Richard A. Dawe, D.C. Wilson · Medical Entomology and Zoology · 1985
Preface. List of contributors. Petroleum engineering: the state of the art. Reservoir volumetrics and recovery factors. Advances in formation evaluation. Pressure analysis: the hardware. Interpretation of transient well test data. Reservoir modelling: finite elements. Reservoir modelling: fully implicit simulation methods. Thermal properties of reservoir rocks and fluids. Index.
Cited by 13
2025
The INTERNATIONAL JOURNAL OF PETROCHEMICAL ENGINEERING AND TECHNOLOGY (IJPET) its scope encompasses all aspects of petroleum research, including origin and accumulation of petroleum, petroleum geochemistry, reservoir engineering, rock mechanics/petrophysics, well logging, testing and evaluation, mathematical modelling, enhanced oil recovery, petroleum geology, compaction/diagenesis, petroleum economics, drilling and drilling fluids, fluid mechanics in porous media, reservoir simulation, production engineering, formation evaluation, exploration methods.
Cited by 0Open access
Yasin Khalili, Mohammad Ahmadi · SHILAP Revista de lepidopterología · 2023
Reservoir modeling and simulation play a pivotal role in the field of reservoir engineering, enabling efficient hydrocarbon recovery and reservoir management. This article provides an overview of the definition, significance, and evolution of reservoir modeling techniques, emphasizing the importance of accurate reservoir characterization. It explores different data acquisition methods, such as core analysis, well logging, seismic data, and production history, highlighting their integration for robust reservoir description. Mathematical modeling techniques for reservoir simulation, including single-phase and multi-phase flow models, along with numerical simulation methods such as finite difference, finite element, and finite volume, are discussed. The article also delves into uncertainty analysis, history matching, and the assimilation of field production data to improve model accuracy. Advanced techniques, emerging trends, and their applications, such as upscaling/downscaling methods, integrated reservoir modeling and optimization approaches, and the use of artificial intelligence and machine learning, are presented. The inclusion of case studies showcases the practical implementation of reservoir modeling and simulation in various areas, such as field development planning, enhanced oil recovery, and reservoir management. Finally, the challenges associated with reservoir modeling and simulation techniques and future perspectives for advancements in the field are addressed.
Cited by 13Open access
Paul Fjerstad, A. S. Sikandar, Huy Nhat Cao et al. · SPE International Improved Oil Recovery Conference in Asia Pacific · 2005
Abstract This paper describes application of Project INTERSECT, a next generation highly scalable reservoir simulator on real large scale field models. High resolution reservoir simulation is required to better define and describe fluid flow and enable improved field development and tactical operational planning. Massively parallel computing techniques overcome limitations of problem size and space resolution. This paper demonstrates that large-scale simulation models can be performed on commodity hardware taking advantage of evolution in multicpu hardware architecture and software engineering. This allows both geologists and reservoir engineers to include more realistic geologic and engineering detail for better and more reliable production optimization. Intense computer simulation is essential for effective reservoir management. The advances in reservoir characterization techniques and the industry drive towards the ‘smart oilfield’ with rapid model updates will require more efficient model processing to achieve timely field operational decisions. Parallel reservoir simulators have the potential to solve larger, more realistic problems than previously possible. The size and application of reservoir simulation problems have been limited by the availability of computing hardware, reservoir simulation architecture and of solution methods for solving large-scale heterogeneous problems. The next generation reservoir simulator demonstrates that key modeling challenges has been overcome by a software architecture and capability to model more realistic subsurface and surface models. Applications of the new reservoir simulator illustrates how typical reservoir engineering options such as local grid refinement, local grid coarsening, multilateral wells and aquifer modeling affect the overall parallel performance and scalability using highly heterogeneous large-scale models. Application of new modeling techniques highlight increased accuracy of modeling results and more reliable field development planning and reservoir management decisions.
Cited by 25
Christopher D. White, Steve A. Royer · 2003
Summary Numerical simulation integrates extensive geoscience and engineering data with complex process models to examine reservoir behavior. Reservoir studies commonly consider many scenarios, cases, and realizations. However, reservoir simulation can be expensive. Complexity, combinatorics, and expense motivate improved reservoir study methods. The experimental design framework selects relevant models, records factor settings for models, creates data files, controls execution, gathers summary data, and creates response models. Response surface models facilitate Monte Carlo simulation, uncertainty analysis, optimization, parameter estimation, upscaling, and performance forecasting. A predevelopment study of a Gulf of Mexico turbidite reservoir uses this framework to examine the sensitivity of oil production predictions to well location, absolute horizontal permeability, pore compressibility, aquifer size, skin, and vertical permeability. Well location is optimized for cases with uncertain parameters.
