In this paper, we introduce an efficient method to generate reservoir simulation grids and modify the fault juxtaposition on the generated grids. Both processes are based on a mapping method to displace vertices of a grid to desired locations without changing the grid topology. In the gridding process, a grid that can capture stratigraphical complexity is first generated in an unfaulted space. The vertices of the grid are then displaced back to the original faulted space to become a reservoir simulation grid. The resulting reversely mapped grid has a mapping structure that allows fast and easy fault juxtaposition modification. This feature avoids the process of updating the structural framework and regenerating the reservoir properties, which may be time-consuming. To facilitate juxtaposition updates within an assisted history matching workflow, several parameterized fault throw adjustment methods are introduced. Grid examples are given for reservoirs with Y-faults, overturned bed, and complex channel-lobe systems.
{"title":"A Fast Gridding Method for Capturing Geological Complexity and Uncertainty","authors":"Xu Yifei, Priyesh Srivastava, Xiao Ma, Karan Kaul, Hao Huang","doi":"10.2118/203902-ms","DOIUrl":"https://doi.org/10.2118/203902-ms","url":null,"abstract":"\u0000 In this paper, we introduce an efficient method to generate reservoir simulation grids and modify the fault juxtaposition on the generated grids. Both processes are based on a mapping method to displace vertices of a grid to desired locations without changing the grid topology. In the gridding process, a grid that can capture stratigraphical complexity is first generated in an unfaulted space. The vertices of the grid are then displaced back to the original faulted space to become a reservoir simulation grid. The resulting reversely mapped grid has a mapping structure that allows fast and easy fault juxtaposition modification. This feature avoids the process of updating the structural framework and regenerating the reservoir properties, which may be time-consuming. To facilitate juxtaposition updates within an assisted history matching workflow, several parameterized fault throw adjustment methods are introduced. Grid examples are given for reservoirs with Y-faults, overturned bed, and complex channel-lobe systems.","PeriodicalId":11146,"journal":{"name":"Day 1 Tue, October 26, 2021","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90336672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Santoso, Xupeng He, M. AlSinan, H. Kwak, H. Hoteit
History matching is critical in subsurface flow modeling. It is to align the reservoir model with the measured data. However, it remains challenging since the solution is not unique and the implementation is expensive. The traditional approach relies on trial and error, which are exhaustive and labor-intensive. In this study, we propose a new workflow utilizing Bayesian Markov Chain Monte Carlo (MCMC) to automatically and accurately perform history matching. We deliver four novelties within the workflow: 1) the use of multi-resolution low-fidelity models to guarantee high-quality matching, 2) updating the ranges of priors to assure convergence, 3) the use of Long-Short Term Memory (LSTM) network as a low-fidelity model to produce continuous time-response, and 4) the use of Bayesian optimization to obtain the optimum low-fidelity model for Bayesian MCMC runs. We utilize the first SPE comparative model as the physical and high-fidelity model. It is a gas injection into an oil reservoir case, which is the gravity-dominated process. The coarse low-fidelity model manages to provide updated priors that increase the precision of Bayesian MCMC. The Bayesian-optimized LSTM has successfully captured the physics in the high-fidelity model. The Bayesian-LSTM MCMC produces an accurate prediction with narrow uncertainties. The posterior prediction through the high-fidelity model ensures the robustness and precision of the workflow. This approach provides an efficient and high-quality history matching for subsurface flow modeling.
