Day 1 Tue, October 26, 2021最新文献

{"title":"Adding GPU Acceleration to an Industrial CPU-Based Simulator, Development Strategy and Results","authors":"H. Cao, Rustem Zaydullin, Terrence Liao, N. Gohaud, E. Obi, G. Darche","doi":"10.2118/203936-ms","DOIUrl":"https://doi.org/10.2118/203936-ms","url":null,"abstract":"\u0000 Running multi-million cell simulation problems in minutes has been a dream for reservoir engineers for decades. Today, with the advancement of Graphic Processing Unit (GPU), we have a real chance to make this dream a reality. Here we present our experience in the step-by-step transformation of a fully developed industrial CPU-based simulator into a fully functional GPU-based simulator. We also demonstrate significant accelerations achieved through the use of GPU technology.\u0000 To achieve the best performance possible, we choose to use CUDA (NVIDIA GPU’s native language), and offload as much computations to GPU as possible. Our CUDA implementation covers all reservoir computes, which include property calculation, linearization, linear solver, etc. The well and Field Management still reside on CPU and need minor changes for their interaction with GPU-based reservoir. Importantly, there is no change to the nonlinear logic. The GPU and CPU parts are overlapped, fully utilizing the asynchronous nature of GPU operations. Each reservoir computation can be run in three modes, CPU_only (existing one), GPU_only, CPU followed by GPU. The latter is only used for result checking and debugging.\u0000 In early 2019, we prototyped two reservoir linearization operations (mass accumulation and mass flux) in CUDA; both showed very strong runtime speed-up of several hundred times, 1 P100-GPU (NVIDIA) vs 1 POWER8NVL CPU core rated at 2.8 GHz (IBM). Encouraged by this success, we moved into linear solver development and managed to move the entire linear solver module into GPU. Again, strong speed-up of ~50 times was achieved (1 GPU vs 1 CPU). The focus for 2019 has been on standard Black-Oil cases. Our implementation was tested with multiple \"million-cell range\" models (SPE10 and other real field cases). In early 2020, we managed to put SPE10 fully on GPU, and finished the entire 2000 day time-stepping in ~35 sec with a single P100 card. After that our effort has switched to compositional AIM (Adaptive Implicit Method), with focus on compositional flash and AIM implementation for reservoir linearization and linear solver, both show early promising results.\u0000 GPU-based reservoir simulation is a future trend for HPC. The development of a reservoir simulator is complex, multi-discipline and time-consuming work. Our paper demonstrates a clear strategy to add tremendous GPU acceleration into an existing CPU-based simulator. Our approach fully utilizes the strength of the existing CPU simulator and minimizes the GPU development effort. This paper is also the first publication targeting GPU acceleration for compositional AIM models.","PeriodicalId":11146,"journal":{"name":"Day 1 Tue, October 26, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89985867","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}

