Reservoir Simulation Research Articles

Study Reviews Reservoir Engineering Aspects of Geologic Hydrogen Storage

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper IPTC 23943, “Reservoir Engineering Aspects of Geologic Hydrogen Storage,” by Johannes F. Bauer, Mohd M. Amro, SPE, and Taofik Nassan, SPE, Technical University Bergakademie Freiberg, et al. The paper has not been peer reviewed. Copyright 2024 International Petroleum Technology Conference. Reproduced by permission. _ Safe and effective large-scale storage of hydrogen (H2) is one of the greatest challenges of the global energy transition and can be realized only through storage in geological formations. The aim of the study detailed in the complete paper is to address and discuss the reservoir engineering aspects of geological H2 storage (GHS). The study is based on two sources: first, a comprehensive literature review and, second, experimental and numerical work performed by the authors’ institute. H2 Pressure/Volume/Temperature (PVT)/Phase Behavior The definition of the PVT/phase behavior of reservoir fluids is crucial in GHS because thermodynamic properties significantly affect safety and effectiveness. The properties of H2 are widely known and modeled, but its reaction with other gases, such as in-situ gases like natural gas in depleted gas reservoirs (DGR), currently is under investigation. Although the ideal gas law can account for H2 behavior at low pressure, accurate depiction of its thermodynamic properties requires more-sophisticated equations of state (EOS), especially when it is mixed with other gases such as methane. Most commercial reservoir simulators use EOS packages that can model the complex properties of these mixtures, mostly within required reliability. In most instances, calibration is still required if experimental PVT data are available. Reservoir Engineering of GHS. GHS projects must meet three crucial technical benchmarks for underground storage: capacity (storage volume), injectivity/productivity (rate of injection/withdrawal in relation to wellhead pressure), and containment integrity (prevention of leakage). Economic sustainability requires that projects must adhere to these standards, which may vary according to the selected geological formations. Although various subsurface structures can store H2, only specific formations such as salt caverns (SCs), saline aquifers (SAs), and depleted gas/oil reservoirs (DGRs and DORs), fulfill the requirements. While the complete paper discusses all three of these formation types in detail, this synopsis will concentrate on SCs. GHS in SCs. SC storage typically involves up to three wells for one cavern with a volume of up to 500,000 std m3, providing a delivery rate of 8,500–17,000 std m3/day. Working gas accounts for up to 65% of the total gas, while water should be kept to a minimum. SCs usually allow between six and 12 operating cycles per year, each lasting approximately 10 days for withdrawal periods. SCs offer high H2 purity and sealed storage. The storage capacity of caverns in Germany is determined by their volume and pressure limitations while avoiding any negative geomechanical effects. These caverns can range in depth from 500 to 2000 m, with heights of up to 400 m. Approximately 30 to 50% of the total stored volume is used as cushion gas to maintain production pressure. Estimating the capacity of these caverns relies on their geometry and thermodynamics, resulting in relatively lower uncertainties when compared with storages in porous media.

Influence of Diffusion, Adsorption, Connate Water, and Salinity on Enhanced Gas Recovery and Carbon Storage Reservoir Simulations

Influence of Diffusion, Adsorption, Connate Water, and Salinity on Enhanced Gas Recovery and Carbon Storage Reservoir Simulations

Bridging the gap: Integrating static and dynamic data for improved permeability modeling and super k zone detection in vuggy reservoirs

