Use of MASTER Web to Improve History Matching (2000)

Use of MASTER Web to Improve History Matching Shih-Hsien Chang; Shih-Hsien Chang New Mexico Petroleum Recovery Research Center Search for other works by this author on: This Site Google Scholar Reid B. Grigg; Reid B. Grigg New Mexico Petroleum Recovery Research Center Search for other works by this author on: This Site Google Scholar Andrew H. Sung Andrew H. Sung New Mexico Institute of Mining and Technology Search for other works by this author on: This Site Google Scholar Paper presented at the SPE/AAPG Western Regional Meeting, Long Beach, California, June 2000. Paper Number: SPE-62617-MS https://doi.org/10.2118/62617-MS Published: June 19 2000 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Chang, Shih-Hsien, Grigg, Reid B., and Andrew H. Sung. "Use of MASTER Web to Improve History Matching." Paper presented at the SPE/AAPG Western Regional Meeting, Long Beach, California, June 2000. doi: https://doi.org/10.2118/62617-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Western Regional Meeting Search Advanced Search AbstractHistory matching is a long-standing problem in reservoir simulation. Although automatic history matching is a difficult process due to the large number of variables and high degrees of uncertainty, it is possible to develop methodologies to improve the matching process. This paper presents MASTER Web, a software tool for configuring a web-based parallel computing system on a cluster of PCs to support a new approach for history matching. A simple, Java-based software has been implemented to connect/communicate heterogeneous (different hardware and operating system) PCs for carrying out the matching process. MASTER Web, a loosely coupled parallel system (with no shared memory) configured in a master-slaves fashion, is sufficient for history matching since there is no need for inter-slave-processor communication. Utilizing parallel computing across the network (the Web), this novel technique not only aims to improve history matching, but does it economically on clusters of ordinary PCs, making it affordable for smaller oil companies. There are a great number of applications of this technique in reservoir simulation. Every application that requires a large number of repeated simulation runs can be improved with MASTER Web. The results we obtained are highly encouraging and indicate the great potential of our approach. Our experiments, carried out with a small cluster of PCs, have achieved a significant practical speedup in terms of wall clock time.IntroductionDue to the great advancement of computers in the last decade, reservoir simulation has become a very common and important tool for reservoir management. Reservoir simulation is very useful in matching production history, which is an important step in reservoir characterization and production forecast. Reliable forecast can only be obtained if dynamic field data such as pressure and production data are honored. Since the available information in a reservoir study is rarely sufficient to construct a simulation model that exactly represents field reality, it is necessary to adjust some reservoir properties in the model to reproduce past field production performance. This process is called history matching. It is an inverse modeling process, where the results (field data) are known and the input parameters (reservoir properties) are unknown.The challenging nature of history matching can be attributed to two compounding factors:Inadequate computing power. In view of the size of common simulation models for history matching, even supercomputers may be hard-pressed for the task. Smaller oil companies who can use the considerable benefit of history matching usually cannot afford supercomputers; and it would be, therefore, highly desirable to develop methodologies for history matching that can be executed on, say, clusters of ordinary PCs.Intensive human intervention. History matching is usually a trail-and-error process. In this process, a simulation expert would perform the following steps: check the output of each run, adjust the input parameters, executethe next run, and repeat the steps until a satisfactory match is obtained. The whole process is very slow due to the high number of variables and degree of uncertainty.The complexity of the history matching process has led most people to believe that automatic history matching is neither viable nor feasible. However, hardware and software advancements, combined with recent developments of soft computing techniques, can be utilized to accelerate the history matching process, making it considerably faster and reliable. Although completely automated history matching has yet to be implemented, semi-automation is readily achievable. Keywords: adjustable parameter, history matching, input parameter, simulated production curve, society of petroleum engineers, history, production history, artificial intelligence, 6-pc cluster, machine learning Subjects: Reservoir Simulation, Information Management and Systems, History matching, Artificial intelligence This content is only available via PDF. 2000. 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