{"title":"Optimization of Water-Alternating-CO2 Injection Field Operations Using a Machine-Learning-Assisted Workflow","authors":"You Junyu, Ampomah William, Sun Qian","doi":"10.2118/203913-ms","DOIUrl":"https://doi.org/10.2118/203913-ms","url":null,"abstract":"\u0000 This paper will present a robust workflow to address multi-objective optimization (MOO) of CO2-EOR-sequestration projects with a large number of operational control parameters. Farnsworth Unit (FWU) field, a mature oil reservoir undergoing CO2 alternating water injection (CO2-WAG) enhanced oil recovery (EOR), will be used as a field case to validate the proposed optimization protocol. The expected outcome of this work would be a repository of Pareto-optimal solutions of multiple objective functions, including oil recovery, carbon storage volume, and project economics.\u0000 FWU's numerical model is employed to demonstrate the proposed optimization workflow. Since using MOO requires computationally intensive procedures, machine-learning-based proxies are introduced to substitute for the high-fidelity model, thus reducing the total computation overhead. The vector machine regression combined with the Gaussian kernel (Gaussian -SVR) is utilized to construct proxies. An iterative self-adjusting process prepares the training knowledgebase to develop robust proxies and minimizes computational time. The proxies’ hyperparameters will be optimally designed using Bayesian Optimization to achieve better generalization performance. Trained proxies will be coupled with Multi-objective Particle Swarm Optimization (MOPSO) protocol to construct the Pareto-front solution repository.\u0000 The outcomes of this workflow will be a repository containing Pareto-optimal solutions of multiple objectives considered in the CO2-WAG project. The proposed optimization workflow will be compared with another established methodology employing a multi-layer neural network to validate its feasibility in handling MOO with a large number of parameters to control. Optimization parameters used include operational variables that might be used to control the CO2-WAG process, such as the duration of the water/gas injection period, producer bottomhole pressure (BHP) control, and water injection rate of each well included in the numerical model. It is proven that the workflow coupling Gaussian -SVR proxies and the iterative self-adjusting protocol is more computationally efficient. The MOO process is made more rapid by squeezing the size of the required training knowledgebase while maintaining the high accuracy of the optimized results. The outcomes of the optimization study show promising results in successfully establishing the solution repository considering multiple objective functions. Results are also verified by validating the Pareto fronts with simulation results using obtained optimized control parameters. The outcome from this work could provide field operators an opportunity to design a CO2-WAG project using as many inputs as possible from the reservoir models.\u0000 The proposed work introduces a novel concept that couples Gaussian -SVR proxies with a self-adjusting protocol to increase the computational efficiency of the proposed workflow and to guarantee the high accuracy of the obtained","PeriodicalId":11146,"journal":{"name":"Day 1 Tue, October 26, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77588830","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}

{"title":"High-Order Adaptive Scheme for Reactive Transport in Heterogeneous Porous Media","authors":"Ricardo H. Deucher, H. Tchelepi","doi":"10.2118/203972-ms","DOIUrl":"https://doi.org/10.2118/203972-ms","url":null,"abstract":"\u0000 Subsurface sequestration of carbon dioxide, contaminant transport, and enhanced oil recovery processes often involve complex reaction dynamics. The rock-fluid interactions span a very wide range of length and time scales, and it is important for the numerical solutions to resolve these scales properly. To address these challenges, we extend the adaptive transport scheme for the simulation of reactive transport in heterogeneous porous media developed previously (Deucher and Tchelepi, 2021) to account for (a) higher-order approximation of the convective fluxes and (b) coupling with a chemical solver connected to geochemical databases.\u0000 The numerical results demonstrate that adaptivity is more effective when a higher-order approximation of the fluxes is used. This is because of lower levels of numerical dispersion compared with low-order approximations, which helps resolve the displacement fronts more accurately. As a result, the regions that experience significant concentration and saturation gradients are more confined, and that leads to improvements in the computational efficiency of the adaptive scheme. The robustness of the approach is demonstrated using a highly heterogeneous two-phase case with multiple wells and a variable total liquid-rate.\u0000 Due to the modularity of the adaptive scheme, coupling with a chemical solver module is straightforward. The scheme is tested for a three-dimensional case that considers injection of carbonated water in a reservoir matrix of calcite. The results show that the adaptive scheme leads to an accurate representation of the reference concentration distributions of the six reactive components throughout the simulation and leads to a large reduction in the number of cell updates required to achieve the solution.","PeriodicalId":11146,"journal":{"name":"Day 1 Tue, October 26, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88909512","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}

{"title":"A GPU-Based Integrated Simulation Framework for Modelling of Complex Subsurface Applications","authors":"M. Khait, D. Voskov","doi":"10.2118/204000-ms","DOIUrl":"https://doi.org/10.2118/204000-ms","url":null,"abstract":"\u0000 Alternative to CPU computing architectures, such as GPU, continue to evolve increasing the gap in peak memory bandwidth achievable on a conventional workstation or laptop. Such architectures are attractive for reservoir simulation, which performance is generally bounded by system memory bandwidth. However, to harvest the benefit of a new architecture, the source code has to be inevitably rewritten, sometimes almost completely. One of the biggest challenges here is to refactor the Jacobian assembly which typically involves large volumes of code and complex data processing. We demonstrate an effective and general way to simplify the linearization stage extracting complex physics-related computations from the main simulation loop and leaving only an algebraic multi-linear interpolation kernel instead. In this work, we provide the detailed description of simulation performance benefits from execution of the entire nonlinear loop on the GPU platform. We evaluate the computational performance of Delft Advanced Research Terra Simulator (DARTS) for various subsurface applications of practical interest on both CPU and GPU platforms, comparing particular workflow phases including Jacobian assembly and linear system solution with both stages of the Constraint Pressure Residual preconditioner.","PeriodicalId":11146,"journal":{"name":"Day 1 Tue, October 26, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87757233","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}

