Reservoir Model Research Articles

Machine learning with hyperparameter optimization applied in facies-supported permeability modeling in carbonate oil reservoirs

Most carbonate reservoirs exhibit heterogeneous pore distribution, whereby the matrix displays low permeability, thus impeding the flow of oil. On the other hand, highly permeable fractures function as the main flow conduits within such reservoirs. Permeability measurements are obtained from core and well test analysis, which are too expensive and not available for many wells. Therefore, accurate permeability prediction is a vital step in developing an efficient field development plan, as it plays a pivotal role in the accurate distribution of 3D petrophysical properties throughout a reservoir. Machine learning (ML) algorithms are now widely applied to predict core permeability using conventional well logs to build a model for permeability prediction in uncored wells. This review considers the performance of six ML algorithms (LightGBM, CATBoost, XGBoost, Adaboost, random forest and gradient boosting) for permeability prediction from a high-quality dataset. The dataset incorporates multiple well-log inputs (gamma ray, caliper, density, neutron porosity, shallow and deep resistivity, total porosity, spontaneous potential, water saturation, depth, and facies) in addition to direct core permeability and porosity measurements. Data pre-processing techniques applied include missing data imputation, scale correction, normalization with three different transformations (log, Box-Cox, and NST) and outlier detection. To enhance the ML performance, two search algorithms (random search and Bayesian optimization) are compared in their ability to tune the ML hyperparameters. There is a need to identify a suitable parameter space, especially when the target variable range is changing. ML performance was evaluated with four evaluation metrics (RMSE, MAE, R2, and Adjusted R2). Results showed that the XGBoost algorithm with configuration of (RS as search algorithm, Box Cox as the normalization method, Z-score for outlier detection, without scale correction, old parameter space) delivered the best prediction performance for permeability with RMSE values of 6.9 md and 9.78 md for training and testing, respectively.

Opportunities and Challenges for Joint Ventures in the SHIVA World: Applicability to Kazakhstan

The purpose of the article is to analyze the opportunities and challenges faced by Joint Ventures (JVs) in the context of the SHIVA world and to assess their relevance and applicability to Kazakhstan. The study aims to identify key factors for JV success in the context of modern global challenges and uncertainties. The following research methods were used in the article: literature review – analysis of existing studies and theoretical models of JVs and the SHIVA concept; comparative analysis – study of global and regional JV practices to identify common patterns and differences; case study – analysis of successful JVs in various industries and their applicability to Kazakhstan. The study reveals that JVs offer significant strategic benefits, including access to novel technologies, resource optimization, and market expansion. Nevertheless, challenges such as cultural disparities, governance intricacies, and knowledge safeguarding persist as critical obstacles. An analysis of global JV instances, including Tesla’s partnerships and collaborations in China’s automotive industry, underscores the significance of trust, strategic coherence, and flexible governance structures. Furthermore, in the energy industry, particularly the oil and gas sector, JVs contribute significantly to risk-sharing, technological advancement, and regulatory conformity. The integration of AI and big data analytics into energy JVs enhances predictive maintenance, reservoir modeling, supply chain visibility, and contract administration. JVs continue to be a crucial strategy for promoting global business expansion, but their success hinges on meticulous planning, effective management, and the capacity to adapt to swiftly evolving market circumstances. Digital transformation, especially through the use of artificial intelligence (AI) and big data analytics, is revolutionizing JV operations by improving efficiency and mitigating risks. Future JV models must focus on strategic adaptability, sustainable governance mechanisms, and digital integration in order to remain competitive in the hyper-connected global economy. In particular, the energy sector is undergoing a transformation through digitalization and AI-powered solutions, enabling companies to navigate operational intricacies, enhance decision-making processes, and achieve long-term sustainability in their JVs.

