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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]

Geological facies modeling has long been studied to predict subsurface resources. In recent years, generative adversarial networks (GANs) have been used as a new method for geological facies modeling with surprisingly good results. However, in conventional GANs, all layers are trained concurrently, and the scales of the geological features are not considered. In this study, we propose to train GANs for facies modeling based on a new training process, namely progressive growing of GANs or a progressive training process. In the progressive training process, GANs are trained layer by layer, and geological features are learned from coarse scales to fine scales. We also train a GAN in the conventional training process, and compare the conventionally trained generator with the progressively trained generator based on visual inspection, multi-scale sliced Wasserstein distance (MS-SWD), multi-dimensional scaling (MDS) plot visualization, facies proportion, variogram, and channel sinuosity, width, and length metrics. The MS-SWD reveals realism and diversity of the generated facies models, and is combined with MDS to visualize the relationship between the distributions of the generated and training facies models. The conventionally and progressively trained generators both have very good performances on all metrics. The progressively trained generator behaves especially better than the conventionally trained generator on the MS-SWD, MDS plots, and the necessary training time. The training time for the progressively trained generator can be as small as 39% of that for the conventionally trained generator. This study demonstrates the superiority of the progressive training process over the conventional one in geological facies modeling, and provides a better option for future GAN-related researches. [ABSTRACT FROM AUTHOR]

Subsurface models are central pieces of information in different earth-related disciplines such as groundwater management and hydrocarbon reservoir characterization. These models are normally obtained using geostatistical simulation methods. Recently, methods based on deep learning algorithms have been applied as subsurface model generators. However, there are still challenges on how to include conditioning data and ensure model variability within a set of realizations. We illustrate the potential of Generative Adversarial Networks (GANs) to create unconditional and conditional facies models. Based on a synthetic facies dataset, we first train a Deep Convolution GAN (DCGAN) to produce unconditional facies models. Then, we show how image-to-image translation based on a U-Net GAN framework, including noise-layers, content loss function and diversity loss function, is used to model conditioning geological facies. Results show that GANs are powerful models to capture complex geological facies patterns and to generate facies realizations indistinguishable from the ones comprising the training dataset. The U-Net GAN framework performs well in providing variable models while honoring conditioning data in several scenarios. The results shown herein are expected to spark a new generation of methods for subsurface geological facies with fragmentary measurements. [ABSTRACT FROM AUTHOR]

Abstract 3D seismic data, well logs, core-based lithofacies and photographs have been combined to interpret and model the depositional facies of the Mangahewa Formation of the Maui Gas Field, Taranaki Basin, New Zealand. The primary objective of the study is to generate a robust facies model for the Middle to Late Eocene (47–37 Ma) Mangahewa Formation of the field. The facies model has included eighteen depositional facies spatially distributed over the gas field. These facies are further subgrouped into three broad depositional facies associations, namely marginal marine, shallow marine and offshore environment. We have identified that marginal marine is the most dominant facies association (64%) within the model. The model visualizes estuarine and shoreface sand geobodies dominating over other facies within the model. Both geobodies comprise over 40% of all the facies interpreted in the field. The entire modeling process involves a novel stochastic approach using unique workflow that follows 3D gridding, coding of the facies classes and multiple iterations over the interpreted facies. The model therefore realistically visualizes potential facies responsible for “good”-quality reservoir sands in the Mangahewa Formation with possible retrogradation from older to younger succession.

Reservoir characterization of petroleum reservoirs can be claimed as one of the most important parts of reservoir management for optimized production and future developments. Through reservoir evaluation, geological zoning for a better comprehension of subsurface structure is needed. For developing a model, porosity plays a vital role; there are two common methods for obtaining this parameter, core samples and well logging. However, the results of these methods are in well scale which cannot be used through field scale modeling. A solution can be the combining seismic field data well log data, which makes it possible to estimate the reservoir properties in field scale. In this study, multi-attribute analyses were applied based on multilayer perceptron to determine the reservoir facies alteration and heterogeneity in the Ghar reservoir of the Hendijan oil field located in the Persian Gulf. Facies modeling was done through the sequential indicator simulation (SIS) algorithm which coupled with the possible trend and indicator kriging (IK) as geostatistical methods. Within the comparison of these two generated models with core facies, the obtained accuracy of SIS algorithm coupled with the possible trend and indicator kriging are 94% and 72%, respectively. Porosity distribution was also done by the sequential Gaussian simulation (SGS) algorithm which resulted the average porosity of 18% in Ghar formation. The SIS method results are compatible with the porosity distribution model obtained from the SGS simulation. The final results prove the robustness of the applied methods for facies and porosity modeling. [ABSTRACT FROM AUTHOR]

