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Kadanwari is a major gas-producing field in Pakistan's Lower Indus Basin (LIB), extensively explored for optimized production. However, the reservoir sands of the Lower Goru Formation (LGF), deposited in a complex river-dominated delta, bear severe variability and hinder accurate facies approximation for optimal production. Furthermore, the regionally extended NNW-SSE directed horst and graben structures significantly compartmentalized these reservoir facies. The main E-sand interval is analyzed for its geological properties, depositional environment, and distribution. The integration of various approaches, including seismic interpretation, attribute extraction, well-based facies modeling, and petrophysical evaluation, proved significant in evaluating the heterogeneous and tectonically influenced E-sands. The discontinuity attribute substantially highlighted the structural style and aided in analyzing the geometries of faults. The low values of the frequency attribute (< 10 Hz) signified the entrapped gas-bearing sands along the faulted zones. The high responses of instantaneous amplitude and sweetness profoundly illuminated the gas-significant deposits throughout the field in association with the well-identified gas-prone sand facies. The outcomes of the neutron-density crossplot depicted gas-bearing sands having low density (< 2.3 g/cc) and good porosity (12%) with the assessment of various cements. The facies modeling distinguished between clean and intermixed sand-shale reservoir zones. Petrophysical analysis revealed a net pay of 14 m within E-sand having gas saturation of about 68%. The adopted approach is robust and efficient, employing a limited data set for developing well-associated seismic responses for potential zone delineation within structural arrangements. The techniques can be optimistic about the LGF's complex potential sands demarcation throughout the Indus Basin. [ABSTRACT FROM AUTHOR]

Abstract Kadanwari is a major gas-producing field in Pakistan's Lower Indus Basin (LIB), extensively explored for optimized production. However, the reservoir sands of the Lower Goru Formation (LGF), deposited in a complex river-dominated delta, bear severe variability and hinder accurate facies approximation for optimal production. Furthermore, the regionally extended NNW-SSE directed horst and graben structures significantly compartmentalized these reservoir facies. The main E-sand interval is analyzed for its geological properties, depositional environment, and distribution. The integration of various approaches, including seismic interpretation, attribute extraction, well-based facies modeling, and petrophysical evaluation, proved significant in evaluating the heterogeneous and tectonically influenced E-sands. The discontinuity attribute substantially highlighted the structural style and aided in analyzing the geometries of faults. The low values of the frequency attribute (

Middle Miocene reservoirs in the southern part of the Gulf of Suez province are characterized by geometrical uncertainties due to their structural settings, lateral facies change, different lithologies, and diverse reservoir quality. Therefore, in this study, detailed 3D geo-static models were constructed by integrating multiple datasets, including 2D seismic sections and digital well-logs. The 3D models were constructed for the Belayim Formation (Hammam Faraun Member), Kareem Formation (Markha Member), and Rudies Formation (Upper Rudies Member) with detailed structuration, zonation, and layering for Amal Field in the southern Gulf of Suez province to assess the hydrocarbon potential, calculate accurate reserves, recommend development and exploration plans, and propose locations for future drilling. The resultant structural model exhibited a compartmentalized area of major and minor normal faults trending NW–SE, forming structurally high potential hydrocarbon trapping locations in the study area. The petrophysical models indicated the good potentiality of Hammam Faraun as a reservoir with porosity values of 15–23%, increasing towards the central part of the area, volume of shale (Vsh) of 21–31%, water saturation (Sw) of 34–49%, and sand thickness increasing toward the northeastern part of the area. The Markha Member was also interpreted as a good reservoir, with porosity values of 15–22%, increasing towards the southeastern part of the area, Vsh of 13–29%, Sw of 16–38%, and sandy facies accumulating in the central horst block. Upper Rudies exhibits good reservoir properties with porosity values of 16–23%, Vsh of 29–37%, Sw of 35–40%, and good sandy facies in the central horst block of the area. The study results showed hydrocarbon potential in the central horst block of the study area for the Middle Miocene multi-reservoirs. [ABSTRACT FROM AUTHOR]