Cited by 137
Liang Xue, Daolun Li, Hongen Dou · ADVANCES IN GEO-ENERGY RESEARCH · 2023
Artificial neural networks have been widely applied in reservoir engineering. As a powerful tool, it changes the way to find solutions in reservoir simulation profoundly. Deep learning networks exhibit robust learning capabilities, enabling them not only to detect patterns in data, but also uncover underlying physical principles, incorporate prior knowledge of physics, and solve complex partial differential equations. This work presents the latest research advancements in the field of petroleum reservoir engineering, covering three key research directions based on artificial neural networks: data-driven methods, physics driven artificial neural network partial differential equation solver, and data and physics jointly driven methods. In addition, a wide range of neural network architectures are reviewed, including fully connected neural networks, convolutional neural networks, recurrent neural networks, and so on. The basic principles of these methods and their limitations in practical applications are also outlined. The future trends of artificial intelligence methods for oil and gas reservoir development are further discussed. The large language models are the most advanced neural networks so far, it is expected to be applied in reservoir simulation to predict the development performance. Document Type: Perspective Cited as: Xue, L., Li, D., Dou, H. Artificial intelligence methods for oil and gas reservoir development: Current progresses and perspectives. Advances in Geo-Energy Research, 2023, 10(1): 65-70. https://doi.org/10.46690/ager.2023.10.07
Cited by 29Open access
Jonas Cordazzo, Leonardo Karpinski, Susie Cristine Keller et al. · 2007
Summary. In recent years, multigrid methods have received considerable attention due to its capability to reduce drastically the computation time in the solution of linear systems. In this article the Additive Correction Multigrid method is analyzed and applied to petroleum reservoir simulation problems, employing structured and unstructured grids. The results of test-problems shown in this paper are strongly encouraging and demonstrate that the multigrid methods can be used with great advantage in reservoir simulation too, similarly to other areas of engineering where this kind of method has been routinely used.
Cited by 0
Xu Chen, Kai Zhang, Zhenning Ji et al. · Mathematics · 2023
Machine learning techniques have garnered significant attention in various engineering disciplines due to their potential and benefits. Specifically, in reservoir numerical simulations, the core process revolves around solving the partial differential equations delineating oil, gas, and water flow dynamics in porous media. Discretizing these partial differential equations via numerical methods is one cornerstone of this simulation process. The synergy between traditional numerical methods and machine learning can enhance the precision of partial differential equation discretization. Moreover, machine learning algorithms can be employed to solve partial differential equations directly, yielding rapid convergence, heightened computational efficiency, and accuracies surpassing 95%. This manuscript offers an overview of the predominant numerical methods in reservoir simulations, focusing on integrating machine learning methodologies. The innovations in fusing deep learning techniques to solve reservoir partial differential equations are illuminated, coupled with a concise discussion of their inherent advantages and constraints. As machine learning continues to evolve, its conjunction with numerical methods is poised to be pivotal in addressing complex reservoir engineering challenges.
Cited by 18Open access
GAI Changcheng, LI Hongda, REN Lu, CAO Wei, HAO Jie · SHILAP Revista de lepidopterología · 2024
Geothermal energy and petroleum are both vital subsurface energy resources. Numerical simulation of geothermal and reservoir systems is a key technology for evaluating and optimizing the development and utilization of these resources, playing an essential guiding role in the energy sector. By comparing the foundational mathematical models, numerical methods, and case studies of geothermal and reservoir simulations, this study highlights the similarities and differences in their application to the development of these two energy sources. In terms of simulation methods, geothermal numerical simulations focus on heat conduction and geothermal field variation, whereas reservoir numerical simulations emphasize fluid dynamics and the oil extraction process. Regarding simulation results, geothermal simulations are used for geothermal resource development planning and the optimization of key production parameters, while reservoir simulations are primarily applied to reserve estimation, injection-production parameter optimization, and well production management. This comparative analysis provides theoretical and practical guidance for research and applications in geothermal energy and petroleum engineering, promoting the efficient and sustainable utilization of both energy resources.