{"title":"Bayesian Long-Short Term Memory for History Matching in Reservoir Simulations","authors":"R. Santoso, Xupeng He, M. AlSinan, H. Kwak, H. Hoteit","doi":"10.2118/203976-ms","DOIUrl":"https://doi.org/10.2118/203976-ms","url":null,"abstract":"\u0000 History matching is critical in subsurface flow modeling. It is to align the reservoir model with the measured data. However, it remains challenging since the solution is not unique and the implementation is expensive. The traditional approach relies on trial and error, which are exhaustive and labor-intensive. In this study, we propose a new workflow utilizing Bayesian Markov Chain Monte Carlo (MCMC) to automatically and accurately perform history matching. We deliver four novelties within the workflow: 1) the use of multi-resolution low-fidelity models to guarantee high-quality matching, 2) updating the ranges of priors to assure convergence, 3) the use of Long-Short Term Memory (LSTM) network as a low-fidelity model to produce continuous time-response, and 4) the use of Bayesian optimization to obtain the optimum low-fidelity model for Bayesian MCMC runs.\u0000 We utilize the first SPE comparative model as the physical and high-fidelity model. It is a gas injection into an oil reservoir case, which is the gravity-dominated process. The coarse low-fidelity model manages to provide updated priors that increase the precision of Bayesian MCMC. The Bayesian-optimized LSTM has successfully captured the physics in the high-fidelity model. The Bayesian-LSTM MCMC produces an accurate prediction with narrow uncertainties. The posterior prediction through the high-fidelity model ensures the robustness and precision of the workflow. This approach provides an efficient and high-quality history matching for subsurface flow modeling.","PeriodicalId":11146,"journal":{"name":"Day 1 Tue, October 26, 2021","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86849083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
O. Andersen, M. Kelley, V. Smith, S. Raziperchikolaee
In this study, we demonstrate geomechanical modeling with fully automatic parameter calibration to estimate the full geomechanical stress fields of a prospective US CO2 storage site, based on sparse measurement data. The goal is to compute full stress tensor field estimates (principal stresses and orientations) that are maximally compatible with observations within the constraints of the model assumptions, thereby extending point-wise, incomplete partial stress measurement to a simulated full formation stress field, as well as a rough assessment of the associated error. We use the Perch site, located in Otsego Country, Michigan, as our case study. Input data consists of partial stress tensor information inferred from in-situ borehole tests, geophysical well logs and processing of seismic data. A static earth model of the site was developed, and geomechanical simulation functionality of the open-source MATLAB Reservoir Simulation Toolbox (MRST) used to model the stress field. Adjoint-based nonlinear optimization was used to adjust boundary conditions and material properties to calibrate simulated results to observations. Results were interpreted through a Bayesian framework. The focus of this article is to demonstrate how the fully automatic calibration procedure works and discuss the results obtained but does not attempt a detailed analysis of the stress field in the context of the proposed CO2 storage initiatives. Our work is part of a larger effort to non-invasively determine in-situ stresses in deep formations considered for CO2 storage. Guided by previously published research on geomechanical model calibration, our work presents a novel calibration approach supporting a potentially large number of linear or nonlinear calibration parameters, in order to produce results optimally agreeing with available measurements and thus extend partial point-wise estimates to full tensor fields compatible with the physics of the site.
{"title":"Automatic Calibration of a Geomechanical Model from Sparse Data for Estimating Stress in Deep Geological Formations","authors":"O. Andersen, M. Kelley, V. Smith, S. Raziperchikolaee","doi":"10.2118/204006-ms","DOIUrl":"https://doi.org/10.2118/204006-ms","url":null,"abstract":"\u0000 In this study, we demonstrate geomechanical modeling with fully automatic parameter calibration to estimate the full geomechanical stress fields of a prospective US CO2 storage site, based on sparse measurement data. The goal is to compute full stress tensor field estimates (principal stresses and orientations) that are maximally compatible with observations within the constraints of the model assumptions, thereby extending point-wise, incomplete partial stress measurement to a simulated full formation stress field, as well as a rough assessment of the associated error. We use the Perch site, located in Otsego Country, Michigan, as our case study. Input data consists of partial stress tensor information inferred from in-situ borehole tests, geophysical well logs and processing of seismic data. A static earth model of the site was developed, and geomechanical simulation functionality of the open-source MATLAB Reservoir Simulation Toolbox (MRST) used to model the stress field. Adjoint-based nonlinear optimization was used to adjust boundary conditions and material properties to calibrate simulated results to observations. Results were interpreted through a Bayesian framework.\u0000 The focus of this article is to demonstrate how the fully automatic calibration procedure works and discuss the results obtained but does not attempt a detailed analysis of the stress field in the context of the proposed CO2 storage initiatives. Our work is part of a larger effort to non-invasively determine in-situ stresses in deep formations considered for CO2 storage. Guided by previously published research on geomechanical model calibration, our work presents a novel calibration approach supporting a potentially large number of linear or nonlinear calibration parameters, in order to produce results optimally agreeing with available measurements and thus extend partial point-wise estimates to full tensor fields compatible with the physics of the site.","PeriodicalId":11146,"journal":{"name":"Day 1 Tue, October 26, 2021","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76278788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