Fracture Modeling of a Carbonate Reservoir: A Case Study of the East Urikhtau Field

Background: Fracture Modeling of carbonate reservoirs plays a key role in predicting well productivity and enhancing field development efficiency. The East Urikhtau field, located in the eastern flank zone of the Pre-Caspian Depression, features a complex tectonic structure with an extensive system of faults and fractures. These geological features significantly impact the filtration and storage properties of carbonate reservoirs, making advanced geological modeling techniques necessary. A comprehensive fracture model allows a more precise evaluation of structural heterogeneities and their effect on hydrocarbon migration and accumulation. Aim: A three-dimensional fracture model of a carbonate reservoir was developed to identify highly fractured zones and evaluate their correlation with well productivity. This model is essential for improving the accuracy of reservoir filtration-capacity property predictions and designing effective strategies for the field’s further development. Materials and methods: Modern geological modeling techniques were applied in this study, including FMI data interpretation, core analysis, seismic attributes, and Discrete Fracture Network (DFN) modeling. Initial geological and geophysical data were processed using Petrel software, utilizing Ant Tracking and Distance to Object methods to determine fracture orientations and intensities. The developed trend model served as the foundation for discrete fracture modeling, enabling the quantitative assessment of fracture intensity and the identification of the most promising zones for further development. Results: The results of this study demonstrate that the developed fracture model facilitated the detailed identification of highly fractured zones and established their correlation with well productivity. It was found that the most intensely fractured zones are located near faults, as confirmed by fluid flow rate analysis. The application of Ant Tracking and DFN methods reduced uncertainties in the inter-well space and improved predictions of the reservoir’s filtration-capacity properties. Conclusion: The developed methodology allows for a more detailed characterization of the geological structure, enhances the accuracy of well productivity forecasting, and optimizes development planning. The obtained data can be used for designing new wells and adjusting field development strategies for reservoirs with dual porosity and permeability.

A new dynamic model of supply boundary at low pressure in tight gas reservoir

Tight gas is a clean and low-carbon energy with great development potential. However, in the middle and late stages of development of tight gas reservoirs, there are characteristics of low pressure, low production and high water cut. At present, there are few studies on the dynamic supply boundary considering the threshold pressure gradient, reservoir and fracture stress sensitivity and slip effect for the gas-water two-phase model of tight gas reservoirs. In order to improve the assessment of the supply boundary and production capacity features of tight gas wells at low pressure, a new coupling model of gas-water two-phase was established in this paper and the threshold pressure gradient, stress sensitivity and slip effect are taken into account the built model. Subsequently, the steady-state substitution method and the material balance method were used to develop the supply boundary calculation model. Finally, an analysis is done on how the supply boundary is affected by the threshold pressure gradient production, stress sensitivity and production pressure difference. The results demonstrate that, in contrast to the slower propagation observed in the matrix zone, the supply boundary propagates relatively quickly within the fracture modification zone. The threshold pressure gradient and stress-sensitive increase the resistance of gas seepage and delay the propagation of the supply boundary, which reduces the production of gas well. The propagation of the supply boundary accelerates with increasing production pressure difference, but the pressure difference’s effect decreases. The production of gas is significantly impacted by the threshold pressure gradient, stress sensitivity and production pressure difference. Efficient production of tight gas reservoirs necessitates minimizing water production and preserving formation pressure.

Sealing of Unconformity Structure and Hydrocarbon Accumulation in the Baikouquan Formation of the Mahu Sag