Cited by 0Open access
Strata Production Co., Roswell, NM (United States) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 1998
The overall objective of this project is to demonstrate that a development program--based on advanced reservoir management methods--can significantly improve oil recovery at the Nash Draw Pool (NDP). The plan includes developing a control area using standard reservoir management techniques and comparing its performance to an area developed using advanced reservoir management methods. Specific goals are (1) to demonstrate that an advanced development drilling and pressure maintenance program can significantly improve oil recovery compared to existing technology applications and (2) to transfer these advanced methodologies to oil and gas producers in the Permian Basin and elsewhere throughout the US oil and gas industry. Results obtained to date are summarized for the following: geostatistics and reservoir mapping; reservoir engineering; reservoir characterization/reservoir simulation; miscible recovery simulations; and technology transfer.
Cited by 0Open access
Xin Li · 2008
A reservoir engineering Grid computing toolkit, ResGrid and its extensions, were developed and applied to designed reservoir simulation studies and continuous reservoir model updating. The toolkit provides reservoir engineers with high performance computing capacity to complete their projects without requiring them to delve into Grid resource heterogeneity, security certification, or network protocols. Continuous and real-time reservoir model updating is an important component of closed-loop model-based reservoir management. The method must rapidly and continuously update reservoir models by assimilating production data, so that the performance predictions and the associated uncertainty are up-to-date for optimization. The ensemble Kalman filter (EnKF), a Bayesian approach for model updating, uses Monte Carlo statistics for fusing observation data with forecasts from simulations to estimate a range of plausible models. The ensemble of updated models can be used for uncertainty forecasting or optimization. Grid environments aggregate geographically distributed, heterogeneous resources. Their virtual architecture can handle many large parallel simulation runs, and is thus well suited to solving model-based reservoir management problems. In the study, the ResGrid workflow for Grid-based designed reservoir simulation and an adapted workflow provide tools for building prior model ensembles, task farming and execution, extracting simulator output results, implementing the EnKF, and using a web portal for invoking those scripts. The ResGrid workflow is demonstrated for a geostatistical study of 3-D displacements in heterogeneous reservoirs. A suite of 1920 simulations assesses the effects of geostatistical methods and model parameters. Multiple runs are simultaneously executed using parallel Grid computing. Flow response analyses indicate that efficient, widely-used sequential geostatistical simulation methods may overestimate flow response variability when compared to more rigorous but computationally costly direct methods. Although the EnKF has attracted great interest in reservoir engineering, some aspects of the EnKF remain poorly understood, and are explored in the dissertation. First, guidelines are offered to select data assimilation intervals. Second, an adaptive covariance inflation method is shown to be effective to stabilize the EnKF. Third, we show that simple truncation can correct negative effects of nonlinearity and non-Gaussianity as effectively as more complex and expensive reparameterization methods.
Cited by 8Open access
Jill S. Buckley, W.W. Weiss, Ahmed Ouenes · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 1994
The purpose of this project is to conduct a variety of laboratory and field tests and utilize all the geological, geophysical, and engineering information collected to develop a reservoir model by the use of global optimization methods. The interdisciple effort will integrate advanced geoscience and reservoir engineering concepts to quantify reservoir heterogeneity and the dynamics of fluid-rock and fluid-fluid interactions. The reservoir characterization will include geological methods (outcrop and reservoir rock studies), geophysical methods (interwell acoustic techniques), and other reservoir/hydrologic methodologies including analyses of pressure transient data, field tracer tests, and laboratory core studies. The field testing will be conducted at the Sulimar Queen Unit with related laboratory testing at the Petroleum Recovery Research Center (PRRC) on core samples from the Sulimar site and Queen sandstone outcrops. Research methods will involve the acquisition of data obtained at different scales and integration of the data into a reservoir model. The goals of the project are to: (1) characterize lithologic heterogeneity, (2) quantify changes in heterogeneity at various scales, (3) integrate the wide variety of data into a model that is jointly constrained by the interdisciplinary interpretive effort, and (4) achieve greater accuracy and confidence during simulation and modeling as steps toward optimizing recovery efficiency from existing petroleum reservoirs.
Cited by 0Open access
Strata Production Co., Roswell, NM (United States) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 1996
The overall objective of this project is to demonstrate that a development program based on advanced reservoir management methods can significantly improve oil recovery. The demonstration plan includes developing a control area using standard reservoir management techniques and comparing the performance of the control area with an area developed using advanced reservoir management methods. Specific goals to attain the objective are: (1) to demonstrate that a development drilling program and pressure maintenance program, based on advanced reservoir management methods, can significantly improve oil recovery compared with existing technology applications, and (2) to transfer the advanced methodologies to oil and gas producers in the Permian Basin and elsewhere in the US oil and gas industry. Results obtained to date are summarized on the following: geology, engineering, 3-D seismic, reservoir characterization and simulation, and technology transfer.
