Artificial Neural Network Surrogate Modeling of Oil Reservoir: A Case Study

Oleg Sudakov, Dmitri Koroteev, Boris Belozerov, Evgeny Burnaev

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    8 Citations (Scopus)


    We develop a data-driven model, introducing recent advances in machine learning to reservoir simulation. We use a conventional reservoir modeling tool to generate training set and a special ensemble of artificial neural networks (ANNs) to build a predictive model. The ANN-based model allows to reproduce the time dependence of fluids and pressure distribution within the computational cells of the reservoir model. We compare the performance of the ANN-based model with conventional reservoir modeling and illustrate that ANN-based model (1) is able to capture all the output parameters of the conventional model with very high accuracy and (2) demonstrate much higher computational performance. We finally elaborate on further options for research and developments within the area of reservoir modeling.

    Original languageEnglish
    Title of host publicationAdvances in Neural Networks – ISNN 2019 - 16th International Symposium on Neural Networks, ISNN 2019, Proceedings
    EditorsHuchuan Lu, Huajin Tang, Zhanshan Wang
    PublisherSpringer Verlag
    Number of pages10
    ISBN (Print)9783030228071
    Publication statusPublished - 2019
    Event16th International Symposium on Neural Networks, ISNN 2019 - Moscow, Russian Federation
    Duration: 10 Jul 201912 Jul 2019

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume11555 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference16th International Symposium on Neural Networks, ISNN 2019
    Country/TerritoryRussian Federation


    • Artificial neural networks
    • Machine learning
    • Reservoir modeling
    • Surrogate modeling


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