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    <journal-meta>
      <journal-id journal-id-type="issn">2411-3336</journal-id>
      <journal-id journal-id-type="eissn">2541-9404</journal-id>
      <journal-title-group>
        <journal-title xml:lang="ru">Записки Горного института</journal-title>
        <journal-title xml:lang="en">Journal of Mining Institute</journal-title>
      </journal-title-group>
      <publisher>
        <publisher-name xml:lang="ru">Санкт-Петербургский горный университет императрицы Екатерины ΙΙ</publisher-name>
        <publisher-name xml:lang="en">Empress Catherine II Saint Petersburg Mining University</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.31897/PMI.2022.103</article-id>
      <article-id custom-type="pmi" pub-id-type="custom">pmi-14930</article-id>
      <article-id pub-id-type="uri">https://pmi.spmi.ru/pmi/article/view/14930</article-id>
      <article-categories/>
      <title-group>
        <article-title xml:lang="en">Reproduction of reservoir pressure by machine learning methods and study  of its influence on the cracks formation process in hydraulic fracturing</article-title>
        <trans-title-group xml:lang="ru">
          <trans-title>Воспроизведение пластового давления методами машинного обучения  и исследование его влияния на процесс образования трещин  при гидравлическом разрыве пласта</trans-title>
        </trans-title-group>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name name-style="eastern">
            <surname>Filippov</surname>
            <given-names>Еvgenii V.</given-names>
          </name>
          <name-alternatives>
            <name name-style="eastern" xml:lang="ru">
              <surname>Филиппов</surname>
              <given-names>Е. В.</given-names>
            </name>
            <name name-style="western" xml:lang="en">
              <surname>Filippov</surname>
              <given-names>Еvgenii V.</given-names>
            </name>
          </name-alternatives>
          <email>martyushevd@inbox.ru</email>
          <contrib-id contrib-id-type="orcid">0000-0002-3211-5430</contrib-id>
          <xref ref-type="aff" rid="aff1"/>
        </contrib>
        <aff-alternatives id="aff1">
          <aff>
            <institution xml:lang="ru">ООО «ЛУКОЙЛ-ПЕРМЬ» (Россия)</institution>
          </aff>
          <aff>
            <institution xml:lang="en">OOO “LUKOIL-PERM” (Russia)</institution>
          </aff>
        </aff-alternatives>
        <contrib contrib-type="author">
          <name name-style="eastern">
            <surname>Zakharov</surname>
            <given-names>Lev A.</given-names>
          </name>
          <name-alternatives>
            <name name-style="eastern" xml:lang="ru">
              <surname>Захаров</surname>
              <given-names>Л. А.</given-names>
            </name>
            <name name-style="western" xml:lang="en">
              <surname>Zakharov</surname>
              <given-names>Lev A.</given-names>
            </name>
          </name-alternatives>
          <email>martyushevd@inbox.ru</email>
          <contrib-id contrib-id-type="orcid">0000-0002-8680-3474</contrib-id>
          <xref ref-type="aff" rid="aff2"/>
        </contrib>
        <aff-alternatives id="aff2">
          <aff>
            <institution xml:lang="ru">Филиал ООО «ЛУКОЙЛ-Инжиниринг» (Россия)</institution>
          </aff>
          <aff>
            <institution xml:lang="en">Branch of OOO “LUKOIL-Engineering” (Russia)</institution>
          </aff>
        </aff-alternatives>
        <contrib contrib-type="author" corresp="yes">
          <name name-style="eastern">
            <surname>Martyushev</surname>
            <given-names>Dmitrii A.</given-names>
          </name>
          <name-alternatives>
            <name name-style="eastern" xml:lang="ru">
              <surname>Мартюшев</surname>
              <given-names>Д. А.</given-names>
            </name>
            <name name-style="western" xml:lang="en">
              <surname>Martyushev</surname>
              <given-names>Dmitrii A.</given-names>
            </name>
          </name-alternatives>
          <email>martyushevd@inbox.ru</email>
          <contrib-id contrib-id-type="orcid">0000-0002-5745-4375</contrib-id>
          <xref ref-type="aff" rid="aff3"/>
        </contrib>
        <aff-alternatives id="aff3">
          <aff>
            <institution xml:lang="ru">Пермский национальный исследовательский политехнический университет (Россия)</institution>
          </aff>
          <aff>
            <institution xml:lang="en">Perm National Research Polytechnic University (Russia)</institution>
          </aff>
        </aff-alternatives>
        <contrib contrib-type="author">
          <name name-style="eastern">
            <surname>Ponomareva</surname>
            <given-names>Inna N.</given-names>
          </name>
          <name-alternatives>
            <name name-style="eastern" xml:lang="ru">
              <surname>Пономарева</surname>
              <given-names>И. Н.</given-names>
            </name>
            <name name-style="western" xml:lang="en">
              <surname>Ponomareva</surname>
              <given-names>Inna N.</given-names>
            </name>
          </name-alternatives>
          <email>martyushevd@inbox.ru</email>
          <contrib-id contrib-id-type="orcid">0000-0003-0546-2506</contrib-id>
          <xref ref-type="aff" rid="aff4"/>
        </contrib>
        <aff-alternatives id="aff4">
          <aff>
            <institution xml:lang="ru">Пермский национальный исследовательский политехнический университет (Россия)</institution>
          </aff>
          <aff>
            <institution xml:lang="en">Perm National Research Polytechnic University (Russia)</institution>