The Oligo-miocene Asmari formation is one of the most important hydrocarbon reservoirs in the southwest of Iran. In order to evaluate reservoir quality and the factors controlling reservoir properties, detailed geological and petrophysical studies were carried out on 242 m of core samples from Asmari reservoir. This study is a part of a larger project that examines depositional history and reservoir properties of the Asmari formation in the Cheshmeh-Khush field. Macroscopic and microscopic studies resulted in the determination of 5 shallow marine carbonate facies (from proximal open marine to tidal setting) and also 5 silisiclastic lithofacies (including channel, barrier, tidal, and shoreface sandstones). Based on the integrated results from sedimentological and paleontological studies, Sr isotopes dating, gamma-ray logs, and seismic data analysis, 5 depositional sequences with constituent system tracts were distinguished. In this research, the reservoir characterization of the Asmari reservoir were carried out through the integration of geological and petrophysical properties. In the first step, 21 hydraulic flow units (HFU or FU) were identified and then, to achieve better lateral correlation and modeling, HFU’s were merged to 17. The results from this study showed different behaviors of the silisiclastic and carbonate facies next to the fluid flow. The findings of this study indicate that the lateral and vertical distribution of channel-filled sandstones (such as units 2 and 8) are strongly controlled by the geometry of depositional facies. Thus, the correlation and modeling of flow units, solely on the basis of lithology and thickness, and regardless of facies and its geometry, will cause different facies (such as coastal and channel-filled sandstones) with different geometry, and reservoir quality are placed incorrectly in a single flow unit. In the carbonate parts of Asmari formation, the effect of diagenetic processes on reservoir quality is much higher than the facies. Hence, the LST limestones of unit 17, as a result of calcite cementation, were changed to a thick, distinct, correlatable, and barrier unit. On the other hand, dolomitic intervals that have not been affected by anhydrite cementation have formed porous and permeable carbonate reservoir units (such as units 18 and 21).

As the reservoir and its attribute distribution are obviously controlled by sedimentary facies, the facies modeling is one of the important bases for delineating the area of high-quality reservoir and characterizing the attribute parameter distribution. There are a large number of continental sedimentary reservoirs with strong heterogeneity in China, the geometry and distribution of various sedimentary microfacies are relatively complex. The traditional geostatistics methods which have shortage in characterization of the complex and non-stationary geological patterns, have limitation in facies modeling of continental sedimentary reservoirs. The generative adversarial network (GANs) is a recent state-of-the-art deep learning method, which has capabilities of pattern learning and generation, and is widely used in the domain of image generation. Because of the similarity in content and structure between facies models and specific images (such as fluvial facies and the images of modern rivers), and the various images generated by GANs are often more complex than reservoir facies models, GANs has potential to be used in reservoir facies modeling. Therefore, this paper proposes a reservoir facies modeling method based on GANs: (1) for unconditional modeling, select training images (TIs) based on priori geological knowledge, and use GANs to learn priori geological patterns in TIs, then generate the reservoir facies model by GANs; (2) for conditional modeling, a training method of “unconditional-conditional simulation cooperation” (UCSC) is used to realize the constraint of hard data while learning the priori geological patterns. Testing the method using both synthetic data and actual data from oil field, the results meet perfectly the priori geological patterns and honor the well point hard data, and show that this method can overcome the limitation that traditional geostatistics are difficult to deal with the complex non-stationary patterns and improve the conditional constraint effect of GANs based methods. Given its good performance in facies modeling, the method has a good prospect in practical application.

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Copyright of Iraqi Bulletin of Geology & Mining is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) 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.)

