A thesis presented to the Department of Petroleum Engineering

A thesis presented to the Department of Petroleum Engineering, University Of Benin In partial fulfilment of the requirements For the degree of MASTER OF ENGINEERING By Abubakar Tanko Supervised by Dr Bello Kelani ABSTRACT The pore pressures in most sedimentary basins including the Niger Delta are rarely normal. They are mostly abnormal pressures, predominantly in the form of overpressured zones. When these abnormal pressures are not predicted accurately prior to drilling, disastrous occurrences, such as formation fluid kicks, well blowouts and loss of drilling mud may take place. This study appraised ten industry utilized pore pressure prediction models with the view of ascertaining their accuracy using offset well data in the form of wireline logs and Repeat Formation Test data. The petrophysical data were evaluated using Ikons Science Rokdoc software. Density and compressional velocity logs were utilized in generating the overburden pressure, shale trend and Normal compaction Trend which are used for the pore pressure prediction. The resulting pore pressure predictions were compared to the measured pressure data for validation. Subsequently one of the models was optimized to yield more accurate predictions. The study identified the overpressured zones in the study area and investigated the cause of overpressure in those zones. The formations fracture pressure and the mud weight window were also estimated using the optimized Eatons model. The ten models employed for the pore pressure prediction were the Eatons, Bowers, Taus, Millers, Ratio, Equivalent- Depth, Hottmann and Johnsons, Zhangs, Duttas, and Holbrooks models while the Eatons fracture pressure model was used for fracture pressure prediction. The velocity density crossplot of the well showed a compaction disequilibrium as the major cause of the overpressure, while Clay diagenesis was also observed as the secondary overpressure generation mechanism in the study area. The onset of overpressure occurs at a depth of 7366ft with a corresponding pore pressure of 3719psi. The most notable overpressured zones occur between a depth range of 10602ft and 11434ft, with the pressure peaking at 6349psi at depth of 11174ft. Five of the ten pore pressure prediction models showed close degrees of agreement with the measured pressure data particularly at depths between 7176ft to 11093ft with the Eatons, Zhangs, Ratio, Hottmann and Johnsons, and Equivalent Depth methods showing very good correlations. At the deeper depths between 11122ft to 11434ft, pore pressures were under-predicted by most of the aforesaid models with only the Ratios model showing close matches. The Bowers, Taus, Millers, Duttas, and Holbrooks models however either under-predicted the pore pressure or over-predicted it at the interval between 7081ft and 9756 ft. At deeper depths between 10524ft and the final depth, Taus, Holbrooks and Bowers models yielded results that reasonably matched the measured pressure. The Millers and Duttas models mismatched the measured pressure by over- predicting the pore pressures in the well. Eatons original model was optimized by lowering the effective stress by virtue of increasing the exponent in the original equation from 3 to 3.9, with the modified model producing the most concordant result with the measured pressure data. More so, results of the statistical analysis conducted to rank the models in terms of their prediction accuracy revealed that the modified Eatons model had the least percentage and Root Mean Square Error (RMSE) values. This further substantiates the accuracy of the model and its suitability for pore pressure prediction in the study area. The outcome of the predicted fracture pressure revealed a pressure range between 5117 psi to 8625 psi which implies the prospects of hydraulic failures of the top seal developing are slim as the magnitude of fracture pressure is considerably greater than the pore pressure. Findings of the mud weight window estimations revealed kicks and lost circulation could be averted when drilling the well by employing a mud weight window with a lower bound between 8.78ppg and 10.91ppg and an upper limit between 13.1ppg and 14.6ppg. The narrowest drilling window was estimated between 10.66ppg to 14.01 ppg. CHAPTER ONE 1.0 Introduction The knowledge of the formation pore pressure and its corresponding fracture pressure are of paramount importance for an effective drilling plan and geomechanical and geological analyses. An understanding of the pore pressure is a requirement of the drilling plan in order to choose proper casing points and design a casing program that will allow the well to be drilled most effectively and maintain well control during drilling and completion operations. Accurate pore pressure is important not only to safer, faster drilling of cleaner wells but also as an important input into mechanical earth models. These are used to design hydraulic fractures and as an input for rock physics model such as those used to predict lithology and fluid fill. Well control events such as formation fluid kicks, lost circulation, surface blowouts and underground blowouts can be avoided with the use of accurate pore pressure and fracture gradient predictions in the design process (Fooshee, S. 2009). One of the aims of a pre-drill pore pressure prediction is to create a mud weight and casing program to allow wells to be drilled safely and with as much forewarning of potentially over-pressured zones as possible. Knowledge and understanding of over-pressured zones can be derived from many sources such as formation pressure tests, daily drilling and End of Well Reports. In addition, a common practice is the interpretation of wireline data (sonic, density, resistivity) from offset wells and increasingly, seismic interval velocities, as a means of predicting pore pressure. Pore pressure is the pressures of the fluids in the pore spaces of porous formations. It varies from hydrostatic pressure, which is normal pressure to abnormal pressure (overpressure or under-pressure). When the pore pressure of a formation at any depth exceeds the hydrostatic pressure of the formation, the pressure is deemed to be over-pressured. Conversely, when the formation pressure is below the hydrostatic pressure at any depth, the formation is considered to be under-pressured. Prediction of abnormal pore pressures prior to drilling, particularly overpressures, can significantly reduce drilling non-productive time as well as catastrophic drilling incidents such as blowouts, kicks, fluid influx, stuck pipe and overall wellbore instability. Borehole instability could in turn lead to increased costs and loss of the well before it reaches its objective (Luo et al., 2012). Accurate prediction of pore pressure values gives the drilling engineer an idea on the best drilling mud weight to use to overcome fracturing and forestall kicks, the most pertinent casing grades/weights to apply during casing selection and design and provides proper logistics for the drilling operation. In the course of drilling a well, control of the mud weight is crucial to maintaining the wellbore stability (Charlez, 1999). The primary parameters that determine the value of the mud weight while drilling include the pore pressure and fracture pressure (Glossary.oilfield.slb.com, 2016b). The pressure range between the pore pressure and the fracture pressure is called Mud Weight Window (Charlez, 1999). The mud weight must be higher than the pore pressure and lower than the fracture pressure (Charlez, 1999). If the mud pressure is more than the fracture pressure, then it may lead to fracturing of the formation and losses of borehole fluids into formation. If it is less than the pore pressure, collapse or blowout may happen (Charlez, 1999 Zhang, 2011). To prevent fluid influx, kicks and wellbore instability in the wellbore, a heavier mud weight than the pore pressure is required. However, when mud weight exceeds the fracture gradient of the drilled formation, it may fracture the formation, causing lost circulation. To prevent wellbore from hydraulic fracturing by the heavy mud weight, as needed where there is overpressure, casing needs to be set to protect the overlying formations from fracturing. Overpressures may be generated by several mechanisms, such as under-compaction, hydrocarbon generation and gas cracking, aquathermal expansion, tectonic compression (lateral stress), mineral transformations, and osmosis, hydraulic head and hydrocarbon buoyancy (Gutierrez et al, 2006 Swarbrick and Osborne, 1998). In nearly all cases where compaction disequilibrium has been determined to be the primary cause of over pressuring, the age of the rocks is geologically young. Areas where compaction disequilibrium is cited as the primary cause of abnormal pressure include the Niger Delta, U.S. Gulf Coast, Alaska Cook Inlet Beaufort Sea, Mackenzie Delta, North Sea, Adriatic Sea , Mahakam Delta, the Nile Delta, Malay Basin, Eastern Venezuelan Basin (Trinidad) and the Potwar Plateau of Pakistan (Law and Spencer, 1998 Burrus, 1998 Heppard, et al., 1998 Powley, 1990 Nelson and Bird, 2005 Morley et al., 2011). In these areas, the abnormally pressured rocks are mainly located in Tertiary and late Mesozoic sedimentary formations, the depositional setting is dominantly deltaic, and the lithology is dominantly shale. Equilibrium shifts from the hydrostatic pressure owing to excess rate of sedimentation over dewatering process for disequilibrium compaction to occur. In this case, the pressure generated in the subsurface is as a result of seal compartmentalization due to the shale lithology. Here, porosity is driven by the effective stress because of overburden stress of the load carried by the subsurface and permeability experiences a set-back. The formation pressure resulting from the above process is simply of mechanical compaction. However, as the depth of burial increases (2000 to 3000m), compaction becomes a chemical process under an influence of temperature at a window range of 70-1000 oC rather than the former. Pore pressure analyses involve three major categories pre-drill pore pressure prediction, pore pressure prediction while drilling and post-well pore pressure analysis. The pre-drill pore pressure can be predicted by using the seismic interval velocity data in the planned well location as well as using geological, well logging and drilling data in the offset wells. The pore pressure prediction while drilling mainly uses the logging while drilling (LWD), measurement while drilling (MWD), drilling parameters, and mud logging data for analyses. The post-well analysis is to analyze pore pressures in the drilled wells using all available data to build pore pressure model, which can be used for pre-drill pore pressure predictions in the future wells. 1.1 Objectives The primary objectives of the study are to Predict the Pore pressure of the Niger Delta formation using ten (10) industry standard models and subsequently compare predictions with measured pressure data (Repeat Formation Tester) from an offset well in the study area and optimize one of the models to yield more accurate predictions. Identify zones of overpressure in the formation. Investigate the overpressure generation mechanism in the study area. Estimate the fracture pressure of the formation. Estimate the drilling mud weight window required to balance the formation pressure during drilling operations in the study location. 1.2 Significance of work Pore pressure significantly affects drilling safety and the economics of drilling design as well as well construction. Drilling Engineers more often than not employ estimated pore and fracture pressure profiles along the well path as primary input to the well design due to their predominant influence on casing seat selection. Consequently, the pore pressure plays a crucial role in the casing program, and overall well cost. In an over-pressured region such as the Niger Delta, knowledge of the formation pore pressure regime can greatly reduce drilling non-productive time and devastating problems associated with drilling through over-pressured zone such as wellbore stability problems, well kicks, blowouts, and lost circulations. As such, accurately predicting the pore pressure regime of the area will help to ensure a better well design, mud weights program, risk assessment and field development plan. 1.3 Geology of the Niger Delta The Niger Delta basin is one of the seven sedimentary basins in Nigeria. It is considered as the most significant owing to its petroliferous nature and consequent active hydrocarbon exploration and production operations occurring both onshore and offshore. The basin is situated at the Gulf of Guinea and fed by the River Niger (Doust Omatsola, 1990 Hooper et al., 2002). It is one of the largest regressive deltas in the world, covering an area of 300,000 km2 with sediment volume of 500,000 km3 and sediment thickness of up to 10 km (Kulke, 1995 Hospers, 1965 Kaplan et al., 1994 Tuttle et al., 1999). The Niger Delta is ranked as the 12th richest province in petroleum resource by the United States Geological Survey, based on 2.2 and 1.4 of world known oil and gas reserves, respectively (Tuttle et al., 1999). The structure in the Niger Delta Basin is largely influenced by instability induced by overpressure, the ductility of the Akata Formation shale, and slope instability (Doust Omatsola, 1990 Corredor et al., 2005 Cobbold et al., 2009). The predominant tectonics in the basin is related to extensional deformation in the sedimentary fill, clearly evident in the paralic Agbada Formation as extensional faulting movements are observable in the inner fold thrust belt, transitional fold zone, and the outer fold thrust belt of the deep water Niger Delta. The structure in the Akata Formation is obscure while the Benin Formation was deposited after cessation of most tectonic activity (Doust Omatsola, 1990). The low density obtained from gravity modelling of Akata Formation may be indicative of presence of salt and/or undercompacted shales (Jacques et al., 2003 cited in Cobbold et al., 2009). 1.31 Stratigraphy and sedimentology The Niger Delta is the worlds largest regressive delta and comprises a series of offlap cycles of sediments (e.g., Doust Omatsola, 1990 Kulke, 1996). The stratigraphy and sedimentology of the Niger Delta, like other major deltas, is a function of the rate of sediment supply and basin tectonics. Several workers (e.g., Short Stauble, 1967 Weber Daukoru, 1975 Doust Omatsola, 1989) have extensively discussed the interplay of these two factors as it relates to the Niger Delta stratigraphy, sedimentology, and structure. Three major depositional cycles have been documented in the Niger Delta Basin (Short Stauble, 1967). Firstly, during the Early Cretaceous, a marine incursion covered the whole basin, and was terminated by weak folding in the Santonian. Secondly, the depositional cycle that created the proto-Niger delta during the Late Cretaceous terminated in a major Palaeocene marine transgression. The third depositional cycle has been ongoing since the Eocene, and is responsible for the southward growth of the main Niger Delta. The Cenozoic strata in the Niger Delta area comprise three main formations (Figure 2.9). The base sequence, the Akata Formation, is up to 7000 m thick in the central part of the basin, and is a delta-front, deep marine deposit that mainly consists of a thick marine shale and clay with a rather silty base. It contains thin sand bodies of continental slope, channel-fill, and turbiditic origin. The shale forms diapirs offshore that are considered to be Palaeocene to Holocene in age (Doust Omatsola, 1990). The Akata Formation is organic-rich and is generally accepted to be the main source rock in the Niger Delta area. Overlying the Akata Formation is a paralic sequence of up to 3000 m thickness, the Agbada Formation, which comprises intercalated shallow marine fluvial sands, silts and clays. These are delta front, delta topset, fluvio-deltaic deposits, ranging in age from Eocene to Pleistocene, and contain both reservoir and cap rocks (Doust Omatsola, 1990). Though still quite controversial, Short Stauble (1967), Frankl Cordry (1967) and Lambert-Aikhionbare Ibe (1984) have all suggested that the Agbada Formation may contain source rocks. The criteria for delineating the top and bottom of the Agbada Formation can be ambiguous and in practice vary from one company to another. Doust Omatsola (1990) suggested the practice of picking the top on wireline logs as the base of fresh water invasion, and the bottom corresponding to the onset of hard overpressure during drilling. Using cuttings from wells, Doust Omatsola (1990) pointed out that the Agbada Formation is sandier in the upper section than the lower section, which may be consolidated to unconsolidated, and is generally poorly sorted with lignite streaks, shell fragments, and glauconite. The topmost sequence of massive non-marine sand is the Benin Formation. In the central part of the basin, it has a maximum thickness of about 2100 m, and thins seawards to disappear near the shelf edge. The oldest sediments of the Benin Formation are thought to be Oligocene in age, and generally lack fauna (Doust Omatsola, 1990). As pointed out by Doust Omatsola (1990), these alluvial, coastal plain deposits contain insignificant quantities of hydrocarbon, but are of great hydrogeological interest. Figure 1.1. The sedimentary basins of Nigeria. The Niger Delta Basin is delineated by the Cameroon Volcanic Zone, the Dahomey Basin, and the 4000 m (13100 ft) bathymetric contour. After Corredor et al. (2005) Figure 1.2 Schematic of the regional stratigraphy in the Niger Delta showing the main stratigraphic units in the outer fold and thrust belt. Source Corredor et al. (2005). Figure Averaged cross-section of the Niger Delta (Obtained from Thomas,1995) Figure 1.3 Averaged cross-section through the Niger Delta (derived from Thomas, 1995). 1.4 Overview of the Thesis The thesis is divided into five chapters. Chapter one gives a brief introduction on pore pressures, fracture pressure and overpressure zones. It also states the objectives of the study and discussed the regional geology of the study area. Chapter two presents a theoretical background of pore pressure and fracture pressure prediction methods, with their pertinent formulations. Drilling mud weights and detection of Overpressured zones and their causes were also discussed at length. Chapter three describes the methodology employed in predicting pore and fracture pressures, mud weight window as well as the investigation of the overpressure generating mechanism. Techniques used to evaluate the accuracy of the pore pressure prediction models were also described. The presentation and discussion of results, as well as statistical evaluation and analysis of the results obtained from pore pressure prediction models are done in chapter four. Chapter five provides the conclusions and recommendations of the thesis. CHAPTER TWO 2.0 Literature Review The fundamental principle underlying pore pressure prediction especially from log is based on the compaction theory, that states that over-pressured sedimentary rocks maintain anomalously higher porosity than they do in normally pressured zones. In formations with normal pressures, pore fluids communicate efficiently with surface during burial. Therefore, the fluids in the pore spaces are squeezed out following normal compaction rate and results to hydrostatic pressure regime. . The magnitude of compaction/porosity loss has been quantitatively related to the load applied by Terzaghi (1943), whose theory states that pore pressure in the formation is a function of total stress (or overburden stress) and effective stress. Pore pressure estimation methods have since adopted the use of the Terzaghi theory. In order to fully comprehend the methods applied in this work, certain basic terminologies need to be understood. 