Reservoir Model Research Articles

The concept of big data and predictive analytics in reservoir engineering: The future of dynamic reservoir models

Reservoir engineering is a critical discipline in the oil and gas industry, aimed at optimizing the extraction and management of subsurface resources. Traditionally, reservoir models have relied on static data and conventional simulation methods, which often fall short in capturing the dynamic complexities of reservoirs. With the advent of big data and predictive analytics, there is a significant opportunity to revolutionize reservoir modeling by integrating real-time data for continuous updates and more accurate predictions. This review introduces a theoretical exploration of how big data and predictive analytics can transform dynamic reservoir models, highlighting their potential to improve reservoir management and hydrocarbon recovery. Big data technologies enable the collection, storage, and processing of vast amounts of structured and unstructured data from multiple sources, including well logs, seismic surveys, drilling operations, and production data. Predictive analytics, through machine learning and statistical techniques, can extract actionable insights from these datasets to predict reservoir behavior, enhance decision-making, and optimize production strategies. This integration facilitates real-time monitoring and adaptive reservoir management, allowing engineers to respond to changing reservoir conditions and uncertainties more effectively. The review further explores the implications of big data-driven predictive models for enhanced hydrocarbon recovery techniques, such as water flooding and gas injection. By automating data processing and integrating real-time field data, predictive analytics can improve the accuracy of reservoir simulations, reduce downtime, and increase operational efficiency. Ultimately, the future of dynamic reservoir modeling lies in the seamless integration of big data and predictive analytics, providing a pathway to smarter, more sustainable resource management and maximized recovery factors in increasingly complex reservoir environments. Keywords: Big Data, Predictive Analytics, Reservoir Engineering, Dynamic Reservoir, Models.

Research on the Blocking Mechanism of Stagnant Water and the Prediction of Scaling Trend in Fractured Reservoirs in Keshen Gas Field

In this paper, well Keshen 221 was taken as the research object. The stagnant water–rock static experiment showed that, after 8 weeks of the residual water–rock static reaction, the pore size of the inner profile of the rock slice increased from 5 μm to 90 μm, and calcium carbonate crystals were deposited in the hole. Combined with the microscopic visualization model, it is observed that the reservoir blockage mostly occurs at the pore throat diameter, and the small fracture (30 μm) is blocked first, then the large fracture (50 μm). So, it is inferred that the blockage of the reservoir flow channel is caused by the migration of the crystals precipitated by the interaction between the stagnant water and the reservoir rock. On this basis, the TOUGHREACT reservoir model was further constructed to simulate the scaling of the stagnant water in the reservoir matrix and used to compare the scaling of the fractures with 7% and 30% porosity and the retained water at 0.658 m and 768 m. The pre-results of reservoir scaling show that the scaling is more serious when the fractures occur in the far well zone than when the fractures occur in the well entry zone. At the same location, the deposition of large fractures is six times that of small fractures, and the scaling is more severe in large fractures.