Cited by 0Open access
Christine Ehlig‐Economides · International Meeting on Petroleum Engineering · 1995
ABSTRACT With data acquisition at many scales, comprehensive databases, and integrated data visualizations, a much more realistic understanding of the implications of reservoir characterization on production is emerging. This paper provides a multidisciplinary overview of four tasks: reservoir delineation, reservoir modeling, production optimization, and uncertainty analysis. Each of these tasks highlights specific engineering applications. An overview is provided of geophysical, geological, and petrophysical approaches that engineers need to understand in order to communicate effectively with geoscientists. Flow units and compartments are the key elements used in this paper to relate the reservoir characterization provided by geoscientists to what is needed by engineers. These two concepts enable engineers and geoscientists to develop a common view of what various disciplines can reveal about a reservoir. Applications for integrated reservoir characterization include improving drilling and completion locations, optimizing well productivity, and enhancing recoverable reserves. An integrated reservoir characterization focused on flow units and reservoir compartments amplifies considerably what can be understood from conventional engineering techniques, including reserves estimation, material balance, production data analysis, and fluid flow simulation. Examples from field studies demonstrate results derived from integrated reservoir characterization that would not have been apparent from conventional engineering methods.
Cited by 2
Charles A. Brown, Charles B. Erbe, James Crafton · SPE Annual Technical Conference and Exhibition · 1981
ABSTRACT Natural gas is currently being developed and produced in the Hay Reservoir Area from deep, high pressure, low permeability reservoirs. The reservoirs are predominantly offshore sand deposits which have been significantly altered since deposition. As a result, the reservoirs are difficult to interpret from logs, require stimulation to provide commercial flow rates, and necessitate more sophisticated evaluation methods than conventional reservoirs. Traditional reservoir engineering approaches are discussed and found to be inadequate in the evaluation of these low permeability sandstone reservoirs based on early life performance. A method of reservoir study is developed by integrating methods from several technical disciplines including geologic reservoir modeling, petrography/reservoir quality analyses, log interpretation modeling, and ultimately, reservoir simulation to rigorously evaluate individual well deliverabilities and reserves. Results show that significantly more gas is economically recoverable than traditional methods of analysis would indicate.
Cited by 7
E.K. Ogay, A.K. Kalybek, Raman Sadykov · Engineering Journal of Satbayev University · 2020
The problem of reservoir engineering and production of coalbed methane (CBM) in Kazakhstan are important subject today. Identification of methane-rich zones from the coal geology side is not always enough. In this article, traditional methods for studying oil and gas basins are applied for identification of methane rich zones in southern part of Karaganda coal basin. Reservoir simulation is used to check the possible production performance of the zones.
Cited by 0Open access
Strata Production Co., Roswell, NM (United States) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 1997
The overall objective of this project is to demonstrate that a development program--based on advanced reservoir management methods--can significantly improve oil recovery. The plan includes developing a control area using standard reservoir management techniques while comparing its performance to an area developed using advanced reservoir management methods. Specific goals are (1) to demonstrate that an advanced development drilling and pressure maintenance program, can significantly improve oil recovery compared to existing technology applications and (2) to transfer these advanced methodologies to oil and gas producers in the Permian Basin and elsewhere throughout the US oil and gas industry. Results so far are described on geology, engineering, 3-D seismic, reservoir characterization and simulation, and technology transfer.
Cited by 0Open access
Q. Liu, T. Stone, G. Han et al. · Proceedings of SPE Asia Pacific Oil and Gas Conference and Exhibition · 2004
Reservoir simulation traditionally involves prediction of fluid component and pressure profiles over time scales that are typical of oil and gas recovery processes. As commercial reservoir simulators evolve and become more sophisticated and accurate, they are increasingly being applied to marginal fields including reservoirs that have stress dependent problems.Recently, efforts have been made towards coupling 3-D finite difference elastic stress solutions with fluid flow in a commercial reservoir simulator (SPE 65107, SPE 79697). In this paper the implementation of a finite element method to solve the rock elastoplastic stress equations that are coupled to reservoir fluid flow is introduced. This method has been chosen as an alternative to the finite difference method in order to produce higher accuracy in situations where the finite difference method has unacceptable errors. In addition, a non-isotropic work hardening plastic algorithm has been derived as part of this finite element implementation. Together they provide an improved analysis of weaker, tectonically stressed and faulted reservoirs. Geomechanical issues such as subsidence/compaction, wellbore failure, and the impact of stress-dependent flow parameters that affect fluid production can be modelled.Advantages and disadvantages of computing the stress coupled to fluid flow by finite difference and finite element methods are discussed. An analysis of error in the two methods, larger scale coupled geomechanical reservoir simulation, solution of the equations and some future directions are included. A workflow is offered that takes into account data collection for the simulator, calibration and interpretation of results. This workflow is important because knowledge of reservoir engineering, reservoir simulation and geomechanical engineering are needed in order to produce a meaningful coupled geomechanical prediction.