Unconformity stratigraphic traps are widely developed in the Mahu Sag, on the northwestern margin of the Junggar Basin. It is of great significance for subsequent oil and gas exploration to explore the role of conglomerate accumulation mode and unconformity inner structure in the formation of oil and gas reservoirs. Therefore, this study uses oil and gas geophysical technology combined with geological theory to identify the P/T unconformity structure in the study area, determine the development characteristics and accumulation control of the unconformity structure, and explore the accumulation mode of stratigraphic oil and gas reservoirs. The results show the following: (1) Based on the different logging response characteristics of the upper, middle, and lower layers of the unconformity structure, five types of unconformity structure are divided according to different lithologic combinations. (2) Through experimental and numerical simulation analysis, it was verified that fracture pressure and thickness are important indicators for evaluating the sealing property of unconformity structure. P/T unconformity structure provides good floor conditions for the Baikouquan Formation reservoir, further confirming its key role in the process of oil and gas accumulation and storage. (3) Based on the analysis of actual cases, the accumulation model of stratigraphic oil and gas reservoirs under the control of unconformity structure is summarized as cross-layer accumulation above the source, fault communication source reservoir, unconformity lateral transmission and distribution, and mudstone lateral docking. The research results provide technical support and important reference values for the exploration and development of unconformity-related oil and gas reservoirs in the Junggar Basin.

From Site Selection to Characterization: Assessment of Potential Reservoirs in the Lurestan Basin of Iran for Carbon Sequestration

ABSTRACTThe increasing global focus on carbon sequestration underscores the need for comprehensive CO2 storage atlases, extending beyond Europe and America to other countries that are significant contributors to carbon dioxide emissions. This article addresses this imperative by examining the CO2 storage potential in the Lurestan area of Iran, with a specific focus on static reservoir modelling and capacity calculation. Our approach involves evaluating potential reservoirs for carbon sequestration using seismic data and well logs followed by site ranking using the Analytic Hierarchy Process (AHP) technique. An in‐depth evaluation was then performed on the highest ranking available reservoir. This included construction of a 3D geological model based on integrated well log and seismic data followed by population of the defined reservoir and seal layers with crucial properties based on available well logs. Finally, reservoir storage capacity was estimated using a volumetric approach.

Mud Loss Analysis Through Predictive Modeling of Pore Pressure and Fracture Gradients in Tin Fouye Tabankort Field, Western Illizi Basin, Algeria

This study examines the distribution of pore pressure (PP) and fracture gradient (FG) within intervals of lost circulation encountered during drilling operations in the Ordovician reservoir (IV-3 unit) of the Tin Fouye Tabankort (TFT) field, located in the Illizi Basin, Algeria. The research further aims to determine an optimized drilling mud weight to mitigate mud losses and enhance overall operational efficiency. PP and FG models for the Ordovician reservoir were developed based on data collected from five vertical development wells. The analysis incorporated multiple datasets, including well logs, mud logging reports, downhole measurements, and Leak-Off Tests (LOTs). The findings revealed an average overburden gradient of 1.03 psi/ft for the TFT field. The generated pore pressure and fracture gradient (PPFG) models indicated a sub-normal pressure regime in the Ordovician sandstone IV-3 reservoir, with PP values ranging from 5.61 to 6.24 ppg and FG values between 7.40 and 9.14 ppg. The analysis identified reservoir depletion due to prolonged hydrocarbon production as the primary factor contributing to the reduction in fracture gradient, which significantly narrowed the mud weight window and increased the likelihood of lost circulation. Further examination of pump on/off cycles over time, coupled with shallow and deep resistivity variations with depth, confirmed that the observed mud losses were predominantly associated with induced fractures resulting from the application of excessive mud weight during drilling operations. Based on the established PP and FG profiles, a narrow mud weight window of 6.24–7.40 ppg was recommended to ensure the safe and efficient drilling of future wells in the TFT field and support the sustainability of drilling operations in the context of a depleted reservoir.

