SPE71569 - Society of Petroleum Engineers SPE 71569...

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Society of Petroleum Engineers SPE 71569 Automated Reservoir Model Selection in Well Test Interpretation Barıs . G¨uyag¨uler, SPE, Roland N. Horne, SPE, Stanford University, and Eric Tauzin, SPE, KAPPA Engineering Copyright 2001, Society of Petroleum Engineers, Inc. This paper was prepared for presentation at the SPE Annual Technical Conference and Exhibition, held in New Orleans, Louisiana, U.S.A., 30 September to 3 October, 2001. This paper was selected for presentation by an SPE Program Committee following review of information contained in an abstract submitted by the authors(s). Contents of the paper, as presented, have not been reviewed by the Society of Petroleum Engineers and are sub- ject to correction by the author(s). The material, as presented, does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Papers pre- sented at SPE meetings are subject to publication review by Editorial Committees of the Society of Petroleum Engineers. Permission to copy is restricted to an abstract of not more than 300 words. Illustrations may not be copied. The abstract should contain conspicuous acknowledgement of where and by whom the paper was presented. Write Librarian, SPE, P.O. Box 833836, Richardson, TX 75083-3836, U.S.A., fax 01-214-952-9435. Abstract Technological achievements in the area of well testing, such as permanent downhole gauges, demand automated techniques to cope with the large amounts of data acquired. In such an application, the need to interpret large quantities of data with little human intervention suggests the desirability of automated model recognition. Also in some cases the characteristic be- havior of the pressure or its derivative curves for specific mod- els may be hidden behind noise or human bias may lead to the selection of an invalid or inappropriate model. This paper demonstrates an approach based on Genetic Al- gorithm (GA) that is able to select the most probable reservoir model among a set of candidate models, consistent with a given set of pressure transient data. The type of reservoir model to be used is defined as a variable and is estimated together with the other unknown model parameters (permeability, skin, etc.). Several reservoir models are used simultaneously in the regres- sion process. GA populations consist of individuals that repre- sent parameters for different models. As the GA iterates, indi- viduals that belong to the most likely reservoir model dominate the population, while less likely models become extinct. Since different models may require different numbers of parameters, the solution vectors have varying lengths. The GA is able to cope with such solution vectors of differing size. Information exchange (GA crossover operator) is allowed only between pa- rameters that are physically related.
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SPE71569 - Society of Petroleum Engineers SPE 71569...

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