3D seismic data, well logs, core-based lithofacies and photographs have been combined to interpret and model the depositional facies of the Mangahewa Formation of the Maui Gas Field, Taranaki Basin, New Zealand. The primary objective of the study is to generate a robust facies model for the Middle to Late Eocene (47-37 Ma) Mangahewa Formation of the field. The facies model has included eighteen depositional facies spatially distributed over the gas field. These facies are further subgrouped into three broad depositional facies associations, namely marginal marine, shallow marine and offshore environment. We have identified that marginal marine is the most dominant facies association (64%) within the model. The model visualizes estuarine and shoreface sand geobodies dominating over other facies within the model. Both geobodies comprise over 40% of all the facies interpreted in the field. The entire modeling process involves a novel stochastic approach using unique workflow that follows 3D gridding, coding of the facies classes and multiple iterations over the interpreted facies. The model therefore realistically visualizes potential facies responsible for “good”-quality reservoir sands in the Mangahewa Formation with possible retrogradation from older to younger succession. [ABSTRACT FROM AUTHOR]

New geophysical data from the leeward slope of Great Bahama Bank show how contour currents shape the slope and induce re-sedimentation processes. Along slope segments with high current control, drift migration and current winnowing at the toe of slope form a deep moat. Here, the slope progradation is inhibited by large channel incisions and the accumulation of large mass transport complexes, triggered by current winnowing. In areas where the slope is bathed by weaker currents, the accumulation of mass transport complexes and channel incision is rather controlled by the position of the sea level. Large slope failures were triggered during the Mid-Pleistocene transition and Mid-Brunhes event, both periods characterized by changes in the cyclicity or the amplitude of sea-level fluctuations. Within the seismic stratigraphic framework of third order sequences, four sequences of higher order were identified in the succession of the upper Pleistocene. These higher order sequences also show clear differences in function of the slope exposure to contour currents. Two stochastic models emphasize the role of the contour currents and slope morphology in the facies distribution in the upper Pleistocene sequences. In areas of high current influence the interplay of erosional and depositional processes form a complex facies pattern with downslope and along strike facies alterations. In zones with lower current influence, major facies alternations occur predominately in downslope direction, and a layer-cake pattern characterizes the along strike direction. Therefore, this study highlights that contour currents are an underestimated driver for the sediment distribution and architecture of carbonate slopes. [ABSTRACT FROM AUTHOR]

This study includes a new understanding of the depositional model of Abu Roash “C” Member deposited in Sitra Field in Abu Gharadig Basin during Turonian time, and illustrates the major affecting factors that control the behavior of this reservoir and consequently offer a great opportunity for Sitra field’s future development activities through a new methodology to maximize the field’s ultimate recovery. The Sitra development lease is located in north Western Desert and occupies the central western part of Abu Gharadig Basin and covers the southern extension of Badr El-din Petroleum Company (BAPETCO) leases with an area of 322.4 km 2 . Sitra Area is divided into several structural closures; the main producing one is the Sitra 8 block in which 39 wells were drilled since 1993. Various data from these wells were evaluated to construct the depositional facies models for the Abu Roash “C” reservoir. The log analyses have been integrated with the core descriptions, and ditch cutting data to interpret the depositional facies model that controlled the reservoir characteristics. In Sitra Area the Abu Roash “C” Member exhibits all of the characteristics of the Shallow Marine-Tidal dominated estuaries which are linked to the south with a fluvio-marine environment, the tide-dominated estuaries are represented in tidal channels and tidal flat facies, march deposits, and distributary mouth bars. The best reservoir rock in the Abu Roash “C” Member was deposited as distributary channel fill/Mouth bars that cut through the underlying strata. Two major parasequences were identified, the first lower one was developed during a shallowing upward sequence represented by shale/sand intercalations into which the main Abu Roash “C” reservoir sand bodies are included, and reached its end by the appearance of a laterally extended coal marker nearly one meter thick. This parasequence was deposited subsequently to a falling sea level phase which occurred after the deposition of Abu Roash “D” limestone. The succeeding parasequence (deepening upward) reached its maximum flooding surface (MFS) by the deposition of the widely extensive shale marker being rich in pelagic Pelecypod shells. The resultant stratigraphic units consist of: genetically related depositional cycles (3 cycles) and their components of facies sequences (5 facies types), each cycle has its own distribution, facies classification and reservoir characteristics. [ABSTRACT FROM AUTHOR]

Categorical variable modeling is of great significance for resource estimation as it defines a major aspect of geological heterogeneity and uncertainty. Stochastic simulation can be used to generate multiple equally-probable realizations that describe the uncertainty in the spatial distribution of categorical variables such as facies. These realizations reasonably sample the space of uncertainty if the input statistics are well known. However, the input statistics are often poorly defined due to sparse data. The prior parameter uncertainty related to proportions of different categories is required to achieve an accurate evaluation of the space of uncertainty. For decision making and risk analysis, it is critical to have an accurate and precise model of uncertainty associated with 3924 the subsurface geology. A methodology is proposed, implemented and checked to quantify parameter uncertainty related to facies proportions in presence of a locally varying proportion model (a trend model). Unconditional sequential indicator simulation (SIS) is employed to implement the spatial bootstrap and quantify the prior proportion uncertainty. A trend building algorithm provides multiple realizations of the trend model based on the spatial bootstrap realizations of sampled data. Passing this prior parameter uncertainty through geostatistical simulation provides a realistic posterior model of uncertainty that accounts for the data configuration, conditioning, spatial correlation, and the domain limits. [ABSTRACT FROM AUTHOR]

