Society of Petroleum Engineers
Automated Reservoir Model Selection in Well Test Interpretation
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.
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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.