Development and Application of Inverse Model Using Genetic Algorithm for Reservoir Characterization

Sung, Wonyong; Kwon, Sun Il; Huh, Dae Gee; Kim, Hyun Tae; Kim, Se Joon; Lee, Won Suk

doi:10.1080/00908310490438597

Cited by 7 publications

(1 citation statement)

References 13 publications

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“…Among optimization algorithms, a genetic algorithm (GA) has advantage that it is insensitive to initial value and it is possible to search the global minimum value. In petroleum engineering, Sung et al 1 had examined applicability and validity of GA as a optimization algorithm for inverse calculation, and Kwon et al 2 had developed 3D reservoir characterization model using GA, and they successfully performed models to the characterization of heterogeneous reservoir. Romero et al 3,4 had also developed a reservoir characterization model using GA and applied it successfully to the actual field.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Heterogeneity Using Inverse Model Equipped with Parallel Genetic Algorithm

Kwon

Sung

Huh³

et al. 2004

Proceedings of SPE Asia Pacific Oil and Gas Conference and Exhibition

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TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractThis study presents the development of reservoir characterization inverse model equipped with ANN type of ISGA and MSGA parallel processing algorithms. In order to efficiently run the developed model, homogeneous PC-cluster was constructed by connecting four PC which have the same features. The model was adapted ANN to automatically determine optimum GA parameters of operator, number of individuals and operation rate, which is appropriate for the reservoir heterogeneity.By utilizing the developed model in this study, inverse calculation was conducted for the synthetic reservoir system with the aid of an ISGA-PP. As a result, it was found that convergence is stably progressed. In the result of permeability distribution, it shows that low permeable zone in central area for the system studied is appeared to be little different comparing to the result obtained by Kriging method which is using only static data. In the matching result of pressure, maximum relative error of 1.54% was presented at OP-4, and hence, the calculated permeability distribution is thought to be quite reliable. When MSGA-PP was applied to the same reservoir system as ISGA-PP, it converged stably similar to ISGA-PP. The difference between ISGA-PP and MSGA-PP is appeared only at convergence rate and the resulting permeability distribution is very similar to each other.In the evaluation of computing efficiency of ISGA-PP and MSGA-PP against GA-SP, the result shows that the efficiency of parallel processing system is more greater as the number of individuals increases. Also, regardless of the number of individuals, the calculating time in parallel processing system was greatly reduced by 3.6 times comparing to serial processing system of GA-SP. Finally, inverse calculation was carried out with MSGA-PP-ANN. As a result, it converged much more faster than MSGA-PP without having a artificial neural network system. It was expected that the model takes only superior individuals at the beginning obtained by optimum GA operator set, which have greater fitness values.

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Section: Introductionmentioning

confidence: 99%