The Ensemble Kalman Filter (EnKF) often fails to reproduce realistic facies distribution and proportion corresponding to the prior spatial distribution. In this paper, a new step is proposed for inclusion in the history matching of multiple facies reservoir models using EnKF. The new step consists of constructing a facies probability map and application of probability field (P-Field) simulation to re-sample a new ensemble. After certain number of assimilation steps in EnKF, the updated ensemble members are used to propose a probability map for facies distribution. P-Field simulation is performed subsequently using the facies probability map to generate a new ensemble, which honors the static geologic data and is more consistent with the early production data. After the re-sampling step, the forecast model is applied to the new ensemble from the beginning. EnKF is again applied on the ensemble members to assimilate the remaining production history. Two case studies with different facies distribution and well configurations were conducted. Initial ensemble was created using known facies classification at the well locations and populating binary facies data throughout the reservoir using prior information of spatial facies distribution and facies proportions. The qualities of the history-matched models were assessed by comparing the spatial facies distribution and proportions of the updated ensemble, in addition to the root mean square error of the predicted data mismatch. Implementation of EnKF together with re-sampling of the new realizations using probability maps demonstrates reasonable improvement in the history matching and uncertainty assessment of multiple facies reservoir models. [ABSTRACT FROM AUTHOR]

Bayesian modeling requires the specification of prior and likelihood models. In reservoir characterization, it is common practice to estimate the prior from a training image. This paper considers a multi-grid approach for the construction of prior models for binary variables. On each grid level we adopt a Markov random field (MRF) conditioned on values in previous levels. Parameter estimation in MRFs is complicated by a computationally intractable normalizing constant. To cope with this problem, we generate a partially ordered Markov model (POMM) approximation to the MRF and use this in the model fitting procedure. Approximate unconditional simulation from the fitted model can easily be done by again adopting the POMM approximation to the fitted MRF. Approximate conditional simulation, for a given and easy to compute likelihood function, can also be performed either by the Metropolis-Hastings algorithm based on an approximation to the fitted MRF or by constructing a new POMM approximation to this approximate conditional distribution. The proposed methods are illustrated using three frequently used binary training images. [ABSTRACT FROM AUTHOR]

Facies bodies in geostatistical models of deep-water depositional environments generally represent channel-levee-overbank-lobe morphologies. Such models adequately capture one set of the erosional and depositional processes resulting from turbidity currents traveling downslope to the ocean basin floor. However, depositional morphologies diverge from the straight forward channel-levee-overbank-lobe paradigm when the topography of the slope or the shape of the basin impacts the timing and magnitude of turbidity current deposition. Subaqueous mass-transport-deposits (MTDs) present the need for an exception to the channel-levee-overbank-lobe archetype. Irregular surface topography of subaqueous MTDs can play a primary role in controlling sand deposition from turbidity currents. MTD topography creates mini-basins in which sand accumulates in irregularly-shaped deposits. These accumulations are difficult to laterally correlate using well-log data due to their variable and unpredictable shape and size. Prediction is further complicated because sandstone bodies typical of this setting are difficult to resolve in seismic-reflection data. An event-based model is presented, called DFTopoSim, which simulates debris flows and turbidity currents. The accommodation space on top of and between debris flow lobes is filled in by sand from turbidity currents. When applied to a subsurface case in the Molasse Basin of Upper Austria, DFTopoSim predicts sand packages consistent with observations from core, well, and seismic data and the interpretation of the sedimentologic processes. DFTopoSim expands the set of available geostatistical deep-water depositional models beyond the standard channel-levee-overbank-lobe model. [ABSTRACT FROM AUTHOR]

We present a methodology based on the ensemble Kalman filter (EnKF) and the level set method for the continuous model updating of geological facies with respect to production data. Geological facies are modeled using an implicit surface representation and conditioned to production data using the ensemble Kalman filter. The methodology is based on Gaussian random fields used to deform the facies boundaries. The Gaussian random fields are used as the model parameter vector to be updated sequentially within the EnKF when new measurements are available. We show the successful application of the methodology to two synthetic reservoir models. [ABSTRACT FROM AUTHOR]