{"title":"Streamline Tracing and Applications in Dual Porosity Dual Permeability Models","authors":"Tsubasa Onishi, Hongquan Chen, Jiang Xie, Shusei Tanaka, D. Kam, Zhiming Wang, X. Wen, A. Datta-Gupta","doi":"10.2118/203993-ms","DOIUrl":"https://doi.org/10.2118/203993-ms","url":null,"abstract":"Streamline-based methods have proven to be effective for various subsurface flow and transport modeling problems. However, the applications are limited in dual-porosity and dual-permeability (DPDK) system due to the difficulty in describing interactions between matrix and fracture during streamline tracing. In this work, we present a robust streamline tracing algorithm for DPDK models and apply the new algorithm to rate allocation optimization in a waterflood reservoir.\u0000 In the proposed method, streamlines are traced in both fracture and matrix domains. The inter-fluxes between fracture and matrix are described by switching streamlines from one domain to another using a probability computed based on the inter-fluxes. The approach is fundamentally similar to the existing streamline tracing technique and can be utilized in streamline-assisted applications, such as flow diagnostics, history matching, and production optimization.\u0000 The proposed method is benchmarked with a finite-volume based approach where grid-based time-of-flight was obtained by solving the stationary transport equation. We first validated our method using simple examples. Visual time-of-flight comparisons as well as tracer concentration and allocation factors at wells show good agreement. Next, we applied the proposed method to field scale models to demonstrate the robustness. The results show that our method offers reduced numerical artifacts and better represents reservoir heterogeneity and well connectivity with sub-grid resolutions. The proposed method is then used for rate allocation optimization in DPDK models. A streamline-based gradient free algorithm is used to optimize net present value by adjusting both injection and production well rates under operational constraints. The results show that the optimized schedule offers significant improvement in recovery factor, net present value, and sweep efficiency compared to the base scenario using equal rate injection and production. The optimization algorithm is computationally efficient as it requires only a few forward reservoir simulations.","PeriodicalId":11146,"journal":{"name":"Day 1 Tue, October 26, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90393387","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}

{"title":"An Optimization-Based Facility Network Solver for Well Allocation in Reservoir Simulation","authors":"K. Wiegand, Y. Zaretskiy, K. Mukundakrishnan, L. Patacchini","doi":"10.2118/203954-ms","DOIUrl":"https://doi.org/10.2118/203954-ms","url":null,"abstract":"\u0000 When coupling reservoir simulators to surface network solvers, an often used strategy is to perform a rule or priority-driven allocation based on individual well and group constraints, augmented by back-pressure constraints computed periodically by the network solver. The allocation algorithm uses an iteration that applies well-established heuristics in a sequential manner until all constraints are met. The rationale for this approach is simply to maximize performance and simulation throughput; one of its drawbacks is that the computed allocation may not be feasible with respect to the overall network balance, especially in cases where not all wells can be choked individually. In the work presented here, the authors integrate the well allocation process into the network flow solver, in the form of an optimization engine, to ensure that the solution conforms to the network rate and pressure balance equations. Results for three stand-alone test cases are discussed.","PeriodicalId":11146,"journal":{"name":"Day 1 Tue, October 26, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81259692","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}

