Нейросетевые методы снижения размерности для создания гибриднойгидродинамической модели залежи углеводородов

Translated title of the contribution: Reduced order reservoir simulation with neural-network based hybrid model

Pavel Temirchev, Anna Gubanova, Ruslan Kostoev, Anton Gryzlov, Dmitry Voloskov, Dmitry Koroteev, Maxim Simonov, Alexey Akhmetov, Andrey Margarit, Alexander Ershov

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

    4 Citations (Scopus)

    Abstract

    This paper considers the development of a computationally fast model for simulation of multiphase flow in porous media for a heterogeneous reservoir with the unlimited number of wells characterized by a different type of completion. This fast solution has been obtained by means of replacing the differential equation governing the flow in porous media by approximate governing equations which are parametrized by convolutional neural networks. The matching of the dynamic properties of the original and reduced models is ensured by conservation of spatial invariance property of the equations. The suggested approach is characterized by the minimal number of limitations and shortcomings related to geological-hydrodynamical structure and size of the original model. Also, there is no necessity of additional model training for reservoirs not included in a training dataset. Suggested approach has been evaluated on the synthetic benchmark test model SPE10, where a significant decrease in computational time has been demonstrated comparing to a traditional commercial reservoir simulator. Based on the results of all demonstrated test case scenarios, it could be noted that hybrid hydrodynamic modeling leads to a significant reduction in computational cost (by a factor of few hundreds), maintaining at the same time required accuracy of calculations.

    Translated title of the contributionReduced order reservoir simulation with neural-network based hybrid model
    Original languageRussian
    Title of host publicationSociety of Petroleum Engineers - SPE Russian Petroleum Technology Conference 2019, RPTC 2019
    PublisherSociety of Petroleum Engineers
    ISBN (Electronic)9781613996928
    Publication statusPublished - 2020
    EventSPE Russian Petroleum Technology Conference 2019, RPTC 2019 - Moscow, Russian Federation
    Duration: 22 Oct 201924 Oct 2019

    Publication series

    NameSociety of Petroleum Engineers - SPE Russian Petroleum Technology Conference 2019, RPTC 2019

    Conference

    ConferenceSPE Russian Petroleum Technology Conference 2019, RPTC 2019
    Country/TerritoryRussian Federation
    CityMoscow
    Period22/10/1924/10/19

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