High-performance computing is at the heart of digital technology which allows to simulate complex physical phenomena. The current trend for hardware architectures is toward heterogeneous systems with multi-core CPUs accelerated by GPUs to get high computing power. The demand for fast solution of Geoscience simulations coupled with new computing architectures drives the need for challenging parallel algorithms. Such applications based on partial differential equations, requires to solve large and sparse linear system of equations. This work makes a step further in Matrix Powers Kernel (MPK) which is a crucial kernel in solving sparse linear systems using communication-avoiding methods. This class of methods deals with the degradation of performances observed beyond several nodes by decreasing the gap between the time necessary to perform the computations and the time needed to communicate the results. The proposed work consists of a new formulation for distributed MPK kernels for the cluster of GPUs where the pipeline communications could be overlapped by the computation. Also, appropriate data reorganization decreases the memory traffic between processors and accelerators and improves performance. The proposed structure is based on the separation of local and external components with different layers of interface nodes-due to the MPK algorithm-. The data is restructured in a way where all the data required by the neighbor process comes contiguously at the end, after the local one. Thanks to an assembly step, the contents of the messages for each neighbor are determined. Such data structure has a major impact on the efficiency of the solution, since it permits to design an appropriate communication scheme where the computation with local data can occur on the GPUs and the external ones on the CPUs. Moreover, it permits more efficient inter-process communication by an effective overlap of the communication by the computation in the asynchronous pipeline way. We validate our design through the test cases with different block matrices obtained from different reservoir simulations : fractured reservoir dual-medium, black-oil two phase-flow, and three phase-flow models. The experimental results demonstrate the performance of the proposed approach compared to state of the art. The proposed MPK running on several nodes of the GPU cluster provides a significant performance gain over equivalent Sparse Matrix Vector product (SpMV) which is already optimized and provides better scalability.
{"title":"Distributed GPU Based Matrix Power Kernel for Geoscience Applications","authors":"A. Sedrakian, T. Guignon","doi":"10.2118/203947-ms","DOIUrl":"https://doi.org/10.2118/203947-ms","url":null,"abstract":"\u0000 High-performance computing is at the heart of digital technology which allows to simulate complex physical phenomena. The current trend for hardware architectures is toward heterogeneous systems with multi-core CPUs accelerated by GPUs to get high computing power. The demand for fast solution of Geoscience simulations coupled with new computing architectures drives the need for challenging parallel algorithms. Such applications based on partial differential equations, requires to solve large and sparse linear system of equations. This work makes a step further in Matrix Powers Kernel (MPK) which is a crucial kernel in solving sparse linear systems using communication-avoiding methods. This class of methods deals with the degradation of performances observed beyond several nodes by decreasing the gap between the time necessary to perform the computations and the time needed to communicate the results. The proposed work consists of a new formulation for distributed MPK kernels for the cluster of GPUs where the pipeline communications could be overlapped by the computation. Also, appropriate data reorganization decreases the memory traffic between processors and accelerators and improves performance. The proposed structure is based on the separation of local and external components with different layers of interface nodes-due to the MPK algorithm-. The data is restructured in a way where all the data required by the neighbor process comes contiguously at the end, after the local one. Thanks to an assembly step, the contents of the messages for each neighbor are determined. Such data structure has a major impact on the efficiency of the solution, since it permits to design an appropriate communication scheme where the computation with local data can occur on the GPUs and the external ones on the CPUs. Moreover, it permits more efficient inter-process communication by an effective overlap of the communication by the computation in the asynchronous pipeline way. We validate our design through the test cases with different block matrices obtained from different reservoir simulations : fractured reservoir dual-medium, black-oil two phase-flow, and three phase-flow models. The experimental results demonstrate the performance of the proposed approach compared to state of the art. The proposed MPK running on several nodes of the GPU cluster provides a significant performance gain over equivalent Sparse Matrix Vector product (SpMV) which is already optimized and provides better scalability.","PeriodicalId":11146,"journal":{"name":"Day 1 Tue, October 26, 2021","volume":"152 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80946299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Consistent set of algorithms to calculate phase relative permeability and capillary pressure values in the four-phase representation suitable for surfactant flooding simulation has been derived. The novel formulation resolves difficulties with applying existing three-phase approaches, and it ensures continuity of transport characteristics at solubilization changes in phase composition.