          </aff>
        </aff-alternatives>
      </contrib-group>
      <pub-date pub-type="epub" iso-8601-date="2022-12-29">
        <day>29</day>
        <month>12</month>
        <year>2022</year>
      </pub-date>
      <pub-date date-type="collection">
        <year>2022</year>
      </pub-date>
      <volume>258</volume>
      <fpage>924</fpage>
      <lpage>932</lpage>
      <history>
        <date date-type="received" iso-8601-date="2021-05-13">
          <day>13</day>
          <month>05</month>
          <year>2021</year>
        </date>
        <date date-type="accepted" iso-8601-date="2022-11-28">
          <day>28</day>
          <month>11</month>
          <year>2022</year>
        </date>
        <date date-type="rev-recd" iso-8601-date="2022-12-29">
          <day>29</day>
          <month>12</month>
          <year>2022</year>
        </date>
      </history>
      <permissions>
        <copyright-statement xml:lang="ru">© 2022 Е. В. Филиппов, Л. А. Захаров, Д. А. Мартюшев, И. Н. Пономарева</copyright-statement>
        <copyright-statement xml:lang="en">© 2022 Еvgenii V. Filippov, Lev A. Zakharov, Dmitrii A. Martyushev, Inna N. Ponomareva</copyright-statement>
        <copyright-year>2022</copyright-year>
        <copyright-holder xml:lang="ru">Е. В. Филиппов, Л. А. Захаров, Д. А. Мартюшев, И. Н. Пономарева</copyright-holder>
        <copyright-holder xml:lang="en">Еvgenii V. Filippov, Lev A. Zakharov, Dmitrii A. Martyushev, Inna N. Ponomareva</copyright-holder>
        <license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/4.0" xml:lang="ru">
          <license-p>Эта статья доступна по лицензии Creative Commons Attribution 4.0 International (CC BY 4.0)</license-p>
        </license>
        <license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/4.0" xml:lang="en">
          <license-p>This article is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0)</license-p>
        </license>
      </permissions>
      <self-uri xlink:type="simple" xlink:href="https://pmi.spmi.ru/pmi/article/view/14930">https://pmi.spmi.ru/pmi/article/view/14930</self-uri>
      <abstract xml:lang="ru">
        <p>Гидравлический разрыв пласта является эффективным способом интенсификации добычи нефти, который в настоящее время широко применяется в разных условиях, в том числе в сложнопостроенных карбонатных коллекторах. В условиях рассматриваемого месторождения проведение гидравлического разрыва пласта приводит к значительной дифференциации показателей технологической эффективности, что обуславливает целесообразность детального изучения закономерностей трещинообразования. По всем скважинам – объектам воздействия выполнена оценка пространственной ориентации образовавшихся трещин с помощью разработанной косвенной методики, достоверность которой подтверждена геофизическими методами. В ходе анализа установлено, что во всех случаях трещина ориентирована в направлении участка элемента системы разработки, характеризующегося максимальным пластовым давлением. При этом значения пластового давления по всем скважинам определены на один момент времени (на начало гидроразрыва пласта) с использованием методов машинного обучения. Достоверность используемых методов машинного обучения подтверждена высокой сходимостью с фактическими (историческими) пластовыми давлениями, полученными при гидродинамических исследованиях скважин. Полученный вывод о влиянии величины пластового давления на закономерности трещинообразования следует учитывать при планировании гидравлического разрыва пласта в рассматриваемых условиях.</p>
      </abstract>
      <abstract xml:lang="en">
        <p>Hydraulic fracturing is an effective way to stimulate oil production, which is currently widely used in various conditions, including complex carbonate reservoirs. In the conditions of the considered field, hydraulic fracturing leads to a significant differentiation of technological efficiency indicators, which makes it expedient to study in detail the crack formation patterns. For all affected wells, the assessment of the resulting fractures spatial orientation was performed using the developed indirect technique, the reliability of which was confirmed by geophysical methods. In the course of the analysis, it was found that in all cases the fracture is oriented in the direction of the development system element area, which is characterized by the maximum reservoir pressure. At the same time, reservoir pressure values for all wells were determined at one point in time (at the beginning of hydraulic fracturing) using machine learning methods. The reliability of the used machine learning methods is confirmed by high convergence with the actual (historical) reservoir pressures obtained during hydrodynamic studies of wells. The obtained conclusion about the influence of the formation pressure on the patterns of fracturing should be taken into account when planning hydraulic fracturing in the considered conditions.</p>
      </abstract>
      <kwd-group xml:lang="ru">
        <title>Ключевые слова</title>
        <kwd>гидравлический разрыв пласта</kwd>
        <kwd>случайный лес</kwd>
        <kwd>дебиты жидкости</kwd>
        <kwd>пластовое давление</kwd>
        <kwd>искусственный интеллект</kwd>
        <kwd>нейронная сеть</kwd>
        <kwd>изменение коэффициента корреляции</kwd>
      </kwd-group>
      <kwd-group xml:lang="en">
        <title>Keywords</title>
        <kwd>hydraulic fracturing</kwd>
        <kwd>random forest</kwd>
        <kwd>liquid flow rates</kwd>
        <kwd>reservoir pressure</kwd>
        <kwd>artificial intelligence</kwd>
        <kwd>neural network</kwd>
        <kwd>change in correlation coefficient</kwd>
      </kwd-group>
    </article-meta>
  </front>
  <body/>
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