Numerical studies of a class of linear solvers for fine-scale petroleum reservoir simulation

2016 ◽  
Vol 18 (2-3) ◽  
pp. 93-102 ◽  
Author(s):  
Zheng Li ◽  
Shuhong Wu ◽  
Chen-Song Zhang ◽  
Jinchao Xu ◽  
Chunsheng Feng ◽  
...  
Keyword(s):  
Reservoir Simulation ◽  
Fine Scale ◽  
Petroleum Reservoir ◽  
Linear Solvers ◽  
Numerical Studies

Hybrid Fluid Flow Simulation Combining Full Physics Simulation and Artificial Intelligence

2021 ◽  
Author(s):  
Mokhles Mezghani ◽  
Mustafa AlIbrahim ◽  
Majdi Baddourah
Keyword(s):  
Artificial Intelligence ◽  
Reservoir Simulation ◽  
History Matching ◽  
Hybrid Approach ◽  
Flow Simulation ◽  
Latin Hypercube Sampling ◽  
Simulation Models ◽  
Fine Scale ◽  
Simulation Results ◽  
Optimization Loop

Abstract Reservoir simulation is a key tool for predicting the dynamic behavior of the reservoir and optimizing its development. Fine scale CPU demanding simulation grids are necessary to improve the accuracy of the simulation results. We propose a hybrid modeling approach to minimize the weight of the full physics model by dynamically building and updating an artificial intelligence (AI) based model. The AI model can be used to quickly mimic the full physics (FP) model. The methodology that we propose consists of starting with running the FP model, an associated AI model is systematically updated using the newly performed FP runs. Once the mismatch between the two models is below a predefined cutoff the FP model is switch off and only the AI model is used. The FP model is switched on at the end of the exercise either to confirm the AI model decision and stop the study or to reject this decision (high mismatch between FP and AI model) and upgrade the AI model. The proposed workflow was applied to a synthetic reservoir model, where the objective is to match the average reservoir pressure. For this study, to better account for reservoir heterogeneity, fine scale simulation grid (approximately 50 million cells) is necessary to improve the accuracy of the reservoir simulation results. Reservoir simulation using FP model and 1024 CPUs requires approximately 14 hours. During this history matching exercise, six parameters have been selected to be part of the optimization loop. Therefore, a Latin Hypercube Sampling (LHS) using seven FP runs is used to initiate the hybrid approach and build the first AI model. During history matching, only the AI model is used. At the convergence of the optimization loop, a final FP model run is performed either to confirm the convergence for the FP model or to re iterate the same approach starting from the LHS around the converged solution. The following AI model will be updated using all the FP simulations done in the study. This approach allows the achievement of the history matching with very acceptable quality match, however with much less computational resources and CPU time. CPU intensive, multimillion-cell simulation models are commonly utilized in reservoir development. Completing a reservoir study in acceptable timeframe is a real challenge for such a situation. The development of new concepts/techniques is a real need to successfully complete a reservoir study. The hybrid approach that we are proposing is showing very promising results to handle such a challenge.

Next Generation Reservoir Simulation Using Russian Linear Solvers (Russian)

2006 ◽  
Author(s):  
B.L. Beckner ◽  
A.K. Usadi ◽  
M.B. Ray ◽  
O.V. Diyankov
Keyword(s):  
Reservoir Simulation ◽  
Next Generation ◽  
Linear Solvers

Sequentially Adapted Flow-Based PEBI Grids for Reservoir Simulation

SPE Journal ◽  
2006 ◽  
Vol 11 (03) ◽  
pp. 317-327 ◽  
Author(s):  
Martin Mlacnik ◽  
Louis J. Durlofsky ◽  
Zoltan E. Heinemann
Keyword(s):  
Reservoir Simulation ◽  
Large Body ◽  
Structural Features ◽  
Flow In Porous Media ◽  
Fine Scale ◽  
Reservoir Properties ◽  
Fine Grid ◽  
Grid Coarsening ◽  
Grid Optimization ◽  
High Level

Summary A technique for the sequential generation of perpendicular-bisectional (PEBI) grids adapted to flow information is presented and applied. The procedure includes a fine-scale flow solution, the generation of an initial streamline-isopotential grid, grid optimization, and upscaling. The grid optimization is accomplished through application of a hybrid procedure with gradient and Laplacian smoothing steps, while the upscaling is based on a global-local procedure that makes use of the global solution used in the grid-determination step. The overall procedure is successfully applied to a complex channelized reservoir model involving changing well conditions. The gridding and upscaling procedures presented here may also be suitable for use with other types of structured or unstructured grid systems. Introduction Modern geological and geostatistical tools provide highly detailed descriptions of the spatial variation of reservoir properties, resulting in fine-grid models consisting of 107 to 108 gridblocks. As a consequence of this high level of detail, these models cannot be used directly in numerical reservoir simulators, but need to be coarsened significantly. Coarsening requires the averaging of rock parameters from the fine scale to the coarse scale. This process is referred to as upscaling. For simulation of flow in porous media, the upscaling of permeability is of particular interest. A large body of literature exists on this topic; for a comprehensive review of existing techniques, see Durlofsky (2005). To preserve as much of the geological information of the fine grid as possible, the grid coarsening should not be performed uniformly, but with more refinement in areas that are expected to have large impact on the flow, including structural features, such as faults. Although grid-generation techniques based on purely static, nonflow-based considerations have been shown to produce reasonable results(Garcia et al. 1992), the application of flow-based grids is often preferable. Flow-based grids require the solution of some type of fine-scale problem. They are then constructed by exploiting the information obtained from streamlines (and possibly isopotentials) either directly or indirectly. Depending on the type of grid used, points will be defined as cell vertices or nodes, resulting in either a corner-point geometry or point-distributed grid. Several gridding techniques for reservoir simulation have been introduced along these lines, as we now discuss.

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