Permeability modeling in fractured and vuggy reservoirs presents significant challenges, especially in carbonate reservoirs like Brazil’s Barra Velha Formation. The interpretation of vug properties is often subject to misconceptions. It is common to mistakenly consider vugs as part of the matrix, attributing uniform porosity and permeability properties. Alternatively, they are sometimes regarded as fractures, attempting to replicate the influence on permeability enhancement. However, vugs exhibit distinct behavior compared to fractures. Their characteristics are contingent upon factors such as geometry, size, and their relationship with the matrix. Existing methods often overlook the substantial impact of vuggy porosity, leading to inadequate representation of permeability values obtained from core plug measurements or downhole static data, such as Nuclear Magnetic Resonance (NMR) logs. In contrast, dynamic data derived from drill-stem tests (DSTs) and inferred from the Productivity Index (J) provide a more holistic view of the reservoir’s permeability, yet typically yielding a single permeability value for each interpreted interval. This research introduces a novel methodology that addresses these challenges by integrating static and dynamic data, with a primary focus on vuggy porosity using a permeability indicator from a dynamic source. A key innovation lies in the development of a method for estimating total permeability in vuggy intervals, accounting for the effects of vugs and calibrating permeability with dynamic data such as J, DST and Production Logging Tests (PLT). We substantiated the impact of vugs on the augmentation of permeability and production through the application of a localized reservoir simulation model around a key well. Two sensitivity analyses were conducted, one employing a grid representing matrix permeability and another incorporating total permeability with adjustments accounting for the influence of vugs. Additionally, we present two approaches for estimating vuggy porosity: one based on acoustic WellBore Image (WBI) and the second through image segmentation, which can be applied universally across different wellbore image formats. Furthermore, this study explores the concept of superkzones, representing intervals with exceptionally high permeability. The partitioning coefficient (CP), quantifying the relationship between vuggy porosity and total porosity, is employed to generate a probability curve for the occurrence of superkzones in intervals lacking production data. This curve accounts for critical factors such as pressure drop and fluid viscosity, providing a more comprehensive understanding of reservoir behavior. Through a comprehensive case study, our research aims to shed light on the intricate permeability characteristics of the Barra Velha Formation, while identifying the presence of superkzones. The proposed methodologies offer a bridge between static and dynamic data, resulting in a continuous permeability curve that aligns more closely with production data. Ultimately, this work contributes valuable insights for reservoir management and the production strategies optimization in complex carbonate reservoirs within the Santos Basin.

Characterization of petrophysical and seismic properties for CO2 storage with sensitivity analysis

Saline aquifers are considered as highly favored reservoirs for CO2 sequestration due to their favorable properties. Understanding the impact of saline aquifer properties on the migration and distribution of CO2 plume is crucial. This study focuses on four key parameters—permeability, porosity, formation pressure, and temperature—to characterize the reservoir and analyse the petrophysical and elastic response of CO2. First, we performed reservoir simulations to simulate CO2 saturation, using multiple sets of these four parameters to examine their significance on CO2 saturation and the plume migration speed. Subsequently, the effect of these parameters on the elastic properties is tested using rock physics theory. We established a relationship of compressional wave velocity (Vp) and quality factor (Qp) with the four key parameters, and conducted a sensitivity analysis to test their sensitivity to Vp and Qp. Finally, we utilized visco-acoustic wave equation simulated time-lapse seismic data based on the computed Vp and Qp models, and analysed the impact of CO2 saturation changes on seismic data. As for the above numerical simulations and analysis, we conducted sensitivity analysis using both homogeneous and heterogeneous models. Consistent results are found between homogeneous and heterogeneous models. The permeability is the most sensitive parameter to the CO2 saturation, while porosity emerges as the primary factor affecting both Qp and Vp. Both Qp and Vp increase with the porosity, which contradicts the observations in gas reservoirs. The seismic simulations highlight significant variations in the seismic response to different parameters. We provided analysis for these observations, which serves as a valuable reference for comprehensive CO2 integrity analysis, time-lapse monitoring, injection planning and site selection.

Forecasting the efficiency of waterflooding, thermal and chemical enhanced oil recovery methods in Bobrikovian reservoirs

