Table 3
Source publication
Abnormally high pressures, measured by repeat formation tester (RFT) and detected by well log data from 10 wells in the Krishna-Godavari (K-G) basin, occur in the Vadaparru Shale of Miocene and Raghavapuram Shale of Early Cretaceous age. Overpressures generated by disequilibrium compaction, and pore pressures have been estimated using the conventio...
Contexts in source publication
Context 1
... ANOVA one-way approach has been adopted with group means m i for i 5 1, 2, 3 and variance. The ANOVA calculations for multiple regressions as shown in Table 3, the model degrees of freedom (df) are equal to 3, the error (residual) degrees of freedom, i.e., the df for residual are equal to 40 and the total degrees of freedom are 43. ...
Context 2
... AND CHATTERJEE In Table 3, the F statistic is equal to 698.370/3.523 5 198.224. The distribution is F (3, 40), and the probability of observing a value greater than or equal to 198.224 is less than 0.001 because significance (sig.) of this test has value of 0.000. ...
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Citations
... V P /V S versus acoustic impedance (AI), the typical form of a template is an excellent indicator of lithology and fluid indicators 46,49 . Minerals content, lithology, coordination number, pressure and temperature from earlier research on reservoirs are also utilized as constraints [53][54][55] . For reservoirs in the KG Basin, the Voigt-Reuss-Hill average yields the average modulus of mineral combination (quartz, feldspar and clay) or simply the Voigt-Reuss-Hill average of sand grains 43,56 . ...
One of the main challenges in reservoir characterization is the accurate prediction of lithology and saturation heterogeneities. In this study, a methodology has been developed that combines Monte Carlo simulation (MCS) and rock physics modelling (RPM) to explore subsurface properties and characterize reservoirs of the Krishna-Godavari (KG) Basin, India. RPM and MCS were effectively applied to well-log data in this study to discriminate distinct lithologies and fluid types, as well as uncertainty analysis. Various diagnostic models, such as the contact cement model, constant cement model and friable sand model, were used for this purpose. The cementation of reservoir sand ranged from 1% to more than 4%, according to the analysis. The gas sand reservoir, cap shale and brine sand were categorized using a rock physics template (RPT) model built over V P /V S against the AI cross-plot. Gas saturation was appropriately indicated by the RPT model produced from the shallow marine environment. The present study proves that RPM developed in the first part of the study may be used to perform uncertainty analysis using MCS. We simulated three different lithologies in this study, viz. shale, brine sand and gas sand, and then categorized them using V P /V S versus P-impedance cross-plot. IN the present study, two major techniques, namely rock physics modelling (RPM) and Monte Carlo simulation (MCS), discriminate subsurface lithology and fluid content. Rock physics describes the link between rock parameters like porosity, mineral fractions and water saturation and elastic properties like P-and S-wave velocity and density, which drive seismic response. As a result of RPM, rock properties that were previously only obtainable through drilling can now be approximated using remotely sensed data. On the other hand, MCS is a model used to predict the probability of different outcomes when the intervention of random variables is present. It aids in the understanding of how risk and uncertainty affect prediction and forecasting models. The area of experimental mathematics known as Monte Carlo methods focuses on experimentation with random numbers. This simulation requires well data for the generation of random numbers, simulating various distributions and estimating the accuracy of results. MCS aids in the assessment of the impact of data uncertainty on calculations performed with analytical approaches. Measurement of re-sistivity, bulk density, interval transit time, self-potential, natural radioactivity and hydrogen content of rocks in a borehole yields data that provide quantitative information and hence help interpret subsurface properties. Which is further produce porosity, formation-water resistivity, shale volume, water saturation and permeability by applying various analytical relationships. The hydrocarbon volume and productivity are then calculated using these reservoir characteristics. Well-log analysis is a crucial step in defining and analysing hydrocarbon-bearing zones. This method entails the interpretation of well-log data within a well-bore to establish a connection between reservoir properties, such as rock physical and chemical properties and lithology and petrophysical parameters, such as porous media properties. In addition, rock physics models are utilized to bridge the gap in seismic data obtained in the time domain and is an indirect measurement of subsurface properties in the form of elastic properties. Petrophysical data are gathered from direct measurements in a well-bore. The link between rock parameters such as porosity, mineral fractions and water saturation, and elastic qualities such as P-and S-wave velocity and density, which drive seismic response, is described by rock physics. Rock properties that were previously only obtainable through drilling can now be determined using seismic data that can also be called remotely sensed or indirectly sensed data using the application of RPM as a tool. Petrophysicists studied porous media features such as po-rosity, permeability, water saturation, fluid recognition, resistivity, and shaliness, particularly in reservoir rocks and contained fluids 1-3. Based on the observed petrophysical and reservoir parameters, rock physics models are used to estimate seismic properties 4,5. Rock physics and seismic
... Machine learning (ML) has been used for the prediction of PP from well log data in different geological settings (Cranganu, 2007;Mustafa et al., 2012;Hu et al., 2013;Keshavarzi and Jahanbakhshi, 2013;Singha and Chatterjee 2014;Ahmed et al., 2019a, b;Karmakar and Maiti, 2019;Zhang et al., 2022;Karmakar and Maiti, 2023). It is effectively used to capture the underlying physics/physical property of reservoirs/geological media as it takes well log as inputs to predict the PP as the target. ...