2.1 Pore Pressure Terminology Several formation pressures could be encountered in the course of exploration and exploitation of potential hydrocarbon reservoirs. Below are basic terminologies commonly used in subsurface pressure estimations. 2.1.1 Hydrostatic pressure This is the pressure exerted by static column of fluid at a reference depth. It is dependent on the density of the formation fluid, usually water or brine, and the true vertical height of the column of fluid. The size and the shape of this fluid column have no effect on the magnitude of this pressure. It is thus calculated using the following equation 2.1.2 Pore pressure It is the pressure exerted by the fluid contained in the pore spaces of the rock, and also known as the formation pressure. Pore pressure can be estimated indirectly by comparing expected (normal) and actual density, neutron, resistivity, or sonic responses from wireline logs. However, direct measurements can only be obtained in permeable rocks with wireline formation testing (WFT) and sampling tools, e.g., XPT, MDT, RCI, RFT, and DST. Measurement of pressure in impermeable beds, such as mudrocks, can only be surmised through pressure measurements within adjacent permeable beds or from indirect indications such as mud weight, cavings, gas, or rate of penetration (ROP) during the drilling operation. 2.1.3 Overpressure In cases where the formation pressure, P EMBED Equation.3 , exceeds the hydrostatic pressure at the same depth, the formation is considered to be over-pressured, and the overpressure equates to the excess pressure OP P EMBED Equation.3 EMBED Equation.3 (2.1) In the course of pre-drill planning, zones with overpressure must be identified and adequate preparations should be made to drill through such zones efficiently. To achieve such safe and efficient drilling requires careful pre-drill planning, drilling practice and procedure, and surveillance while drilling. When the mud-weight is not sufficient while drilling through a zone, this is usually shown on the mud log in the form of increased background gas, excessive cavings arriving at the shale shaker, increased mud tank volume, increased bit torque, and overpull during trip. 2.1.4 Underpressure A zone is under-pressured if the formation pressure is less than hydrostatic pressure at the same depth. It may be caused by pressure depletion in reservoirs under production 2.1.5 Overburden pressure It overburden, is the pressure exerted at a particular depth by the weight of overlying sediments including the fluid it contains. Sometimes it is referred to as the lithostatic or vertical stress. This pressure originates from the combined weight of the formation matrix (rock) and the fluids (water, oil, gas) in the pore space overlying the formation of interest. In general, when density data are unavailable or unreliable, an overburden gradient of 1 psi/ft is often used as the default. But when data are available, a more realistic evaluation based on lithology and density coupled with knowledge of local geology is preferable. OVB estimation in this project, the density at mudline varies over the range 1.451.75 g/cc. In contrast, bottom-hole densities vary over the range 2.52.85 g/cc. The low density values at mudline are based on the limited data available from the shallow sections of wells. The overburden stress, Sv, is estimated by using the relationship EMBED Equation.3 EMBED Equation.3 Z (2.2) Where EMBED Equation.3 formation average bulk density Z vertical thickness of the overlying sediment Bulk density is determined using the equation EMBED Equation.3 EMBED Equation.3 (2.3) Where EMBED Equation.3 porosity (expressed as a fraction) EMBED Equation.3 density of formation fluid EMBED Equation.3 density of formation matrix Sediment porosity decreases under the effect of compaction which proportional to the increase in overburden pressure. In the case of clays, this reduction is essentially dependent on the weight of the sediments. However in sandstones and carbonates, this relationship is a function of many parameters other than compaction, such as diagnostic effects, sorting, and original composition. Sv overburden stress In normally compacted rock, increase in effective stress results in porosity reduction. Any process causing a reduction of effective stress in a seal formation will results in overpressure, because that will result in the overburden stress been transferred to the pore fluid rather than the rock matrix. Figure 2.1 Schematic of Pressure-Depth plot depicting major terminologies used in the work. 2.2 Compaction The mechanical response of sediments experiencing an increase in effective stress is to undergo a reduction in volume by means of reorganization and deformation of the matrix material and the loss of porosity. Chemical processes may also occur and contribute to the loss of porosity. This process of porosity reduction is referred to as compaction. Shale has a lot of porosity to lose and fluid to expel. If the fluid cannot escape to balance the stress, a higher porosity is retained and the rock is over-pressured. During normal compaction, sediments undergo a monotonic increase in effective stress which causes an elasto-plastic reduction in porosity (and void ratio). The resultant relationship between effective stress and void ratio is known as the normal compaction curve. Hence the use of seismic velocity for pore pressure prediction is focused on an interpretation of velocity as a proxy for porosity, with comparison between the actual and expected interval velocity on the normal compaction curve. Pressure in the shales can be quantified exploiting the relationship between total stress (normally vertical stress tied to the estimation of the overburden) and vertical effective stress based on the interval velocity (porosity) comparison. Quantitative methods of pressure estimation from wireline logs require the establishment of a Normal Compaction Trend (NCT) for each log type as an input in the workflow. The NCT should be established in the cleanest possible shale intervals. Shale pressure prediction is based on the assumption that shale pressure is at equilibrium with pressure in the adjoining sand interval, an assumption that is not always true. 2.2.1 Terzaghi effective stress The process of soil and sediment compaction has been studied for a considerable amount of time in the fields of soil mechanics, engineering and more recently petroleum geology. The amount of compaction/porosity loss was first quantitatively related to the load applied by Terzaghi (1925). This relationship was achieved by the definition of effective stress (cr) as the difference between the applied normal stress (s) in a specified direction and the pore pressure EMBED Equation.3 Sv Pp This basic theory led to the development of experimental apparatus (oedometers) where porosity loss in sediments could be related to measured increases in applied stress. 2.2.2 Normal Compaction Trends (NCT) The NCT represents the normally compacted shale sequences, which in turn should show a power law decrease in porosity with depth. Effective stress methods assume that normally pressured formations follow a unique compaction curve, allowing for a pore pressure estimation based on a normal compaction trend (NCT) (Bowers, 1995). The following shows the workflow for determining a NCT in 4 basic steps (modified from Bartetzko et al., 2012) 1. Shale determination Compaction models rely on using shale intervals to initially define the power law compaction curve (Bartetzko et al., 2012). Often this includes using the gamma ray log to distinguish between clean, non-shale formations, and clay-rich shale formations. 2. Determination of overburden stress The overburden gradient is required to relate effective stress to pore pressure using Terzaghis effective stress principle. To determine the overburden gradient, integrate the calculated logged density curve. 3. Definition of normal compaction trend Place a power law best fit trend line between a porosity indicator, such as acoustic information or resistivity, and normal effective stress. To obtain normal effective stress, the shallow section of the well, where the formation is assumed to be in a normal hydrostatically pressured, is often used (Bartetzko et al., 2012). 4. Calibration If additional information on pore pressure, from either formation evaluation tests in permeable formations, or geologic and structural information, is available, the NCT can be adjusted accordingly to better fit the data in interest. With an NCT properly fit to the data set, deviations from the NCT will imply areas of abnormal pore pressure. Normal Compaction Trend Transforms The establishment of a NCT for any log type that is sensitive to porosity is based on the expectation of linear, exponential, logarithmic, or power-law functional relationship in the mudrock as a function of depth. For example, NCTs may be determined for density, sonic velocity or transit time, and resistivity as functions of depth. Depending on the log type available, a few of the commonly cited transforms in literature for NCT computation are presented below. NCT derived from sonic logs When one has a sonic transit log at his disposal, Ramdhan Goulty (2011) suggested the use of the equation EMBED Equation.3 (2.5) Where EMBED Equation.3 transit time EMBED Equation.3 Initial surface transit time EMBED Equation.3 Matrix transit time b compaction constant (m EMBED Equation.3 ) z depth in (m) NCT derived from Resistivity logs EMBED Equation.3 (2.6) Where R resistivity Ro initial surface resistivity b compaction constant (m EMBED Equation.3 ) z depth in (m) NCT derived from density logs The NCT for density logs is established using the following equation EMBED Equation.3 (2.7) EMBED Equation.3 porosity (fraction) EMBED Equation.3 matrix density EMBED Equation.3 fluid density EMBED Equation.3 bulk density NCT derived from porosity logs The NCT can be established by using either the linear or exponential form of Athys (1930) equation EMBED Equation.3 in linear form (2.8) EMBED Equation.3 in exponential form (2.9) Where EMBED Equation.3 initial surface porosity () c compaction constant (m EMBED Equation.3 ) z depth in (m) 2.3 Fracture pressure and fracture gradient Fracture pressure is the pressure required to fracture the formation and cause mud loss from wellbore into the induced fracture. Fracture gradient can be obtained by dividing the true vertical depth from the fracture pressure. Fracture gradient is the maximum mud weight therefore, it is an important parameter for mud weight design in both drilling planning stage and while drilling. If mud weight is higher than the formation fracture gradient, then the wellbore will have tensile failure (be fractured), causing losses of drilling mud or even lost circulation. Fracture pressure can be measured directly from downhole leak-off test (LOT). There are several approaches to calculate fracture gradient. The following two methods are commonly used in the drilling industry i.e., the minimum stress method and tensile failure method. 2.3.1 Minimum horizontal stress It is estimated as the minimum mud pressure required to hold open fractures in the formation. There is usually an interval of open hole of 35 m depth below the casing shoe in a compacted formation, and the LOT is run in this section of the hole to select a suitable mud weight for drilling the next section. The well is shut in and drilling mud is pumped into the well, with increasing pressure on the well bore. The pressure increase with time is monitored as shown in Figure 3.3. Point B is the fracture pressure, i.e., the pressure at which mud invades the formation hence the drop in pressure. The leak-off pressure (LOP) obtained during an LOT indicates the maximum mud weight that can safely be used for drilling the next section of hole without tensile failure in the formation. There are empirical methods suggested in the literature on how to estimate the minimum horizontal stress. Yoshida et al. (1996) concluded in their survey of drilling engineers working for major operating companies, that the methods due to Matthews Kelly (1967), Eaton (1975), and Daines (1982) are the most popular, of which Eatons method and then the method of Matthews and Kelly (1967) are the most widely used. In a similar study of deepwater Niger Delta wells, Ajienka et al. (2009) concluded that the method due to Matthews Kelly (1967) is the most reliable method for fracture gradient estimation. The minimum stress method is calculated as follows EMBED Equation.3 (2.10) Where EMBED Equation.3 minimum in-situ stress or the lower bound of fracture pressure EMBED Equation.3 the Poissons ratio which can be obtained from compressional and shear velocities (vp and vs) EMBED Equation.3 Pore pressure EMBED Equation.3 (2.11) Matthews and Kelly (1967) introduced a variable of effective stress coefficient for fracture gradient prediction The minimum horizontal component of the stress, 5/, is known as the fracture pressure. It is commonly measured by a leak-off tests (LOT) after each run of casing has been set in a well (White et al., 2002). 2.4 Mud Weight Equivalent Mud Weight or simply EMW. Typically EMW expressed in pound per U.S. gallon (ppg) pressure in psi and the total vertical depth (TVD) is expressed in feet. The constant value of 0.052 is a conversion constant that can be derived by dimensional analysis. Using metric units, pressure expressed in Pascal and TVD in meter will lead to conversion constant equal to1175.7 (unit less). However, it should be noted that using density unit to describe pressure is absolutely wrong but drillers prefer using this unit since it gives them more sense about drilling difficulty. For instance knowing the formation pore pressure is 4500 psi (31026.4 kPa) does not imply that whether this formation is easy to drill or not. In 10000 ft (304.8 m) this pressure is equal to hydrostatic pressure, but in 500 ft (152 m) this pressure considered as a substantial pore pressure. Lets clarify this problem by converting and comparing pressures to EMW. At 5000 ft (152 m), EMW 17.5 lb/gal (260.014 N/m3) and at 10000ft (304.8 m), EMW8.65 lb/gal (177.95 N/m3) (Bruce and Bowers, 2002). EMW is expressed using following equation EMBED Equation.3 (2.12) Figure 2.2 Schematic sketch of the hydrostatic pressure, overburden pressure, normal and overpressure. According 2.5 Overpressure generating mechanisms Numerous mechanisms have been proposed as the major causes of overpressure in the subsurface formations. Dickinson (1953), in his study of Gulf of Mexico described overpressure as incomplete dewatering. The term disequilibrium compaction, or compaction disequilibrium, has since been coined for Dickinsons mechanism, which is still thought to be the most common mechanism of overpressure generation in young, rapidly buried sequences of mud-rich sediment at depths down to approximately 2 km. A multitude of additional mechanisms of overpressure generation have later been suggested such as thermal effects (Baker, 1972), clay mineral changes (Burst, 1969, Bruce, 1984) and osmosis (Marine and Fritz, 1981). In other basins, the influence of hydrocarbon generation/maturation has been proposed to explain over-pressured reservoir (Spencer, 1987), and gas generation as a mechanism has been invoked for the Gulf of Mexico situation (Hunt et al., 1991). Some authors suggest one dominant mechanism, e.g., disequilibrium compaction (Bredehoft and Hanshaw, 1968 Summa et al., 1993), or hydrocarbon generation (Meissner, 1978a, b), whilst others have attempted to ascribe the relative proportion of each mechanism (Hart et al., 1995) both for the GOM and for other basins around the world. Notwithstanding all the current debate on the magnitude of the various mechanisms, a driller should be aware that the additive contributions from the various mechanisms can make the difference between a successful and a catastrophic drilling operation. Overpressure generation mechanisms can be classified into three main categories firstly, loading mechanisms, which are related to stress and involves compression leading to pore volume reduction and secondly, fluid expansion mechanisms (Swarbrick et al., 2002) and hydrocarbon buoyancy mechanisms. 2.5.1 Loading mechanisms These are stress related mechanisms, in other words, they involve compression leading to pore volume reduction. If pore fluid cannot escape fast enough to stay in hydrostatic equilibrium as increasing compressive stress is applied, overpressure is generated. Increasing overburden stress as sediment is buried can cause overpressure by disequilibrium compaction. The other loading mechanism that may generate overpressure is increasing tectonic stress, which acts in the horizontal plane assuming that the vertical stress is a principal stress. 2.5.1.1 Under-Compaction (Disequilibrium Compaction) Most overpressure observed in sedimentary basins is caused by disequilibrium compaction, which results from continuous loading of effectively sealed sediments (Mouchet and Mitchell, 1989 Osborne and Swarbrick, 1997). During deposition of sediments, fluids within the pore spaces try to escape as the pores compact with the increase in vertical stress. If the sedimentation rate is slow, normal compaction occurs, i.e. equilibrium is attained between increasing overburden and the reduction of pore fluid volume due to compaction (Mouchet and Mitchel, 1989). This normal compaction generates hydrostatic pore pressure in the formation. Rapid burial results into faster expulsion of formation fluid in response to rapidly increasing overburden stress. When a formation with low permeability is encountered with a high rate of sedimentation, fluid in the sediments can only be partially expelled, while the remaining fluid which is trapped in the formation will have to carry a large part of the load and pore-fluid pressure will increase. The rate of porosity loss varies with rock-type. Each rock-type will have a lower limit beyond which no further mechanical compaction is possible and porosity loss is thereafter due to chemical compaction. Sandstones compact at a relatively slow rate from a starting point of about 4045 porosity to about 2030 porosity due to rearrangement of the sand grains and some dissolution at grain contacts (McBride et al., 1991) By contrast, clays have a typical porosity at the time of deposition of 6580 and compact more quickly than sands. Clays will continue to compact, by grain rearrangement and ductility, to great depths (typically 46 km) where the porosity can be reduced to 510 of the rock volume (Katsube and Williamson, 1994 Hunt et al., 1994) Examples of where disequilibrium compaction is cited as the primary reason of abnormal pressure include the U.S. Gulf coast, Niger Delta, Mahakam Delta, Mackenzie Delta, North Sea, Adriatic Sea, the Nile Delta, Alaska Cook Inlet Beaufort Sea, Malay Basin, Eastern Venezuelan Basin (Trinidad) and the Potwar Plateau of Pakistan (Powley, 1990 Burrus, 1998 Heppard et al., 1998 Law and Spencer, 1998 Yassir and Addis, 2002 Nelson and Bird, 2005). 