The development of reserves contained viscous and highly viscous oil poses one of the major challenges in modern reservoir engineering. The poor performance of conventional waterflooding prompts the application of enhanced oil recovery (EOR) methods, of which thermal and chemical EOR are considered the most efficient. Reservoir simulation is a powerful tool for evaluating EOR performance. Serial runs of a history-matched model of a Bobrikovian reservoir are used to evaluate the incremental oil production due to each EOR technique and formulate the efficiency limits of the process performance criteria. In contrast to previous studies that evaluated EOR applications using reservoir simulation modeling, an entirely new quality model is derived through automatic history matching performed by solving an inverse problem. Another novel feature is the automated selection of the optimum field development scenario given a user-defined objective function. Evaluating the performance of hot water injection in a Bobrikovian reservoir suggests that this technology yields positive effects when the injected water temperature ranges from 50 °C to 90 °C. The surfactant–polymer flooding method is also investigated. The largest incremental oil production with a constant surfactant concentration of 0.5% is observed for a polyacrylamide concentration of 0.25%, which can be attributed to a water cut reduction due to plugging the well-drained and flushed reservoir zones. The greatest incremental oil production is achieved when injecting 1.5% surfactant and 0.25% polyacrylamide. The highest efficiency per ton of injected chemicals is obtained when injecting 0.5% polyacrylamide and 0.10% surfactant. Keywords: High-viscosity oil; EOR; thermal methods; surfactant-polymer flooding; reservoir simulation; history matching; injection simulation scenario.

Petroleum engineering innovations: Evaluating the impact of advanced gas injection techniques on reservoir management

Gas injection techniques have emerged as a critical aspect of modern reservoir management in petroleum engineering. This paper explores the impact of advanced gas injection techniques on reservoir management strategies and their implications for oil recovery. By evaluating recent innovations in gas injection technology, including the use of CO₂, nitrogen, and hydrocarbon gases, this study sheds light on their effectiveness in enhancing oil recovery rates and extending the productive lifespan of reservoirs. Through a comprehensive review of literature and case studies, this paper examines the mechanisms and benefits of various gas injection methods. It delves into the technological advancements that have revolutionized gas injection techniques, such as real-time reservoir simulation, 4D seismic monitoring, smart well technologies, and machine learning integration. These advancements have provided reservoir engineers with unprecedented insights into reservoir behavior, enabling more informed decision-making processes and optimization of EOR operations. However, alongside these advancements come significant challenges that must be addressed. Economic feasibility, technical hurdles, and environmental concerns pose persistent challenges to the widespread adoption of advanced gas injection techniques. The paper discusses these challenges in detail and explores potential solutions to overcome them, emphasizing the importance of sustainable practices and cross-industry collaboration. By analyzing real-world case studies and industry trends, this paper provides valuable insights into the current state of gas injection techniques in reservoir management and offers recommendations for future research and development. It highlights the need for continued innovation, collaboration, and sustainability in petroleum engineering to maximize the potential of advanced gas injection techniques and ensure the efficient and responsible recovery of hydrocarbon resources. This review provides a concise overview of the paper's scope, findings, and implications, inviting readers to delve deeper into the topic of advanced gas injection techniques in reservoir management.

Innovative approaches in enhanced oil recovery: A focus on gas injection synergies with other EOR methods

Enhanced Oil Recovery (EOR) techniques have become crucial in extending the productive life of mature oil fields and maximizing resource extraction. Among these, gas injection methods, particularly those involving CO₂, nitrogen, and hydrocarbon gases, have shown significant promise. This review focuses on the innovative synergies achieved by combining gas injection with other EOR methods to enhance recovery efficiency and economic viability. Gas injection techniques work by increasing reservoir pressure and reducing oil viscosity, facilitating easier flow and extraction. When integrated with other EOR methods such as thermal, chemical, and microbial techniques, the synergistic effects can lead to even greater enhancements in oil recovery. For instance, the combination of gas injection with thermal EOR, like steam injection, can improve the displacement of heavy oils by simultaneously reducing viscosity and enhancing thermal conductivity within the reservoir. Similarly, integrating gas injection with chemical EOR methods, such as polymer flooding, can optimize the sweep efficiency by leveraging the mobility control provided by polymers and the pressure maintenance by gases. Recent advancements in monitoring and simulation technologies have further improved the effectiveness of these combined approaches. Real-time reservoir simulation and 4D seismic monitoring allow for precise tracking of gas and fluid movements, enabling dynamic adjustments to injection parameters for optimal performance. Smart well technologies, equipped with advanced downhole sensors, provide continuous data on reservoir conditions, facilitating more responsive and adaptive EOR strategies. However, these innovative synergies are not without challenges. Economic feasibility remains a significant concern, particularly given the high costs associated with gas procurement and the integration of multiple EOR techniques. Additionally, managing the complex interactions between different EOR methods and heterogeneous reservoir conditions requires sophisticated modeling and robust operational protocols. In conclusion, the integration of gas injection with other EOR methods offers a promising pathway to enhance oil recovery rates and extend the lifespan of mature reservoirs. Continued research and development, coupled with cross-industry collaboration, are essential to overcoming the economic and technical challenges associated with these innovative approaches. This review underscores the potential for gas injection synergies to revolutionize EOR practices and drive sustainable growth in the oil and gas industry.