Pore pressure (PP) information plays an important role in analysing the geomechanical properties of the reservoir and hydrocarbon field development. PP prediction is an essential requirement to ensure safe drilling operations and it is a fundamental input for well design, and mud weight estimation for wellbore stability. However, the pore pressure trend prediction in complex geological provinces is challenging particularly at oceanic slope setting, where sedimentation rate is relatively high and PP can be driven by various complex geo-processes. To overcome these difficulties, an advanced machine learning (ML) tool is implemented in combination with empirical methods. The empirical method for PP prediction is comprised of data pre-processing and model establishment stage. Eaton's method and Porosity method have been used for PP calculation of the well U1517A located at Tuaheni Landslide Complex of Hikurangi Subduction zone of IODP expedition 372. Gamma-ray, sonic travel time, bulk density and sonic derived porosity are extracted from well log data for the theoretical framework construction. The normal compaction trend (NCT) curve analysis is used to check the optimum fitting of the low permeable zone data. The statistical analysis is done using the histogram analysis and Pearson correlation coefficient matrix with PP data series to identify potential input combinations for ML-based predictive model development. The dataset is prepared and divided into two parts: Training and Testing. The PP data and well log of borehole U1517A is pre-processed to scale in between [-1, +1] to fit into the input range of the non-linear activation/transfer function of the decision tree regression model. The Decision Tree Regression (DTR) algorithm is built and compared to the model performance to predict the PP and identify the overpressure zone in Hikurangi Tuaheni Zone of IODP Expedition 372.
... The fluid column size and shape have no effect on the hydrostatic pressure which can be determined by the following equation (Hottman and Johnson 1965): where P h is the hydrostatic pressure in pounds per square inch (psi), δ f is the density of fluid, and D v is the vertical depth. Singha and Chatterjee (2014) used repeat formation tester (RFT) and gamma-ray, density, and sonic logs data for detecting the abnormally high pressures in the Miocene shale and Early Cretaceous shale. Kumar (2015) reported that the over-pressure mechanism is based on disequilibrium compaction. ...
Along with Siwa Oasis depression, there are several ponds, lakes, and Sabkhas features resulted from the current flows
through the springs due to the over-pressure of deep sandstone aquifer. The Nubia Sandstone aquifer (NSA) and Carbonate
aquifer are the main two water-bearing units. They are separated by a huge thickness of shale and controlled by complicated
structural faults. There are ten wells including gamma-ray, short and long resistivity, caliper, neutron, and density logs. These
logs were used in identifying the lithology, calculating the porosities, water saturation percentages, and effective permeability.
The long resistivity, neutron, and density logs were used to detect, identify, and calculate this over-pressure and its zones.