From shallow to deep: Enhancing seismic resolution with weak supervision

High-resolution (HR) seismic data are crucial for accurately identifying subsurface geologic formations and reservoir properties. However, as widely observed in prestack and poststack data, seismic wave attenuation often leads to a gradual decrease in resolution with depth. This degradation leads to resolution differences between data from shallow and deep layers, characterized by high-frequency energy loss in deeper target layers. The latter could complicate the extraction of crucial deep-layer structure information, leading to increased uncertainty in reservoir modeling and significant economic losses due to our inability to image deep targets with sufficient resolution. Traditional methods with simplified assumptions about this nonlinear attenuation hinder modeling geologic complexity and variability. Deep-learning methods excel at capturing complex, nonlinear relationships but often rely heavily on scarce and costly paired labels for supervised learning. As a result, deep learning without requiring paired labels for seismic resolution enhancement has become a key research focus. This study explores a method to enhance 3D seismic data resolution using weakly supervised learning, leveraging the inherent similarity of sedimentary structures across depths. Specifically, our approach uses a relatively shallow, HR data window to transfer and extrapolate learned high-frequency information to a deeper window containing attenuated responses from deeper layers. Thereby, we achieve enhanced resolution in the target region. A tailored 3D convolutional neural network with a bidirectional cycle structure, custom-designed loss functions, and data preprocessing techniques specifically addresses the challenge of resolution differences caused by seismic wave attenuation. The effectiveness of our method is validated with synthetic and real 3D poststack migration data, demonstrating its robustness for regions near the target layer. Compared with conventional spectral whitening, our approach leverages intrinsic data characteristics more adaptively and robustly, making geologic structures more discernible.

Impact of sedimentological heterogeneity on subsurface storage in net-transgressive, shallow-marine sandstones: Cliff House Sandstone, New Mexico, USA

Net-transgressive, shallow-marine sandstone reservoirs overlain by thick mudstone seals are prime candidates for storage of CO 2 , H 2 and thermal energy. Although these reservoirs have high net-to-gross ratios, analogous outcrops demonstrate a wide range of sedimentological heterogeneities that are sampled only sparsely or at low resolution in subsurface data. We use a combination of outcrop data, sketch-based reservoir modelling and flow diagnostics to assess the impact of sedimentological heterogeneities on subsurface storage. The Cliff House Sandstone outcrop example comprises wave-dominated shoreface sandstones arranged in aggradationally-to-retrogradationally stacked parasequences, which overlie and pass up depositional dip into mudstone-dominated coastal plain, lagoonal and tidal flat deposits that encase channelized tidal and tidally influenced fluvial sandbodies. Reservoir models of this outcrop example demonstrate that effective horizontal permeability, flow patterns and displacement, and stratigraphic trapping potential are controlled by: (1) the packaging of shoreface sandstones into laterally extensive parasequences bounded by offshore mudstones; (2) the spatial distribution, connectivity and permeability of channelized sandbodies; and (3) the localized connections between channelized sandbodies and shoreface sandstones. The last two parameters are likely to be poorly constrained in subsurface seismic and well data, and their potential effects require evaluation in reservoir modelling studies.

A Proposed Method to Estimate Climate Change Impacts on Dam's Spillway Design Flood

ABSTRACTOvertopping of a dam, which can be due to inadequate spillway design, is often a precursor to dam failure. Generally, dam spillways are designed using historical hydro‐meteorological records. However, the hydrological regime of rivers and floods' intensity may change in the future due to climate change. Although climate change impacts should be considered in spillway design, their consideration remains a challenge. Spillway flow is affected by many factors, each of which can change due to climate change. This paper aims to propose a method for assessing the impacts of climate change on spillway design floods, which considers a wide range of these changes. The method incorporates a robust weather generator, a daily hydrological model, and a reservoir model. The results indicated that the conjunction of the models can acceptably simulate extreme spillway flows. The proposed method was used to assess climate change's impact on the annual maximum spillway flows of a potential large dam in Iran under three Representative Concentration Pathway (RCP) scenarios of the Hadley Centre Global Environment Model version 2‐Earth System (HadGEM2‐ES) model. The findings indicate that while mean monthly spillway flow decreases under all scenarios, extreme spillway flows significantly increase under the RCP 8.5 scenario.