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In this study, we present a procedure for reservoir property modeling in a channelized carbonate reservoir based on hierarchical geostatistical simulation and seismic data integration. Because soft data integration in facies modeling has always been challenging, we used an innovative approach to incorporate seismic data in facies simulations properly. In this regard, we produced a facies proportion model (FPM) using sequential Gaussian co-simulation of facies proportion as primary data and acoustic impedance as secondary data. The facies proportions were extracted from vertical proportion curves, and the impact of seismic data in facies simulation was determined with the help of correlation coefficient maps. An alternative type of seismic-based soft data was also derived using a supervised neural network to create a facies probability cube (FPC) for each facies. Afterward, pluri-Gaussian simulation (PGS) was implemented to these two prepared soft datasets, and consequently, porosity was simulated twice in each of the models—with and without seismic-derived secondary data. Histogram analysis showed that the facies modeled with the PGS–FPM method reproduced the original well data better than the PGS–FPC method. In addition, blind wells validation showed that the PGS–FPM outputs had up to 79% accuracy, while channel geometries were better constructed using the PGS–FPC approach. The difference between the reservoir quality of channel and background was distinguishable in all hierarchical simulated porosity results. At the same time, the predicted results from simulated porosity in PGS–FPM facies had stronger correlation with true values in blind wells. [ABSTRACT FROM AUTHOR]

This research is focused on the analysis of the sequence stratigraphic units of F3 Block, within a wave‐dominated delta of Plio–Pleistocene age. Three wells of F3 block and a 3D seismic data, are utilized in this research. The conventional techniques of 3D seismic interpretation were utilized to mark the 11 surfaces on the seismic section. Integration of seismic sequence stratigraphic interpretation, using well logs, and subsequent 3D geostatistical modeling, using seismic data, aided to evaluate the shallow hydrocarbon traps. The resulting models were obtained using System Tract and Facies models, which were generated by using sequential stimulation method and their variograms made by spherical method, moreover, these models are validated via histograms. The CDF curve generated from upscaling of well logs using geometric method, shows a good relation with less percentage of errors (1 to 2 for Facies and 3 to 4 for System Tract models) between upscaled and raw data that complements the resulted models. These approaches help us to delineate the best possible reservoir, lateral extent of system tracts (LST and/or HST) in the respective surface, and distribution of sand and shale in the delta. The clinoform break points alteration observed on seismic sections, also validates the sequence stratigraphic interpretation. The GR log‐based Facies model and sequence stratigraphy‐based System Tract model of SU‐04‐2 showed the reservoir characteristics, presence of sand bodies and majorly LST, respectively, mainly adjacent to the main fault of the studied area. Moreover, on the seismic section, SU‐04‐2 exhibits the presence of gas pockets at the same location that also complements the generated Facies and System Tract models. The generated models can be utilized for any similar kind of study and for the further research in the F3 block reservoir characterization. [ABSTRACT FROM AUTHOR]

The early Eocene carbonate reservoir, Sui Main Limestone (SML), is the largest gas reservoir in Pakistan. In the Qadirpur area, more than 30 wells have been drilled, some of which have been declared dry or abandoned due to poor reservoir characteristics or facies variation. The aim of this work is to re-evaluate the reservoir characteristics and facies identification of Sui Main Limestone by using petrophysical analysis and rock physics modeling in the Qadirpur field in the Middle Indus Basin of Pakistan. The reservoir characterization of carbonate rocks is difficult because of their complex pore networking. The well data on five exploratory wells drilled in this gas field are used. The log-derived porosity varies in a very large range of 2%–36%, with an average of 14%–34%. The average porosity of the clean carbonate intervals is 10%–14% and thus has the capacity to accumulate hydrocarbon. The high porosity value indicates the presence of micro-fractures in Sui Main Limestone. These micro-fractures and secondary pores are well interconnected and allow the pore fluids to communicate. The secondary porosity is mainly due to the presence of vuggs and fractures in Sui Main Limestone. The volume of shale varies from 11%–38% in the wells. The net pay zones have hydrocarbon saturation in the range of 40%–70%, which is mainly gas. Using wireline log response, the formation is divided into three facies: limestone, shale, and shaly limestone. This comprehensive work may help improve the prediction of the reservoir quality in heterogeneous carbonate reservoirs and optimize field development.