{"title":"A Physics-Based Proxy for Surface and Subsurface Coupled Simulation Models","authors":"Changdong Yang, Jincong He, S. Du, Zhenzhen Wang, Tsubasa Onishi, X. Guan, Jianping Chen, X. Wen","doi":"10.2118/204004-ms","DOIUrl":"https://doi.org/10.2118/204004-ms","url":null,"abstract":"\u0000 Full-physics subsurface simulation models coupled with surface network can be computationally expensive. In this paper, we propose a physics-based subsurface model proxy that significantly reduces the run-time of the coupled model to enable rapid decision-making for reservoir management.\u0000 In the coupled model the subsurface reservoir simulator generates well inflow performance relationship (IPR) curves which are used by the surface network model to determine well rates that satisfy surface constraints. In the proposed proxy model, the CPU intensive reservoir simulation is replaced with an IPR database constructed from a data pool of one or multiple simulation runs. The IPR database captures well performance that represents subsurface reservoir dynamics. The proxy model can then be used to predict the production performance of new scenarios – for example new drilling sequence – by intelligently looking up the appropriate IPR curves for oil, gas and water phases for each well and solving it with the surface network. All necessary operational events in the surface network and field management logic (such as facility constraints, well conditional shut-in, and group guide rate balancing) for the full-coupled model can be implemented and honored.\u0000 In the proposed proxy model, while the reservoir simulation component is eliminated for efficiency. The entirety of the surface network model is retained, which offers certain advantages. It is particularly suitable for investigating the impact of different surface operations, such as maintenance schedule and production routing changes, with the aim of minimizing production capacity off-line due to maintenance. Replacing the computationally intensive subsurface simulation with the appropriate IPR significantly improves the run time of the coupled model while preserving the essential physics of the reservoir. The accuracy depends on the difference between the scenarios that the proxy is trained on and the scenarios being evaluated. Initial testing with a complex reservoir with more than 300 wells showed the accuracy of the proxy model to be more than 95%. The computation speedup could be an order of magnitude, depending largely on complexity of the surface network model.\u0000 Prior work exists in the literature that uses decline curves to replicate subsurface model performance. The use of the multi-phase IPR database and the intelligent lookup mechanism in the proposed method allows it to be more accurate and flexible in handling complexities such as multi-phase flow and interference in the surface network.","PeriodicalId":11146,"journal":{"name":"Day 1 Tue, October 26, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84855728","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}

{"title":"Modelling Methane Extraction from Stimulated Coalbed Influenced by Multidomain and Thermal Effects","authors":"Wai Li, Jishan Liu, J. Zeng, Y. Leong, D. Elsworth, Jianwei Tian","doi":"10.2118/203990-ms","DOIUrl":"https://doi.org/10.2118/203990-ms","url":null,"abstract":"\u0000 The process of extracting coalbed methane (CBM) is not only of significance for unconventional energy supply but also important in mine safety. The recent advance in fracking techniques, such as carbon dioxide (CO2) fracking, intensifies the complexity of stimulated coalbeds. This work focuses on developing a fully coupled multidomain model to describe and get insight into the process of CBM extraction, particularly from those compound-fractured coalbeds. A group of partial differential equations (PDEs) are derived to characterize gas transport from matrix to fractures and borehole. A stimulated coalbed is defined as an assembly of three interacting porous media: matrix, continuous fractures (CF) and radial primary hydraulic fracture (RF). Matrix and CF constitute a dual-porosity-dual-permeability system, while RF is simplified as an 1-D cracked medium. These media further form three distinct domains: non-stimulated reservoir domain (NSRD), stimulated reservoir domain (SRD) and RF. The effects of coal deformation, heat transfer, and non-thermal sorption are coupled into the model to reflect the multiple processes in CBM extraction. The finite element method is employed to numerically solve the PDEs. The proposed model is verified by comparing its simulation results to a set of well production data from Southern Qinshui Basin in Shanxi Province, China. Great consistency is observed, showing the satisfactory accuracy of the model for CBM extraction. After that, the difference between various stimulation patterns is presented by simulating the CBM extraction process with different stimulation patterns including (1) unstimulated coalbed; (2) double-wing fracture + NSRD; (3) multiple RFs + NSRD; (4) SRD + NSRD and (5) multiple RFs + SRD + NSRD. The results suggest that Pattern (5) (often formed by CO2 fracking) boosts the efficiency of CBM extraction because it generates a complex fracture network at various scales by both increasing the number of radial fractures and activating the micro-fractures in coal blocks. Sensitivity analysis is also performed to understand the influences of key factors on gas extraction from a stimulated coalbed with multiple domains. It is found that the distinct properties of different domains originate various evolutions, which in turn influences the CBM production. Ignoring thermal effects in CBM extraction will either overestimate or underestimate the production, which is the net effect of thermal strain and non-isothermal sorption. The proposed model provides a useful approach to accurately evaluate CBM extraction by taking the complex evolutions of coalbed properties and the interactions between different components and domains into account. The importance of multidomain and thermal effects for CBM reservoir simulation is also highlighted.","PeriodicalId":11146,"journal":{"name":"Day 1 Tue, October 26, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78841948","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}