Study of the Optimization of Pressurization Timing and Parameters for Enhanced Well Production Based on an Integrated Wellbore-Gas Reservoir Coupling Dynamic Analysis Method for Shale Gas Wells

An integrated dynamic analysis and prediction method for shale gas reservoirs based on wellbore coupling has been developed, focusing on optimizing well production. This method integrates a three-dimensional geological model, a mechanical model, simulations of fracture propagation, and a numerical simulation in a gas reservoir to establish a continuous-flow model that links the gas reservoir, fractures, and wellbore after hydraulic fracturing. It enables comprehensive integrated production dynamic analysis and predictions. The process begins by trajectory modeling and attribute assignment. Subsequently, based on the regional tectonic map, contour lines are drawn, regional tectonic surfaces are established, segmentation and clustering are performed, and appropriate fracturing fluids and proppants are selected to simulate fracture network expansion. Finally, the integrated dynamic simulation model of the gas reservoir and wellbore is constructed using the Petrel RE module, considering the combined effects of wellbore flow and reservoir seepage. The model was developed from a single well and through three-dimensional geological modeling, the simulation of fracture propagation in hydraulic fracturing, and a numerical simulation. An integrated dynamic licensing and prediction methodology was established for a shale gas integration model with wellbore-gas reservoir coupling. Additionally, this study analyzes and establishes the optimal pressurization timing and regime for the pressurizer to enhance gas well production. The model was successfully applied for historical matching and dynamic predictions in a block in the southern Sichuan region, producing results closely aligned with the actual data, thus providing a robust tool for predicting the future production profiles of shale gas wells.

Prediction of Reservoir Properties Using the DQ Trace Attribute and RDQ Inversion Workflow

Accurate characterization of reservoir properties is critical for making informed decisions in exploration and production. Conventional seismic inversion workflows often rely on simplifying assumptions and may struggle to accurately predict reservoir properties in complex geologic settings. We present a quantitative petrophysical inversion workflow based on the Distance and Quadrant (DQ) trace attribute and the Rotated Distance and Quadrant (RDQ) crossplot. The DQ trace provides a new way of visualizing and interpreting seismic data that is sensitive to both amplitude and AVO gradient variations, while the RDQ crossplot allows for the separation of different lithologies and fluid types based on their unique signatures in the DQ-AVO space. We incorporate a new attribute, DQOpx, into the inversion workflow to improve the delineation of fluid contacts. Another filter called the signed half isochron is also incorporated to improve the accuracy of thin-bed inversion results. The workflow is designed to be flexible and adaptable to different reservoir scenarios and project objectives, from reconnaissance-level screening to detailed reservoir characterization. We demonstrate the effectiveness of the RDQ inversion workflow, which is an inversion based on a model lookup catalog of extensive pseudowell information, through application to two synthetic reservoir models, showcasing its ability to accurately predict reservoir properties in both high-amplitude (class 3) and low-amplitude (class 1) settings. The RDQ inversion workflow represents a significant advancement in the field of seismic reservoir characterization, providing a powerful and flexible tool for the accurate prediction of reservoir properties in a wide range of geologic scenarios.

Technology Focus: History Matching and Forecasting (April 2025)