2.5.1.2 Tectonic compressive stress This mechanism is similar to the process responsible for generating overpressure by disequilibrium compaction, but in this case the loading is by additional horizontal compressive stress caused by tectonics. Overpressuring due to lateral stress is reported along major fault zones, both within the fault and in the adjacent porous wall-rock (Byerlee, 1993). Episodic release of overpressured fluids is associated with earthquakes and fault rupture (Byerlee, 1990). In addition, overpressure is a characteristic of accretionary sedimentary prisms, where horizontal compression coupled with loading and under plating is caused by subduction (Davis et al., 1983). Examples are found in geologically younger regions of the Earths crust, e.g., Rocky Mountains, USA mountain chains in Indonesia (Agip manual, 1980) Barbados Accretionary Prism (Trinidad), the Andes, Papua New Guinea, California and Gulf of Alaska. 2.5.2 Unloading mechanisms Unloading involves fluid expansion mechanism as the rock matrix constrains the increased volume of pore fluid. It also refers to the decrease in effective stress, either by a reduction in confining stress or by an increase in pore pressure. Confining stress is reduced by exhumation, and if fluid escape is restricted, overpressure may result. Other unloading mechanisms may be categorized as fluid expansion mechanisms, where the rock matrix restricts expansion of incoming fluid or an internal increase in fluid volume. Examples in this category include hydrocarbon generation, the cracking of oil and bitumen to gas, clay diagenesis, and aquathermal expansion (e.g., Bowers, 1995 Swarbrick Osborne, 1998 Swarbrick et al., 2002). 2.5.2.1 Hydrocarbon generation (kerogen maturation) Kerogen maturation adds to overpressure by the creation of lower density fluid from higher density kerogen. The kinetics of this reaction is highly dependent on time and temperature (Swarbrick et al., 1998, 2002). Unless sufficient fluid is expelled, the porosity will not be consistent with the expected amount of compaction at a given depth. Conversion of kerogen to lighter hydrocarbon is expected to occur at depths of 24 km and temperatures of 70120C (Tissot et al., 1987) and 3.05.5 km and 90150C for oil cracking to gas (Barker, 1990). The increased volume of hydrocarbons and residue relative to unaltered organic material, and the inhibited structural collapse of the rock framework as overburden-supporting solid organic matter are converted to hydrocarbon pore fluid result into overpressure. If the kerogen sustains part of the overburden stress before maturation, that stress will be transferred to the liquid hydrocarbon, and if the liquid cannot escape, there will be an increase in pore pressure. Meissner (1987) suggested that the catagenesis of solid kerogen is accompanied by 25 volume expansion. Whereas, maturation of kerogen to gas can results in a large volume increase as much as 50 (Ungerer et al 1983). It is this high pressure resulting from increase in pore fluid and creation of additional pore spaces during maturation of source rocks to oil, that aid the expulsion of petroleum from low permeability source rock into carrier beds (England et al 1987). 2.5.2.2 Oil to gas cracking Of all the unloading overpressure generation mechanisms, only gas generation is considered sufficient enough to produce high magnitude overpressures (Swarbrick et al 2002 Tingay et al 2007 Lahann and Swarbrick 2011). Ungerer et al (1990) examined hydrocarbon generation for type-2 Toarcian black shales in the Paris Basin in France and observed that, generation of oil is associated with a 3 to 6 decrease in fluid volume maturation of kerogen into gas can result in a large fluid volume increase of potentially as much as 50. Primary migration of hydrocarbon is the principal evidence for gas generation as a mechanism of overpressure generation (England et al 1987). 2.5.2.3 Aquathermal expansion Increase in temperature that occurs within over-pressured zones is another crucial factor that contributes to fluid pressure. The overlying, normally pressured sediments, that are compacted possess a lower thermal conductivity and act as a blanket, decreasing the transfer of heat from the deep mantle. A leak-proof permeability seal is required in order to have a closed system, and the heat trapped by the blanket effect above the geo-pressured zone produces an abnormally high temperature in the formation. This contributes to fluid pressure within the zone. The principle which governs aquathermal expansion as an overpressure mechanism is the thermal expansion of water when heated above 4oC (It is what causes water in a tin heated to certain temperature to pop its lid open). If the body of water is contained in a sealed vessel the pressure rises rapidly. Take for instance, Barker (1972) shows a pressure rise of 8,000 psi in water heated from 54.4oC to 93.3oC caused by a volume increase of only1.65. This pressure increase over about 1.0-1.5 KM would lead to a sharp transition zone at the top of overpressure section. This is only possible if the environment is completely isolated, with no change in pore volume. Several authors (e.g., Daines, 1982 Luo and Vasseur, 1992) have shown that the conditions for aquathermal pressuring will rarely be met. In particular when water is heated its viscosity reduces and facilitates more rapid expulsion, even at low permeabilities. An additional objection to aquathermal expansion is that in many overpressured rocks there is a gradual transition zone to high amounts of overpressure. This implies permeability and hence the section is not fully sealed and cannot fulfill the requirements for aquathermal pressuring. Aquathermal pressuring is only feasible if a seal with permeability close to zero can be proven to exist. Hunt (1990) suggested that there are diagenetic seals with highly effective sealing properties, typically at depth of about 3.0 km, which are laterally extensive. Although banded cements have been described where the top of overpressure is observed (Tigert and Alshaieb, 1990), the origin of these diagenetic seals and their ability to form regional impermeable barriers is subject to further questioning (Swarbrick and Osborne, 1998). 2.5.2.4 Clay diagenesis A multitude of mineral transformations in sediments involve the release of clay bound water. The most common of these involves smectite to illite transformation, gypsum to anhydrite in evaporitic sediments and coalification (Law et al., 1983). Surficial, young argillaceous sedimentsm are often rich in smectite mineral called montmorillonite. Smectite absorb water due to their imbalance ionic charge (Van dar Waals bond). During burial, smectite alters chemically by addition of Al and K ions and the release of Na, Ca, Mg, Fe, and Si ions plus water, to produce illite, which does not have the same capacity to absorb water. The reaction is kinetically controlled and is dependent on the combined effects of time and temperature, as well as the influence of mineral fabric and permeability (Hall, 2004). In several mud-dominated basins a gradual and systematic change from smectite to illite downwards in the stratigraphic section is observed, broadly coincident with the transition to high amount of overpressure (Bruce, 1984). The overall volume change involve with smectite transformation is not well known, because the exact chemistry of the reaction is not known. Conversion of one volume of smectite to illite would release 0.36 volumes of water according to the reaction proposed by Hower et al. (1979), leading to an overall volume decrease of about 23 if all the reaction occurs in a closed system. Boles and Franks (1979) claim a release of 0.56 volumes of water, which would create a volume increase of about 25. The transformation of smectite to illite clay is accompanied by changes in the physical characteristics of the sediments. Firstly, the collapse of the smectite clay framework and release of bound water influences the compressibility of the sediment. If the compressibility factor is increased, the overburden induces additional compaction requiring expulsion of newly released water from the rock to achieve equilibrium. If the low permeability of the rock acts to retain the fluid, then overpressure will result, i.e. disequilibrium compaction induced by mineral dehydration. 2.5.3 Fluid movement and buoyancy mechanisms 2.5.3.1 Osmosis It takes place when two solutions of different ionic concentrations are separated by a semi permeable membrane that allow solvent to pass through by diffusion from the more dilute side to the other side of the membrane with high concentration. The osmotic flow will continue until the chemical potential of diffusion is equal on both sides of the membrane. Pressure increase thereby occurs if the solvent moving into the more concentrated solution enters a closed compartment. Large contrasts in the brine concentrations of formation fluids, from dilute to saltier water, across a semi-permeable membrane can induce transfer of fluids across the membrane. Marine and Fritz (1981) suggested that osmotic pressure could be an explanation for some overpressured sections. Swarbrick and Osborne (1998) argued that this mechanism have only local effect. As it is required to have recharge of the more saline, and discharge of the originally less saline waters to maintain the pressure, which they believe is unlikely to happen during normal compaction processes. In addition, it has been observed that brines in overpressured zones tend to be of a lower salinity than adjacent normally pressured brine, which would act to reduce the pressure in the overpressured zones. 2.5.3.2 Hydrocarbon buoyancy (density contrasts) Gases and most oils have a lower density than the associated formation waters and hence have a lower pressure gradient. Since overpressure is the excess pressure above hydrostatic for a given depth, there is always some amounts of overpressure wherever a column of oil or gas is present. This mechanism is restricted to structural and stratigraphic traps of hydrocarbons, and cannot cause regional overpressure. The amount of overpressure within the hydrocarbon accumulation is a function of the pressure gradients of oil, gas and water and the height of the hydrocarbon column. 2.6 Overpressure characteristics trends The major mechanisms responsible for overpressure can be categorized into two categories loading by burial or tectonic compressive stress, leading to overpressure generation by disequilibrium compaction and unloading processes that reduce the effective stress acting on the sediment, such as clay mineral transformations, gas generation, and lateral transfer (Swarbrick et al., 2002 Lahann Swarbrick, 2011). The two categories of overpressure-generating mechanisms cause different characteristic trends in wireline logs and cross-plots. In the course of burial, compaction of sediment proceeds rapidly initially, with decreasing porosity and increasing resistivity, density, and sonic velocity. As depth increases, the rate of compaction reduces. The normal compaction trend for any porosity log means the compaction trend when the pore pressure is hydrostatic and may be influenced by clay mineral transformations in addition to mechanical compaction processes (e.g., Dutta, 1987 cited in Bowers, 2001 Lahann, 1998). If a deviation from the normal compaction trend towards higher porosity occurs, that is an indication of undercompaction (Figure 2.3). There may also be apparent deviations from the normal compaction trend due to differences in lithology Figure 2.3. Schematic of density, velocity and resistivity vs. depth showing trend reversal Normally Pressured Shale Abnormally Pressured Shales Porosity Decreases with depth Higher than expected Conductivity Decreases with depth Higher than expected Density Increases with depth Lower than expected Resistivity Increases with depth Lower than expected Sonic travel time Decreases with depth Higher than expected Table 1 Characteristics of normal and abnormal pressured shale (G.E.Omolaiye, E.A.Ayolabi, C.S.Ugwuabgo, 2013) Hoesni (2004) observed that sonic and density signatures associated with pore pressure gradient increase are caused by different over-pressure generation mechanisms. He proposed that, overpressures generated by disequilibrium compaction are expected to plot on top of the normally pressured sonic-density loading curve (fig 2.4). Clay diagenesis is associated with increased density which shifts from the loading curve towards the right. Kerogen maturation and aquathermal fluid expansion decrease the density making them distinguishable from clay diagenesis (Lahann et al., 2011). This is noticeable as a downward trend in the cross-plot. Overpressures associated with clay diagenesis or load transfer are predicted to undergo an increase in density and little change in sonic velocity with increasing overpressure. Sequences that are overpressured through gas generation are predicted to undergo a sharp reduction in velocity, with little or no change in density. Lahann and Swarbrick 2011 further hypothesize that, if pore pressure gradient reduces with depth, sonic density value will return to the original loading curve if overpressures were caused by gas generation, whereas sequences affected by clay diagenesis or load transfer will plot along a new loading curve, possibly sub parallel to the original loading curve. Figure 2.4 Typical Velocity vs. Density signatures and their associated, causal mechanisms of overpressure generation (from Hoesni, 2004). 2.7 Worldwide examples of overpressure The occurrence of overpressure has been reported in virtually all sedimentary basins worldwide (Figure 2.4) and can occur in all lithological types and formations of all ages though more common in young sedimentary basins. For instance, cases of overpressure have been reported in the GOM, USA (e.g., Dickinson, 1930 Mello Karner, 1996 Bowers, 2001) Wyoming Basin, USA (Evers et al., 1983) Niger Delta, onshore (Onuoha et al., 2009) Niger Delta, offshore (Swarbrick et al., 2011) Baram Delta, Brunei (Tingay et al., 2008) Taranaki Basin, New Zealand (Webster et al., 2010) Central Graben, UK North Sea (Daniel, 2000) and the Kutai Basin Indonesia (Ramdhan Goulty, 2010). Due to lack of data and drilling activity, not much is known regarding overpressure in the Greenland and Antarctica areas (Mouchet and Mitchell, 1989). Prior to drilling operations, it is pertinent to recognize the various overpressure mechanisms within the system. For example, Daniel (2001), Bowers (2001) and Katahara (2003) all suggested that overpressure calculation should take into account the mechanism of overpressure and this should inform the choice and/or modification of method. Examples of basins where disequilibrium compaction is the predominant mechanism of overpressure include the US Gulf Coast (Dickinson, 1953 Pennebaker, 1968) and the Baram Basin (Tingay et al., 2009). Goulty (2012) noted that although the most commonly cited cause of overpressure is disequilibrium compaction, it is only a plausible explanation of overpressure down to depths of 24 km. At greater depths, mudrocks are in the chemical compaction regime where porosity generally decreases independently of effective stress, in association with ongoing chemical compaction. 2.7.1 Overpressures occurrence in Niger Delta Ever since the early years of exploration activities in the Niger Delta, over-pressured zones have been encountered where the depth of exploration wells were determined primarily by the occurrence of first kicks in such wells. It was believed that, the occurrence of first kicks marks the onset of overpressure and thus the termination of drilling activity. This approach spanned for a long period before the adoption of controlled drilling approach, where precautionary increases in mud weight, target depths were often accomplished with many of such wells erroneously regarded as non-overpressure on the basis that no kicks were experienced (Nwozor et al. 2013). Based on kick data and reversal in logs, early studies in the basin develop a series of pressure graphs and rudimentary maps aimed at delineating the onset of overpressure and their distribution in the basin as they believed that, under-compaction was the cause of overpressure in Niger Delta. The onset of overpressures has been noted to vary widely between 4494 ft (1370m) TVDss to 14006 ft (4270m) TVDss with a very diffuse trend in spatial distribution, thus making isobar maps irrelevant in pore pressure prediction (Nwozor et al. 2013). Anowai et al. postulated that the top of overpressure is relatively shallower in the Northern and Greater Ugheli Depobelts when compared with the Central Swamp Depobelt and other near-to-offshore fault-bounded mega structures. This follows from recommendations in Krusi which took a more quantitative approach by investigating the relationship between the top of overpressures with both shaliness and rate of subsidence and noted a pattern of pressure increases where net-to-gross ratio drops below 35 -20 in proximity with discontinuous or detached reservoirs such as beach and channel sands. This was perceived as resulting from restriction to normal dewatering/expulsion of fluid of the compacting sediments that result in pore fluid been trapped and thus bearing part of the overburden weight. Until recent time, under-compaction has been taken as the mechanism responsible for Overpressure generation in Niger Delta. Nwozor et al. in their work suggest an evidence of secondary mechanism of overpressure generation following Bowers velocity effective stress cross-plot. This approach does not give detailed account about the mechanism resulting into the overpressures. However, in this study, Hoesnis method was applied which uses velocity density cross-plot as a basis for determining the mechanism that are responsible for the overpressures. 2.8 Pore Pressure Estimation Techniques Methods employed for evaluating abnormal pore pressures are separated into two categories prediction and detection methods. The prediction methods normally use data obtained from seismic surveys, offset well logs and well history .They are primarily geophysical techniques utilized at the initial exploration phase. Detection methods primarily utilize drilling parameters and well log information obtained during the actual drilling of a well (Yoshida, 1996). They apply to those aspects and parameters which can be monitored during the drilling phase and can alert the driller of the fact that they have encountered an abnormally pressured zone or transition zone. In his paper on pore pressure determination in deepwater, Traugott (1997) explained that existing pore pressure prediction models tend to fall into two categories vertical and horizontal models. Vertical models are those for which the assumption is made that a given value of porosity determines effective stress uniquely. They are also referred to as explicit models (e.g. Alixant and Desbrandes, 1991 Rasmus and Stephens, 1991). Horizontal models, on the other hand, are those in which the effective stress is empirically related to the ratio of the measured parameter (e.g. velocity, resistivity) to the same expected value at the trend line at the same depth, for instance the Eaton relationship (Eaton, 1975). Traugott (1997) identified limitations to the two models extrapolation of the compaction trend line to the depth of interest, assuming a straight or curved shape, for the horizontal methods and the effects of formation temperature for the vertical ones. Temperature has been the key difference between the horizontal and vertical methods of pore pressure prediction. 