The shale resistivity, porosity, and density of the Nubia Sandstone formation were used in determining the expected normal
compaction trend. The top of over-pressuring, over-pressuring forewarning, over-pressuring transition, and over-pressuring
zones were detected. The formation pressure and its gradient were calculated at the first depth in the over-pressuring zone. The
piezometric heads and pressures were calculated and mapped. The depths to the top of over-pressures, formation pressures,
and their gradient maps were constructed. The statistical analyses between the two pressures were carried out. Accordingly,
the thickness and type of shale, the effective porosity, the compaction of the deep aquifer, the huge thickness of the overlaid
formation and its porosities, and the complicated structural conditions are considered the main factors in generating the
over-pressuring phenomenon at Siwa Oasis. Therefore, this study considers that the used logs are compatible powerful tools
in studying this phenomenon
... Permeability for a sandstone reservoir is high for coarse grains with low porosity, whereas it is low for fine grains with high porosity (Holmes et al. 2009). Also, permeability directly relates to total pore volume, fluid storage space, fluid movement capability, rock type, and fluid properties (Chatterjee and Mukhopadhyay 2002;Verma et al. 2012;Singha and Chatterjee 2014). The determination and prediction of this reservoir property is highly complicated. ...
Permeability is the most important petrophysical attribute for analyzing fluid flow behavior. So far, no universal approach can provide an accurate and reliable estimation of permeability for an entire hydrocarbon reservoir. The present study utilizes five empirical, three statistical, and three connectionist methods to estimate the permeability of a heterogeneous oil reservoir. The empirical models include ‘Tixier’, ‘Morris and Biggs’, ‘Timur’, ‘Coates and Dumanoir’, and ‘Coates and Denoo’. The statistical methods incorporate ‘multiple variable regression (MVR)’, ‘gaussian process regression (GPR)’, and ‘bagged tree (BT)’. The connectionist techniques are ‘support vector machine (SVM)’, ‘convolutional neural network (CNN)’, and ‘feed-forward backpropagation artificial neural network (ANN) with training algorithms Levenberg–Marquardt (LM), Bayesian Regularization (BR), and Scaled Conjugate Gradient (SCG)’. Prediction efficiency of study methods are compared using six statistical indexes, such as regression coefficient, mean squared error, root mean squared error, average absolute error percentage, minimum absolute error percentage, and maximum absolute error percentage. Ranking of the log variables based on their importance in permeability modeling has been performed. To achieve the objectives, 439 data points comprising of laboratory derived core permeability information and seven well log parameters, namely gamma ray (GR\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$GR$$\end{document}), bulk density (RHOB\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$RHOB$$\end{document}), sonic travel time (DT\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$DT$$\end{document}), true resistivity (LLD\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$LLD$$\end{document}), neutron porosity (φN\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varphi_{N}$$\end{document}), NMR porosity, and bulk volume of irreducible fluid are selected from a Jeanne d’Arc Basin’s reservoir. All these methods are tested on different data sets of study wells to confirm the reproducibility of the results. The results of the statistical indexes analysis imply that the empirical relationships are inappropriate for a heterogeneous reservoir as they provide a poor match with real data. However, the ‘Coates and Dumanoir’ model provides a relatively better match with core permeability among all five empirical approaches. The MVR is less efficient among statistical models, BT is reasonably efficient, and GPR is highly efficient. Amidst soft computing techniques, SVM, ANN with LM, and ANN with BR show very high efficiency, whereas ANN with SCG is moderately acceptable, and CNN provides extremely poor efficiency. A comprehensive comparison among all studied models shows that the best predictor is ANN with BR as it provides an excellent match between predicted and real data, and it requires only 14.9 s to process the data. Both statistical and connectionist methods imply that the GR\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$GR$$\end{document} and φN\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\varphi_{N}$$\end{document} are the most vital log parameters in permeability modeling, whereas DT\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$DT$$\end{document}, RHOB\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$RHOB$$\end{document}, and LLD\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$LLD$$\end{document} are the least important predictor variables. The outcomes of this study will help engineers and researchers to apply an accurate permeability prediction tool in petroleum industries during the exploration phase to obtain accurate reservoir permeability data, correct analysis of fluid flow behavior, accurate reservoir characterization, and reduced uncertainty associated with a reservoir evaluation.
Article highlightsEmpirical models are incapable of predicting permeability accurately.
SVM, ANN with LM, and ANN with BR are the most efficient predictive methods.
An accurate and cost-effective permeability prediction strategy is achieved.
... P-wave sonic, resistivity, density and mud pressure can provide information on rock and fluid properties that are indications of overpressure (Guo et al., 2010;Li et al., 2022). For the normally pressured sediments, mudstone parameters such as P-wave sonic, resistivity, and density fit exponential model (Singha and Chatterjee, 2014). Therefore, logging parameters of normally pressured mudstone were selected from drillings to fit the compaction trend guidelines. ...
