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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" article-type="research-article" dtd-version="1.1d1" xml:lang="kk"><front><journal-meta><journal-id journal-id-type="publisher">Қазақстанның мұнай-газ саласының хабаршысы</journal-id><journal-title-group><journal-title>Қазақстанның мұнай-газ саласының хабаршысы</journal-title></journal-title-group><issn publication-format="print">2707-4226</issn><issn publication-format="electronic">2957-806X</issn><publisher><publisher-name>KMG Engineering</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">108887</article-id><article-id pub-id-type="doi">10.54859/kjogi108887</article-id><article-categories><subj-group subj-group-type="heading"><subject>Review Article</subject></subj-group></article-categories><title-group><article-title>Using Big Data and analytics for forecasting and productivity enhancement in the oil and gas industry</article-title></title-group><contrib-group><contrib contrib-type="author"><name name-style="western"><surname>Seitimbetova</surname><given-names>Aigerim B.</given-names></name><email>sab.buketov.2022@gmail.com</email><uri content-type="orcid">https://orcid.org/0009-0000-8755-7992</uri><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author"><name name-style="western"><surname>Shulgina-Tarashchuk</surname><given-names>Alevtina S.</given-names></name><email>alevtinash79@mail.ru</email><uri content-type="orcid">https://orcid.org/0009-0000-4759-9389</uri><xref ref-type="aff" rid="aff-1"/></contrib><contrib contrib-type="author"><name name-style="western"><surname>Smailova</surname><given-names>Aizhan S.</given-names></name><email>smailova.buketov@gmail.com</email><uri content-type="orcid">https://orcid.org/0000-0003-2936-0336</uri><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff id="aff-1">Karaganda Buketov University</aff><pub-date date-type="epub" iso-8601-date="2026-06-30" publication-format="electronic"><day>30</day><month>06</month><year>2026</year></pub-date><volume>8</volume><issue>2</issue><fpage>48</fpage><lpage>58</lpage><history><pub-date date-type="received" iso-8601-date="2025-06-30"><day>30</day><month>06</month><year>2025</year></pub-date><pub-date date-type="accepted" iso-8601-date="2026-04-07"><day>07</day><month>04</month><year>2026</year></pub-date></history><permissions><copyright-statement>Copyright © 2026, Seitimbetova A.B., Shulgina-Tarashchuk A.S., Smailova A.S.</copyright-statement><copyright-year>2026</copyright-year></permissions><abstract>&lt;p&gt;Amid the digital transformation of the global economy, Big Data analytics and advanced analytics technologies are becoming key tools for enhancing business efficiency and sustainability. Their application is particularly relevant in the capital-intensive and high-risk oil and gas industry, where data-driven decision-making offers significant competitive advantages.&lt;/p&gt;&#13;
&lt;p&gt;This study examines the opportunities and benefits of implementing Big Data and analytical solutions across various stages of the oil and gas production cycle – from geological exploration and drilling to processing and transportation. The study presents the main data sources and types characteristic of the industry, as well as modern analytical methods, including descriptive, predictive, prescriptive, and real-time analytics. Special attention is given to machine learning and artificial intelligence algorithms used for predicting equipment failures, optimizing drilling parameters, and modeling reservoir behavior.&lt;/p&gt;&#13;
&lt;p&gt;Based on the analysis of case studies from leading international companies such as BP, Equinor, Gazprom Neft, and others, it is shown how digital tools can improve decision-making accuracy, reduce operational costs, and minimize technological risks. The study also examines the key challenges hindering the widespread adoption of Big Data in the sector, including a shortage of qualified personnel, integration difficulties between legacy and modern systems, cybersecurity concerns, and the high cost of digital transformation.&lt;/p&gt;&#13;