2.8.1 Prediction Methods Pore pressure predictions basic theory was derived from Terzaghis and Biots effective stress law (Biot. 1941 Terzaghi et al. 1996). This fundamental theory represents the formation pore pressure is a function of overburden stress and effective stress. The Terzaghis and Biots effective stress law generated a correlation to explain the relationship of overburden stress, effective vertical stress and pore pressure. It can be expressed in the following form EMBED Equation.3 (2.13) Where EMBED Equation.3 is overburden stress EMBED Equation.3 is effective stress 2.8.1.1 Seismic method Besides the actual drilling of a well, the only way to predict potential hazards like over-pressured zones is through the use of seismic surveys. Seismic methods detect changes of interval velocities with depth from velocity analysis of CMP data. This is based on the fact that, elastic wave velocities in rocks increase during loading due to a reduction in porosity and increased contact at grain boundaries for sediments that compacted normally (i.e. normal compaction trend). A deviation from the normal compaction trend causes velocity of sound waves to reduce and is interpreted in seismic data as indicator of overpressure. To be accurate, precise sound velocity data are needed and this may determine the successful application of this method. Previous works reveal that, accurate prediction of pore pressure from velocity requires two sets of information. One is an accurate velocity trend. The other is sufficient data to calculate the effective pressure at enough points to establish and calibrate a relationship between velocity and effective pressure. Consequently this requires density and pore pressure measurements in a similar setting. 2.8.1.2 Pore Pressure Prediction from wireline logs Pore pressure prediction from shale properties derived from well log data (acoustic travel time/velocity and resistivity) was pioneered by Hottmann and Johnson (1965). They proved that porosity decreases as a function of depth from analyzing acoustic travel time in Miocene and Oligocene shale in Upper Texas and Southern Louisiana Gulf Coast. This trend represents the Normal Compaction Trend (NCT) as a function of burial depth, and fluid pressure exhibited within this normal trend is the hydrostatic. If intervals of abnormal compaction are penetrated, the resulting data points diverge from the normal compaction trend. They contended that porosity or transit time in shale is abnormally high relative to its depth if the fluid pressure is abnormally high. Afterwards, several empirical formulas for pore pressure prediction were presented based on petro-physical properties such as resistivity, sonic transit time (interval velocity) and other well logging data. Yoshida et al. (1996) in their survey, of mostly drilling engineers in the GOM, concluded that The major operating companies still rely on the use of offset wells and history in combination with seismic for pore pressure work. The prediction and calculating of pore pressure from wireline log data are restricted to the Shale sections. Establishing a normal compaction trendline is important when calculating pore pressure from log derived shale properties. Of the several available well logs, sonic log data is considered to be the most accurate, as it is largely unaffected by borehole size, formation temperature and pore water salinity. The commonly used methods for shale pore pressure prediction include Eatons method, Bowers method and Equivalent Depth method. 2.8.1.2.1 Eatons Method (1975) Eaton published a technique for pore pressure estimation based on the relationship between overburden and pore pressure and came up with empirical equations for the Gulf of Mexico. His equations are based on Terzaghis effective stress concept. Eatons model is an empirically determined method based on the relationship that pressure is related to the difference between overburden and, the product of effective stress (from normal pressure zone) and ratio of the observed log value and log value on projected normal trend. The model uses different well logs to quantify the formation pore pressure, where the log results of acoustic velocity, resistivity or dc-exponent would be used. The method is an improvement of Hottmann and Johnsons method of equivalent depth, proposed in 1965. The methods both rely on the widely accepted assumption that overburden pressure is dependent on pore pressure and effective vertical stress, as shown in Terzaghis equation of 1948. By its nature this is an empirical equation to relate well log and pore pressure gradient. This equation could be used in the environments where sediment compacted rapidly. Under compaction is usually observed in these kinds of environments. So Eatons equation is very useful to quantify pore pressure where sediments show under compaction. Eatons method is one of the most commonly used for pore pressure prediction. Some commonly used forms of Eatons equation are listed below. Eatons equation based on sonic log EMBED Equation.3 (2.14) Where EMBED Equation.3 Pore pressure EMBED Equation.3 Overburden stress EMBED Equation.3 Hydrostatic pressure EMBED Equation.3 Sonic velocity obtained from normal compaction trend EMBED Equation.3 Sonic velocity observed Eatons equation based on resistivity log EMBED Equation.3 (2.15) EMBED Equation.3 Resistivity observed EMBED Equation.3 Resistivity in normal trend Eatons Method from DX or DXC Exponent EMBED Equation.3 (2.16) Where D D exponent Dn Normal trend of the D exponent. 2.8.1.2.2 Bowers method (1995) Bowers (1995) proposed an effective stress method for estimating pore pressure which uses Terzaghis equation in the pore pressure prediction. Bowers method is based on the effective stress. The main step in this method is to calculate the effective stress from velocity and then use the Terzaghis equation in pore pressure calculation. He proposed that the sonic velocity and effective stress have a power relationship as follows EMBED Equation.3 (2.17) Where EMBED Equation.3 EMBED Equation.3 is the compressional velocity at a given depth. EMBED Equation.3 is the compressional velocity in the mudline (usually 5000 ft/s, or 1520 m/s) A and B are the parameters calibrated with offset velocity versus effective stress data. Rearranging Eq. 2.12 and considering EMBED Equation.3 , the pore pressure can be obtained from the velocity as described in Eq. 2.12, as EMBED Equation.3 (2.18) A 10 – 20 and B 0.7 0.75 The method is applicable to many petroleum basins. Bowers (2001) pointed out that, where mechanisms other than disequilibrium compaction are present (i.e., unloading mechanisms), the equivalent depth method under-predicts pressure. 2.8.1.3.3 Tau model A velocity-dependent pore pressure prediction method was proposed by Shell through introducing a Tau variable into the effective stress equation (Lopez et al., 2004 Gutierrez et al, 2006). EMBED Equation.3 (2.19) Where As and Bs are the fitting constants EMBED Equation.3 is the Tau variable EMBED Equation.3 (2.20) EMBED Equation.3 is the compressional transit time either from sonic log or seismic velocity C is the constant related to the mudline transit time (normally C 200 EMBED Equation.3 ) D is the constant related to the matrix transit time (normally D 50 EMBED Equation.3 s/ft) Then, the pore pressure can be calculated from Eq. 14 using Eq. 1, EMBED Equation.3 (2.21) Tau model and Millers method are similar to Bowers method. The advantage of Millers method and Tau model is that both the effects of the matrix and mudline velocities are considered on pore pressure prediction. 2.8.1.3.4 Millers Method The Miller sonic method describes a relationship between velocity and effective stress that can be used to relate sonic/seismic transit time to formation pore pressure. In Millers sonic method an input parameter maximum velocity depth, dmax, controls whether unloading has occurred or not. If dmax is less than the depth (Z), unloading has not occurred, the pore pressure can be obtained from the following equation (Zhang et al., 2008) EMBED Equation.3 (2.22) Where EMBED Equation.3 EMBED Equation.3 is the compressional velocity at a given depth EMBED Equation.3 is the empirical parameter defining the rate of increase in velocity with effective stress (normally 0.00025) EMBED Equation.3 is the sonic interval velocity in the matrix of the shale (14000-16000 ft/s) 2.8.1.3.5 Equivalent depth method This method is based on the assumption that every point on a log has an equivalent point on the Normal Compaction Trend line (NCT). The Equivalent depth approach is an example of the analysis utilizing trend line. A depth section is first assumed in this method where the pore pressure is hydrostatic, afterwards the assumption is that the same shale with equal physical properties at different depths will have equal effective stress and the sediments are generally compacted due to the deliberate rise in effective stress with depth. From figure 2.7, every point A in an under compacted clay is associated with a normally compacted point B. The compaction at point A is identical to that at point B. The depth of point B, EMBED Equation.3 is called the equivalent depth. The fluid contained within the pores of clay A have been subjected to all geostatic loads in the course of burial from EMBED Equation.3 to EMBED Equation.3 .Normal Compaction trends (NCTs) might be shown as straight lines fitted to the data over the ordinarily compacted points after the log of a measured quality are plotted as a function of depth. The rationale behind this method is that in over pressured formations, the overburden below a pressure seal will also be supported by fluid present in the formation, which is trapped by a good seal. So the pore pressure calculated for a depth where it is known that there is overpressure is the sum of hydrostatic fluid column pressure to the observed top of overpressure and the difference of depths between total depth and top of overpressure depth multiplied by overburden gradient. From Terzaghis relation we know that EMBED Equation.3 (2.23) EMBED Equation.3 (2.24) EMBED Equation.3 (2.25) With the knowledge of the overburden pressure at A ( EMBED Equation.3 ), the pore pressure at A ( EMBED Equation.3 ) can be calculated. EMBED Equation.3 (2.26) Then by eliminating EMBED Equation.3 and EMBED Equation.3 EMBED Equation.3 (2.27) Where EMBED Equation.3 is Pore pressure EMBED Equation.3 is the overburden pressure EMBED Equation.3 Stress at A. EMBED Equation.3 Stress at B. A depth of interest/Depth of the under compacted Clay B depth along the normal compaction trend at which the measured parameter is the same as it is at the depth of interest. The effective stress method is valid for overpressure caused by disequilibrium compaction provided that the mudrock lithology is the same at the two depths and that the only operative compaction process is mechanical compaction. Where overpressure is caused by an unloading mechanism, the effective stress method will fail to estimate the pore pressure accurately because porosityeffective stress relationships during unloading deviate from the NCT. Figure 2.7 Pore pressure prediction using the equivalent depth method 2.8.1.3.6 Ratio Method Here, pore pressure is computed based on the supposition that for resistivity, sonic delta-t, and density singly, the pore pressure is as a result of the normal pressure increased (multiplied) or separated by the degree (ratio) of the measured value to the normal value for the same depth. EMBED Equation.3 (2.28) EMBED Equation.3 (2.29) EMBED Equation.3 (2.30) Where EMBED Equation.3 is the normal hydrostatic pore pressure n is the normal value Log is the measured values It is vital to perceive that in distinction of trend line systems, the ratio approach doesnt utilize effective stress or overburden explicitly thus is not an effective stress methodology. This can result in unphysical conditions, where the overburden is lower than the computed pore pressure. 2.8.1.3.7 Hottmann and Johnson Model (1965) Hottmann and Johnson (1965) were probably the first ones to make pore pressure prediction from shale properties derived from well log data (acoustic travel time/velocity and resistivity). They indicated that porosity decreases as a function of depth from analyzing acoustic travel time in Miocene and Oligocene shale in Upper Texas and Southern Louisiana Gulf Coast. The method empirically correlates departures from the velocity normal trend line to an equivalent pore pressure gradient. This trend represents the normal compaction trend as a function of burial depth, and fluid pressure exhibited within this normal trend is the hydrostatic. If intervals of abnormal compaction are penetrated, the resulting data points diverge from the normal compaction trend. They contended that porosity or transit time in shale is abnormally high relative to its depth if the fluid pressure is abnormally high. They proposed an equation that can be written in the following form to predict pore pressure In contrast to Terzaghis methods, the Hottmann and Johnsons method does not use overburden or effective stress explicitly and thus is not an effective stress method. This can lead to unphysical situations, such as calculated pore pressures that are higher than the overburden. More so, the method is biased towards fluid expansion overpressure, as such it is likely to overestimate pore pressure in wells with under-compaction as the primary overpressure mechanism. 2.8.1.3.8 Zhangs Model (2011) Zhang derived a theoretical equation for pore pressure prediction (PPP) from porosity according to normal compaction trend of porosity. Overpressure sets in where porosity EMBED Equation.3 at an interested depth is greater than the normal porosity EMBED Equation.3 at the same depth. The expression for the pore pressure gradient is given as EMBED Equation.3 (2.32) Where EMBED Equation.3 is the porosity in shale at depth, Z EMBED Equation.3 is the porosity in the mudline (in the ground surface or sea floor) Z is the true vertical depth below the mudline c is the compaction constant in 1/m or 1/ft. Porosity can be obtained from sonic logs and is often estimated by an empirical compressional transit time equation presented by Wyllie et al. (1956) given as EMBED Equation.3 (2.33) EMBED Equation.3 is the compressional transit time in the matrix. EMBED Equation.3 is the compressional transit time of interstitial fluids. Substituting Wyllies porosity-transit time equation into Eqs. 26, yields the following equation to calculate pore pressure gradient (Ppg) and pore pressure. EMBED Equation.3 (2.34) Where EMBED Equation.3 is the normal compaction trendline, calculated as EMBED Equation.3 (2.35) Since it is commonly accepted that porosity decreases exponentially as depth increases in normally compacted formations (Athy, 1930). The advantage of Zhangs model is that the calculated pore pressures are dependent on depth, and both effects of the matrix and mudline transit time are taken into account. EMBED Equation.3 (2.36) By combining equation 2.18 with the mean effective stress law from basic stress analysis (Goulty, 1998) the following relationship can be derived (Harrold, 1999) EMBED Equation.3 EMBED Equation.3 (2.37) 2.8.1.3.10 Holbrooks Method (1995) Holbrook (1995) also proposed a pore pressure estimation method for naturally fractured reservoirs. There is no need to set any trend lines in Holbrooks method, because this method is based on the relationship between the porosity, mineralogy, and effective stress in granular sedimentary rocks. Holbrook successfully used this effective stress-law in the North Sea to predict pore pressure in limestone, shaly limestone, and sandstone intervals (Holbrook, 1999). Holbrooks method uses the following equation to calculate the effective stress of the rock EMBED Equation.3 (2.38) Where EMBED Equation.3 is the effective stress EMBED Equation.3 is the maximum effective stress required to reduce the mineral porosity to zero EMBED Equation.3 is porosity from well logs EMBED Equation.3 is the compaction strain-hardening coefficient for the type of minerals. It is assumed to be 5.0 2.8.1.3.11 Dutta (1987) Dutta (1987), published constitutive relationships between porosity and effective stress. The calculated effective pressure could be converted to pore-pressure using the simplified Terzaghi equation EMBED Equation.3 (2.39) Where EMBED Equation.3 the porosity in the sea floor and depends on lithology EMBED Equation.3 is the diagenetic function and a function of temperature denotes the contribution to the rock transit time variation from chemical compaction. It is assumed to be 5.0 EMBED Equation.3 is void ratio given by EMBED Equation.3 The following relationship presented by Wyllie et al. (1956) relates transit time with porosity and is used to estimate porosity. EMBED Equation.3 (2.40) Where EMBED Equation.3 is the transit time of interstitial fluids 189sec/ft EMBED Equation.3 is compressional transit time in the matrix (167sec/ft) The corresponding pore pressure is given by subtracting EMBED Equation.3 from the overburden pressure as given in Terzaghi equation. 2.8.2 Detection Methods These methods utilize drilling parameters and well log information obtained during the actual drilling of a well. while drilling mainly uses the logging while drilling (LWD), measurement while drilling (MWD), drilling parameters, and mud logging data such as ROP, gas, cuttings/cavings, drag and torque, and drilling exponent. 