The scarcity of drilling in the deep water area of Qiongdongnan Basin restricts the cognition and prediction of overpressure. In this paper, a shallow zone of overpressure at the depth of 900–1,200 m below the sea floor in the deep water area was found by analyzing electronic logs, mud pressure (Mud pressure is a product of the height of the column of mud, density and gravity acceleration) and test pressure from drill stem testing (DST) and modular dynamic testing (MDT), and the interpretation of anomalous seismic interval velocities. The shallow overpressure is a newly observed geological phenomenon in the South China Sea for which the generation mechanisms are not well understood, despite similar observations and analyses elsewhere in the world. Two representative wells, one each located in the shallow water and the deep water areas, respectively were selected to investigate the vertical distribution of the shallow overpressure. The top of the overpressure in Well A in the shallow water area is about 2,111 m below sea floor, while the top of the overpressure in Well B in the deep water area is about 1,077 m below sea floor. A pressure coefficient (i.e., ratio of pore pressure to the normal hydrostatic pressure measured from the sea surface) profile was constructed from the shallow water area to the deep water area using the calibrated relationship between seismic interval velocities and pressure data from 30 wells. The distance between the top of the overpressure and the seabed is predicted to be between 900 and 1,200 m in the deep water area Basin. Disequilibrium compaction is the interpreted primary cause of the shallow overpressure and the results of basin modeling indicate that the shallow overpressure was generated since 5.5 Ma.
... Vol.: (0123456789) Baouche et al. 2021;Das and Chatterjee 2018;Singha and Chatterjee 2014). The circumferential stress around the borehole is useful to identify the direction of maximum compressive failure. ...
Horizontal stresses are key parameters of reservoir geomechanics and wellbore stability modeling. For scientific well drilling, where direct measurements are not always available, modeling of horizontal stresses is challenging, especially for highly porous un-compacted gas hydrate-bearing sediments below the seafloor. We have estimated the minimum (Sh) and maximum (SH) horizontal stresses by using rock poro-elastic models based on three wells data which are situated at the national gas hydrate program (NGHP)-01 sites of the offshore Mahanadi basin. The stress magnitudes are validated by wellbore breakout. We have computed the stress magnitudes using 2D seismic for mapping the gas hydrate-bearing sediments. The average gradients of SH and Sh (10.58 MPa/km and 10.48 MPa/km) are less than the gradient of vertical stress, SV (10.67 MPa/km). The present-day stress distribution at the NGHP-01 site is principally a normal faulting (SV > SH > Sh) regime as obtained from stress polygons. The breakouts identified from both formation image and caliper data, suggest a NNW-SSE orientation for SH in the Pleistocene age, which is slightly anti-clock wise relative to the northward oriented of the Indian sub-continent. This change in the orientation of SH could be due to a local structure/fault system cross-cutting the bottom simulating reflector, and mass sliding/slumping on the seafloor. The orientation of SH varies from N11.25°W to N25.7°W of D-quality. We have analysed wellbore stability using the Mohr–Coulomb circle and hoop stress techniques. These results will enable numerical modeling of production from gas hydrate reservoirs planned for the future.
Article Highlights
Identification of breakout from formation image and caliper log data and hence, orientation of horizontal stress in the gas hydrates-bearing sediments of the offshore Mahanadi basin.
Magnitude of the maximum horizontal stress at breakout intervals and the continuous profile of the horizontal stresses using both well data at NGHP-01 sites and multi-channel seismic data.
Analysis of stress polygons, Mohr circles and hoop stress distributions at the selected depth intervals near the gas hydrate-bearing sediments.
... where P h is the hydrostatic pressure gradient, assumed to be 10 MPa km −1 (Singha and Chatterjee 2014), Δt is the compressional sonic travel time, Δt n is the travel time computed from the normal compaction trend (NCT) and n is Eaton's exponent. A detailed methodology for PP and stress magnitude prediction for the same Upper Assam wells, M1. ...