Condensate Banking Analysis Optimizes Reservoir Development in Permian Basin

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper URTeC 4044947, “Numerical Analysis of Condensate Banking and Development Optimization in the Deep Formation of the Permian Basin,” by Jichao Han, SPE, Sunil Lakshminarayanan, and Jiasen Tan, SPE, Oxy, et al. The paper has not been peer reviewed. _ In the context of unconventional reservoir development, the issue of condensate banking—a factor contributing to reduced productivity in gas condensate reservoirs when the reservoir pressure falls below the dewpoint pressure—poses a constant concern. Before implementation of any mitigation strategies, it is prudent to assess the potential of condensate banking in the future, based on the limited early production data. A specialized workflow was devised for a deep formation in the Permian Basin. This workflow aims to quantify the severity of condensate banking and subsequently optimize reservoir development strategies. Introduction Well A represents a single appraisal well situated within deep formations of the Permian Basin. With only 5 months of production data available, pressure/volume/temperature analysis and modeling identified it as a gas condensate reservoir, with the dewpoint very close to the critical point. This prompts two critical business inquiries: Are there any concerns regarding condensate banking for this well? And what are the optimal drawdown strategies tailored to this specific format surrounding the appraisal well? Model Setup and Calibration Modeling Workflow. This study uses the comprehensive simulation workflow derived from Hydraulic Fracturing Test Site 2, which can effectively handle integrated models to provide valuable insight for condensate banking analysis and reservoir development optimization (Fig. 1). Hydraulic fracture and reservoir simulations were conducted using GOHFER 3D and Navigator software, respectively. The integration of fracture and reservoir models poses a significant challenge in accurately transferring fracture geometry and conductivity information from fracture simulators to reservoir simulators. To address this challenge, an in‑house preprocessor was developed, designed to project results from the fracture simulator into the reservoir simulator seamlessly, ensuring the fidelity of complex fracture geometries. This preprocessor enables the extension or contraction of fracture geometries in proportion to reduce uncertainties imposed by production data. Additionally, fracture conductivity is determined through regional correlations among proppant concentration, net closure stress, and fracture conductivity. Model Introduction. The geological model has been simplified to enhance modeling efficiency while still meeting the project requirements. The 3D geological model adopts a layer-cake structure with a flat geostructure. To ensure accuracy, the well trajectory is adjusted to align with the desired formation. The reservoir model measures 4,000×10,000×330 ft, with a grid resolution of 200×25×10 ft. Fracture geometry is imported from the fracture simulator, while fracture conductivity is primarily determined based on proppant concentration data exported from the fracture simulator. Fracture height remains unaltered in the reservoir simulator, reflecting available geochemical measurements. A compositional simulation approach is adopted instead of a black-oil model. To improve the accuracy of pressure drop and condensate dropout predictions, the local grid refinement (LGR) approach is selected, resulting in denser grids. This approach proves superior to the dual-porosity, dual-permeability (DPDP) method.

Impact of dual-fracture location on heat extraction from Enhanced geothermal system in low-permeability reservoirs

As environmental and economic issues exacerbated by traditional fossil fuels intensify, Dry Hot Rock geothermal energy has garnered significant attention due to its immense potential. Current research predominantly focuses on the heat extraction effectiveness in complex fractures within Enhanced Geothermal Systems, with relatively less emphasis on fluid behavior between large fractures. This study employs finite element software to establish a multi-horizontal well model in a low permeability reservoir (5 × 10-17 m2), analyzing the impact of different spatial configurations of two fractures on fluid flow, heat transfer, and heat extraction efficiency. The results indicate that fractures are the primary channels for fluid flow in low permeability reservoirs. Optimal heat extraction occurs when fractures are parallel, spaced 100 m apart, and perpendicular to the horizontal well, with significant threshold effects of fracture spacing and angle on temperature distribution. The highest heat extraction is achieved at a 37° intersection angle of fractures at the production well. Over a 60-year production period, reservoir extraction degrees for parallel or intersecting fractures range from 8 % to 15 %, while that for fracture communication between injection and production well horizontal segments is only 1.91 % to 3.01 %. Under the optimal injection scenario, cumulative 60-year heat production for parallel, intersecting, and communicating fractures is 2.13 × 1018J, 2.20 × 1018J, and 2.22 × 1018J, respectively, with the best heat extraction efficiency when fractures communicate. Additionally, under constant flow rate and inlet temperature, outlet temperature and system thermal power show near-linear declines, while reservoir extraction degree rises linearly. This study provides crucial theoretical support and practical guidance for the efficient extraction of geothermal energy.

Gas Production Prediction Model of Volcanic Reservoir Based on Data-Driven Method

Based on on-site construction experience, considering the time-varying characteristics of gas well quantity, production time, effective reservoir thickness, controlled reserves, reserve abundance, formation pressure, and the energy storage coefficient, a data-driven method was used to establish a natural gas production prediction model based on differential simulation theory. The calculation results showed that the average error between the actual production and predicted production was 12.49%, and the model determination coefficient was 0.99, indicating that the model can effectively predict natural gas production. Additionally, we observed that the influence of factors such as reserve abundance, the number of wells in operation, controlled reserves, the previous year’s gas production, formation pressure, the energy storage coefficient, effective matrix thickness, and annual production time on the annual gas production increases progressively as the F-values decrease. These insights are pivotal to a more profound understanding of gas production dynamics in volcanic reservoirs and are instrumental in optimizing stimulation treatments and enhancing resource recovery in such reservoirs and other unconventional hydrocarbon formations.