2.8.2.1 Cavings and cuttings The type, shape, size, and quantity of carvings/cuttings may be indicative of overpressure or UBD. In particular splintery, curved and large quantity of cuttings are typical in UBD situations (Figure 3.14). Cavings can also be produced due to bedding plane instability but these generally have distinct shape (i.e. blocky) and appears to be man made, this occur more at connection (ESD PP) and, increasing MW typically worsens the situation. Information on nature of cavings and cuttings obtained from mud log, drilling data, and end-of-well-report (EOWR) is used in corroboration with other data to infer UBD. 2.8.2.2 Rate of penetration (ROP) ROP is an indication of how fast the bit drills through an interval. The expectation is that, if drilling parameters are kept constant, ROP should decrease with depth due to increased Compaction of rock with depth. Therefore, increased ROP or any deviation from increasing trend could potentially be due to under-compaction which is typically associated with overpressure. However, because parameters such as weight on bit (WOB), revolutions per minute (RPM), hydraulics, and mud weight are hardly ever kept constant, ROP may increase or decrease independent of the sediment compaction state. When available, normalized ROP used in conjunction with other indicators is a more accurate means of inferring under-compaction during drilling. Examples of normalized ROP including the D-exponent are discussed later in the section. Torque The torque should increase with depth due to contact between drill string and wellbore. In the underbalanced situation, the hole will flow into the bore leading to increased string-to-wall contact and consequently increased torque. Bit balling, BHA configuration, hole deviation, and instability that is unrelated to formation pressure may also cause increase in torque. Modern drilling rigs and mud logging units now have the capability to differentiate between down hole and surface torque. Balling up may be indicative of a pressure transition zone and is typically characterized by a steady torque reading. Shale density Sediments density increases with depth as it compacts, as such, a deviation from this trend is assumed to results from pore pressure which prevents further compaction at that depth. Theoretically, this method is an excellent overpressure indicator, but because of the difficulty involved in making precise measurements and selecting the shale that represents drill cuttings, it is not commonly practice. Measurement while drilling (MWD) MWD is a system to make drilling measurements and transmit data to the surface while drilling the well. MWD tools are as part of BHA. The tools are either contained inside a drill collar (sonde type) or are built into the collars themselves. The measurements of GR, directional survey, tool face, pressure in borehole, temperature, vibration, shock, torque etc. can be taken by MWD. Some advanced MWD tools can even measure formation pressure and take formation samples. The MWD also provides the telemetry for operating rotary steering tools (RST). The measured results can be stored in MWD tools and the results transmit digitally to surface using mud pulsar telemetry or other advanced technology. MWD systems have the capability of receiving control commands which can be sent by turning on and off mud pumps or by changing the rotation speed of drill pipe or by other advanced telemetry technology such as wired pipe. Logging while drilling (LWD) LWD is a technique of transporting well logging tools into downhole of the well as part of the BHA. The combination of LWD tools and MWD system transmit partial or complete measurement results to the surface via a drilling mud pulser or other improved techniques, while LWD tools are still in the borehole, which is called Real Time Data. Real-time data from LWD services let us make timely, informed decisions, reducing time and costs. Complete measurement results can be downloaded from LWD tools after they are pulled out of hole, which is called Memory Data. LWD data will be collected during drilling operations. Collecting and processing data due drilling operations eliminate the requisition of drilling assembly to insert a wireline logging tool 2.9 Direct pressure measurement These are pressure measurement done using wireline pressure tools for measuring vertical pressure distribution in reservoirs. Two dominant types of tools for pressure data collection are used (1) high resolution tools run in open well bores prior to completion – amongst others, the modular formation dynamics tester (MDT), the formation multi-tester (FMT), the repeat formation tester (RFT) and the GeoTap pressure while drilling tool developed by Halliburton – and (2) downhole gauges (e.g. the drill stem test (DST)) that provide measurements from producing wells. These tools include the Repeat Formation Tester (RFT), Modular Dynamics Tester (MDT) or Drill Stem Test (DST). Both the RFT and MDT measure pressure at known depths and are commonly used to determine fluid gradients in individual sands These tools are only applicable in permeable formations such as sandstones, where the pore fluid can flow to enable the pressure measurement. In situations where Sandstone reservoirs are isolated with shale dominated sequences the Pore pressure through such sequences are determined using both direct measurements in the reservoirs, and estimation of shale properties in to non-reservoirs section. 2.9.1 Repeat Formation Tester The tool has been primarily designed to measure Formation pressure Test Collect Reservoir Fluid Samples. The tool operates by isolating a small area of the formation from the well bore fluids and allowing formation fluids to flow into two chambers. It can test several levels in a well. At the zone to be tested, a backup shoe from the tool is pressed against the well wall to force a rubber pad with a valve against the opposite wall. The valve is opened, and formation fluids can flow into the tool as the pressures are measured. The pressure records are used to calculate formation permeability. Two large sample chambers, each holding several gallons, are used to obtain a sample of formation fluids at that level. The samples are used to measure water resistivity (Rw) and saturation (Sw). This can be repeated at several locations in the well. The RFT tools provide accurate, definitive data on formation pore pressure. However, the formation pressure data can only be obtained from permeable layers, such as reservoir sandstones or limestone. This formation may contain pressures which bear no resemblance to the pore pressure in the overlaying and underlaying formations, and such their application is restricted to the formation sampled. The RFT tools provide accurate, definitive data on formation pore pressure. However, the formation pressure data can only be obtained from permeable layers, such as reservoir sandstones or limestone. This formation may contain pressures which bear no resemblance to the pore pressure in the overlaying and underlaying formations, and such their application is restricted to the formation sampled. In HPHT wells the RFT tool should be considered for use prior to performing potentially problematic drilling operations, such as coring, in order to fine tune the required med density and minimize the risk of swab or surge problems. 2.9.2 Drill Stem Test (DST) DST is a method of the testing formation pressure and fluid. A drill stem with a packer is run and set just above the zone to be tested. The packer is set and a DST valve is opened to allow the reservoir to communicate with the inside of the drillstem which is run either empty or with a small calculated cushion. The drill stem is run with several pressure gauges. The purpose of the pressure gauges is to record the downhole pressure during the sequence of flow and shut in periods that comprise the DST. The pressures recorded during the test are used to calculate reservoir characteristics such as formation pressure, permeability, skin damage and productivity index. Analysis of the pressure build up from shut in leads to accurate determination of the formation pore pressure. The second shut-in period is used for determining the final shut-in reservoir pressure. The actual static reservoir pressure is determined from Horner analyses of the DST pressure data. These downhole gauges provide the invaluable ability to monitor pressure decline over time. Data from drill stem tests enable accurate determination of the reservoir pressure. However, the pressure data can only be obtained from permeable formations that exhibit sufficient hydrocarbon reservoir potential to warrant the expense of the DST. 2.10 Wireline logs Prediction based on wireline logs is regarded as one of the major methods employed to estimate pore pressure in shale formation. This is due to the fact that direct pressure measurement in shale using Modular Dynamic Tester (MDT) or Repeat Formation Testers (RFT) appears very difficult/ almost impossible owing to their low permeability. Shale comprises a large portion of most sedimentary basins and forms the seal and source rock for many hydrocarbon reservoirs. It is commonly assumed that over-pressured shale is under compacted relative to its depth of burial. As such, exhibit several of the following properties when compared with the normally pressured section at the same depth Higher porosities, Lower bulk densities, Lower interval velocities and impedances, Lower resistivity. Wireline methods of pore pressure prediction involves establishment of a base line from normal pressure shale reading, which is being extrapolated into the abnormal pressured region. The observed values in the abnormal pressure region, are then related to the extrapolated values. Figure 2.9 Schematic depicting log responses to compaction disequilibrium 2.10.1 Natural gamma ray tool All rocks emit natural gamma radioactivity as a function of their mineralogy where radioactive elements, chiefly potassium, uranium and thorium, are present. As the radioactive elements decay, they emit alpha particles, beta particles or, of prime importance for this tool, gamma rays. A scintillation detector in the tool detects gamma rays, and the number of detected gamma rays is recorded in standard API (American Petroleum Institute) units. The radioactive isotopes of potassium, uranium and thorium are found in a number of minerals such as illite (potassium), organic matter (uranium) and heavy minerals such as zircon and sphene (thorium). All of these minerals are normally far more abundant in shale than sandstones (the dominant lithology encountered in the wells in this study) so shale have higher gamma ray API responses than sand bodies. 2.10.2 Resistivity and conductivity log The resistivity and induction tools are used in almost all wells drilled to measure the electrical current carrying capacities of the formations. The resistivity tools measure the formations resistance to the flow of electrical current (the resistivity), whilst the induction tools measure the formations ability to conduct current (the conductivity). The measurements are equivalent since the conductivity is the reciprocal of the resistivity. The flow of current in the formation is controlled by a number of factors the resistivity of the pore fluid, the resistivity of the matrix material, the resistivity of the mud-filtrate, porosity, salinity of the pore fluid and the temperature. The resistivity effect of the mud-filtrate is overcome by using deep resistivity reading which are taken outside the mud zone. As salinity does not normally vary greatly with depth, it effects is often discounted. Temperature on its own normally increases uniformly with depth and hence, resistivity value can be corrected for the temperature increase. Therefore, only porosity affects resistivity majorly, thereby making it effective as a viable porosity determinant tool to complement acoustic log. 2.10.3 Sonic log Along with the gamma ray, caliper and resistivity tools, the acoustic logging tool is used as standard for logging wells. For this study the sonic log is considered to be the most accurate as it is largely unaffected by borehole size, formation temperature and pore fluid salinity. The tool measures the interval transit time (ITT) of the formation being investigated. A compressional wave signal is emitted at one end of the tool and travels through the mud into the formation, is refracted along the borehole wall, and generates head waves which are refracted back into the borehole to be detected by the two receivers at the other end of the tool. Since the spacing between the two receivers is constant, the difference in signal arrival time corresponds to the speed of sound in the formation between the two receivers. And the measured interval transit time is taken to represent the velocity at the midpoint between the two. In normal compaction profile, the transit time should decrease with depth due to decreased porosity and increasing density. Abnormally pressured shale tends to have higher porosity and lower density than normally pressured shale, and hence are easily depicted by acoustic log signature. Figure 2.10 Schematic of a sonic logging tool with receiver and transmitter 2.10.4 Density log The density tool works by bombarding the formation with medium energy, focused gamma rays and measuring the back scattered flux of gamma rays at near and far detectors. As the density of the formation increases, so the count per second of gamma rays decreases. The importance of the density log to this study is for the calculation of vertical stress in the well. Density increases with depth due to the increased compaction resulting from the weight of the overlying sediments as new sediments were being deposited. In abnormally pressured shale, compaction is often retarded, resulting in an increased porosity and thus lower density than normally pressured shale at an equivalent depth. Such a decrease in shale bulk density value from normal compaction value is observed when entering a zone of abnormal pressure. Where it has been supplied for analysis, density-derived porosity is used for comparison with porosity computed from the sonic log. Such a comparison has been proposed for use in determining whether unloading had contributed to observed overpressures (Bowers, 1998). The theory here is that unloading induces a change in the fabric of the shale which affects the sonic waves more than the density log, causing a separation in the two tool responses over the interval i.e. unloaded. 2.11 Mud weight window It is defined as the difference between maximum allowable annular wellbore pressure and the minimum allowable annular wellbore pressure. To ensure wellbore stability, the mud pressure should be kept higher than the formation pressure to keep formation fluids from entering the wellbore and higher than the collapse pressure. If the mud pressure becomes lower than the collapse pressure shear failure at the wellbore wall will occur. The upper limit to this window is set by the fracture gradient while the lower limit is set by the pore pressure or collapse pressure, whichever is greater. In cases of high overpressure or low formation strength, the mud weight window may be quite narrow and with the resulting high risk for well flow (kicks) and losses. Typically, the narrower a drilling window is, the more difficult a given well will be to drill. Wells with extremely narrow drilling windows often require complicated well designs, which incorporate many casing and liner strings, or unconventional drilling applications such as managed pressure drilling. A narrow operating window is a hazardous scenario for drilling. In this scenario, the narrow margin makes it more challenging to drill within the window as a slight change in bottom hole pressure may lead to formation fracturing, severe drilling fluid loss, or flow influx. It is standard practice to set numerous casing strings at shallow depths to avoid extensive lost circulation. 2.12 Collapse Pressure Collapse pressure represents the minimum mud weight required to maintain a gun barrel hole and keep the formation intact before potential collapse6. The formation collapse pressure should not be ignored. In some cases, the collapse pressure is equal to or greater than pore pressure. Drilling operations encroaching on the collapse pressure curve are likely to see large splinters of formation popping off into the wellbore, as opposed to cuttings created by the drill bit. Wellbore instability may cause the drill string to become stuck by packing off the wellbore from collapse of the formation. Figure 2.11 Drilling/Mud weight window Equivalent Mud Weight The equivalent mud weight (EMW) is the fluid density that would be required to provide an equivalent hydrostatic head to equal the bottomhole pressure being applied. During LOTs, the well is shut-in and fluid is pumped into the well to increase the annular pressure. The goal of a LOT is to test the cement job and determine the strength of the formation. Operationally, the results of these tests are typically expressed in pounds per gallon and referenced as the EMW, because the value is the highest mud weight that can be used while drilling the subsequent hole section without fracturing the well. Nearly all the methods in the literature relate shale excess porosity with excess pore pressure. This is explained by the shale compaction behavior. Since shale is compacted to a greater extent as compared to the other lithology, any excess porosity is interpreted as indicating undercompaction resulting in overpressure. When wireline logs are available, empirical correlations can be used to estimate pore pressure before drilling. These empirical relations were obtained from offset well data. 2.13 Fracture Pressure prediction Methods Fracture pressure is the pressure needed to rupture the formation causing loss of mud from wellbore into the induced crack. Dividing the true vertical profundity by the fracture pressure gives the fracture gradient. The facture gradient can be taken as highest mud weight. Various hypothetical and field-created mathematical statements have been utilized to estimate fracture pressure/gradients. The vast majority of these are suitable for quick application in a given zone. 2.13.1 Mathews and Kelly Method Mathews and Kelly created the accompanying mathematical statement for computing fracture gradients in sedimentary formations after they understood that the cohesiveness of the rock matrix is regularly identified with the matrix stress and differs just in the level of compaction EMBED Equation.3 (2.41) Where PF is the formation fracture gradient Ppg is the formation pore pressure gradient OBG is the overburden stress gradient Ko is the effective stress coefficient EMBED Equation.3 (2.42) Where EMBED Equation.3 minimum effective in-situ stress EMBED Equation.3 Maximum effective in-situ stress or effective overburden stress 2.13.2 Hubbert and Willis method Hubbert and Willis investigated the variables included in starting a crack in a formation. They accepted the fractured gradient is a function of formation pressure, overburden stress, and a relationship between the vertical and horizontal stress. They accepted the stress relationship to be within to 1/3 of the aggregate overburden. The fracture gradient is in this manner dictated by EMBED Equation.3 (2.43) Where PF is the formation fracture gradient Pp is the formation pore pressure gradient OBG is the overburden stress gradient 2.13.3 Ben Eaton Method Ben Eaton amplified the ideas offered by Mathews and Kelly to bring Poissons ratio into the equation for the fracture pressure gradient EMBED Equation.3 (2.44) Where EMBED Equation.3 is the fracture pressure EMBED Equation.3 is the Poisson ratio, which is calculated using the equation EMBED Equation.3 EMBED Equation.3 (2.45) EMBED Equation.3 is the overburden pressure EMBED Equation.3 is the depth EMBED Equation.3 is the pore pressure Where EMBED Equation.3 and EMBED Equation.3 are the compressional and shear velocities, respectively 2.14 Drilling problems associated with abnormal pressures When planning the drilling operation of a formation, an adequate pore pressure prediction and mud density must be estimated to forestall the following occurrences i. Breakdown of formation (exceeding the fracture gradient) and consequent borehole collapse. ii. The influx of formation fluids such as kicks and blowouts. iii. Lost circulation (flow of mud into formation). iv. Excessive differential pressure causing stuck pipe 2.14.1 Kick and Blowouts Kick occurs where formation pore fluid enters the wellbore. This is the result of drilling into a formation where the pore pressure exceeds mud hydrostatic pressure. Wells are planned and drilling operations are controlled with the intention that primary well control is always maintained i.e. hydrostatic mud pressure is greater than formation fluid pressure. Fluid pressures are fundamental to many aspect of oil well drilling. If downhole pressures are not kept under control, an uncontrolled release of oil and gas to the surface (called blow out) can result, which might lead to loss of life, massive environmental damage to underground reservoir, and damage to rig and other facilities. 