&lt;p&gt;The analysis leads to the conclusion that data and analytics constitute strategic assets for the future development of the oil and gas industry. Digital technologies open up new horizons in forecasting, management, and sustainable production, paving the way for next-generation intelligent oil and gas enterprises.&lt;/p&gt;</abstract><kwd-group xml:lang="en"><kwd>Big Data</kwd><kwd>analytics</kwd><kwd>oil and gas industry</kwd><kwd>forecasting</kwd><kwd>productivity</kwd><kwd>digitalization</kwd></kwd-group><kwd-group xml:lang="kk"><kwd>Big Data</kwd><kwd>талдау</kwd><kwd>мұнай-газ өнеркәсібі</kwd><kwd>болжау</kwd><kwd>өнімділік</kwd><kwd>цифрландыру</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>Big Data</kwd><kwd>аналитика</kwd><kwd>нефтегазовая промышленность</kwd><kwd>прогнозирование</kwd><kwd>производительность</kwd><kwd>цифровизация</kwd></kwd-group></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>Nguyen T.N., Gosine R.G., Warrian P. A Systematic Review of Big Data Analytics for Oil and Gas Industry 4.0 // IEEE Access. 2020. Vol. 8. P. 61183–61201. doi: 10.1109/ACCESS.2020.2979678.</mixed-citation></ref><ref id="B2"><label>2.</label><mixed-citation>Женис Д.К., Касенов А.К., Ибраев А.Е., Шаяхмет КН. Машинное обучение в системах мониторинга забойного давления в эксплуатационных скважинах: обзор // Вестник нефтегазовой отрасли Казахстана. 2025. Т. 7, №2. С. 61–72. doi: 10.54859/kjogi108797.</mixed-citation></ref><ref id="B3"><label>3.</label><mixed-citation>Хасанов Б.К., Серниязов Ж.М. Анализ снижения продуктивности скважин месторождения Кашаган // Вестник нефтегазовой отрасли Казахстана. 2020. Т. 2, №2. C. 28–33. doi: 10.54859/kjogi95647.</mixed-citation></ref><ref id="B4"><label>4.</label><mixed-citation>Колбикова Е.С. Литофациальный анализ и возможности прогнозирования свойств по данным геофизических исследований и сейсморазведки методами машинного обучения // Вестник нефтегазовой отрасли Казахстана. 2021. Т. 3, №4. C. 32–37. doi: 10.54859/kjogi99690.</mixed-citation></ref><ref id="B5"><label>5.</label><mixed-citation>Дукесова Н.К., Кунжарикова К.М., Бисикенова Л.М., Бектас Г.Ж. Оценка данных PVT и геохимический фингерпринтинг: подходы и результаты // Вестник нефтегазовой отрасли Казахстана. 2025. Т. 7, №1. C. 79–89. doi: 10.54859/kjogi108768.</mixed-citation></ref><ref id="B6"><label>6.</label><mixed-citation>Alrabeh M., Abuzaid A. New Artificial Intelligence and Big Data Analytics Process to Enhance Non Metallic Pipe Deployments in Digital Oil Fields Using Workflows for Disparate Data Sets // Abu Dhabi International Petroleum Exhibition &amp; Conference; November 9–12, 2020; Abu Dhabi, UAE. Available from: onepetro.org/SPEADIP/proceedings-abstract/20ADIP/20ADIP/D041S114R003/452660.</mixed-citation></ref><ref id="B7"><label>7.</label><mixed-citation>Giunta G., Bernasconi G., Giro R.A., Cesari S. Digital Transformation of Historical Data for Advanced Predictive Maintenance // Abu Dhabi International Petroleum Exhibition &amp; Conference; November 9–12, 2020; Abu Dhabi, UAE. Available from: onepetro.org/SPEADIP/proceedings-abstract/20ADIP/20ADIP/D011S019R002/452657.</mixed-citation></ref><ref id="B8"><label>8.</label><mixed-citation>Sletcha B., Vivas C., Saleh F.K., et al. Digital Oilfield: Review of Real time Data flow Architecture for Upstream Oil and Gas Rigs // SPE International Conf. and Exhibition on Formation Damage Control; February 19–21, 2020; Lafayette, USA. Available from: onepetro.org/SPEFD/proceedings-abstract/20FD/20FD/D021S013R005/446223.</mixed-citation></ref><ref id="B9"><label>9.</label><mixed-citation>Mahzari P., Emambakhsh M., Temizel C., Jones A.P. Oil production forecasting using deep learning for shale oil wells under variable gas oil and water oil ratios // Petroleum Science and Technology. 2021. Vol. 39, Issue 3. P. 445–468. doi: 10.1080/10916466.2021.2001526.</mixed-citation></ref><ref id="B10"><label>10.</label><mixed-citation>Gupta I., Samandarli O., Burks A., et al. Autoregressive and Machine Learning Driven Production Forecasting – Midland Basin Case Study // SPE/AAPG/SEG Unconventional Resources Technology Conference; July 2021; Houston, USA. Available from: chooser.crossref.org/?doi=10.15530%2Furtec-2021-5184.</mixed-citation></ref></ref-list></back></article>