The Longdong area in the Ordos basin is a typical fluvial reservoir with strong heterogeneity. In order to clarify the distribution law of underground reservoirs in the Longdong area, it is necessary to establish and optimize a 3D geological model to characterize the heterogeneity of reservoirs. This is of great significance for accelerating the exploitation of tight sandstone gas in the southwest of the Ordos basin. This study takes the P2h8 member of the Ct3 research area in the Longdong area as an example, analyzes the core and logging curve shape to divide the sedimentary microfacies, and establishes the facies model. In particular, in view of the difficulty in obtaining 3D training images under the existing conditions in the study area, we use the multi-point geostatistics method combining sequential two-dimensional condition simulation and the direct sampling method to establish the facies model. This method can simulate the 3D geological model by using the 2D training images composed of the digital plane facies diagrams and the well-connection facies diagrams. In addition, we choose the object-based method and sequential indicator method for comparative experiments to verify the feasibility of this method (sequential two-dimensional condition simulation combined with the direct sampling method) from many aspects. The results show that the multi-point geostatistics method based on 2D training images can not only match the well data, but also show the geometric characteristics and contact relationship of the simulation object. The distribution characteristics of sandbody thickness and modeling results are consistent with the actual geological conditions in the study area. This study explores the feasibility of this method in the 3D geological simulation of large-scale fluvial facies tight sandstone reservoirs. Additionally, it also provides a new idea and scheme for the modeling method of geologists in similar geological environments. [ABSTRACT FROM AUTHOR]

We present a Generative Adversarial Network (GAN)‐based 3D reservoir simulation framework, GANSim‐3D, where the generator is progressively trained to capture geological patterns and relationships between various input conditioning data and output earth models, and is thus able to directly produce multiple 3D realistic and conditional earth models from given conditioning data. Conditioning data can include 3D sparse well facies data, probability maps, and global features, such as facies proportion. The generator only includes 3D convolutional layers, and once trained on a data set consisting of small‐size data cubes, it can be used for geomodeling of 3D reservoirs of large arbitrary sizes by simply extending the inputs. To illustrate how GANSim‐3D is practically used and to verify GANSim‐3D, a field karst cave reservoir in Tahe area of China is used as an example. The 3D well facies data and 3D probability map of caves obtained from geophysical interpretation are taken as conditioning data. First, we create training, validation, and test datasets consisting of 64 × 64 × 64‐size 3D cave facies models integrating field geological patterns, 3D well facies data, and 3D probability maps. Then, the 3D generator is trained and evaluated with various metrics. Next, we apply the pretrained generator for conditional geomodeling of two field cave reservoirs of size 64 × 64 × 64 and 336 × 256 × 96, respectively. The produced reservoir realizations prove to be diverse, consistent with field geological patterns and field conditioning data, and robust to noise in the 3D probability maps. Each realization with 336 × 256 × 96 cells only takes 0.988 s using 1 GPU. Key Points: Generative Adversarial Network (GAN)‐based 3D reservoir simulation framework (GANSim‐3D) produces multiple 3D realistic conditional reservoir models of large arbitrary sizes from various conditioning dataApplication in field karst cave reservoirs shows robust performance of GANSim‐3DGeomodeling with GANSim‐3D is quite fast: <1 s for one realization with 336 × 256 × 96 cells [ABSTRACT FROM AUTHOR]

This study aims to characterize the Farewell Formation by producing reservoir models based on the 3D seismic data, available well logs, and completion reports. Seven wells were penetrating the studied formation, but a complete well log dataset was only available for three wells: MB-R(1), MB-V(3), and MB-P(8). The volume-based structural modeling was done using Structural Framework (SF) and Corner Point Gridding (CPG). Sequential Indicator Simulation (SIS) and Object-Based Modeling (OBM) algorithms were used to model lithofacies and depositional facies, respectively, for a better definition of fluvial channels. GR responses and core descriptions were used for constructing facies models. The whole formation was divided proportionally into 100 layers (K property layer), with each layer having approximately 2-m thickness. The field has got NE-SW trending paleo-shoreline, and the whole formation experienced an overall transgressive depositional system, with local regressions, marked by upward increasing marine influence. Petrophysical modeling was done using Sequential Gaussian Simulation (SGS) algorithm. Structural, facies, and petrophysical models show that the potential areas are located towards the southern and central parts of the field and better defined respectively within K property layers 38–71 and layers 37–100 of the formation. The distribution of effective porosity, permeability, and net-to-gross values range as high as 15–21%, 10–1000 mD, and 80–100% respectively, while the water saturation and volume of shale are as low as 0–20% and 0–10%, respectively. The aforementioned characteristics are often associated with shoreface (especially middle shoreface) and fluvial channel sand deposits. This study hopes to contribute significantly to the Maui Gas Field development and also laid the foundation for a basin-scale study of the Farewell Formation. [ABSTRACT FROM AUTHOR]

