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Abstract

Run-time performance of a reservoir simulator is significantly impacted by the selection of the linear solver preconditioner, iterative method and their adjustable parameters. The choice of the best solver algorithm and its optimal parameters is a difficult problem that even the experienced simulator users cannot adequately solve by themselves. The typical user action is to use the default solver settings or a small perturbation of them that are frequently far from optimal and consequently the performance may deteriorate. There has been extensive research to develop automatic performance tuning and self-adaptive solver selection systems. For example Self-Adapting Large-scale Solver Architecture (SALSA) developed at the University of Tennessee requires running of large number of problems to initialize the system before using it. In contrast we propose an adaptive control on-line system to optimize the simulator performance by dynamically adjusting the solver parameters during the simulation. We start with a large set of parameters and quickly choose the best combinations that are continuously adapted during the simulation using the solver runtime performance measurements (e.g. solver CPU time) to guide the search. This software system, called the Intelligent Performance Assistant (IPA), has been successfully integrated into ExxonMobil’s proprietary reservoir simulator and deployed with it worldwide. The system can handle a large number of combinations of solver parameters, currently in the order of 108, and consistently improves run time performance of real simulation models, frequently by 30% or more, compared to the performance with the default solver settings. Moreover, IPA includes a persistent memory of solver performance statistics. The runtime statistics from these individual runs can be gathered, processed using data mining techniques and integrated in the IPA system, thus allowing its continuous improvement.

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/content/papers/10.3997/2214-4609.20146368
2008-09-08
2020-10-01
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.20146368
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