2.14.2 Lost Circulation Continued loss of drilling mud to the formation not only damages future production potential, but could also lead to a well control issue. The hydrostatic pressure throughout the wellbore decreases when the (static) mud column in the annulus decreases in height, hence the loss of drilling mud in the wellbore will have to be refilled. The decreased height of the mud hydrostatic column sets the stage for a pressure imbalance between the hydrostatic mud column and the fluid contained in the exposed rock formation. An influx of some magnitude will arise once the bottomhole pressure exceeds the hydrostatic pressure created by the static mud column. On condition that there is not an intervention that influx can grow in volume leading to a kick and if it is not monitored it may result in a blow-out8. 2.14.3 Stuck Pipe As it is published in the underbalanced drilling and completion manual of DEA, the most common sticking mechanism in conventional drilling is the differential sticking. When drilling fluid leaks into the formation, leaving a fairly impermeable layer of solids on the wellbore, differential sticking occurs. If the drill pipe or tubing is in contact with the wellbore, the filtrate can leak away from behind the pipe and create a low-pressure zone. Pressure sticking or differential sticking of the pipe is seen when the differential pressure over the area involved creates forces. This cannot happen if the well is underbalanced. Stuck pipe can be freed by changing the well condition to underbalance. 2.14.4 Wellbore Instability Once the mud column pressure against the formation is reduced there are important setbacks to consider. In order to function as a close up against well kicks or blowouts, heaving shale (geopressured shale), broken or fractured formations, and general borehole Instability due to tectonic stresses or weak formations and salt, most of the drilling procedures exploit the mud column pressure. The wellbore pressure differential should be controlled very accurately to mitigate wellbore instability problems. MPD methods and tools are used to manage the pressure profiles in the wellbore to reduce the likelihood of the unwanted pressure differential. Figure 2.12 Effects of Wellbore Pressure Differential Figure 2.13 Schematic of wellbore behavior due to the differential pressures across the well. Depicted above is an illustration of the behavior of the wellbore by virtue of varying pressures across the well. Surges in wellbore pressure causes hydraulic fracturing, while a decrease in the wellbore pressure causes wellbore collapse and fluid kicks. CHAPTER THREE 3.0 Methodology The project was conducted using data from a well situated in the Niger Delta. Data used primarily consisted of wireline logs such as sonic velocity, density, gamma ray logs and wireline formation tester (WFT) data in the form of Repeat Formation Tester (RFT). Prior to selecting the well for the study, it was ensured the well had a good quality of wireline logs as well as pressure data. The tools employed for this study were Ikon Sciences RokDoc software and Microsoft Office Excel. The Rokdoc software was used for log conditioning, overburden pressure profile generation, discrimination and estimation of shale volume, normal compaction trends generation, pore pressure, fracture pressure, and mud weight window estimation, generation of cross-plots and general analysis. Microsoft Office Excel was used in the statistical analysis to ascertain the accuracy of the models and subsequently ranking them. The basic workflow that was adopted in this work is as follows Acquisition and review of offset well data of Niger Delta in the form of petrophysical well logs and direct pressure measurements (RFT). Generation of Overburden pressure profile, Normal Compaction Trend and Shale velocity trend which were used as input parameters in the pore pressure prediction models. Inputting the aforementioned generated parameters into pore pressure prediction models (Eaton, Bowers, Tau, Equivalent Depth, Ratio, Miller, Zhang, Hottmann and Johnson, Holbrook, and Dutta) and predicting the pore pressures of the area. Comparison of the predicted pore pressures with the measured pressure data (RFT) and conducting a statistical analysis of the results by way of computing the percentage and Root Mean Square Errors of the results and ranking the models on the basis of the percentage and Root mean square error values, with the least being the most accurate. Selecting one of the models and optimizing it so as to obtain more accurate predictions and lower percentage and Root Mean Square Error values. Detection of over-pressured zones by noting the deviations of compressional velocity from the normal compaction trend line and the deviation of pore pressures predicted using the optimized model from the hydrostatic pressure gradient zone. Plotting and analysis of velocity versus density cross-plot to determine overpressure generation mechanism and comparing them with model signatures by Hoesni, 2004. Estimation of the Fracture pressure of the formation by using the most accurately predicted pore pressure model values as inputs in the Eatons fracture pressure method. Estimation of the mud weight window by utilizing the predicted pore pressures and fracture pressures as the lower and upper boundaries respectively. Interpretation of results. The full procedure is illustrated in Figure 3 below SHAPE MERGEFORMAT SHAPE MERGEFORMAT Figure 3.1 Schematic of Workflow employed in the project. 3.1 Data Sources The Data used in the pressure evaluations were derived from two main sources wireline log suite and the measured pressure data. The table below summarizes the wireline log data employed in the work and their uses. Wireline LogGamma ray (GR)Density (Rho)Compressional Velocity (Vp)Shear wave Velocity (Vs)UnitAPIg/cm3m/sm/sUse of dataLithology discriminationOverburden pressure estimationNCT computation, pore pressure and fracture pressure predictions.Poissons ratio computation and fracture pressure prediction. Table 3.1 Wireline log data used in the work 3.1.1 Wireline logs The suite of well log used is shown below in the figure 3.1. The first track on the log suite shows the gamma-ray log, second track indicates the sonic compressional velocity log. This is followed by shale fraction log in track three. Track four indicates shear wave log while track five is the density log. Prior to using the wireline logs, they were scrutinized for the accuracy of the log heading as well as depth discrepancies. Logs that do not meet the quality control standard were discarded from the study. 3.1.2 Pressure Data Direct measurements of pressure taken in the wells are the most crucial data source used in this study. As without these data, the pore pressure prediction from wireline log data would just be estimates with no means of validation by comparison with the actual pore pressures. These data are used to validate the pore pressure prediction methods used in the study. 3.1.2.1 Repeat Formation Test Data (RFT) The RFT data (Pressure data) used in the project was made available by an oil Exploration and production company. Prior to using the pressure data, the Pressure depth data were compared with Gamma Ray, compressional velocity, density and shear wave logs data points that were off depth were discarded. 3.2 Shale discrimination and volume generation Given that most compaction occurs in the clay formations, and a compaction trend is crucial to pore pressure prediction, the cleanest clay lithology is used. To do this a volume fraction of shale is generated using Gamma Ray log with the aid of Ikon Science Rokdoc software. The volume fraction of shale, Vsh, is the fractional volume of shale as measured or inferred from formation properties. It is crucial to the accurate establishment of the Normal Compaction Trend (NCT) which is taken using the cleanest shale interval measured along the formation of interest. Gamma Ray log is used to discriminate between sand and shale lithology also with the aid of the RokDoc software. The gamma ray log is commonly used to measure the clay content with a typical threshold of about 65 API units. The main assumption associated with the lithological discrimination is that the gamma ray response for each of the lithology encountered is a function of a mixture of low gamma quartz ( EMBED Equation.3 ) and high gamma clays. The lowest values for the API response are assigned the value of a clean sand ( EMBED Equation.3 ) and the highest average values are assigned the value of a pure clay. After the identification of the sands and shale, points for analysis are chosen by virtue of having a clay fraction greater than 50. Data points with lesser values than the gamma ray from clay discrimination line are filtered out, with the remaining points utilized. The volume fraction of shale, Vsh, is generated from the Ikon Sciences RokDoc software using the standard equation EMBED Equation.3 (3.1) Where GRlog is the log response GRsh is the log value in clean shale GRsd is the log value in clean sand. The Vsh output is also used as additional filter in picking clean shale during NCT generation, and for pore pressure estimation. Figure 3.2 Wireline logs used in the work obtained from a well in the Niger Delta. Track 1 shows the natural gamma ray (GR) log, track 2 the sonic compressional velocity, track 3 shows the shale volume fraction, track 4 is the shear wave velocity and track 5 is the density log. True Vertical depth (ft.)Pressure (psi)Pressure typeData typeQuality70813267Pore PressureRFTGood71763327Pore PressureRFTGood73663716Pore PressureRFTGood78513832Pore PressureRFTGood79703669Pore PressureRFTGood82603822Pore PressureRFTGood83913867Pore PressureRFTGood93234285Pore PressureRFTGood93864434Pore PressureRFTGood94434500Pore PressureRFTGood95284608Pore PressureRFTGood97564725Pore PressureRFTGood105244901Pore PressureRFTGood105525011Pore PressureRFTGood106025277Pore PressureRFTGood107515388Pore PressureRFTGood110365275Pore PressureRFTGood110635733Pore PressureRFTGood110935950Pore PressureRFTGood111226171Pore PressureRFTGood111506243Pore PressureRFTGood111746349Pore PressureRFTGood112926157Pore PressureRFTGood113205963Pore PressureRFTGood114346002Pore PressureRFTGood Table 3.2 Measured Pressure data used for analysis (validation) in the work 3.3 Overburden stress The computation of the overburden stress as a function of depth is a pre-requisite for pore pressure prediction in most models. These estimates are applied as inputs in Terzaghis equation for correlating vertical effective stress and pore pressure. It is an estimate of the combined weight of the fluid and the matrix rock above the depth of interest. The Overburden pressure in this work was estimated from bulk density using cumulative summation of the sediment weight for each depth interval. Methods for overburden pressure estimation include fixed gradients (use of 1.0 psi/ft. with the assumption of a constant mudrock porosity of 24) and use of density log. The overburden pressure was estimated using a model fit of the density log (Rho fit) by employing the RokDoc software using the equation EMBED Equation.3 (3.2) Where EMBED Equation.3 overburden at depth below mudline EMBED Equation.3 density of matrix EMBED Equation.3 density at mudline (seabed) EMBED Equation.3 Compaction coefficient (1/ft) 3.4 Normal compaction trend (NCT) As pore pressure is determined by comparing the measured value of shale properties (from log data) with the anticipated value for that measurement in a normally pressured environment, the NCT is a required input for pressure prediction and must be estimated as accurately as possible. More so it used in detecting overpressured zones by noting sonic data deviations from the normal compaction trend line which indicates abnormal pore pressure in the formation. The normal compaction trend in this study was picked from the compressional velocity, EMBED Equation.3 log. It was generated in the RokDoc software using the reciprocal input log transform of compressional velocity, EMBED Equation.3 which is used together with the hydrostatic and lithostatic pressures to predict Pore pressures. The NCT is estimated using the equation EMBED Equation.3 (3.3) Where EMBED Equation.3 is the compressional wave velocity at or near the surface for onshore data (Ft/s) EMBED Equation.3 is the compressional wave velocity at maximum extrapolated (in ft/s), EMBED Equation.3 is the compressional wave velocity at or near the surface point along depth (in ft/s) b is the compaction coefficient (ft-1) The NCT was iteratively tried until a reasonable trend was picked in relation to the cleanest shale interval. 3.5 Hydrostatic Pressure Water density value of 0.433 psi/ft (1.025 g/cc) was used as the average formation water density which is also the hydrostatic pressure gradient. The hydrostatic pressure was then found by multiplying the pressure gradient by the respective depth below mudline. That is EMBED Equation.3 (3.4) Where EMBED Equation.3 is the hydrostatic pressure EMBED Equation.3 is the true vertical depth below mudline (ft) Parameters such as the estimated overburden pressure, hydrostatic pressure, and normal compaction trend were utilized as inputs in the models. The Rokdoc software was used for the prediction of the pore pressures. 3.6.1 Eatons model (1975) The model is one of the most commonly used techniques for predicting pore pressure, especially in the industry (Yoshida et al, 1996). It compares a petrophysical property such as velocity or resistivity to the normally compacted equivalent of that property at the same depth. It requires both a Normal compaction trendline (NCT) and an Overburden pressure for its computation. The Eatons model in accordance with Terzaghi (1943) gives a direct transform of sonic velocity into pore pressure. EMBED (3.5) Where EMBED predicted pore pressure in psi. Pob overburden pressure Phyd hydrostatic pressure EMBED observed velocity at the depth of interest in m/s from shale trend velocity EMBED normal compacted shale velocity The above equation was applied Rokdoc for the prediction of pore pressure using Vp log as input 3.6.2 Bowers Model The model represents a relationship between effective stress and velocity that could be utilized to associate sonic velocity to formation pore pressure. According to Bowers sonic velocity EMBED Equation.3 and effective stress EMBED have a power relationship given by EMBED (3.6) EMBED (3.7) However EMBED (3.8) Therefore pore pressure can be obtained by substituting equation (3.7) into (3.8) EMBED The above equation is used by Rokdoc in the prediction with Vp log as input Where EMBED velocity of unconsolidated fluid saturated sediments (about 5000 ft/s1524m/s) in Niger Delta. Vp compressional sonic velocity A and B are constants which describe the variation in velocity with increasing effective stress. Where A and B are assumed to be 0.69 and 1.0 respectively. 3.6.3 Tau Model Shell proposed a pore pressure prediction technique that depends on velocity as it introduced a Tau variable in the mathematical statement of effective stress (Lopez et al., 2004 Gutierrez et al., 2006). The empirical relationship links vertical effective stress to velocity. EMBED (3.9) Where EMBED Effective stress A and B Fitting constants derived from the local area EMBED Tau variable EMBED (3.10) C and D constants related to the mudline and matrix transit time respectively EMBED Transit time from velocity log Therefore, pore pressure calculation of Terzaghis equation, using the Tau model then becomes EMBED (3.11) 3.6.4 Millers Method The Miller sonic method describes a relationship between velocity and effective stress that can be used to relate sonic/seismic transit time to formation pore pressure. In Millers sonic method an input parameter maximum velocity depth, dmax , controls whether unloading has occurred or not. If dmax is less than the depth (Z), unloading has not occurred, the pore pressure can be obtained from the following equation (Zhang et al., 2008) EMBED Equation.3 (3.12) Where EMBED Equation.3 is the empirical parameter defining the rate of increase in velocity with effective stress (normally 0.00025) EMBED Equation.3 is the compressional velocity at a given depth EMBED Equation.3 is the compressional velocity in the mudline (i.e., the sea floor or the ground surface 5000 ft/s, or 1520 m/s EMBED Equation.3 is the sonic interval velocity in the matrix of the shale (14000-16000 ft/s) 3.6.5 Hottmann and Johnson Model (1965) Hottmann and Johnson (1965) were the pioneers of pore pressure prediction from shale properties derived from well log data (acoustic travel time/velocity and resistivity). They indicated that porosity decreases as a function of depth from analyzing acoustic travel time in Miocene and Oligocene shale in Upper Texas and Southern Louisiana Gulf Coast. The method empirically correlates departures from the velocity normal trend line to an equivalent pore pressure gradient. They proposed an equation that can be written in the following form to predict pore pressure EMBED Equation.3 (3.13) Where EMBED Equation.3 is the normal fluid pressure gradient (psi/ft.) A and B are the constants, A1 82776 and B1 15695 EMBED Equation.3 is the depth (ft) EMBED Equation.3 (3.14) EMBED Equation.3 (3.15) EMBED Equation.3 (3.16) With the knowledge of overburden pressure at A ( EMBED Equation.3 ), the pore pressure at A ( EMBED Equation.3 ) can be calculated. EMBED Equation.3 (3.17) Then by eliminating EMBED Equation.3 and EMBED Equation.3 EMBED Equation.3 (3.18) Where EMBED Equation.3 is Pore pressure. EMBED Equation.3 is the overburden pressure EMBED Equation.3 Effective Stress at A. EMBED Equation.3 Effective Stress at B. A depth of interest/Depth of the under compacted Clay B depth along the normal compaction trend at which the measured parameter is the same as it is at the depth of interest. 3.6.7 Zhangs Model (2011) Zhang derived a theoretical equation for pore pressure prediction (PPP) from porosity according to normal compaction trend of porosity. Overpressure sets in where porosity EMBED Equation.3 at an interested depth is greater than the normal porosity EMBED Equation.3 at the same depth. The expression for the pore pressure gradient given as EMBED Equation.3 (3.19) The Porosity is often estimated by the empirical time average equation presented by Wyllie et al. (1956) EMBED Equation.3 (3.20) Where EMBED Equation.3 is the transit time of interstitial fluids. Substituting Wyllies porosity-transit time equation into Eqs EMBED Equation.3 (3.21) Where EMBED Equation.3 is the hydrostatic pore pressure. Where EMBED Equation.3 is the porosity in shale at depth, Z EMBED Equation.3 is the porosity in the mudline (in the ground surface or sea floor) Z is the true vertical depth below the mudline c is the compaction constant in 1/m or 1/ft. EMBED Equation.3 is the compressional transit time in the matrix. 