{"title":"Four Phase Relative Permeability and Capillary Pressure Framework for Surfactant EOR Simulation","authors":"B. Samson, M. Shaykhattarov","doi":"10.2118/203978-ms","DOIUrl":"https://doi.org/10.2118/203978-ms","url":null,"abstract":"\u0000 Consistent set of algorithms to calculate phase relative permeability and capillary pressure values in the four-phase representation suitable for surfactant flooding simulation has been derived. The novel formulation resolves difficulties with applying existing three-phase approaches, and it ensures continuity of transport characteristics at solubilization changes in phase composition.","PeriodicalId":11146,"journal":{"name":"Day 1 Tue, October 26, 2021","volume":"98 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77346327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accounting for poro-mechanical effects in full-field reservoir simulation studies and uncertainty quantification workflows is still limited, mainly because of their high computational cost. We introduce a new approach that couples hydrodynamics and poro-mechanics with dual-porosity flow diagnostics to analyse how poro-mechanics could impact reservoir dynamics in naturally fractured reservoirs without significantly increasing computational overhead. Our new poro-mechanical informed dual-porosity flow diagnostics account for steady-state and singlephase flow conditions in the fractured medium while the fracture-matrix fluid exchange is approximated using a physics-based transfer rate constant which models two-phase flow using an analytical solution for spontaneous imbibition or gravity drainage. The deformation of the system is described by the dualporosity poro-elastic theory, which is based on mixture theory and micromechanics to compute the effective stresses and strains of the rock matrix and fractures. The solutions to the fluid flow and rock deformation equations are coupled sequentially. The governing equations for fluid flow are discretised using a finite volume method with two-point flux-approximation while the governing equations for poro- mechanics are discretised using the virtual element method. The solution of the coupled system considers stress-dependent permeabilities for fractures and matrix. Our framework is implemented in the open source MATLAB Reservoir Simulation Toolbox (MRST). We present a case study using a fractured carbonate reservoir analogue to illustrate the integration of poro-mechanics within the dual-porosity flow diagnostics framework. The extended flow diagnostics calculations enable us to quickly screen how the dynamics in fractured reservoirs (e.g. reservoir connectivity, sweep efficiency, fracture-matrix transfer rates) are affected by the complex interactions between poro-mechanics and fluid flow where changes in pore pressure and effective stress modify petrophysical properties and hence impact reservoir dynamics. Due to the steady-state nature of the calculations and the effective coupling strategy, these calculations do not incur significant computational overheads. They hence provide an efficient complement to traditional reservoir simulation and uncertainty quantification workflows as they enable us to assess a broader range of reservoir uncertainties (e.g. geological, petrophysical and hydro-mechanical uncertainties). The capability of studying a much broader range of uncertainties allows the comparison and ranking from a large ensemble of reservoir models and select individual candidates for more detailed full-physics reservoir simulation studies without compromising on assessing the range of uncertainties inherent to fractured reservoirs.
{"title":"A Fast Screening Tool for Assessing the Impact of Poro-Mechanics on Fractured Reservoirs Using Dual-Porosity Flow Diagnostics","authors":"Lesly Gutierrez-Sosa, S. Geiger, F. Doster","doi":"10.2118/203981-ms","DOIUrl":"https://doi.org/10.2118/203981-ms","url":null,"abstract":"\u0000 Accounting for poro-mechanical effects in full-field reservoir simulation studies and uncertainty quantification workflows is still limited, mainly because of their high computational cost. We introduce a new approach that couples hydrodynamics and poro-mechanics with dual-porosity flow diagnostics to analyse how poro-mechanics could impact reservoir dynamics in naturally fractured reservoirs without significantly increasing computational overhead.\u0000 Our new poro-mechanical informed dual-porosity flow diagnostics account for steady-state and singlephase flow conditions in the fractured medium while the fracture-matrix fluid exchange is approximated using a physics-based transfer rate constant which models two-phase flow using an analytical solution for spontaneous imbibition or gravity drainage. The deformation of the system is described by the dualporosity poro-elastic theory, which is based on mixture theory and micromechanics to compute the effective stresses and strains of the rock matrix and fractures. The solutions to the fluid flow and rock deformation equations are coupled sequentially. The governing equations for fluid flow are discretised using a finite volume method with two-point flux-approximation while the governing equations for poro- mechanics are discretised using the virtual element method. The solution of the coupled system considers stress-dependent permeabilities for fractures and matrix. Our framework is implemented in the open source MATLAB Reservoir Simulation Toolbox (MRST).