This year’s History Matching and Forecasting selections highlight innovations in surrogate modeling, artificial intelligence, and well-test analysis. These three papers leverage machine learning and hybrid methods to tackle challenges in forecasting, optimization, and reservoir characterization. In paper SPE 220002, the authors introduce the embed-to-control observe (E2CO) framework, a deep-learning surrogate model for reservoir performance forecasting and life-cycle optimization. The E2CO model demonstrates remarkable accuracy in predicting reservoir dynamics and optimizing production under geological uncertainty, validated against the SPE10 benchmark. While currently focusing on sector-scale models, its scalable architecture supports future field-scale extensions. The integration of stochastic-gradient-based optimization enhances flexibility in handling nonlinear constraints. Paper SPE 220876 presents a deep-neural-network-based history-matching workflow that combines a forward surrogate model for rapid multiphase-flow predictions with an inference network for parameter estimation. Its success in 2D heterogeneous reservoirs underscores its potential to streamline history matching with minimal accuracy loss. The use of synthetic data ensures controlled validation of core principles, a critical step before field deployment. Future work on extending the framework to 3D models could further solidify its applicability to complex scenarios, building on robust 2D results. Paper SPE 221287 introduces a semi-iterative model (SIM) for well-test curve matching, improving parameter inversion accuracy in homogeneous reservoirs by leveraging key points and segments on curves. The SIM’s efficiency and precision, particularly in dual-porosity systems, set a new benchmark for automated well-test interpretation. The dependence on high-quality input data ensures reliable outcomes in controlled environments. Its success in homogeneous reservoirs paves the way for future adaptations to more-complex challenges. Collectively, these studies exemplify the transformative potential of machine learning and hybrid methods in reservoir management, balancing innovation with practical applicability. Their methodologies provide scalable frameworks, with current limitations serving as milestones for ongoing research rather than barriers to adoption. Summarized papers in this April 2025 issue. SPE 220876 - Deep-Neural-Network-Based Workflow Increases Efficacy in Solving Complex Problems by Bicheng Yan, King Abdullah University of Science and Technology, et al. SPE 220002 - Deep-Learning-Based Reservoir Surrogate Achieves Optimization Under Uncertainty by Quang Minh Nguyen, The University of Tulsa, et al. SPE 221287 - Semi-Iterative Well-Test-Matching Method Uses Featured Points To Increase Accuracy, Efficiency by Xue Guo, China University of Petroleum, et al. Recommended additional reading at OnePetro: www.onepetro.org. SPE 220790 - Graph-Level Feature Embedding With Spatial/Temporal GCN Method for Interconnected Well-Production Forecasting by Ziming Xu, University of Alberta, et al. URTeC 4033921 - Application of a Sparse Hybrid Data-Driven and Physics Model in Unconventional Reservoirs for Production Forecasting by Hardikkumar Zalavadia, Texas A&M University, et al. SPE 220995 - A Hybrid Tabular -Spatial-Temporal Model With 3D Geomodel for Production Prediction in Shale Gas Formations by Muming Wang, University of Calgary, et al.

A constitutive model of natural gas hydrate reservoirs during exploitation by methane-carbon dioxide replacement

A constitutive model of natural gas hydrate reservoirs during exploitation by methane-carbon dioxide replacement

Deep-Learning-Based Reservoir Surrogate Achieves Optimization Under Uncertainty

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 220002, “A Deep-Learning-Based Reservoir Surrogate for Performance Forecast and Nonlinearly Constrained Life-Cycle Production Optimization Under Geological Uncertainty,” by Quang Minh Nguyen, SPE, The University of Tulsa and ExxonMobil, and Mustafa Onur, SPE, The University of Tulsa. The paper has not been peer reviewed. _ This study presents an efficient gradient-based production optimization method that uses a deep-learning-based proxy model for the prediction of state variables (such as pressures and saturations) and well outputs [such as bottomhole pressures (BHPs) and injection rates] to solve nonlinearly constrained optimization with geological uncertainty. The surrogate model is the embed-to-control observe (E2CO) deep-learning proxy model, consisting of four blocks of neural networks: encoder, transition, transition output, and decoder. Background and Problem Statement Life-cycle production optimization is a crucial step in closed-loop reservoir management (CLRM). Even though numerous problems of interest exist, the specific application considered here refers to the optimization of the well-control problem that maximizes the net present value (NPV), given an ensemble of reservoir models representing the geological uncertainty, as a result of involving various data sources such as geology, seismic data, well logs, well tests, and—most commonly and most importantly—the nonunique nature of the history-matching (or data-assimilation) process. This optimization problem is known as a robust production-optimization problem, and it is even significantly more challenging when nonlinear state constraints are present at each control step and must be honored. Typically, in traditional CLRM, the evaluation of the objective function and constraint values relies on the outputs from a high-fidelity reservoir simulator (HFS). However, using HFS, whether developed in-house or commercially, for production optimization may impose significant computational demands. A possible way forward in reducing the computational costs associated with forward simulations is the use of reduced-order models (ROMs). These methods work with a reduced dimension of system states based on the full form of the governing equations, thus enabling sequential prediction of the states’ evolution that could closely represent the true physics. Proper orthogonal decomposition-trajectory piecewise linearization (POD-TPWL) is a commonly used ROM for subsurface flow applications. There are two stages in POD-TPWL: a training (offline) stage during which numerical simulation runs are performed and their solutions are recorded (along with the derivative information), and a testing (online) stage where blind test predictions are made. With recent advancements in computer resources, the idea of using machine learning (ML) technologies to construct low-fidelity data-driven proxy models (or surrogates), rather than relying solely on full-physics numerical solvers such as HFS, gains appeal. In many scenarios, a production-optimization problem is a regression task, so using regression ML techniques is suitable for constructing low-fidelity data-driven proxies.