The Coniacian–Santonian Matulla Formation is one of the important reservoirs in the July oilfield, Gulf of Suez Basin. However, this formation is characterized by uncertainty due to the complexity of reservoir architecture, various lithologies, lateral facies variations and heterogeneous reservoir quality. These reservoir challenges, in turn, affect the effectiveness of further exploitation of this reservoir along the Gulf of Suez Basin. In this work, we conduct an integrated study using multidisciplinary datasets and techniques to determine the precise structural, petrophysical, and facies characteristics of the Matulla Formation and predict their complex geometry in 3D space. To complete this study, 30 2D seismic sections, five digital well logs, and core samples of 75 ft (ft = 0.3048 m) length were used to build 3D models for the Matulla reservoir. The 3D structural model shows strong lateral variation in thickness of the Matulla Formation with NW–SE, NE–SW and N–S fault directions. According to the 3D facies model, shale beds dominate the Matulla Formation, followed by sandstone, carbonate, and siltstone beds. The petrophysical model demonstrates the Matulla reservoir's ability to store and produce oil; its upper and lower zones have good quality reservoir, whereas its middle zone is a poor quality reservoir. The most promising areas for hydrocarbon accumulation and production via the Matulla reservoir are located in the central, southeast, and southwest sectors of the oilfield. In this approach, we combined multiple datasets and used the most likely parameters calibrated by core measurements to improve the reservoir modeling of the complex Matulla reservoir. In addition, we reduced many of the common uncertainties associated with the static modeling process, which can be applied elsewhere to gain better understanding of a complex reservoir. [ABSTRACT FROM AUTHOR]

A study using high-resolution macroscopy and microscopy was conducted on the Neogene Mukah coal in Sarawak, Malaysia, to describe the changes in the coal facies, peat development and its precursors, and depositional conditions of the peat/coal. Eight coal seams from three boreholes (MC05, MC12, MC01) were analyzed. Bright, banded bright, banded dull, and dull coal lithotypes, with a predominance of the brighter lithotypes, were identified and primarily attributed to increases in huminite content and decreases in liptinite and mineral matter contents. Coals were characterized by high huminite (70.3%–91.9%), moderate liptinite (4.0%–26.3% mmf), minor inertinite (1.4%–5.9% mmf), and argillaceous mineral matter (0.0%–50.6%). Clarite (3.5%–88.7%) was observed to be the most predominant microlithotypes with carbominerite content ranging from 0.0% to 67.9%. The complete succession from topogenous to ombrogrenous peat evolution was identical between the eight coal seams, and temporary development of rheotrophic–ombrotrophic mires was suggested. The basal section of coal bench was dominated by humodetrinite/liptinite-rich coal or humocollinite/mineral matter-rich coal, whereas the middle section was characterized by humotelinite- and humocollinite/liptinite-rich coal and overlain by humotelinite-rich coal. The Mukah coal was suggested to have been deposited in a coastal floodplain within a (mainly) upper–lower delta plain setting more prone to telmatic coal facies. [ABSTRACT FROM AUTHOR]

Copyright of Petroleum Geology & Recovery Efficiency is the property of Petroleum Geology & Recovery Efficiency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The incorporation of hard data in geostatistical modeling is crucial for enhancing the accuracy of interpolating or stochastically estimating subsurface spatial features. The hard data at specified points in the model domain serve as a guide in optimizing the unknown parameters to follow the patterns of the hard data. Recently, a novel approach to solving hydrogeologic/reservoir modeling problems has emerged by using deep generative models, specifically generative adversarial networks (GANs), to generate realistic and diverse images of channelized aquifers. This subsequently can be coupled with inverse models to solve parameter estimation problems. This study focused on using an improved GAN, called a progressive growing generative adversarial network (PGGAN), conditioned with hard data to perform parameter estimation of complex facies models by coupling an ensemble smoother with multiple data assimilation (ES-MDA). First, the PGGAN was trained to an image with 128 × 128 resolution. The trained PGGAN was used to generate hydraulic conductivity fields when fed an ensemble of latent variables and hard data. The ES-MDA then was used to update the latent variable with the help of hydraulic head data obtained from the groundwater model. The approach was tested on synthetic hydraulic conductivity data. Results show that this approach was able to perform efficient estimation of an unknown facies model domain. Additionally, the proposed method was applied to a different test case of a facies model exhibiting different statistical characteristics. The results were satisfactory, demonstrating that the method is not constrained to the particular hydraulic conductivity fields introduced in the generator's training. • Coupling deep learning with data assimilation for groundwater modeling. • Both hard data and soft data are conditioned. • Synthetic examples are used to demonstrate the effectiveness. [ABSTRACT FROM AUTHOR]