3.6.8 Ratio Method Here, pore pressure is computed based on the supposition that for resistivity, sonic delta-t, and density singly, the pore pressure is as a result of the normal pressure increased (multiplied) or separated by the degree (ratio) of the measured value to the normal value for the same depth. EMBED Equation.3 (3.22) 3.6.9 Holbrooks Method (1995) He proposed a pore pressure estimation method for naturally fractured reservoirs. The method doesnt require any trend lines, as it is based on the relationship between the porosity, mineralogy, and effective stress in granular sedimentary rocks. Holbrooks method uses the following equation to calculate the effective stress of the rock EMBED Equation.3 (3.23) Where EMBED Equation.3 EMBED Equation.3 is the effective stress EMBED Equation.3 is the maximum effective stress required to reduce the mineral porosity to zero EMBED Equation.3 is porosity from well logs EMBED Equation.3 is the compaction strain-hardening coefficient for the type of minerals. 3.6.10 Duttas Model Dutta (1987), published constitutive relationships between porosity and effective stress. The calculated effective pressure could be converted to pore-pressure using the simplified Terzaghi equation EMBED Equation.3 (3.24) Where EMBED Equation.3 the porosity in the sea floor and depends on lithology EMBED Equation.3 is the diagenetic function and a function of temperature denotes the contribution to the rock transit time variation from chemical compaction. EMBED Equation.3 is void ratio given by EMBED Equation.3 (3.25) The following relationship presented by Wyllie et al. (1956) relates transit time with porosity and is used to estimate porosity. EMBED Equation.3 (3.26) Where EMBED Equation.3 is the transit time of interstitial fluids 189sec/ft EMBED Equation.3 is compressional transit time in the matrix (167sec/ft) 3.7 Prediction performance of Models In order to ascertain the accuracy of the pore pressure predictive models, a statistical error analysis of the results was conducted by calculating the percentage error and the Root Mean Square Error (RMSE) of the results obtained from the models. This was used as the primary index to further rank the models in terms of their accuracy. These were done using Microsoft Excel. The statistical measures are defined as follows 3.7.1 Percentage Error It is the magnitude of the difference between an exact and an approximate value divided by the magnitude of the exact value. It allows one to see the how far off an approximate value and an exact value are in percentage of the exact value. Error EMBED Equation.3 (3.27) Where approx. is the approximate value Exact is the exact value. The lower the Percentage error, the better is the correlation. 3.7.2 Root Mean Square Error (RMSE) It is a measure of the accuracy when comparing forecasting errors of different models for a particular data set. It is a measure of the differences between values predicted by a model or an estimator and the values observed. The RMSE was calculated using the following equation EMBED Equation.3 (3.28) Where EMBED Equation.3 predicted values (the expected values or unknown results) EMBED Equation.3 Observed values (known results) EMBED Equation.3 Sample size 3.8 Overpressure mechanism Investigation The cause of overpressure was detected using velocity and density analysis. Cross-plot of shale velocity against density was employed to distinguish between overpressures generated by disequilibrium compaction and other mechanisms. The cross-plot of velocity against density was plotted using Rokdoc and analyzed by comparing them with the model by Hoesni (2004) so as to identify the signature and consequent cause of overpressure in the study area. Figure 3.3 Velocity vs. Density signatures and their associated, causal mechanisms of overpressure generation (from Hoesni, 2004). 3.9 Overpressure Detection EMBED Equation.3 (3.29) Where EMBED Equation.3 is the fracture pressure EMBED Equation.3 is the Poissons ratio EMBED Equation.3 is the pore pressure EMBED Equation.3 (3.30) 3.11 Mud weight window The mud weight window represents the density of mud that would produce the required pressure at a given depth that is required to balance the formation pressure but would avoid fracturing the formation. As the mud weight window is defined by both pore pressure and fracture gradient predictions, it was determined by calculating the mud density that falls within these two crucial parameters. The predicted pore pressure and fracturing pressure were then used to calculate the mud weight. The equation to calculate the mud density is given as Mud density EMBED (3.31) Where EMBED EMBED Predicted pore pressure CHAPTER FOUR 4.0 Results and Discussion The results of the project are presented and interpreted here in keeping with the set out objectives. These include results for the pore pressure predictions from the ten models used in the work, the fracture pressure prediction, identification of over-pressured zones, investigation of overpressure generating mechanism, and the estimated mud weight windows required to balance the formation pressure during drilling operations. 4.1 Overburden Pressure Profile The overburden pressure profile was generated from the input density log (Rhob) with the aid of Ikon Sciences RokDoc software. The computed Overburden is utilized as a representative value for the vertical effective stress and in turn for pore pressure prediction. A top density of 1.5 g/cm3 and a formation matrix density of 2.65 g/cm3 were employed in computing the Overburden pressure of the well. A model fit is calculated from the Rho data (Rho fit), and this is integrated to generate an overburden profile, Pob fit, for the well. Finally, based on calculations conducted with density log, the overburden gradient falls between 0.88 to 0.93 psi/ft. Figure 4.0 depicts the generated overburden pressure profile. The overburden gradient was extrapolated at intervals without density log data. Figure 4.1 Generated overburden The black line on the right represents the estimated overburden gradient from density log with the error margin represented by the dotted lines. On the left hand is the density log and the density fit. 4.2 Shale Trend By employing a shale cut off of 0.5, the velocity within the sand intervals are eliminated thereby enabling the fitting of the compaction trend within the cleanest shale. The filtered and smoothed output log is a shale trend, based on the values in shale, i.e. the values for which VShale is above a specified cut-off (0.5). Figure 4.2 Shale Trend 4.3 Normal Compaction Trend (NCT) The Normal Compaction Trend was established through the shale data by utilizing the RokDoc software using the NCT formulation (equation 3.3) presented in the previous chapter. A shale cut off of 0.5 was applied to do away with the sand intervals which are the greyed out on the plot. The sediments compact normally until a depth of about 7300 ft. where we have the onset of overpressure. The NCT was derived using the compressional velocity ( EMBED Equation.3 ) logs. The compressional velocity data has a linear trend line, concordant with the normal compaction trend line which reveals that pore pressure is normal in these formations. The compressional velocity log data then showed a reversal (decrease) from the established normal compaction trendline (NCT) which are designed to detect the abnormal pressure zones (overpressure zone) and normal pressure zone. The reversal of the compressional velocity behind the established normal-compaction trend line (NCT) is indicative of the overpressure zones. Figure 4.3 Normal Compaction Trend generated from sonic compressional velocity 4.4 Shale volume The plot of volume fraction of shale is illustrated below. On the first log column on the left is a volume fraction set, depicting the key lithology in Niger Delta with sand colored yellow and shale colored green, next is the volume of shale and clay calculated from Gamma Ray log. The last column is the Gamma Ray log constrained by a crosshair line indicating the degree of the computation. Figure 4.4 Shale volume It is obvious that the well exhibits a dominantly shale/sand/shale sequence typical of the Niger delta formation. The wells were analyzed in terms of lithology. Shale lithology were delineated by the high gamma ray value. Regions showing low gamma ray re mapped as sand lithology. 4.5 Overpressure Generating Mechanism The Cross-plots of velocity against density for the well shows a concurrent increase in both parameters with depth which is in keeping with the signature for a disequilibrium compaction mechanism, as illustrated by Hoesni curve, which is characterized by an increase in density with a corresponding increase in velocity. However, there is evidence of another overpressure mechanism besides disequilibrium Compaction going by the notable bottom right trend which depicts clay diagenesis paths when compared with the typical Hoesni curve-types from velocity-density cross plots. The plot was colored by depth to show the behavior of density and sonic velocity with depth. Based on the data in the cross-plot, it can be inferred that the predominant mechanism of overpressure generation is disequilibrium compaction up to 10600 ft depth, judging by the increase in velocity with corresponding increase in density. At about 10700ft, there is a larger drop in velocity than in density relative to the shallower trend that takes the data points along a secondary trend which is suggestive of clay diagenesis. Figure 4.5 Cross plot of velocity versus density showing the overpressure generating. Points are colored by TVD depth. 4.6 Prediction of pore pressures using various models The results of the pore pressure prediction in the study area using the ten (10) models are shown in figures 4.2 and 4.3. Using the generated Normal Compaction trend, NCT (figure 4) and Overburden pressure profile (figure 4), the pore pressure profiles were predicted with the RokDoc software using the ten (10) prediction models. The predicted pore pressure profiles were then compared with the measured pore pressures (RFT) at various depths to ascertain the accuracy of the models. Afterwards, a statistical analysis of the results was conducted by calculating the percentage error and Root Mean Square Error (RSME) of the results obtained from the models which were used as the ultimate index to further rank the models in terms of their accuracy. One of the models used was then chosen and optimized so as to enhance the accuracy of the pore pressure predictions obtained from it and to curtail the percentage and Root Mean Square Error values and in turn. Figure 4.6 Pressure-depth plot depicting results of predicted pore pressures using the 10 models. The blue labeled line is the hydrostatic gradient line, while the red line with label is the Lithostatic gradient line and the multicolored lines between the two are the prediction results. As can be seen from the Figure above, the Eatons method, represented by a purple wavy curve coincides with the measured pressure at depths of 7176ft to 9443ft but did not accurately match the measured pressure at deeper depths between 9528ft and 9756ft where there were reversals in the predicted pore pressures. However, at depths of 10524ft 11093 ft there were close matches between the predicted results and the measured pressure data, with the only reversal at 10751ft.At deeper depths, the model under-predicted the pore pressure. The Zhangs model, just like Eatons model concords with the measured pressure up to depths of 9443ft. At deeper depths of 9528ft 9756ft, the model under predicted the pore pressures and then closely matched the measured pressures from 10524ft 11093ft. The model practically replicated the results of the Eatons model but showed slightly better agreement with the RFT data. Bowers model, represented by a grey curve under-predicted the pore pressures from the initial depth up to 9323ft, but showed better agreement with the measured pressure data from depths of 9386ft to 9528ft. It showed significant better matches between 10524 ft to 11320 ft. Millers model over-predicted the pore pressure from 7081ft to 9528ft but matched the measured pressure at a depth of 9756ft .It then relapsed to the over-prediction trend by over- predicting the pore pressure between 10524 ft and 11434 ft. The Hottmann and Johnsons model yielded results that were synonymous with that of Eatons and Zhangs models. The Pressure predictions concurred with RFT data up to 9528ft but deviated from 9756ft. The model nonetheless matched the measured pressure data at depths of 10524ft 11093ft but later under-predicted the pore pressure at 11122ft. Ratios model predictions agreed with the measured pressure from depths of 7176ft 9443ft but under-predicted it at depths of 9528ft and 9756ft .The model however yielded better matches from 10524ft -11417ft. Holbrooks model under-predicted the pore pressure from the initial depth up to a depth of 9756ft. At deeper depths of 10524ft 11417ft, the model closely matched the measured pressure data. It however over-predicted the pore pressure at 11036ft and subsequently yielded good matches between 11063ft-11292ft. The Taus model significantly under-predicted the pore pressures from depths of 7081ft to 9756ft. Afterwards it showed a good agreement with the measured pressure from 10524ft to 11434ft. Duttas model considerably over-predicted the pore pressure across the entire well depth. The Duttas model only reasonable match was at 7366ft. The Equivalent-Depth models predictions closely matched the measured pressure data from 7176ft to 11063ft but over-predicted the pore pressure at depths of 11093ft to the final depth (11434ft). TVD (ft.)RFT (psi)Eaton (psi)Bowers (psi)Tau (psi)Miller (psi)Holbrook (psi)Equivalent Depth (psi)Dutta (psi)Zhang (psi)Ratio (psi)Hottmann Johnson (psi)7081326734002284289643242538360536273415349334157176332733282261269848182470338035763322334733237366371636342581321645602843393639283648377236487851383237702901335246703102398242113750387037857970366936502886315545213032369441603665368436658260382236303039310544973128354142773645361336458391386738513247341747273365391145043865388738669323428541153973374848253989402651634129409841299386443443864141412752254210451953664401445244019443450044544213422053094288462754414468453644699528460845364308432953954386474655384550463445509756472539384182348647364094347153023953382539531052449014861512847345662511249616296487649134877105525011495251854860576451835118636349665031496710602527751595315514159905348546465165174530851741075153885260546252916102549155946660527754245275110365275512256035124593855615253675452225185513711063573355475812567163915849599470155505577055611109359505732590959076588598163037135575160385746111226171588259926095674860926551723659456259589611150624360496097630169256226681473606052650560621117463496065612063246942624868367383607865276080112926157583260856038668461466412730458256141584611320596357746090596866196132630072965793604657881143460025807618160176648621263247380582360405821 Table 4.1 Predicted pore pressures from the ten models Tables 4.2 below depicts results of the statistical error analysis involving the percentage errors of the models pore pressure predictions. TVD (ft)RFT (psi)Eaton ErrorBowers (psi) ErrorTau (psi) ErrorMiller (psi) ErrorHolbrook (psi) Error708132673400422843028961143243225382271763327332802261322698194818452470267366371636342258131321613456023284323785138323770229012433521346702231021979703669365012886213155144521233032178260382236305303920310519449718312818839138673851032471634171247272233651393234285411543973737481348251339897938644344386141417412775225184210594434500445414213642206530918428859528460845362430874329653951743865975647253938174182113486264736040941310524490148611512854734356621651124105525011495215185348603576415518331060252775159253151514135990145348110751538852602546215291261021354912110365275512235603651243593813556151106357335547358121567116391115849211093595057324590915907165881159811111226171588255992360951674896092111150624360493609726301169251162260111746349606546120463240694296248211292615758325608516038266849614601132059635774360902596806619116132311434600258073618136017066481162123Total error79247179403203 TVD (ft.)RFT (psi)Hottmann Johnson (psi) ErrorRatio (psi) ErrorZhang (psi) ErrorDutta (psi) ErrorEquivalent Depth (psi) Error70813267341553493734155362711360510717633273323033471332203576733802736637163648237722364823928639366785138323785138701375024211103982479703669366503684036650416013369418260382236455361353645542771235417839138673866038871386504504163911193234285412944098441294516320402669386443444011445204401153662145192944345004469145361446815441214627395284608455014634145501553820474639756472539531638251939531653021234712710524490148770491304876162962849611105525011496715031049661636327511821060252775174253081517426516235464410751538852752542415277266602455944110365275513735185252221675428525301106357335561357701550547015225994511093595057463603815751371352063036111226171589646259159454723617655161115062436062365054605237360186814911174634960804652736078473831668368112926157584656141058255730419641241132059635788360461579337296226300611434600258213604015823373802363245Total Error725872459131 Table 4.2 Statistical analysis of pore pressure prediction model of the predicted models 4.7 Optimization of pore pressure prediction model The Eatons model was chosen for the optimization as several published works have stated its accuracy as regards pore pressure prediction when disequilibrium compaction is the dominant over-pressure generating mechanism. More so, results from the Eatons model were mostly in concordance with the measured pressure data. Furthermore, the Ratio model was not considered as the model disregards the effect of overburden stress which is crucial to accurate PPP while the Zhangs model was already derived from the original Eatons model. Given that the Eatons method is an effective stress approach and it underestimated the pore pressure at certain depths, I decided that lowering the effective stress would yield better pore pressure prediction matches with the RFT data as the effective stress is the most sensitive parameter. By iteratively trying out different exponents in place of the exponent 3 used in the original Eatons equation, I noted that increasing the exponent lowers the effective stress and consequently gives closer matches to the measured pressure than all the previous models used in the work. Raising the exponent from 3 to 3.9 yielded the best match. As such the original Eatons model was modified by employing an exponent of 3.9. EMBED (4.1) Figure 4.7 Pore pressure prediction using the modified Eatons model. From the plot it is seen that based on the comparison of each pore pressure prediction method against the available pressure data (both measured and inferred) and the statistical analysis described above, the most