Deep-Neural-Network-Based Workflow Increases Efficacy in Solving Complex Problems

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 220876, “Efficacy Gain From a Deep-Neural-Network-Based History-Matching Workflow,” by Bicheng Yan, SPE, and Yanjui Zhang, King Abdullah University of Science and Technology. The paper has not been peer reviewed. _ Reservoir history-matching is essential for understanding subsurface uncertainty in rock properties. However, conventional history-matching methods usually require numerous forward model evaluations and rely heavily on the previous estimation of uncertain model parameters. To resolve these issues, the authors write that they resort to cutting-edge deep-learning (DL) technologies for their universal approximation capability in forward and inverse modeling based on automatic differentiation. In the complete paper, the authors develop a deep neural network-based history-matching (DNN-HM) workflow with enhanced efficiency and scalability for solving complex history-matching problems. Methodology The developed DNN-HM workflow consists of a forward modeling part for two-phase waterflooding reservoir simulations and the inverse modeling part for history-matching tasks. The complete paper includes detailed descriptions of this workflow, including associated equations. Forward Reservoir Simulation. The forward problem consists of a two-phase waterflooding process in a 2D heterogeneous reservoir. The reservoir is saturated initially with water and oil phases, where oil is the primary phase. The edges of the reservoir domain are bounded by a no-flowing boundary condition. Five water-injection wells and one oil-production well are drilled in the reservoir. The fluid flow in the porous reservoir follows Darcy’s law and the mass conservation of each fluid phase. In this study, the authors ignore the effect of capillary pressure on two-phase flow in the porous reservoir. The partial differential equation (PDE) system is spatially discretized through the cell-centered finite-difference method and temporally discretized by the backward-Euler scheme. Therefore, the PDE system is solved using the conventional fully implicit method to ensure numerical stability. Because conventional numerical reservoir simulators typically are implemented in a black-box fashion, it is usually difficult to obtain the derivatives of reservoir or well responses with regard to reservoir model parameters from such simulators. Nevertheless, these derivatives are critical to perform a gradient-based history-matching workflow. Conventional reservoir simulators are first run to generate synthetic simulation data for the example of the two-phase waterflooding problem, and, further, such data are used to train a DL-based forward surrogate model capable of executing automatic differentiation to provide the relevant derivatives for the considered history-matching problem. Therefore, the forward model ultimately can be written as an abstract described in the complete paper. In the waterflooding process described in this study, the injection wells are operated with a constant water volumetric injection rate at surface condition, while the production wells are scheduled at constant bottomhole pressure (BHP). The observational data from well locations include the injection flowing BHP and the water and oil volumetric production rates at surface conditions. Furthermore, two Fourier neural operators (FNO) are used to separately predict the pressure of the well gridblocks and water saturation to achieve the nonlinear mapping of function G, which is denoted as NNf. The pretrained NNf ultimately is used to obtain the prediction of the observational data. The forward model G or NNf is trained based on the training data generated from a high-fidelity reservoir simulator.