Cited by 0
Jill S. Buckley, W.W. Weiss, Ahmed Ouenes · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 1994
The purpose of this project is to conduct a variety of laboratory and field tests and utilize all the geological, geophysical, and engineering information collected to develop a reservoir model by the use of global optimization methods. The interdisciplinary effort will integrate advanced geoscience and reservoir engineering concepts to quantify reservoir heterogeneity and the dynamics of fluid-rock and fluid-fluid interactions. The reservoir characterization will include geological methods (outcrop and reservoir rock studies), geophysical methods (interwell acoustic techniques), and other reservoir/hydrologic methodologies including analyses of pressure transient data, field tracer tests, and laboratory core studies. The field testing will be conducted at the Sulimar Queen Unit with related laboratory testing at the Petroleum Recovery Research Center (PRRC) on core samples from the Sulimar site and Queen sandstone outcrops. Research methods will involve the acquisition of data obtained at different scales and integration of the data into a reservoir model. The goals of the project are to: (1) characterize lithologic heterogeneity, (2) quantify changes in heterogeneity at various scales, (3) integrate the wide variety of data into a model that is jointly constrained by the interdisciplinary interpretive effort, and (4) achieve greater accuracy and confidence during simulation and modeling as steps toward optimizing recovery efficiency from existing petroleum reservoirs. Dr. Jerry Harris, Associate Professor in the Department of Geophysics at Stanford University and Dr. Gary Pope, Director of the Center for Petroleum and Geosystems Engineering at the University of Texas at Austin, will collaborate on the project. Several members of the PRRC staff will participate in the development of improved reservoir description by integration of the field and laboratory data as well as in the development of quantitative reservoir models to aid performance predictions.
Cited by 0Open access
Menhal A. Al-Ismael, Ali A. Al-Turki, Abdulaziz Al-Darrab · International Petroleum Technology Conference · 2020
Automatic updates of simulation models with historical field performance and events is a challenging and time-consuming task that reservoir engineers need to tackle; whether it is to maintain history matched reservoir models (evergreen assets), undertake new calibration exercise or update forecasting studies. The challenge takes another dimension with increasing complexity of field operations (production/injection/drilling/workover), and well designs and configuration of downhole equipment. This paper presents an efficient workflow capitalizing on IR4.0 Digital Twin principles to automate the process of seamlessly integrating and updating historical wells’ information in reservoir simulation models. The objective of this workflow is to drive reservoir simulation towards capitalizing on digital transformation and the Live Earth models concept to revolutionize model calibration and history matching for superior quality of prediction with great confidence. Well data digitization in this workflow was achieved through automating well data acquisition, well data quality checking enforcement and well modeling in interconnected simulation applications. The workflow minimizes human manual interaction with data giving engineers the chance to focus more on reservoir engineering aspects of reservoir engineering tasks. The workflow consists of four steps. The first step is data acquisition in which various types of well data are fetched. The second step is data quality check in which data from different data sources is subjected to engineering and scientific measures (i.e. Quality Indices) that translate engineering knowledge and experience to detect possible data inconsistencies. The third and fourth steps cover exporting and importing relevant data within the reservoir simulation applications’ portfolio where various data types are handled and managed seamlessly. Data and event acquisition workflows were automated to provide seamless well data transfer between different data sources and reservoir simulation pre and post-processing applications. The different types of well data were obtained through automatic fetching from data repository (databases, petrophysical models … etc.). The Quality Check (QC) procedures were automatically performed against deviation surveys, perforations, casing/tubing, flowmeter, cores, formation tops and productivity/injectivity index. This helped in identifying data discrepancy, if any, including missing data entries and contradicting well events. The automation of these workflows significantly reduced the time needed for well data transmission/update to the reservoir models, eliminated human errors associated with data entry or corrections, and helped keeping the models up-to-date (evergreen). Incorporating the digital twin concepts enabled advanced automatic digitization of well information. It provided a data exchange solution that meets E&P requirements and provided more effective and efficient methods of connecting diverse applications and data repositories.
Cited by 6