On the Feasibility of Deep Geothermal Wells Using Numerical Reservoir Simulation

This study examines the geothermal energy extraction potential from the basement rock within the Denver–Julesburg Basin, focusing on the flow performance and heat extraction efficiency of different geothermal well configurations. It specifically compares U-shaped, V-shaped, inclined V-shaped, and pipe-in-pipe configurations against enhanced geothermal system setups. Through numerical modeling, we evaluated the thermal behavior of these systems under various operational scenarios and fracture conditions. The results suggest that while closed-loop systems offer moderate temperature increases, Enhanced geothermal system configurations show substantial potential for high-temperature extraction. This underscores the importance of evaluating well configurations in complex geological settings. The insights from this study aid in strategic geothermal energy planning and development, marking significant advancements in geothermal technology and setting a foundation for future explorations and optimizations.

A Multiphase and Multicomponent Model and Numerical Simulation Technology for CO2 Flooding and Storage

In recent years, CO2 flooding has become an important technical measure for oil and gas field enterprises to further improve oil and gas recovery and achieve the goal of “dual carbon”. It is also one of the concrete application forms of CCUS. Numerical simulation based on CO2-EOR plays an indispensable role in the study of the mechanism of CO2 flooding and buried percolation, allowing for technical indicators to be selected and EOR/EGR prediction to be improved for reservoir engineers. This paper discusses the numerical simulation techniques related to CO2 flooding and storage, including mathematical models and solving algorithms. A multiphase and multicomponent mathematical model is developed to describe the flow mechanism of hydrocarbon components–CO2–water underground and to simulate the phase diagram of the components. The two-phase P-T flash calculation with SSI (+DEM) and the Newton method is adopted to obtain the gas–liquid phase equilibrium parameters. The extreme value judgment of the TPD function is used to form the phase stability test and miscibility identification model. A tailor-made multistage preconditioner is built to solve the linear equation of the strong-coupled, multiphase, multicomponent reservoir simulation, which includes the variables of pressure, saturation, and composition. The multistage preconditioner improves the computational efficiency significantly. A numerical simulation of CO2 injection in a carbonate reservoir in the Middle East shows that it is effective for researching the recovery factor and storage quantity of CO2 flooding based on the above numerical simulation techniques.

Multilevel Schur-complement algorithms for scalable parallel reservoir simulation with temperature variation

In reservoir simulation, the non-isothermal multiphase flow problem introduces the temperature variable to account for thermal effects, simultaneously posing challenges in efficiently solving the nonlinear systems for large-scale simulations. In this paper, we introduce and investigate a family of Schur-complement-based field-split algorithms designed for addressing non-isothermal multiphase flow problems, particularly those characterized by high heterogeneity. This algorithm involves decomposing a large system into smaller, more manageable sub-systems for solving non-isothermal multiphase flow problems with multiple physical fields, which enables parallel computation and makes it suitable for high-performance computing environments. Furthermore, a multilevel Schur-complement preconditioner, which involves applying the Schur-complement technique at each level of the hierarchy by capturing the coupling between different fields and physics, is proposed to enhance the efficiency and robustness of the parallel simulator. Large-scale simulations for both benchmark and realistic problems are conducted on a supercomputer, showcasing the method's efficacy in managing heat diffusion, significantly reducing linear iterations, and demonstrating a good parallel scalability.

