{"id":104814,"date":"2023-12-26T13:55:45","date_gmt":"2023-12-26T10:55:45","guid":{"rendered":"https:\/\/azbuki.bg\/?p=104814"},"modified":"2023-12-26T13:55:45","modified_gmt":"2023-12-26T10:55:45","slug":"development-of-oil-fields-using-science-artificial-intelligence-and-machine-learning","status":"publish","type":"post","link":"https:\/\/pedagogy.azbuki.bg\/en\/uncategorized\/development-of-oil-fields-using-science-artificial-intelligence-and-machine-learning\/","title":{"rendered":"Development of Oil Fields Using Science Artificial Intelligence and Machine Learning"},"content":{"rendered":"

Al-Obaidi S.H.1), Chang W.J.2), Hofmann M.1)<\/strong>
\n1) Department of Petroleum Engineering \u2013 Mining University (Russia)<\/em>
\n2)Department of Petroleum Engineering \u2013 University of Xidian (China)<\/em><\/p>\n

https:\/\/doi.org\/10.53656\/nat2023-3-4.01<\/a><\/p>\n

Abstract.<\/strong> Since artificial intelligence has become increasingly prevalent in the oil industry, it is relevant to this study since it is being used for exploration, development, production, field design, and management planning to improve decision-making, reduce costs, and speed up production. For establishing relationships between complex non-linear datasets, machine learning has proved superior to regression methods in petroleum engineering when it comes to high-dimensional data prediction errors, processing power, and memory. In this article, machine learning is compared with conventional statistical models of oil and gas engineering for determining and predicting reservoir pressure values in the development of oil fields. The effectiveness and potential of machine learning to determine reservoir pressure values was analysed. Using non-parametric multivariate model that link well performance over time, a new method is proposed for predicting reservoir pressure using machine learning. According to the proposed method, the predicted reservoir pressure correlates well with values measured by hydrodynamic studies of wells based on the dynamics of indicators describing well performance. Machine learning method based on random forest algorithm tends to provide better prediction reliability for reservoir pressure than linear regression method (absolute deviation: 0.86; relative deviation: 6.8%).
\nKeywords:<\/em> Machine learning; Oil fields; Reservoir pressure; Prediction; Non-parametric<\/p>\nLog in to read the full text<\/a>","protected":false},"excerpt":{"rendered":"

Al-Obaidi S.H.1), Chang W.J.2), Hofmann M.1) 1) Department of Petroleum Engineering \u2013 Mining University (Russia) 2)Department of Petroleum Engineering \u2013 University of Xidian (China) https:\/\/doi.org\/10.53656\/nat2023-3-4.01 Abstract. Since artificial intelligence has become increasingly prevalent in the oil industry, it is relevant to this study since it is being used for exploration, development, production, field design, and […]<\/p>","protected":false},"author":124332423429233,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jnews-multi-image_gallery":[],"jnews_single_post":{"source_name":"","source_url":"","via_name":"","via_url":"","override_template":"0","override":[{"template":"1","single_blog_custom":"","parallax":"1","fullscreen":"1","layout":"right-sidebar","sidebar":"default-sidebar","second_sidebar":"default-sidebar","sticky_sidebar":"1","share_position":"bottom","share_float_style":"share-monocrhome","show_share_counter":"1","show_view_counter":"1","show_featured":"0","show_post_meta":"1","show_post_author":"0","show_post_author_image":"1","show_post_date":"0","post_date_format":"default","post_date_format_custom":"Y\/m\/d","show_post_category":"1","show_post_reading_time":"1","post_reading_time_wpm":"300","show_zoom_button":"1","zoom_button_out_step":"2","zoom_button_in_step":"3","show_post_tag":"1","show_prev_next_post":"1","show_popup_post":"1","number_popup_post":"3","show_author_box":"0","show_post_related":"0","show_inline_post_related":"0"}],"override_image_size":"0","image_override":[{"single_post_thumbnail_size":"crop-500","single_post_gallery_size":"crop-500"}],"trending_post":"0","trending_post_position":"meta","trending_post_label":"Trending","sponsored_post":"0","sponsored_post_label":"Sponsored by","sponsored_post_name":"","sponsored_post_url":"","sponsored_post_logo_enable":"0","sponsored_post_logo":"","sponsored_post_desc":"","disable_ad":"0"},"jnews_primary_category":{"id":"","hide":""}},"categories":[1],"tags":[],"yoast_head":"\nDevelopment of Oil Fields Using Science Artificial Intelligence and Machine Learning - \u0410\u0437-\u0431\u0443\u043a\u0438<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/bel.azbuki.bg\/en\/uncategorized\/development-of-oil-fields-using-science-artificial-intelligence-and-machine-learning\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Development of Oil Fields Using Science Artificial Intelligence and Machine Learning - \u0410\u0437-\u0431\u0443\u043a\u0438\" \/>\n<meta property=\"og:description\" content=\"Al-Obaidi S.H.1), Chang W.J.2), Hofmann M.1) 1) Department of Petroleum Engineering \u2013 Mining University (Russia) 2)Department of Petroleum Engineering \u2013 University of Xidian (China) https:\/\/doi.org\/10.53656\/nat2023-3-4.01 Abstract. 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