НЕФТЯНАЯ ПРОВИНЦИЯ

Abstract

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Optimization of petroleum reservoir development requires a robust numerical field model that allows for prediction of system response to various field development scenarios. Deterministic reservoir model can be considered reliable enough only once the model has been history matched. That is the model should be able to match field-wide historical production data. History matching (numerical tuning) is the most labor-intensive step of reservoir simulation modeling. History matching is time-consuming and is generally based on trial-and-error procedure. This is the most complicated step in reservoir simulation study. The main limitation for application of these algorithms is computation time required for assessment of objective relationship for each simulation run. The paper considers computer-based system for identification of reservoir numerical model parameters. The paper also looks into the application of general-purpose optimization methods for decision making, analysis of sensitivities and relationships between target values. History matching is presented as the optimization process, i.e. the search for the objective function of discrepancy between estimated (actual) and simulated data followed by minimization of the objective function. Combination of supplementary methods and optimization theory can significantly reduce the time required to history match a model.

Key words:

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optimization, history matching, numerical model, oil reservoirs, natural hydrocarbon systems, uncertainty assessment, evolutionary methods, multi-objective optimization

References

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1. Khisamov R.S., Ibatullin R.R., Nikiforov A.I., Ivanov A.F., Nizaev R.Kh. Teoriya i praktika modelirovaniya razrabotki neftyanykh mestorozhdeniy v razlichnykh geologo-fizicheskikh usloviyakh [Theory and practice of modeling the development of oil fields under various geological and physical conditions]. Kazan: FAN Publ., 2009, 239p. (in Russian)

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5. Escart Zitzler, Lothar Thiele, “Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach”. IEEE Transactions on Evolutionary computation, vol. 3, no. 4, 1999

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Authors

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S.А. Aleksandrov, Junior Research Associate, Reservoir Engineering Department, TatNIPIneft Institute–PJSC TATNEFT

32, Musa Jalil st., Bugulma, 423236, Russian Federation

E-mail: alexandrov_sa@tatnipi.ru

 

R.Kh. Nizaev, Dr.Sc., Associate Professor, Leading Research Associate, Reservoir Engineering Department, TatNIPIneft Institute–PJSC TATNEFT

32, Musa Jalil st., Bugulma, 423236, Russian Federation

E-mail: nizaev@tatnipi.ru

 

M.T. Khannanov, PhD (Geol.), Leading Expert at Petroleum Geology Department – PJSC TATNEFT; Assistant Professor of Geology Chair at Almetyevsk State Oil Institute

75, Lenin Street, Almetyevsk, 423450, Russian Federation

E-mail: geofkhannanov@mail.ru

For citation:

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S.А. Aleksandrov, R.Kh. Nizaev, M.T. Khannanov Mnogocelevaja optimizacija metodami iskusstvennogo intellekta v oblasti plastovogo modelirovanija neftjanyh mestorozhdenij [Multi-objective optimization using artificial intelligence techniques in reservoir modeling]. Neftyanaya Provintsiya, No. 2(26), 2021. pp. 100-115. DOI https://doi.org/10.25689/NP.2021.2.100-115 (in Russian)