Porous media flow of polymeric nanofluids in multilayered formations: a simulation study

ABSTRACT The injection of viscoelastic fluids into the oil-bearing formations to push the bypassed and entrapped oil toward the producers is a widely accepted technique known for creating a stable displacement front and piston-like displacement, leading to Enhanced Oil Recovery (EOR). However, the sweep efficiency of such complex fluids in stratified formations is still a topic of debate in the literature and is problematic, particularly in harsh reservoir conditions. This research concerns how the inclusion of nanoparticles into the polymer solution could affect the porous media flow of polymeric fluids in multilayered reservoirs. Accordingly, validated core-scale numerical models for polymer flood and polymer nanohybrid flood were upscaled to develop a reservoir-scale model. Through numerical simulations using a compositional reservoir simulator, the simultaneous flow of crude oil and polymer nanohybrids in both low and high-permeable layers of the reservoir were scrutinized. The numerical outputs showed that, compared with polymer flood, the flow dynamics of the polymer nanohybrids in different layers of the reservoir are more favorable, which resulted in improved oil recovery of over 20% of Residual Oil In Place (ROIP). Additionally, our numerical findings indicated that the polymer nanohybrids could exhibit lower polymer adsorption and acceptable salt tolerance in porous media.

Characterization of low-frequency distributed acoustic sensing signals in hydraulic fracturing stimulation – A coupled flow-geomechanical simulation approach

Low-frequency distributed acoustic sensing (LF-DAS) is one of the promising diagnostic techniques for detecting and characterizing hydraulic fractures. LF-DAS signals can capture fracture hits and the strain field around the hydraulic fracture. However, the interpretation of field LF-DAS data can be challenging due to the complexity of the underground conditions. This study develops a fracture propagation model to simulate the hydraulic fracturing process. The modelling results are analyzed to examine patterns and trends observed in interpreting field LF-DAS data. The fracture propagation model, coupled with the flow and geomechanical computations, is implemented in the MATLAB Reservoir Simulation Toolbox (MRST). The flow and geomechanical calculations are discretized by the finite volume and the virtual element methods, respectively. The hydraulic fracture is set to propagate along a prescribed path with a specific propagation or activation criterion. The accuracy of our model is validated against the KGD analytical solutions for the leak-off-viscosity, storage-viscosity and leak-off-toughness dominated regimes. The simulated stress and strain features are consistent with those interpreted from field LF-DAS signals. Several case studies and sensitivity analyses demonstrate the approach's utility and examine fracture interference, closure, and stress shadowing effects. The modelling work facilitates interpreting field measurement data by investigating characteristics of fracture hits from adjacent wells. The modelling method provides insights into fracture interference and its implications on optimal designs during hydraulic fracturing stimulation.

IORSim: A Mathematical Workflow for Field-Scale Geochemistry Simulations in Porous Media

Reservoir modeling consists of two key components: the reproduction of the historical performance and the prediction of the future reservoir performance. Industry-standard reservoir simulators must run fast on enormous and possibly unstructured grids while yet guaranteeing a reasonable representation of physical and chemical processes. However, computational demands limit simulators in capturing involved physical and geochemical mechanisms, especially when chemical reactions interfere with reservoir flow. This paper presents a mathematical workflow, implemented in IORSim, that makes it possible to add geochemical calculations to porous media flow simulators without access to the source code of the original host simulator. An industry-standard reservoir simulator calculates velocity fields of the fluid phases (e.g., water, oil, and gas), while IORSim calculates the transport and reaction of geochemical components. Depending on the simulation mode, the geochemical solver estimates updated relative and/or capillary pressure curves to modify the global fluid flow. As one of the key innovations of the coupling mechanism, IORSim uses a sorting algorithm to permute the grid cells along flow directions. Instead of solving an over-dimensionalized global matrix calling a Newton–Raphson solver, the geochemical software tool treats the species balance as a set of local nonlinear problems. Moreover, IORSim applies basis swapping and splay tree techniques to accelerate geochemical computations in complex full-field reservoir models. The presented work introduces the mathematical IORSim concept, verifies the chemical species advection, and demonstrates the IORSim computation efficiency. After validating the geochemical solver against reference software, IORSim is used to investigate the impact of seawater injection on the NCS Ekofisk reservoir chemistry.Article HighlightsThe IORSim sorting algorithm decouples the nonlinear geochemical reaction calculations into recurring one-dimensional problems to assure numerical stability and computation efficiency. To the best of our knowledge, this work presents the mathematical concept, implementation, and application of topological sorting for the first time on (industry) field-scale problems.IORSim combines topological sorting with basis swapping and splay trees to significantly reduce computation times. Moreover, a high-speed forward simulation mode was developed to allow the post-advection of chemical components to visualize species distribution, water chemistry, and mineral interactions. If the geochemical reactions interfere with the fluid flow, the IORSim backward mode uses relative permeability curves to update the global fluid flow at each time step.We validate the implemented topological scheme on a reservoir grid, show the computation efficiency, and compare the impact of explicit, implicit, and grid refinement on numerical dispersion.The decoupled flow simulator and geochemical reaction calculations allow seamless integration of full-field reservoir models that contain complex geological structures, a large number of wells, and long production histories.The computation capabilities of IORSim are demonstrated by simulating and reproducing the impact of seawater injection in the southern segment of the giant Ekofisk field (more than 50 years of injection and production history). IORSim shows that seawater injection changed the Ekofisk mineralogy and impacted the produced water chemistry. In the investigated Ekofisk case, seawater promoted calcite dissolution and led to the precipitation of magnesite and anhydrite. Moreover, surface complexation modeling revealed that sulfate is adsorbed on the calcite surface.