{"title":"Adaptive Time Stepping, Linearization and a Posteriori Error Control for Multiphase Flow with Wells","authors":"E. Ahmed, Ø. Klemetsdal, X. Raynaud, O. Møyner, H. Nilsen","doi":"10.2118/203974-ms","DOIUrl":"https://doi.org/10.2118/203974-ms","url":null,"abstract":"We present in this paper a-posteriori error estimators for multiphase flow with singular well sources. The estimators are fully and locally computable, distinguish the various error components, and target the singular effects of wells. On the basis of these estimators we design an adaptive fully-implicit solver that yields optimal nonlinear iterations and efficient time-stepping, while maintaining the accuracy of the solution. A key point is that the singular nature of the solution in the near-well region is explicitly captured and efficiently estimated using the adequate norms. Numerical experiments illustrate the efficiency of our estimates and the performance of the adaptive algorithm.","PeriodicalId":11146,"journal":{"name":"Day 1 Tue, October 26, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82534341","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}

{"title":"A Massively Parallel Reservoir Simulator on the GPU Architecture","authors":"Usuf Middya, A. Manea, Alhubail Maitham Makki, Todd R. Ferguson, T. Byer, A. Dogru","doi":"10.2118/203918-ms","DOIUrl":"https://doi.org/10.2118/203918-ms","url":null,"abstract":"\u0000 Reservoir simulation computational costs have been continuously growing due to high-resolution reservoir characterization, increasing model complexity, and uncertainty analysis workflows. Reducing simulation costs by upscaling is often necessary for operational requirements. Fast evolving HPC technologies offer opportunities to reduce cost without compromising fidelity.\u0000 This work presents a novel in-house massively parallel full-physics reservoir simulator running on the emerging GPU architecture. Almost all the simulation kernels have been designed and implemented to honor the GPU SIMD programming paradigm. These kernels include physical property calculations, phase equilibrium computations, Jacobian construction, linear and nonlinear solvers, and wells. Novel techniques are devised in various kernels to expose enough parallelism to ensure that the control and data-flow patterns are well suited for the GPU environment. Mixed-precision computation is also employed when appropriate (e.g., in derivative calculation) to reduce computational costs without compromising the solution accuracy.\u0000 The GPU implementation of the simulator is tested and benchmarked using various reservoir models, ranging from the synthetic SPE10 Benchmark (Christie & Blunt, 2001) to several industrial-scale models. These real field models range in size from tens of millions of cells to more than billion cells with black-oil and multicomponent compositional fluid. The GPU simulator is benchmarked on the IBM AC922 massively parallel architecture having tens of NVidia Volta V100 GPUs. To compare performance with CPU architectures, an optimized CPU implementation of the simulator is benchmarked on the IBM AC922 CPUs and on a cluster consisting of thousands of Intel's Haswell-EP Xeon® CPU E5-2680 v3. Detailed analysis of several numerical experiments comparing the simulator performance on the GPU and the CPU architectures is presented. In almost all of the cases, the analysis shows that the use of hardware acceleration offers substantial benefits in terms of wall time and power consumption.\u0000 This novel in-house full-physics, black-oil and compositional reservoir simulator employs several novel techniques in various simulation kernels to ensure full utilization of the GPU resources. Detailed analysis is presented to highlight the simulator performance in terms of runtime reduction, parallel scalability and power savings.","PeriodicalId":11146,"journal":{"name":"Day 1 Tue, October 26, 2021","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87979323","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}