Practical aspects of hydraulic fracturing design optimization using machine learning on field data: Digital database, algorithms and planningthe field tests

Viktor Duplyakov, Anton Morozov, Dmitry Popkov, Albert Vainshtein, Andrei Osiptsov, Evgeny Burnaev, Egor Shel, Grigory Paderin, Polina Kabanova, Ildar Fayzullin, Ruslan Uchuev, Albert Mukhametov, Alexander Prutsakov, Ivan Vikhman, Maxim Staritsyn

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

    1 Citation (Scopus)

    Abstract

    The study provides insights into the development of a data-driven model for hydraulic fracturing designoptimization. We make a specific focus on practical aspects of testing the model in the field. Database forhydraulic fracturing treatments is built on the data from 22 oilfields in Western Siberia, Russia. The databasecontains about 5500 points with formation, well and fracturing process parameters, the target feature formodel is a cumulative fluid production for 3 months. System and method for searching offset (similar) wellsis also developed, tested and validated. Authors developed the model for predicting cumulative productionthat is used for futher hydraulic fracturing design optimization.

    Original languageEnglish
    Title of host publicationSociety of Petroleum Engineers - SPE Symposium
    Subtitle of host publicationHydraulic Fracturing in Russia. Experience and Prospects 2020
    PublisherSociety of Petroleum Engineers
    ISBN (Electronic)9781613997925
    Publication statusPublished - 2020
    EventSPE Symposium on Hydraulic Fracturing in Russia: Experience and Prospects, SHF 2020 - Virtual, Online
    Duration: 22 Sep 202024 Sep 2020

    Publication series

    NameSociety of Petroleum Engineers - SPE Symposium: Hydraulic Fracturing in Russia. Experience and Prospects 2020

    Conference

    ConferenceSPE Symposium on Hydraulic Fracturing in Russia: Experience and Prospects, SHF 2020
    CityVirtual, Online
    Period22/09/2024/09/20

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