\u0000 We present a case study using a fractured carbonate reservoir analogue to illustrate the integration of poro-mechanics within the dual-porosity flow diagnostics framework. The extended flow diagnostics calculations enable us to quickly screen how the dynamics in fractured reservoirs (e.g. reservoir connectivity, sweep efficiency, fracture-matrix transfer rates) are affected by the complex interactions between poro-mechanics and fluid flow where changes in pore pressure and effective stress modify petrophysical properties and hence impact reservoir dynamics.\u0000 Due to the steady-state nature of the calculations and the effective coupling strategy, these calculations do not incur significant computational overheads. They hence provide an efficient complement to traditional reservoir simulation and uncertainty quantification workflows as they enable us to assess a broader range of reservoir uncertainties (e.g. geological, petrophysical and hydro-mechanical uncertainties). The capability of studying a much broader range of uncertainties allows the comparison and ranking from a large ensemble of reservoir models and select individual candidates for more detailed full-physics reservoir simulation studies without compromising on assessing the range of uncertainties inherent to fractured reservoirs.","PeriodicalId":11146,"journal":{"name":"Day 1 Tue, October 26, 2021","volume":"110 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79257381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An unstructured grid generation method is presented that automates control-volume boundary alignment to geological objects and control point alignment to complex wells. The grid generation method is coupled with an iterative acute mesh reconstruction technique, to construct essentially acute triangulations, while satisfying quite general geometric constraints. For well aligned grids control points are constrained to the well trajectory and protection circles are used, whereas for boundary aligned grids halo construction is performed. Unstructured Delaunay triangulations (DT) have the desirable locally orthogonal perpendicular bisectional (PEBI) property, required by the industry standard two-point flux approximation for consistency for isotropic fields. The PEBI property can only be exploited if such grids are comprised of acute simplexes, with circumcentres located inside their respective elements. The method presented enables acute DT layered mesh generation while honoring internal boundaries and wells in a two dimensional space. A dual (Voronoi) grid derived from a feature honored simplicial mesh is then projected in the vertical direction generating 2.5D PEBI grids. Effectiveness of the method to construct acute PEBI grids honoring geological objects and complex wells is demonstrated by meshing representative reservoir geometries. Numerical results are presented that verify consistency of the two-point flux on the resulting boundary-aligned acute PEBI grids. Development of an unstructured grid generation method which 1) Automates interior boundary alignment, 2) Honors features with respect to control point and/or control volume, and 3) Generates acute PEBI grids for reservoir geometries is presented. A unique workflow is presented to generate boundary aligned acute PEBI grids for complex geometries. Development of boundary aligned grids that honor both geological objects and multilateral complex wells, together with mesh reconstruction to ensure circumcenter containment is presented. Further, 3D PEBI grid generation method which can limit refinement to well perforations and geological objects is also described.
{"title":"Acute PEBI Grid Generation for Reservoir Geometries","authors":"Shahid Manzooor, M. Edwards, A. Dogru","doi":"10.2118/203908-ms","DOIUrl":"https://doi.org/10.2118/203908-ms","url":null,"abstract":"\u0000 An unstructured grid generation method is presented that automates control-volume boundary alignment to geological objects and control point alignment to complex wells. The grid generation method is coupled with an iterative acute mesh reconstruction technique, to construct essentially acute triangulations, while satisfying quite general geometric constraints. For well aligned grids control points are constrained to the well trajectory and protection circles are used, whereas for boundary aligned grids halo construction is performed. Unstructured Delaunay triangulations (DT) have the desirable locally orthogonal perpendicular bisectional (PEBI) property, required by the industry standard two-point flux approximation for consistency for isotropic fields. The PEBI property can only be exploited if such grids are comprised of acute simplexes, with circumcentres located inside their respective elements. The method presented enables acute DT layered mesh generation while honoring internal boundaries and wells in a two dimensional space. A dual (Voronoi) grid derived from a feature honored simplicial mesh is then projected in the vertical direction generating 2.5D PEBI grids. Effectiveness of the method to construct acute PEBI grids honoring geological objects and complex wells is demonstrated by meshing representative reservoir geometries. Numerical results are presented that verify consistency of the two-point flux on the resulting boundary-aligned acute PEBI grids. Development of an unstructured grid generation method which 1) Automates interior boundary alignment, 2) Honors features with respect to control point and/or control volume, and 3) Generates acute PEBI grids for reservoir