A high-performance deep reservoir computer experimentally demonstrated with ion-gating reservoirs

While physical reservoir computing is a promising way to achieve low power consumption neuromorphic computing, its computational performance is still insufficient at a practical level. One promising approach to improving its performance is deep reservoir computing, in which the component reservoirs are multi-layered. However, all of the deep-reservoir schemes reported so far have been effective only for simulation reservoirs and limited physical reservoirs, and there have been no reports of nanodevice implementations. Here, as an ionics-based neuromorphic nanodevice implementation of deep-reservoir computing, we report a demonstration of deep physical reservoir computing with maximum of four layers using an ion gating reservoir, which is a small and high-performance physical reservoir. While the previously reported deep-reservoir scheme did not improve the performance of the ion gating reservoir, our deep-ion gating reservoir achieved a normalized mean squared error of 9.08 × 10−3 on a second-order nonlinear autoregressive moving average task, which is the best performance of any physical reservoir so far reported in this task. More importantly, the device outperformed full simulation reservoir computing. The dramatic performance improvement of the ion gating reservoir with our deep-reservoir computing architecture paves the way for high-performance, large-scale, physical neural network devices.

Impact of well completion types on oil drainage area in reservoirs

Well completion design plays a crucial role in oil production and reservoir management. This study examines the influence of various well completion types on the oil drainage area within reservoirs, with the goal of enhancing our understanding of how completion methods affect hydrocarbon recovery. Using advanced reservoir engineering techniques and simulations, this research investigates the dynamic interaction between well completion and the accessibility of oil resources. The study provides a comprehensive analysis of different well completion types, including open-hole completions, cased-hole completions, and multilateral completions, and evaluates their collective impact on the drainage area. It also explores the interplay between well completion strategies and reservoir properties, such as permeability, lithology, and fluid characteristics. The findings of this study reveal that the choice of well completion type can significantly influence the drainage area by affecting the flow paths and reservoir contact. Different completion methods can extend or limit the reach of hydrocarbon recovery, impacting oil production rates. The research underscores the importance of optimizing well completion design to maximize reservoir performance. Furthermore, the study addresses operational considerations related to well completion, including equipment selection, production optimization, and workover requirements. It offers insights into the economic and operational implications of different well completion strategies, providing valuable guidance for reservoir management. The outcomes of this research contribute to a deeper understanding of how well completion types impact the oil drainage area in reservoirs. This knowledge is valuable for reservoir engineers, operators, and industry professionals involved in oil production, assisting them in making informed decisions to optimize oil recovery, reservoir performance, and well completion strategies.