accurate and therefore preferred pore pressure prediction strategy for future use in the Niger Delta area is the modified Eatons model. TVD (Ft.)RFT (psi)Eatons modified (psi) Error70813267345767176332733280736637163719078513832378717970366936660826038223601683913867386909323428540855938644344430094434500450909528460846030975647253790201052449014895010552501150070106025277526501075153885378011036527552740110635733563021109359505936011122617161720111506243630011117463496330011292615760072113205963596301143460025988044 Table 4.3 Percentage Error of the modified Eatons model Figure 4.8 Comparison of the predicted pore pressures with the measured pressure data, RFT. 4.8 Ranking of Models Microsoft excel was employed in conducting the statistical analysis and ranking the models according to their percentage error and root mean square error values. Ranking was based on the premise that the lower the error value the better the prediction accuracy of the model. Model ErrorRankingEatons Modified Model441stRatio Model582ndZhangs Model723rdHottmann and Johnsons Model724thEatons model795thEquivalent Depth Model1316thTaus Model1797thHolbrooks Model2038thBowers Model2479thMillers Model40310thDuttas Model45911th Table 4.4 Ranking of models based on statistical parameters From results presented in Table 4.4 above, it can be seen that the model with the lowest rank was selected as the best and most accurate correlation. The Eatons modified model obtained the lowest statistical values, while the highest values were obtained by Duttas model. Given that the best model should have the least statistical values rank (Ikiensikimama et al, 2008), Eatons modified model was considered the best by this method. Figure 4.9 Percentage error of predictions of pore pressure models 4.9 Over-Pressured Zones The overpressure estimations were done by observing the deviations of well log data (compressional velocity) points from the normal compaction trend of the sediments. The deviations of predicted pore pressures from Eatons modified model to the right of the hydrostatic pressure zone was also used to detect Overpressured zones. In the Niger Delta area generally, hydrostatic pressure gradient averages to a value of about 0.44psi/ft for the fresh water formations and 0.46psi/ft for the saline water formations. Figure 4 Overpressured zones Figure 4.10 Detected Overpressure zones obtained from Normal compaction Trend Figure 4.11 Detection of Overpressure zones using predicted pore pressures obtained from modified Eatons model The compressional velocity log data shows that the well maintained normal pressure from 7081ft to 7176ft. At a depth of 7366ft, the velocity log deviated from the normal compaction trend .This is indicative of the onset of overpressure. Sonic velocity generally increases with depth for a normal pressured zone. When overpressure sets in, the velocity always decreases and is observed to fall below the normal compaction trend line (red line). By employing the modified Eatons model to predict overpressure in the study area, it was observed that the onset of overpressure in the well occurs at 7366ft true vertical depth (TVD). The highest over pressured zones were observed to lie approximately between depth range of 10602ft and 11434ft, with the highest pressure being 6349psi at depth of 11174ft and a corresponding pressure gradient of 0.57psi/ft. These formation pressure gradient (0.57psi/ft.) falls into the class of mild overpressure. Given that shale predominantly occupies the lower section of the well, it is responsible for the higher pore pressure values between 10602ft and 11434ft. TVD (Ft.)RFT (psi)Eatons modified (psi)Pressure Gradient (psi/ft)7081326734570.497176332733280.467366371637190.507851383237870.487970366936660.468260382236010.448391386738690.469323428540850.449386443444300.479443450045090.489528460846030.489756472537900.3910524490148950.4710552501150070.4710602527752650.5010751538853780.5011036527552740.4811063573356300.5111093595059360.5411122617161720.5511150624363000.5711174634963300.5711292615760070.5311320596359630.5311434600259880.52 Table 4.5 Estimated pressure gradients from modified Eatons model 4.10 Fracture Pressure Prediction The pore pressure trend obtained from the modified Eatons model was chosen as the reference pressure trend for the prediction of the fracture pressure and used as input into the Eatons fracture pressure equation. This was owing to the fact that the pressure trend from Eatons modified model yielded the least percentage error and Root Mean Square Error (RMSE) values in comparison to the results obtained from the ten (10) other conventional models used for the pore pressure prediction. As such the pore pressure used in equation 12 was computed using the Eatons modified model (Equation) while the Poisson ratio was computed from a relationship of compressional-wave and shear-wave velocities equation. Figure 4 Fracture pressure Figure 4.12 Predicted Fracture pressure using Eatons method As can be seen above, the fracture pressure ranges in values between 5117 to 8625 psi. These values are significantly higher than the predicted pore pressures which implies a significant margin between the pressures. As such it can be inferred that the shale in the study area possesses a reasonable seal capacity i.e. shale seal. This is owing to the fact that the predicted pore pressure is not in close proximity to the fracture pressure, as having such formation pressure values during drilling could result to the fracturing of the formation, and consequent circulation losses. In addition to this elevated drilling risk in such wells, a close proximity of the pore pressure to fracture strength of the rock (seal capacity) increases the risk of hydraulic failure of the top-seal, allowing hydrocarbons to leak. 4.11 Mud weight By utilizing the fracture pressure predictions, reasonable estimates of the expected maximum allowable mud weights for drilling in the area were obtained. The interval between the predicted pore pressure (Eatons modified model) and the fracture pressure predicted represents the mud weight window. Figure 4.13 Estimated mud weight window Figure 4.13 above is the result of the mud weight window of the well, which is constrained by pore pressure and fracture pressure. The upper limit to this window is set by the fracture pressure while the lower limit is set by the predicted pore pressure. The purple profile to the left shows the mud weight below which breakouts or shear failure will occur. On the other side, if the used mud weight exceeds the blue profile, an induced fracture in the formation and a mud loss would occur. Hence, the white area between the aforementioned profiles in this figure is the stable mud weight window for drilling. As is seen from this figure, this window changes as a function of depth. At a depth of 7176 ft., the pore fracture pressure profiles reveals a reasonable mud weight window of 8.9 ppg to 13.6 ppg. The window then gradually widens up to 7970ft , 8.8 ppg to 13.7 ppg and then slightly narrows down between 8260ft and 8391ft, 8.8 13.7 ppg. The narrowest mud window was observed at 11122ft with mud weights between 10.66ppg-14.01ppg, beyond which safer windows were observed at depths between 11174ft to 11320ft with a width of 10.12ppg 14.39 ppg and subsequently narrowed at 11434ft. The widest window was obtained at 8391ft, with corresponding mud weights between 8.84ppg 13.6ppg. The result of estimated mud weight window is indicative of a trend that alternately widens and narrows down with depth. By and large, a mud weight window with a lower bound falling between 8.78 ppg and 10.91 ppg and an upper limit between 13.1 ppg and 14.6 ppg is required to drill the well without formation fluid ingress (kicks) and loss circulation. The narrowest drilling window was estimated between 10.66 ppg to 14.01 ppg at 11122 ft. A mud weight designed in line with the predicted mud window would prove to be successful, as regards to mitigating drilling hazards. Table 4.6 Predicted Pore pressure, fracture pressure and mud weight window. TVD (ft)RFT (psi)P-Hyd (psi)P-Litho (psi)Vp (m/s)Vn (m/s)Predicted PP (psi)Predicted FP (psi)MWW (ppg)708132673186621327022764345751178.78-13.73717633273229630527592771332850668.88-13.55736637163315649226932785371953899.67-13.96785138323533695827592821378756709.36-13.83797036693587707028172830366656228.83-13.73826038223717735828752851360157208.87-13.29839138673776749028442860386959328.84-13.6932342854195842729602925290063788.82-13.14938644344224849128952929408566899.06-13.42944345004249855028892933443067759.14-13.65952846084288863728852939450968709.28-13.73975647254390886230532954460364789.29-12.851052449014736965829803003379074648.94-13.521055250114748968529663005489575469.12-13.751060252774771973629363008500777319.56-13.931075153884838989329333017526578719.62-14.0611036527549661019429913035537878699.18-13.7311063573349781022229413037527482089.95-14.01110935950499210254289130395630835410.30-13.88111226171500510284285430405936847010.66-14.01111506243501810314283630426300860410.75-14.55111746349502810338283430436330862510.91-14.8112926157508110463290930516007850110.47-14.92113205963509410493292030525963847510.12-14.39114346002514510615293330595988854110.08-14.24 Note TVD is the True vertical depth (ft) RFT is the Downhole pressure instrument Predicted PP is the predicted pore pressure (psi) MWW is the mud weight window (ppg) Predicted FP is the predicted fracture pressure (psi) Vp is the observed compressional velocity at a given depth Vn is the normal compressional velocity at a given depth obtained from NCT P-Hyd is the hydrostatic pressure P-Litho is the lithostatic pressure CHAPTER FIVE CONCLUSION AND RECOMMENDATION 5.0 Conclusion In this work, pore pressures and fracture pressures were predicted using well logs from an exploratory well in the Niger Delta field. The pore pressure prediction was done using ten industry models namely Eatons, Bowers, Tau, Millers, Equivalent Depth, Hotmann and Johnson, Ratio, Holbrook, Dutta and Zhangs models with various degrees of accuracy. The methods utilized in ascertaining the prediction accuracy of the pore pressure models were by comparison with measured pressure data (RFT) and computation of percentage and root mean square errors. The Ratios model gave better correlations with the RFT data in comparison to the other nine models used in the study. The Zhangs, Eatons and Hottmann and Johnsons model equally yielded favorable matches with the measured pressure data. However, Duttas and Millers models showed a great degree of inconsistency with the measured pressure data and consequently had the highest percentage and root mean square errors. As such they fell short of accuracy in estimating the pore pressure in the well. Meanwhile, one of the models, being the Eatons, was optimized to yield more accurate pore pressure predictions. The original Eatons equation which is an effective stress method (overburden pressure minus pore pressure) was modified by reducing the effective stress through an increase in the exponent from 3 to 3.9. The modified Eatons equation with an exponent of 3.9 yielded the best correlation with measured pressure data values and had the least percentage and Root mean square Errors of 44 and 203 psi respectively. This further validates the suitability of the model for usage in the study area. Hence, it can be conclusively stated that the modified Eatons model with an exponent of 3.9 is the most accurate for prediction of pore pressure in the Niger Delta as it outperformed the previous models. The cause of the overpressure in the Niger Delta was investigated in the study. Analysis of velocity and density cross plots revealed the major overpressure generation mechanism in the Niger Delta is disequilibrium compaction of sediments up to depths of 10600ft. However there are indications of another overpressure generation mechanism being clay diagenesis starting at about 10700 ft. The best pore pressure prediction model (modified Eatons model) in the study area and the generated Poissons ratio were used to estimate fracture pressure using the Ben Eatons Model. The results obtained imply that the magnitude of fracture pressures which fall between 5117 psi to 8625 psi, are significantly greater than the pore pressure as such the chances of hydraulic failures developing in the top seal are very remote. The mud weight window was also estimated using the modified Eatons model and fracture pressures as its lower and upper pressure limits respectively. The results obtained revealed that successful drilling of the formation requires a mud weight with a lower bound falling between 8.78ppg and 10.91ppg and an upper limit between 13.1ppg and 14.6ppg. At 9323 ft., the narrowest drilling window was observed between 9.45ppg to 13.08ppg. The Overpressured zones were identified by noting the deviations of the compressional velocity log data from the Normal Compaction Trend line. Points where the compressional velocity experienced reversal/decrease and fell below the normal compaction trend line were considered to be Overpressured zones. The modified Eatons model was also used in detecting the Overpressured zones by observing its deviation from the hydrostatic pressure zone. Results revealed the predicted over-pressured zones from the well will most likely occur below the depth of 7366ft. The most prominent over pressured zones were observed to lie at depths between 10602ft and 11434ft, with the pressure climaxing at 6349psi at depth of 11174ft and a corresponding pressure gradient of 0.57psi/ft. which implies that the zones are mildly over-pressured. The major contribution of this thesis is in the identification of the best model to predict pore pressure in the Niger Delta. The thesis will also expand on the work completed by Eaton in the Gulf of Mexico, as the modified model can be successfully employed in the Niger Delta formation. 5.1 Recommendations The following suggestions should be taken into account for future research so as to facilitate the comprehension of pore pressure predictions and safe drilling operations in the Niger Delta. Additional dataset from various depositional environments within the Niger Delta should be used to assess the pore pressure prediction models, as one of the major challenges encountered in this work was the absence of pressure measurements and wireline log data at shallow depths. This could further aid in the validation of the proposed pore pressure prediction model. Direct pressure measurements should be employed in any future pore pressure prediction work so as to verify the results obtained from the models and to complement statistical methods used to determine the models accuracies. REFERENCES Bowers, G.L. (1995). Pore pressure estimation from velocity data accounting for overpressure mechanisms besides undercompaction. SPE Drilling and Completion, paper IADC/SPE 27488. Corredor, F., Shaw, J.H., Bilotti, F. (2005). Structural styles in the deep-water fold and thrust belts of the Niger Delta. AAPG Bulletin, 89, 753-780. Doust, H., and Omatsola, E., (1990). Niger Delta. In Divergent Passive Margin Basins Tulsa, Edwards, J.O. and P.A. Santogross (Eds.), AAPG Memoir, pp 201-238. Dutta, N. C., (1997). Pressure prediction from seismic data implications for seal distribution and hydrocarbon exploration and exploitation in the deepwater gulf of Mexico. Norwegian Petroleum Soc. Special Publication vol. 7, pp. 187-199. Dutta, N.C.(2002) Geopressure prediction using seismic data Current status and road ahead Geophysics, 67, 2012-2041. Eaton, B. A., (1968). Fracture gradient-prediction and its application in oil field operations Journal of Petroleum Technology, vol. 10, pp. 13531360. Eaton, B. A., (1975). The equation for geopressure prediction from well logs. Society of Petroleum. pp. 50 63. Eaton, B. A., (1972). The effect of overburden stress on geopressures prediction from well logs. SPE vol. 3719. Journal Petroleum Technology. Engineers of AIME, paper SPE 5544. Eaton, B.A. (1975) The Equation for geopressure prediction from well logs society of petroleum engineers of AIME. Paper SPE 5544. Harrold, T., Swarbrick, R. E., and Goulty, N. R., (1999). Pore pressure estimation from mudrock properties in Tertiary basins, S. E. Asia AAPG Bulletin, vol. 83, no. 7, pp. 10571067. Hoesni, M.J. (2004) The Origin of Overpressure in the Malay Basin and Its Influence on Petroleum System. Ph.D Thesis, University of Durham, Durham. Hubbert, M.K. and Willis, D.G. (1957) Mechanics of Hydraulic Fracturing. Transactions of Society of Petroleum Engineers of AIME, 210, 153-163. Katahara, K. (2003). Analysis of overpressure on the Gulf of Mexico shelf. Offshore Technology Conference, Paper 15293, 1-10. Lahann, R. W., and Swarbrick, R. E., (2010). Kaolinite diagenesis A previously unrecognized cause of fluid overpressure Proceedings of the AAPG Geoscience Technology Workshop on Pore Pressure and Related IssuesSpecial Focus Singapore, October 28 29, 2010, pp. 6. Lahann, R. W., and Swarbrick, R. E., (2011). Overpressure generation by load transfer following shale framework weakening due to smectite diagenesis, Geofluids vol. 11, pp. 362-375. Lopez, J. L., Rappold, P. M., Ugueto, G. A., Wieseneck, J. B., Vu, K., (2004). Integrated shared earth model 3D pore-pressure prediction and uncertainty analysis. The leading Edge, Jan., 2004, pp. 52-59. Mathews, W.R. Kelly, J. (1967). How to predict formation pressure and pressure gradient. Oil and Gas Journal, 65,8, 92-106. Miller, T.W., (2002). A velocity/effective stress methodology that predicts finite sediment velocities at infinite effective stresses. EAGE/SEG Summer Research Workshop, Galveston, Texas. Mouchet, J.P. Mitchell, A. (1989). Abnormal Pressures While Drilling. Elf Aquitaine, Boussens, Manual Techniques 2, 225p. Osborne, M. J., and Swarbrick, R. E., (1997). Mechanisms for generating overpressure in sedimentary basins A reevaluation. AAPG Bulletin, vol.81, p.1023-1041. Ramdham, A. M., and Goulty, N. R., (2011). Overpressure and shale compaction in the Lower Kutai Basin, Indonesia – a radical reappraisal. AAPG Bullettin, vol. 95, pp.1725-1744. Sayers, C. M., (2006). An introduction to velocity-based porepressure estimation The Leading Edge, vol. 25, pp. 14961500. Terzaghi, K., (1943). Theoretical soil mechanics John Wiley and Sons, Inc. Traugott, M., (1997). Pore pressure and fracture gradient determinations in deepwater. World Oil. Yoshida, C., Ikeda, S. Eaton, B.A. (1996). An Investigative Study of Recent Technologies Used for Prediction, Detection, and Evaluation of Abnormal Formation Pressure in North and South America. IADC/SPE. Zhang, J. (2011) Pore Pressure Prediction from Well Logs Methods, Modifications and New Approaches. Shell Exploration and Production Company, Houston, Texas. Input into pore pressure models and PPP Cross-plot and overpressure mechanism Wireline log data Gamma Ray Density Sonic Compressional velocity Shear wave velocity Generation of overburden profile Generation of NCT Computation of Poissons ratio Shale discrimination Comparison of PPP with RFT data computing Error RMSE Optimization of model using it for overpressure estimation Fracture pressure prediction using optimized model and mud window estimation q2erhWi(iEJWE( 5q/@@Z_dmOpDnJiiEJfvJKip [email protected]/@0LaVVqN
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