geometries is presented. A unique workflow is presented to generate boundary aligned acute PEBI grids for complex geometries. Development of boundary aligned grids that honor both geological objects and multilateral complex wells, together with mesh reconstruction to ensure circumcenter containment is presented. Further, 3D PEBI grid generation method which can limit refinement to well perforations and geological objects is also described.","PeriodicalId":11146,"journal":{"name":"Day 1 Tue, October 26, 2021","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88828847","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Due to its simplicity, adaptability, and applicability to various grid formats, the restriction-smoothed basis multiscale method (MsRSB) (Møyne and Lie 2016) has received wide attention and has been extended to various flow problems in porous media. Unlike the standard multiscale methods, MsRSB relies on iterative smoothing to find the multiscale basis functions in an adaptive manner, giving it the ability to naturally adjust to various complex grid orientations often encountered in real-life industrial applications. In this work, we investigate the scalability of MsRSB on various state-of-the-art parallel architectures, including multi-core systems and GPUs. While MsRSB is — like most other multiscale methods — directly amenable to parallelization, the dependence on a smoother to find the basis functions creates unique control- and data-flow patterns. These patterns require careful design and implementation in parallel environments to achieve good scalability. We extend the work on parallel multiscale methods in Manea et al. (2016) and Manea and Almani (2019) to map the MsRSB special kernels to the shared-memory parallel multi-core and GPU architectures. The scalability of our optimized parallel MsRSB implementation is demonstrated using highly heterogeneous 3D problems derived from the SPE10 Benchmark (Christie and Blunt 2001). Those problems range in size from millions to tens of millions of cells. The multi-core implementation is benchmarked on a shared memory multi-core architecture consisting of two packages of Intel's Cascade Lake Xeon® Gold 6246 CPU, while the GPU implementation is benchmarked on a massively parallel architecture consisting of Nvidia Volta V100 GPUs. We compare the multi-core implementation to the GPU implementation for both the setup and solution stages. To the best of our knowledge, this is the first parallel implementation and demonstration of the versatile MsRSB method on the GPU architecture.
限制光滑基多尺度方法(MsRSB) (Møyne and Lie 2016)因其简单、适应性强、适用于各种网格格式而受到广泛关注，并已推广到多孔介质中的各种流动问题。与标准的多尺度方法不同，MsRSB依赖于迭代平滑，以自适应的方式找到多尺度基函数，使其能够自然地适应现实工业应用中经常遇到的各种复杂网格方向。在这项工作中，我们研究了MsRSB在各种最先进的并行架构上的可扩展性，包括多核系统和gpu。虽然MsRSB像大多数其他多尺度方法一样，直接适用于并行化，但依赖于平滑器来查找基函数创建了独特的控制和数据流模式。这些模式需要在并行环境中仔细设计和实现，以获得良好的可伸缩性。我们在Manea等人(2016)和Manea和Almani(2019)中扩展了并行多尺度方法的工作，以将MsRSB特殊内核映射到共享内存并行多核和GPU架构。我们优化的并行MsRSB实现的可扩展性使用源自SPE10基准的高度异构3D问题进行了演示(Christie and Blunt 2001)。这些问题的大小从数百万到数千万个细胞不等。多核实现在共享内存多核架构上进行基准测试，该架构由两个Intel的Cascade Lake Xeon®Gold 6246 CPU组成，而GPU实现在由Nvidia Volta V100 GPU组成的大规模并行架构上进行基准测试。我们在设置和解决方案阶段将多核实现与GPU实现进行了比较。据我们所知，这是GPU架构上通用MsRSB方法的第一个并行实现和演示。
{"title":"A Massively Parallel Restriction-Smoothed Basis Multiscale Solver on Multi-Core and GPU Architectures","authors":"A. Manea","doi":"10.2118/203939-ms","DOIUrl":"https://doi.org/10.2118/203939-ms","url":null,"abstract":"\u0000 Due to its simplicity, adaptability, and applicability to various grid formats, the restriction-smoothed basis multiscale method (MsRSB) (Møyne and Lie 2016) has received wide attention and has been extended to various flow problems in porous media. Unlike the standard multiscale methods, MsRSB relies on iterative smoothing to find the multiscale basis functions in an adaptive manner, giving it the ability to naturally adjust to various complex grid orientations often encountered in real-life industrial applications. In this work, we investigate the scalability of MsRSB on various state-of-the-art parallel architectures, including multi-core systems and GPUs. While MsRSB is — like most other multiscale methods — directly amenable to parallelization, the dependence on a smoother to find the basis functions creates unique control- and data-flow patterns. These patterns require careful design and implementation in parallel environments to achieve good scalability. We extend the work on parallel multiscale methods in Manea et al. (2016) and Manea and Almani (2019) to map the MsRSB special kernels to the shared-memory parallel multi-core and GPU architectures. The scalability of our optimized parallel MsRSB implementation is demonstrated using highly heterogeneous 3D problems derived from the SPE10 Benchmark (Christie and Blunt 2001). Those problems range in size from millions to tens of millions of cells. The multi-core implementation is benchmarked on a shared memory multi-core architecture consisting of two packages of Intel's Cascade Lake Xeon® Gold 6246 CPU, while the GPU implementation is benchmarked on a massively parallel architecture consisting of Nvidia Volta V100 GPUs. We compare the multi-core implementation to the GPU implementation for both the setup and solution stages. To the best of our knowledge, this is the first parallel implementation and demonstration of the versatile MsRSB method on the GPU architecture.","PeriodicalId":11146,"journal":{"name":"Day 1 Tue, October 26, 2021","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87462929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The