Dual Oxygen Reservoir Model for Nonpolar Resistive Switching in Nickel Tetradecavanadate Based Molecular Switch

Dual Oxygen Reservoir Model for Nonpolar Resistive Switching in Nickel Tetradecavanadate Based Molecular Switch

Identifying the Jeribe Formation Interval in the Khurmala Dome Field Using Lithology and Gamma-Ray Log Correlation Analysis

The Middle Miocene-aged Jeribe Formation is an important reservoir in many of Iraq's oilfields. This study identifies the Jeribe Formation in several drilled wells at the Khurmala Dome Field using gamma-ray log and lithology analyses, comparing gamma-ray log analysis for the Jeribe Formation of a subset of wells at Khurmala Dome Field and two wells from Jari Pika Field and Taza Field. The lithology analyses of selected wells at Khurmala Field, along with the formation’s lithology, were studied at local types and other locations, which indicates the presence of the Jeribe Formation. The analysis results denoted its lateral continuity, lithology similarity, and thickness variations over location differences for the formation at selected wells of the Khurmala Dome Field. Both gamma-ray log and lithology analyses determined the interval's top, bottom, and thickness. Future studies should include biostratigraphy, correlation with wells drilled in the north part of the Khurmala Dome, gas and hydrocarbon analyses, and reservoir modeling to evaluate the amount of hydrocarbons within the formation.

Development of a dynamic compositional reservoir model for enhanced oil recovery in Nahr Umr formation of Abu-Amood oil field

Due to the research gap in the literature regarding the Nahr Umr formation within the Abu-Amood oil field, this study aims to develop a dynamic compositional reservoir model for this formation. Located in the Dhi Qar province of Iraq, the Nahr Umr Formation, primarily composed of sandstone, lies 3025 meters below sea level and holds significant hydrocarbon potential. This research focuses on building a compositional model to predict reservoir behavior under various production strategies, ultimately maximizing oil recovery. The model was constructed using geological report data and implemented with Petrel and CMG software. Utilizing pressure-volume-temperature (PVT) data and the Peng-Robinson equation of state (EOS), an accurate fluid properties model was developed. Through reservoir and well area calculations, this study demonstrates that the Nahr Umr reservoir can be effectively drained using only six producers. This comprehensive model is crucial for designing efficient production strategies and enhancing oil recovery in the Nahr Umr Formation.

A new practical method for reserve estimation and production forecast of gas wells exhibiting gas–water two-phase flow

This paper addresses the challenge of reserve estimation and production forecasting for gas wells affected by gas–water two-phase boundary dominated flow (BDF). Traditional production data analysis models for gas reservoirs often assume water as an immobile phase, a premise invalid in cases such as tight gas sandstone and during flowback periods of multi-fractured horizontal wells where formation water flow is significant. The study focuses on the nonlinearity in the governing flow equations due to pressure-dependent phase properties and saturation-dependent relative permeabilities in gas–water systems. We propose an approximate method to estimate saturation–pressure relationships during reservoir depletion, crucial for evaluating gas and water phase mobilities. Additionally, a density-based production data analysis method is developed for reserve estimation and production forecasting in scenarios involving gas–water two-phase flow. The proposed models were validated against finely gridded numerical simulations with controlled input parameters. The effectiveness of these formulations for calculating original gas-in-place (OGIP) and original water-in-place (OWIP) is demonstrated through a field case from the Sulige tight gas reservoir. This study introduces novel approaches that extend beyond existing literature, providing specific, quantifiable improvements in saturation–pressure relationship evaluation and gas and water phase mobility evaluations during BDF.