Development and Applicability Assessment of an Hourly Water Level Prediction Model for Agricultural Reservoirs Using Automated Machine Learning

Objectives:This study aims to develop and evaluate hourly water level prediction models for agricultural reservoirs using an automated machine learning (AutoML) approach, specifically, employing TPOT.Methods:The study focuses on the Baekrok and Baekryeon reservoirs using rainfall forecast data from the Korea Meteorological Administration, along with observed rainfall and reservoir water level data. TPOT, which utilizes genetic algorithms to automate the generation of optimal machine learning pipelines, was used to build models. Additionally, Random Forest (RF) was implemented as a benchmark to evaluate TPOT’s applicability. Predictions were generated with lead times of 1, 3, 6, and 12 hours, and model accuracy was evaluated using the Nash-Sutcliffe Efficiency (NSE), coefficient of determination (R2), and root-mean-square error (RMSE).Results and Discussion:The predictions from both TPOT and RF showed high accuracy for shorter lead times, with NSE values exceeding 0.99 for the 1-hour predictions. However, predictive accuracy decreased as lead time increased, likely due to greater uncertainty in rainfall forecast data, particularly for the 12-hour predictions. Despite this trend, TPOT-derived models maintained more stable and accurate performance compared to RF models across all lead times.Conclusion:This study demonstrates the applicability of TPOT-based AutoML techniques in predicting hourly water levels in agricultural reservoirs. Future work should explore strategies to mitigate the decline in accuracy for longer lead times.

Study of recovery factor and CO(2) storage behavior in a reservoir using numerical simulation

In this study, the behavior of the recovery factor during the displacement and storage phases of carbon dioxide (CO2) in an Ecuadorian reservoir was investigated by numerical simulation. Continuous injection and CO2 WAG methods were used. The methodology included field data collection, creation of a static reservoir model, configuration of the PVT model with CMG software, construction of a dynamic model in GEM-CMG based on rock-fluid properties, and evaluation of CO2 injection scenarios. After a screening analysis, the optimal scenario was identified and the performance of the recovery factor over time was studied. The results indicated that, during the 8 years of simulation, the recovery factor with continuous injection was 28%, while with the WAG method it reached 31%. It was concluded that WAG was the most efficient method. Regarding storage, it was observed that continuous injection was able to store 83% of the CO2 in the reservoir.

Refracturing Time Optimization Considering the Effect of Induced Stress by Pressure Depletion in the Shale Reservoir

Refracturing is an important technology for tapping remaining oil and gas areas and enhancing recovery in old oilfields. However, a complete and detailed refracturing timing optimization scheme has not yet been proposed. In this paper, based on the finite volume method and the embedded discrete fracture model, a new coupled fluid flow/geomechanics pore-elastic-fractured reservoir model is developed. The COMSOL 3.5 commercial software was used to verify the accuracy of our model, and by studying the influence of matrix permeability, initial stress difference, cluster spacing, and fracture half-length on the orientation of maximum horizontal stress, a timing optimization method for refracturing is proposed. The results of this paper show that the principle of optimizing the refracturing timing is to avoid the time window where the percentage of Type I (Type I indicates that stress inversion has occurred, 0∘≤α≤20∘; Type II indicates that the turning degree is strong, 20∘<α≤70∘; and Type III indicates less stress reorientation, 70∘<α≤90∘) stress reorientation area is relatively large, so that the fractures can extend perpendicular to the horizontal wellbore. At the same time, the simulation results show that with the increase in production time, the percentage of Type I and Type II increases first and then decreases, while the percentage of Type III decreases first and then increases. When the reservoir permeability, stress difference, and cluster spacing are larger, the two types of refracturing measures can be implemented earlier. But, with the increase in fracture half-length, the timing of refracturing Method I is earlier, and the timing of refracturing Method II is later. The research results of this paper are of great significance to the perfection of the refracturing theory and the optimization of refracturing design.