multi-stage fracture treatments create complex fracture networks with various proppant type, size, and concentration distributed within and along fractures through reservoir rock, where larger size and higher concentrations usually result in higher long-term conductivity. To model the fracture conductivity reduction with depletion, we traditionally use a single monotonic relationship between fracture conductivity and pressure, which is proper for a single proppant concentration but obviously hard to describe the situation in the horizontal wells with complex concentration distributions. This paper is to present a new method to speed-up the calibration process of well performance models with multi-million cells and its two applications in the Wolfcamp reservoir in the Delaware Basin. To study well performance and completion effectiveness of 3000 horizontal wells over University Lands acreage in the Permian Basin, we have built a series of well performance models with complex fracture networks (SPE 189855 and 194367). We have used those models to methodically investigate the drivers of well completion parameters and well spacing on well performance and field development value (URTeC 554). In the process of building multiple robust well performance models, we found out it is hard and time-consuming to calibrate a well performance model with multi-million cells based upon a single correlation between fracture conductivity and pressure. We first modeled the complex fracture networks and fracture conductivity distributions based upon the historical completion pumping data; we then developed multiple correlations to characterize fracture conductivity reduction and closure behaviors with pressure depletion based upon initial fracture conductivities (as the result of proppant type, size, and concentration) and reservoir geomechanical properties. We found out that this method significantly reduced our model calibration time. We then applied our method to multiple case studies in the Permian Basin to test and improve the method. We have thus developed a method to mimic the fracture conductivity reduction and closure behavior in the horizontal wells with complex fracture networks. The paper will layout the theoretical foundation and detail our method to develop the multiple correlations to model fracture conductivity reduction and fracture closure behaviors in the horizontal well performance models in the unconventional reservoirs. We will then show two case studies to illustrate how we have applied our method to speed up the model calibration process. Based upon the multiple applications into our model calibration process, we have concluded that the method is very effective to calibrate the well performance model with complex fracture networks. The method can be used for engineers to simplify and speedup calibrating horizontal well performance models. Therefore, engineers can more effectively build more robust well performance models to optimize
{"title":"A Novel Method to Speedup Calibrating Horizontal Well Performance Model with Multi-Stage Fracturing Treatments and Its Applications in Delaware Basin","authors":"Hongjie Xiong, Sangcheol Yoon, Yu Jiang","doi":"10.2118/203935-ms","DOIUrl":"https://doi.org/10.2118/203935-ms","url":null,"abstract":"\u0000 The multi-stage fracture treatments create complex fracture networks with various proppant type, size, and concentration distributed within and along fractures through reservoir rock, where larger size and higher concentrations usually result in higher long-term conductivity. To model the fracture conductivity reduction with depletion, we traditionally use a single monotonic relationship between fracture conductivity and pressure, which is proper for a single proppant concentration but obviously hard to describe the situation in the horizontal wells with complex concentration distributions. This paper is to present a new method to speed-up the calibration process of well performance models with multi-million cells and its two applications in the Wolfcamp reservoir in the Delaware Basin.\u0000 To study well performance and completion effectiveness of 3000 horizontal wells over University Lands acreage in the Permian Basin, we have built a series of well performance models with complex fracture networks (SPE 189855 and 194367). We have used those models to methodically investigate the drivers of well completion parameters and well spacing on well performance and field development value (URTeC 554). In the process of building multiple robust well performance models, we found out it is hard and time-consuming to calibrate a well performance model with multi-million cells based upon a single correlation between fracture conductivity and pressure.\u0000 We first modeled the complex fracture networks and fracture conductivity distributions based upon the historical completion pumping data; we then developed multiple correlations to characterize fracture conductivity reduction and closure behaviors with pressure depletion based upon initial fracture conductivities (as the result of proppant type, size, and concentration) and reservoir geomechanical properties. We found out that this method significantly reduced our model calibration time. We then applied our method to multiple case studies in the Permian Basin to test and improve the method.