Structural heterogeneities and tectonic forces in North-East India give rise to changeable in-situ stresses with varying orientations in this part of India. Wells located in the seismic gap in Upper Assam, Naga Thrust and Chittagong-Mizoram-Tripura fold belt of Mizoram are considered for studying the stress state and borehole collapse models for the area. The absence of stress studies in the Mizoram area acts as a stimulator to take up stress studies. Poroelastic modeling shows an average ratio of maximum horizontal to vertical stress to be 0.79 for normal faulted, 1.18 for thrust faulted and 1.12 in strike-slip faulted regimes. The S H direction varies from 193°N in Upper Assam to 213°N in Mizoram areas. The image log in a well of the Mizoram area shows the rotation of S H direction (≈85°) from 500m to 3707m due to structural heterogeneity. The thrust and strike-slip regimes under the study area pose the major threat for safe borehole drilling in this complex terrain. To mitigate this issue, Mohr-Coulomb (MC) and Mogi-Coulomb (MG) rock failure criteria are discussed to predict minimum mud weight for borehole drilling. MG predicted mud weight (MW) ensures borehole stability in wells in normal faulted sediments while MC predicted MW prevents shear failure in wells in thrust and strike-slip regimes. A disc plot is used to model a stable wellbore drilling path with minimum MW is modeled using a disc plot. A vertical well is stable in a normal faulted regime whereas horizontal drilling is preferable in the fold-thrust belt. Sensitivity analysis of geomechanical input parameters on MW using Monte Carlo Simulation shows that S H has the maximum effect on MW regardless of the faulting regimes.
... Seismic inversion attempts to transform seismic amplitude into acoustic impedance for extract information relevant to spatial reservoir static and dynamic parameters including porosity, permeability and saturation (Soubotcheva 2006;Gogoi and Chatterjee 2019). Seismic inversion results have been widely used to the prediction of lithofacies, elastic and petrophysical properties (Singha and Chatterjee 2014;Kumar et al. 2016). The advantages of acoustic impedance information over seismic amplitude are described by many studies (Duboz et al. 1998;Latimer et al. 2000;Yilmaz 2001;Pendrel 2006). ...
Porosity and facies are two main properties of rock which control the reservoir quality and have significant role in petroleum exploration and production. Well and seismic data are the most prevalent information for reservoir characterization. Well information such as logs prepare adequate vertical resolution but leave a large distance between the wells. In comparison, three-dimensional seismic data can prepare more detailed reservoir characterization in the inter-well space. Generally, seismic data are an efficient tool for identification of reservoir structure; however, such data usable in reservoir characterization. Therefore, these two types of information were incorporated in order to obtain reservoir properties including porosity and facies in the study area. Using Multimin algorithm, petrophysical analysis was carried out for estimation of reservoir porosity. Then, an accurate post-stack inversion was accomplished to obtain the acoustic impedance volume. The results showed that the Ghar sandstone is characterized by a lower acoustic impedance compared to the high acoustic impedance Asmari Formation. Because of a relationship between acoustic impedance and reservoir properties (i.e., porosity), porosity cube calculation was performed by artificial neural network method which is a popular approach for parameter estimation in petroleum exploration. The consequences showed a good agreement between log based and seismic inversion-derived porosity. The inversion results and well logs cross-plots analyses illustrated that the Ghar member considered as a high quality zone with porosity 22 to 32 percent and the Asmari dolomite shows a low quality interval characters with porosity 1 to 6 percent. The findings of this study can help for better understanding of reservoir quality (especially porous Ghar member delineation) by lithology discrimination in the analysis of identification reservoirs and finding productive well location in Hendijan field.
... The prior knowledge of pore pressure is necessary for hydrocarbon exploration, basin modeling and field development. The quantitative study of pore pressure (PP) from geophysical seismic data is always essential for selecting the drilling mud, and to avoid catastrophic incidents such as blowouts (Eberhart-Phillips et al. 1989;Osborne and Swarbrick 1997;Dutta 2002;Singha and Chatterjee 2014;Oughton et al. 2015). A common practice for predicting pre-drilling pore pressure is to use a pressure-velocity relationship. ...
... The hydrostatic pressure is directly dependent on density of the pore fluid, and at some depth, it is examined the exceeding hydrostatic pressure is caused due to the pore pressure, and the zone is considered as overpressure (Dutta 2002;Chatterjee et al. 2011Chatterjee et al. , 2015. A phenomenon like disequilibrium compaction, generation of hydrocarbon, tectonic compression, hydrocarbon buoyancy, and mineral dehydration are responsible for generating overpressure in several sedimentary basins (Osborne and Swarbrick 1997;Swarbrick et al. 2001;Singha and Chatterjee 2014). Widely, more than 90% overpressure occurs due to the incompressible fluid retained within the rock or disequilibrium compaction in all sedimentary basins worldwide (Chatterjee and Mukhopadhyay 2001, 2002Zoback et al. 2003). ...