Performance and economic analysis of various geothermal power plant systems: Case study for the Chingshui geothermal field in Taiwan

Geothermal power plants are a source of sustainable energy. However, their performance is influenced by various factors, including production temperature and well locations, and few studies have investigated the relationships between these factors. To address this research gap, the present study developed integrated models of a geothermal reservoir and a variety of power plant systems. By using COMSOL and MATLAB, we conducted simulations for the Chingshui geothermal field in Taiwan to investigate the performance and economic feasibility of five common types of geothermal power plant systems over their operational lifetime under different well configurations. The key findings of this investigation are as follows. First, the locations of production wells are a primary factor influencing production temperature. Constructing wells in low-permeability zones results in considerable increases in the pressure differential in the injection pump; thus, wells should not be constructed in such zones. Second, as system flow rate and well depth decrease, production temperature remains relatively stable, net power output decreases, and system efficiency increases. This efficiency increase is caused by decreases in pumping power requirements at lower flow rates and shallower well depths. Finally, among the investigated systems, the organic Rankine cycle system had the lowest electricity production cost and shortest payback period within the enthalpy range of 500–1000 kJ/kg. Double-flash (DF) and DF–binary systems are also viable options when the mass flow rate is 10–20 kg/s and the enthalpy is 700–800 kJ/kg. Overall, the results of this study offer valuable insights for the design of geothermal power plants, thereby contributing to the optimization of renewable energy systems.

Adaptive constraint-guided surrogate enhanced evolutionary algorithm for horizontal well placement optimization in oil reservoir

In the face of escalating global energy demands, this study introduces an Adaptive Constraint-Guided Surrogate Enhanced Evolutionary Algorithm (ACG-EBS) for optimizing horizontal well placements in oil reservoirs. Addressing the complex challenge of maximizing oil production, the ACG-EBS integrates geological, engineering, and economic considerations into a novel optimization framework. This algorithm stands out for its adept navigation through a complex and discrete decision space of horizontal well placements, an area where traditional methods often encounter challenges. Key innovations include the Adaptive Constraint Initialization Mechanism (ACIM) and the Evolutionary Constraint-Tailored Candidate Refinement strategy (ECTCR), which collectively elevate the feasibility of candidate solutions. An enhanced balance strategy harmonizes comprehensive and niche surrogate models, optimizing the balance between exploration and exploitation. Through testing on both two-dimensional and three-dimensional reservoir models, the ACG-EBS has proven highly effective in identifying optimal well placements that align with field deployment realities and maximize economic returns. This contribution significantly supports the ongoing evolution of oilfield development optimization, showcasing the algorithm's potential to enhance oil production and economic outcomes.

Comparative Study on Artificial Fracture Modeling Schemes in Tight Reservoirs—For Enhancing the Production Efficiency of Tight Oil and Gas

In order to improve the reliability of the deployment of production schemes after artificial fracturing in tight reservoirs, it is urgent to carry out research on the description of fractures after artificial fracturing. In this study, taking the Chang 61 oil formation group in the Wangyao South area of Ordos Basin as an example, three different fracture modeling schemes are used to establish the geological model of fractured reservoirs, and the fitting ratios of the respective reservoir models are calculated by using the method of reservoir numerical simulation of the initial fitting, and the optimal fractured reservoir modeling scheme is screened in the end. The research area adopts three types of fracture prediction results based on FMI fracture interpretation data, seismic fracture prediction data, and rock mechanics artificial fracturing simulation data. On this basis, geological models of fractured reservoirs are established, respectively. The initial fitting of reservoir values of each geological model are compared, and the highest initial fitting rate of reservoir values is 88.44%, which is based on rock mechanics artificial fracturing simulation data. However, the initial fitting rate of the reservoir model was the lowest at 75.76%, which was established based on the fracture random modeling results of FMl fracture interpretation data. Under the constraints of seismic geostress prediction results and microseismic monitoring data, the simulation results of rock mechanics artificial fracturing fracture are used as the basis, on which the geological model of artificially fractured reservoirs is thus established, and this scheme can more realistically characterize the characteristics of fractured reservoirs after artificial fracturing in the study area.