Multiple-point statistics are used to model facies heterogeneities in the vadose zone of the Komadugu-Yobe River valley (southeastern Niger) which is presently submitted to an undergoing intensive agricultural development; therefore, increasing quantitative and qualitative pressures are exerted on groundwater resources. The sand-clay heterogeneities are analyzed by means of a Landsat image acquired during a high flow period over a 160 km stretch in the downstream part of the valley and a set of 50 boreholes drilled near the town of Diffa (4 km×4 km area). The horizontal variograms of heterogeneities are characterized by a noticeably constant length scale of 380 m and clayey objects are shown to be randomly distributed in space according to a Poisson process. A set of two-dimensional vertical images is built based on a Boolean procedure and the Snesim algorithm is used to simulate synthetic three-dimensional media. When the vertical correlation length is fitted, the three-dimensional model satisfactorily reproduces the second order statistics of heterogeneities and the specific facies patterns. [ABSTRACT FROM AUTHOR]

We present an approach for modeling facies bodies in which a highly constrained stochastic object model is used to integrate detailed seismic interpretation of the reservoir's sedimentological architecture directly in a three-dimensional reservoir model. The approach fills the gap between the use of seismic data in a true deterministic sense, in which the facies body top and base are resolved and mapped directly, and stochastic methods in which the relationship between seismic attributes and facies is defined by conditional probabilities. The lateral geometry of the facies bodies is controlled by seismic interpretations on horizon slices or by direct body extraction, whereas facies body thickness and cross-sectional shape are defined by a mixture of seismic data, well data, and user defined object shapes. The stochastic terms in the model are used to incorporate local geometric variability, which is used to increase the geological realism of the facies bodies and allow for correct, flexible well conditioning. The result is a set of three-dimensional facies bodies that are constrained to the seismic interpretations and well data. Each body is defined as a parametric object that includes information such as location of the body axis, depositional direction, axis-to-margin normals, and external body geometry. The parametric information is useful for defining geologically realistic intrabody petrophysical trends and for controlling connectivity between stacked facies bodies. [ABSTRACT FROM AUTHOR]

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Abstract: High resolution reservoir models are often required for resource assessment and reservoir management. The reservoir models must reproduce geologically realistic heterogeneity that may affect predictions. An important heterogeneity in many deepwater depositional systems consists of facies variations controlled by geological events that lead to lobe geometries. A surface-based geostatistical scheme is presented to model high resolution lobes and their associated stacking patterns in a geometrically realistic manner. The lobes are constrained to match well data. Facies, porosity and permeability are then modeled within the lobe framework. Application to a reservoir with 36 wells demonstrates the practical applicability of the approach. This surface-based method may be readily extended to model other geometries in various reservoir settings. [Copyright &y& Elsevier]

Abstract: This paper presents an algorithm for the construction of event-based fluvial models. The event-based approach may be applied to construct stochastic pseudo-process-based fluvial models for a variety of fluvial styles with conditioning to sparse well data (1–5 wells) and areal and vertical trends. The initial models are generated by placing large-scale features, such as channels and crevasse splays, into the model as geometric objects. These large-scale features are controlled by geometric input parameters provided by the user and are placed into the model to roughly honor well data through a rejection and updating method. Yet, some model to well data mismatch may still occur due to inconsistency in the size and positioning of complicated features relative to the well data. An image processing algorithm is used to post-process realizations to exactly honor all well data. The final, cell-based models, have no data mismatch and contain geologically realistic fluvial features that would be difficult to obtain with other pixel-based methods and precise conditioning that is difficult to obtain with object-based methods. [Copyright &y& Elsevier]

This paper presents a FORTRAN program for hierarchical object-based modeling of complex fluvial facies. Unique features of this program include (1) a simple approach to place channel, levee, and crevasse sands within a matrix of floodplain shales, (2) templates for fast rastering of fluvial facies objects, leading to fast CPU times, and (3) the use of simulated annealing and non-random perturbation rules for conditioning to extensive soft facies-proportion data and local well data. Object-based modeling techniques are widely applicable to modeling fluvial depositional systems. Public domain software for such modeling is uncommon and inflexible with respect to the variety of conditioning data that can be handled. Commercial software is costly and also of limited flexibility. The fluvsim program overcomes many of these limitations with an accessible research code. [Copyright &y& Elsevier]