\u0000 We have thus developed a method to mimic the fracture conductivity reduction and closure behavior in the horizontal wells with complex fracture networks. The paper will layout the theoretical foundation and detail our method to develop the multiple correlations to model fracture conductivity reduction and fracture closure behaviors in the horizontal well performance models in the unconventional reservoirs. We will then show two case studies to illustrate how we have applied our method to speed up the model calibration process.\u0000 Based upon the multiple applications into our model calibration process, we have concluded that the method is very effective to calibrate the well performance model with complex fracture networks.\u0000 The method can be used for engineers to simplify and speedup calibrating horizontal well performance models. Therefore, engineers can more effectively build more robust well performance models to optimize ","PeriodicalId":11146,"journal":{"name":"Day 1 Tue, October 26, 2021","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87037304","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xiangyu Yu, Cong Wang, Xia Yan, Shihao Wang, Lei Wang, P. Winterfeld, Yushu Wu
Enhanced Geothermal Systems (EGS) are those geothermal reservoirs artificially fractured to create paths for injected low-temperature fluid which is then heated up along the flow path until production for electricity generation. This heat recovery involves three tightly coupled processes: thermal, hydraulic and mechanical which interacts with each other and in turn affects the energy production. The local temperature field would be disturbed by injected fuild, resulting in thermal/poroelastic responses near the hydraulic fractured area which are the dominant factors of fluid flow. In this paper, the three-dimensional (3D) Embedded Discrete Fracture Model (EDFM) was adopted to describe the geometry of the fracture and simulate fluid flow and heat transfer between fractures and the matrix, while mechanics, including displacement of the strong discontinuity (fractures), was solved by the 3D eXtended Finite Element Method (XFEM). With the capability of modeling fractures of arbitrary shapes within a 3D reservoir domain using 3D EDFM-XFEM, a coupled THM model was developed based on the unconditionally stable fixed-stress split sequential-implicit method, where the fluid flow/heat transfer module and mechanics module are solved iteratively until convergence within a time step. Fluid flow/heat transfer and XFEM with internal/external tractions are both validated by comparison with existing simulators. We conducted simulations for two synthetic geothermal reservoir heat recovery cases to investigate the effects of the injection temperature and boundary traction condition on the production temperature and fracture deformation. The results indicate that the fracture aperture and permeability is sensitive to temperature variation and hence impacts the production rate/temperature. Thermal strain might be the dominant factor of rock deformation, especially in the shallow depth where geostress is at a low level.
{"title":"A 3D Coupled Thermal-Hydraulic-Mechanical THM Model Using EDFM and XFEM for Hydraulic-Fracture-Dominated Geothermal Reservoirs","authors":"Xiangyu Yu, Cong Wang, Xia Yan, Shihao Wang, Lei Wang, P. Winterfeld, Yushu Wu","doi":"10.2118/203983-ms","DOIUrl":"https://doi.org/10.2118/203983-ms","url":null,"abstract":"\u0000 Enhanced Geothermal Systems (EGS) are those geothermal reservoirs artificially fractured to create paths for injected low-temperature fluid which is then heated up along the flow path until production for electricity generation. This heat recovery involves three tightly coupled processes: thermal, hydraulic and mechanical which interacts with each other and in turn affects the energy production. The local temperature field would be disturbed by injected fuild, resulting in thermal/poroelastic responses near the hydraulic fractured area which are the dominant factors of fluid flow. In this paper, the three-dimensional (3D) Embedded Discrete Fracture Model (EDFM) was adopted to describe the geometry of the fracture and simulate fluid flow and heat transfer between fractures and the matrix, while mechanics, including displacement of the strong discontinuity (fractures), was solved by the 3D eXtended Finite Element Method (XFEM). With the capability of modeling fractures of arbitrary shapes within a 3D reservoir domain using 3D EDFM-XFEM, a coupled THM model was developed based on the unconditionally stable fixed-stress split sequential-implicit method, where the fluid flow/heat transfer module and mechanics module are solved iteratively until convergence within a time step. Fluid flow/heat transfer and XFEM with internal/external tractions are both validated by comparison with existing simulators. We conducted simulations for two synthetic geothermal reservoir heat recovery cases to investigate the effects of the injection temperature and boundary traction condition on the production temperature and fracture deformation. The results indicate that the fracture aperture and permeability is sensitive to temperature variation and hence impacts the production rate/temperature. Thermal strain might be the dominant factor of rock deformation, especially in the shallow depth where geostress is at a low level.","PeriodicalId":11146,"journal":{"name":"Day 1 Tue, October 26, 2021","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82529061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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