Use of Deep-Learning-Accelerated Gradient Approximation for Reservoir Geological Parameter Estimation

The estimation of space-varying geological parameters is often not computationally affordable for high-dimensional subsurface reservoir modeling systems. The adjoint method is generally regarded as an efficient approach for obtaining analytical gradient and, thus, proceeding with the gradient-based iteration algorithm; however, the infeasible memory requirement and computational demands strictly prohibit its generic implementation, especially for high-dimensional problems. The autoregressive neural network (aNN) model, as a nonlinear surrogate approximation, has gradually received increasing popularity due to significant reduction of computational cost, but one prominent limitation is that the generic application of aNN to large-scale reservoir models inevitably poses challenges in the training procedure, which remains unresolved. To address this issue, model-order reduction could be a promising strategy, which enables us to train the neural network in a very efficient manner. A very popular projection-based linear reduction method, i.e., propel orthogonal decomposition (POD), is adopted to achieve dimensionality reduction. This paper presents an architecture of a projection-based autoregressive neural network that efficiently derives an easy-to-use adjoint model by the use of an auto-differentiation module inside the popular deep learning frameworks. This hybrid neural network proxy, referred to as POD-aNN, is capable of speeding up derivation of reduced-order adjoint models. The performance of POD-aNN is validated through a synthetic 2D subsurface transport model. The use of POD-aNN significantly reduces the computation cost while the accuracy remains. In addition, our proposed POD-aNN can easily obtain multiple posterior realizations for uncertainty evaluation. The developed POD-aNN emulator is a data-driven approach for reduced-order modeling of nonlinear dynamic systems and, thus, should be a very efficient modeling tool to address many engineering applications related to intensive simulation-based optimization.

Analysis of the impact of geological and engineering parameters on productivity in tight oil reservoirs

AbstractTight oil resources are abundant, but the factors affecting production capacity are complex. In this paper, focusing on tight oil reservoirs, three research works were conducted. First, using the numerical simulation software, a numerical model of tight oil reservoirs was established. Second, the influence of geological parameters such as porosity and permeability on oil production were analyzed. Third, the influence of rock compression coefficient and injection fluid on tight oil production were analyzed. Results show that: (a) When the porosity is 0.05, the cumulative oil production in the first 6 years is the highest, while in the later stage of the simulation, the cumulative oil production with a porosity of 0.1 is the highest. (b) The higher the permeability, the greater the cumulative oil production. The cumulative oil production under different permeability conditions are 1392.044, 2178.805, 2939.1704, and 4038.0878 m3, respectively. (c) Under tight reservoir conditions, the impact of different rock compression coefficients on the daily oil production of oil and gas reservoirs is not very significant. (d) The recovery effect is optimal when using the N2 injection scheme. The effectiveness of the CH4 scheme is second, and there is a certain gap compared to the N2 scheme. The development plan of injecting water has the worst effect. However, compared to the depletion development model, the cumulative oil production by injecting N2, CO2, CH4, and water has all increased.

Transfer Learning-Based Physics-Informed Convolutional Neural Network for Simulating Flow in Porous Media with Time-Varying Controls

A physics-informed convolutional neural network (PICNN) is proposed to simulate two-phase flow in porous media with time-varying well controls. While most PICNNs in the existing literature worked on parameter-to-state mapping, our proposed network parameterizes the solutions with time-varying controls to establish a control-to-state regression. Firstly, a finite volume scheme is adopted to discretize flow equations and formulate a loss function that respects mass conservation laws. Neumann boundary conditions are seamlessly incorporated into the semi-discretized equations so no additional loss term is needed. The network architecture comprises two parallel U-Net structures, with network inputs being well controls and outputs being the system states (e.g., oil pressure and water saturation). To capture the time-dependent relationship between inputs and outputs, the network is well designed to mimic discretized state-space equations. We train the network progressively for every time step, enabling it to simultaneously predict oil pressure and water saturation at each timestep. After training the network for one timestep, we leverage transfer learning techniques to expedite the training process for a subsequent time step. The proposed model is used to simulate oil–water porous flow scenarios with varying reservoir model dimensionality, and aspects including computation efficiency and accuracy are compared against corresponding numerical approaches. The comparison with numerical methods demonstrates that a PICNN is highly efficient yet preserves decent accuracy.