Effects of salinity, temperature, and pressure on H2–brine interfacial tension: Implications for underground hydrogen storage

Underground hydrogen (H2) storage is a long-term, clean, and sustainable solution for a large-scale H2 economy. Hydrogen geo-storage strategies in saline aquifers have gained considerable attention regarding meeting energy demand challenges. Rock-fluid interaction and interfacial tension (IFT) of any given gas determine the fluid flow, storage potential, and containment security. However, the literature lacks sufficient information on the IFT of H2-brine systems at various thermophysical and salinity conditions. This study experimentally measures density using an Anton Paar DMA-HP densitometer and IFT with a Kruss drop shape analyzer (DSA-100). The density and IFT measurements of H2-brine systems were evaluated at various pressures (0.1, 5, 10, 15, and 20 MPa), temperatures (25, 50, and 70 °C), and salinities, including deionized water, seawater (mixed brine), and individual brines (NaCl, KCl, MgCl2, CaCl2, and Na2SO4) at 1 and 3 M concentrations. The results indicate a significant decrease in H2-brine IFT by the increasing temperature at a constant pressure and salinity. A slightly decreasing trend is observed when the H2-brine IFT is plotted against pressure, maintaining a constant salinity and temperature. Increasing the salinity of the salt solutions (from 1 to 3 M), irrespective of the salt type, increases the H2-brine IFT at a constant pressure and temperature. Due to the screening effect, the monovalent and divalent cations behave differently in H2. To the best of our knowledge, this comprehensive dataset for H2-brine IFT is presented for the first time. The provided data are crucial for reservoir simulations and determining the H2 geo-storage potential under natural reservoir conditions.

Flash Flood Simulation for Hilly Reservoirs Considering Upstream Reservoirs—A Case Study of Moushan Reservoir

With the advancement of society and the impact of various factors such as climate change, surface conditions, and human activities, there has been a significant increase in the frequency of extreme rainfall events, leading to substantial losses from flood disasters. The presence of numerous small and medium-sized water conservancy projects in the basin plays a crucial role in influencing runoff production and rainwater confluence. However, due to the lack of extensive historical hydrological data for simulation purposes, it is challenging to accurately predict floods in the basin. Therefore, there is a growing emphasis on flood simulation and forecasting that takes into account the influence of upstream water projects. Moushan Reservoir basin is located in a hilly area of an arid and semi-arid region in the north of China. Flooding has the characteristics of sudden strong, short confluence time, steep rise, and steep fall, especially floods caused by extreme weather events, which have a high frequency and a wide range of hazards, and has become one of the most threatening natural disasters to human life and property safety. There are many small and medium-sized reservoirs in this basin, which have a significant influence on the accuracy of flood prediction. Therefore, taking Moushan Reservoir as an example, this paper puts forward a flash flood simulation method for reservoirs in hilly areas, considering upstream reservoirs, which can better solve the problem of flood simulation accuracy. Using the virtual aggregation method, the 3 medium-sized reservoirs and 93 small upstream reservoirs are summarized into 7 aggregated reservoirs. Then, we construct the hydrological model combining two method sets with different runoff generation and confluence mechanisms. Finally, after model calibration and verification, the results of different methods are analyzed in terms of peak discharge error, runoff depth error, difference in peak time, and certainty coefficient. The results indicate that the flooding processes simulated by the proposed model are in line with the observed ones. The errors of flood peak and runoff depth are in the ranges of 2.3% to 15% and 0.1% to 19.6%, respectively, meeting the requirements of Class B accuracy of the “Water Forecast Code”. Method set 1 demonstrates a better simulation of floods with an average flood peak error of 5.63%. All these findings illustrate that the developed model, utilizing aggregate reservoirs and dynamic parameters to reflect regulation and storage functions, can effectively capture the impact of small water conservancy projects on confluence. This approach addresses challenges in simulating floods caused by small and medium-sized reservoirs, facilitating basin-wide flood prediction.