... In the basement low permeable rock such as Shale is found, which retain the fluids. It has been reported that the overpressure in the Shale formations contains the hydrocarbons due to their sealing capacity (Tang and Lerche 1993;Hao et al. 2002;Li et al. 2008;Singha et al. 2014). In this study, the overpressure zone is found within the sediment from Paleocene-Eocene age group Shale, and as we know, the composition Fig. 10 The plot displays the validation of pore pressure from log and seismic with two way travel time at well location: a Km, b Te; correlation of pore pressure from log and seismic at c well-Km with the value of R 2 − 0.87, d well-Te with the value of R 2 − 0.78 and quality of Shale vary as a function of pressure than sandstones (Lindsay and Tomar 2001;Singha et al. 2014). ...
Quantitative variation of pore pressure within the formations is essential for the selection of drilling mud, and to avoid catastrophic incidents such as blowouts. In this paper, we estimate to detect in-situ overpressure zone (OPZ) and to establish the adequate spatial distribution of PP from a 3D seismic data containing three wells in the foreland basin of the upper Assam shelf. We applied the fact that the porosity of Shale decreases monotonically as the effective stress increases, so we delimited the Shale volume up to 70% for PP estimation. The OPZ has been identified in the wells by comparing two methods: first, deviation of sonic transit time from normal compaction trend, and second, the separation between sonic-density porosity. The predicted PP is validated by repeat formation test and mud weight data. The 3D pore pressure model that obtained by velocity-effective stress transformation method matches with pore pressure in the wells with excellent goodness of fit. The PP gradient varies from 14.22 to 15.50 MPa/km in OPZ, and the top of OPZ ranges from 1225 to 2182 m, respectively. The spatial distribution of pore pressure is found to be mostly normal pressure for Barail, Sylhet and fracture basement except in the Kopili formation in which OPZ is spatially distributed, and higher pressure is observed in the locations toward the S-SE direction. Our results reveal the occurrence of overpressure zone in Barail and Kopili formations of Oligocene to Eocene, which can be attributed to the disequilibrium compaction phenomenon.
... Petrophysical properties play an important role in recognizing and developing reservoir assessment methods. The most important reservoir properties include permeability, porosity, and water saturation, directly related to the volume of hydrocarbons in situ, rock type, and fluid flow [2]. There are two main approaches to obtaining permeability values for petroleum engineers [3]. ...
... The first approach uses laboratory methods to examine the drilling core data directly, and the other approach uses indirect data to evaluate the permeability values using well logs. Nowadays, network-based intelligent computing methods such as neural networks and deep learning have been considered by oil and gas industry researchers due to their ability to solve high accuracy problems [4], [2]. The recent studies in the field of analysis and estimation of petrophysical values have been performed using artificial neural network method and geostatistics methods evaluations, which have obtained acceptable results in these studies [5], [6]. ...
Permeability is one of the important parameters in reservoir petrophysical studies, and evaluation of this parameter can be used as a key tool in the oil fields development. This study aim is permeability estimation and modeling of the Upper Surmeh Formation in one of the oil fields in the Persian Gulf. The Surmeh Formation with Jurassic age is considered as one of the most important oil and gas reservoirs in the Persian Gulf basin. In this study, we have used petrophysical well logs and 3D post-stack seismic data in the permeability evaluation process. The structural reservoir model has been prepared using the interpretation of seismic sections and well logs in the reservoir section. This model includes the interpretation of fault surfaces, geocell network and reservoir horizons. The geocell network used in this study used columns and geocells with dimensions of 50 * 50 meters in the X and Y directions. The thickness of the geocellular layers of each reservoir zone is designed to fit that zone in the reservoir section. The values permeability estimation was performed using the artificial neural network with a back-propagation algorithm. The results obtained from the artificial neural network were generalized in the studied reservoir well logs. The correlation coefficients value obtained from permeability estimation values with drilling core data is equal to 88%. Comparison of geostatistics results with permeability value shows that the proposed methods can provide acceptable results for reservoir permeability modeling.