Density of pure and mixed NaCl and CaCl2 aqueous solutions at 293 K to 353 K and 0.1 MPa: an integrated comparison of analytical and numerical data

This study reports on newly acquired density data of synthetically prepared pure and mixed NaCl and CaCl2 aqueous solutions that span a wide range of geothermally encountered concentrations and mixing ratios. The analytical data are provided for the temperature range of 293–353 K at ambient pressure. For the reproduction of that data, PHREESCALE was used. The predictive potential of this numerical tool regarding the density of geothermal fluids of known composition was the major target herein. As a result, the measured data are in good agreement with previous analytical studies found in the literature. Possible sources of errors are discussed in this paper. Density data of the mixed solutions at temperatures other than ambient are unique and close existing data gaps. The numerical model reproduces the newly measured and already existing density data within an error band of approximately 1%. For further use in geothermal applications, this can be considered an excellent agreement. Moreover, the model yields a direct calculation of density without the need to establish complex empirical equations of state and mixing rules. Finally, sensitivity calculations performed with a thermal–hydraulic (TH) numerical reservoir model demonstrate the required accuracy of fluid density for reliably predicting the long-term performance of deep geothermal energy systems. In terms of the productivity index and the timing of thermal breakthrough it shows that the present analytical and numerical uncertainty in density is small enough to reliably state both reservoir parameters.

Numerical simulation of shale gas reservoir based on ILU-GMRES method

ABSTRACT The numerical simulation of shale gas reservoirs requires transforming mathematical models into large linear equation systems, and solving large linear equation systems requires a lot of time. A novel ILU-GMRES method for solving large linear equations is presented, which treats the sub matrix discretized at nodes as the basic elements of the coefficient matrix. By combining the incomplete LU decomposition method (ILU) with the generalized minimum residue method (GMRES), the discretized large linear equations are iteratively solved. A comparison was made between the new method and other methods for calculating the actual shale gas reservoir model. The results reveal that the new method has a faster calculation speed in solving linear equation systems. The calculation time of the novel ILU-GMRES method is 1029s, the calculation time of the generalized minimum residual method (GMRES) method is 2709s, the calculation time of the Gauss–Seidel method (GS) is 3786s, and the calculation time of the successive over relaxation method (SOR) method is 3386s, the calculation time of the preprocessing conjugate gradient method (PCG) is 3115s. The calculation speed of the novel method is almost 2.6 times faster than the GMRES method, the novel method is more effective in the numerical simulation of shale gas.

Improved Fracture Permeability Evaluation Model for Granite Reservoirs in Marine Environments: A Case Study from the South China Sea

Permeability is a crucial parameter in the exploration and development of oil and gas reservoirs, particularly in unconventional ones, where fractures significantly influence storage capacity and fluid flow. This study investigates the fracture permeability of granite reservoirs in the South China Sea, introducing an enhanced evaluation model for planar fracture permeability based on Darcy’s law and Poiseuille’s law. The model incorporates factors such as fracture heterogeneity, tortuosity, angle, and aperture to improve permeability assessments. Building on a single-fracture model, this research integrates mass transfer equations and trigonometric functions to assess intersecting fractures’ permeability. Numerical simulations explore how tortuosity, angle, and aperture affect individual fracture permeability and the influence of relative positioning in intersecting fractures. The model makes key assumptions, including minimal consideration of horizontal stress and the assumption of unidirectional laminar flow in cross-fractures. Granite outcrop samples were systematically collected, followed by full-diameter core drilling. A range of planar models with varying fracture apertures were designed, and permeability measurements were conducted using the AU-TOSCAN-II multifunctional core scanner with a steady-state gas injection method. The results showed consistency between the improved model and experimental findings regarding the effects of fracture aperture and angle on permeability, confirming the model’s accuracy in reflecting the fractures’ influence on reservoir flow capacity. For intersecting fractures, a comparative analysis of core X-ray computed tomography (X-CT) scanning results and experimental outcomes highlighted discrepancies between actual permeability measurements and theoretical simulations based on tortuosity and aperture variations. Limitations exist, particularly for cross-fractures, where quantifying complexity is challenging, leading to potential discrepancies between simulation and experimental results. Further comparisons between core experiments and logging responses are necessary for model refinement. In response to the challenges associated with evaluating absolute permeability in fractured reservoirs, this study presents a novel theoretical assessment model that considers both single and intersecting fractures. The model’s validity is demonstrated through actual core experiments, confirming the effectiveness of the single-fracture model while highlighting the need for further refinement of the dual-fracture model. The findings provide scientific support for the exploration and development of granite reservoirs in the South China Sea and establish a foundation for permeability predictions in other complex fractured reservoir systems, thereby advancing the field of fracture permeability assessment.