Strategies for hydrogen storage in a depleted sandstone reservoir from the San Joaquin Basin, California (USA) based on high-fidelity numerical simulations

Depleted hydrocarbon reservoirs are promising candidates for underground hydrogen (H2) storage (UHS) due to their known (and often high) storage capacity, well-identified geological conditions, reduced cushion gas requirements, and adaptable existing infrastructure. While these reservoirs have shown success in natural gas storage, their performance for pure H2 storage remains untested, necessitating further research. To address this knowledge gap, we conduct high-fidelity multiphase compositional reservoir simulations to evaluate the technical feasibility of UHS in a depleted hydrocarbon reservoir within the Temblor Formation at the North Belridge Field from the San Joaquin Basin, California, USA. This reservoir zone comprises five distinct sand layers separated by four shale intervals and is bounded above and below by thick shales (overburden and underburden, respectively). This research has two primary objectives: (a) to assess the feasibility of two distinct UHS strategies—one that involves storing H2 across all sand layers and another that stores an equal amount of H2 in the single thickest sand layer, and (b) to evaluate the effect of interbedded shale permeability on UHS performance. Our findings indicate that UHS in the Temblor Formation is technically viable, showing enhanced H2 withdrawal efficiency and produced H2 purity over successive cycles with minimal leakage risk. The two storage strategies significantly impact UHS performance in the interbedded sandstone sequence. Storing H2 across all sand layers results in higher H2 withdrawal efficiency, but lower produced H2 purity. In contrast, storing H2 in a single sand layer leads to lower withdrawal efficiency but consistently high H2 purity. In addition, H2 storage in a single sand layer results in higher reservoir pressure buildup, implying greater geomechanical risks. The impact of interbedded shale permeability on UHS performance is negligible across the range of uncertainty for shale permeability. This research not only lays the groundwork for potential UHS implementation in the abundant sandstones of the San Joaquin Basin of California, but also offers valuable insights for other depleted hydrocarbon reservoirs under consideration for UHS that share similar properties.

Decoupling numerical method based on deep neural network for nonlinear degenerate interface problems

Many practical problems, including modeling composite materials, nuclear waste disposal, oil reservoir simulations, and flows in porous medium, commonly involve interface problems. However, the solution to interface problems with discontinuous coefficients of PDEs using fully decoupled numerical methods is challenging. The main objective is to solve the interface problems with fully decoupled numerical methods. This paper proposes an efficient decoupled numerical method for solving degenerate interface problems with double singularities. First, we divide the whole domain into singular and regular subdomains. Then, we use the Deep Neural Network (DNN) to find the solution on the singular subdomain and approximate the solution on the regular subdomain using the finite difference method. The scheme combines the solutions of singular and regular subdomains, which is an exciting idea. The key to the new approach is to split nonlinear degenerate partial differential equations with an interface into two independent boundary value problems based on deep learning. In this way, the expansion of the solution on the singular domain does not contain undetermined parameters, and two independent boundary value problems can be solved with any well-known traditional numerical methods. The main advantage of the proposed scheme is that we not only get the order of convergence of the degenerate interface problems on the whole domain, but we also can calculate VERY BIG jump ratio (such as 1012:1 or 1:1012) for the interface problems including degenerate and non-degenerate cases. Finally, with examples, we demonstrate the efficiency and accuracy of methods for 1 and 2D problems. It is also interesting that the proposed method is valid for the interface problems with degenerate and non-degenerate cases, we show it with some examples.

Novel methodology to couple decline curve analysis with CFD reservoir simulations for complex shale gas reservoirs

AbstractShale reservoirs are highly complex and are difficult to study using conventional reservoir simulation tools. This study introduces a novel methodology for estimating production from complex shale gas reservoirs by coupling decline curve analysis (DCA) with computational fluid dynamics (CFD) simulations. The proposed method uses exponential DCA to analyze production data from a dual porosity–permeability shale gas transport model. These complexities include fracture characteristics, geomechanical properties, nanopore confinement effects, and multiple flow mechanisms contributing to the total production performance. The shale gas transport model is validated through historical production data from Marcellus shale. The new methodology also tests fracture characteristics. It shows that increased porosity and permeability will increase the recoverable reserves but will have varying effects on the decline rate. The paper demonstrates the advantages of the proposed methodology over conventional reservoir simulation tools. It provides insights into the factors affecting shale gas production performance through the inclusion of the complexities of an unconventional shale gas reservoir. The paper provides a proof of concept on the particular reservoir of which the field data is provided—Barnett and Marcellus Shale.