Simultaneous quantification of triple pore types in carbonate reservoirs using well logs and seismic data

Accurate characterization of carbonate pore types is of great significance for seismic reservoir characterization. However, the existing pore-type inversion methods cannot characterize the coexistence of three main pore types (vuggy pores, matrix pores, and cracks) for each modeling point because they apply a simple iterative procedure without using S-wave velocity. To overcome this problem, we develop a simultaneous triple pore-type inversion (TPTI) strategy to estimate the porosities of different pore types from the P- and S-wave velocities using a global optimization technique known as very fast simulated annealing. Considering the rock composition variation and pore geometry complexity, we first establish an easy-to-implement multipore effective medium model for carbonate reservoirs based on the Keys-Xu model and Gassmann’s equation. Then, the applicability of this model is verified by two sets of experimental acoustic measurements for synthetic and actual carbonate samples. A good comparison of model predictions with laboratory data indicates that our model can effectively capture the elastic responses of carbonate rocks with different pore shapes, which provides a theoretical basis for quantifying multiple pore types. Based on the developed model, we further develop a TPTI method. We apply it to describe pore-type distribution from well logs and seismic data in a heterogeneous carbonate reservoir from the Sichuan Basin in southwest China. Real applications suggest that our method significantly improves the accuracy of porosity estimates for different pore types, outperforming the traditional dual pore-type inversion method. This advancement holds considerable promise for the seismic characterization of pore types in ultradeep carbonate reservoirs.

Three‐dimensional static reservoir modelling of Kareem sandstone reservoir, Tawilla Oil Field, at the southern region of Gulf of Suez, Egypt

The Gulf of Suez, which contains Egypt's oldest oil fields, is one of North Africa's most well‐known oil regions. There are more than 80 conventional oil fields in Egypt's Gulf of Suez, some of which have reservoirs that stretch back to the Precambrian and Quaternary. In close proximity to the southern entrance of the Gulf of Suez is the Tawilla West oilfield. The oil field Tawilla West is believed to consist of rotating fault blocks that descend in a south‐west direction. The main producing reservoirs are the Miocene section reservoirs, the Belayim and Kareem sandstones. The current research is focusing on the structural elements affecting this giant field to update the field structural model using the newly processed 3D seismic survey, the acquired data from newly drilled wells and the associated different logging techniques. The seismic information quality varied from poor to fair. The quality of the interpreted stratigraphic horizons and geological faults was mainly controlled by the seismic information quality. The research used seismic attribute analyses to improve interpretation and incorporate additional features, enabling better hydrocarbon potential identification and characterization of the reservoirs. Several geological structure contour maps and cross‐sections were generated to help in delineating and understanding the reservoir's extension. Based on the detailed correlation study, we were able to detect the faults that affected the structure of the Tawilla West field in detail, define their throw amounts and directions, and identify the missed sections across the studied area. This study introduces an updated model scenario to show the differences and their effect on the field development plan and recommendations. By examining subsurface geologic structural characteristics and evaluating petrophysical data, a 3D static reservoir model was created to resolve structural settings and hydrocarbon trapping, providing detailed information on the field and identifying new opportunities for future development. The research discovered that the updated detailed 3D structural model may support the Kareem Reservoir development plans and encourage drilling, workover and dynamic operations to assign development possibilities in the correct area. According to the established model, there are at least three options in the study's attic areas that might boost oil output and oil reserves for the field while avoiding further failures.

Developing a coupled geo-hydrostratigraphic model for a complex lithologic reservoir: a case study of Dakhla Basin, Southwestern Morocco

Developing a coupled geo-hydrostratigraphic model for a complex lithologic reservoir: a case study of Dakhla Basin, Southwestern Morocco