[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118014-en":3,"doc-seo-118014-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118014,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Machine Learning in Oil and Gas Exploration - A Review","A comprehensive assessment reviews machine learning applications aimed at shaping AI trends in the oil and gas industry, with emphasis on geological and geophysical exploration and reservoir characterization. Key topics include seismic data processing, facies and lithofacies classification, and prediction of petrophysical properties such as porosity, permeability, and water saturation, where accuracy remains difficult. The review analyzes barriers related to subsurface uncertainty, scale mismatch, and temporal-spatial data complexity, proposes solutions and practices for improved accuracy, and outlines future research directions. Robust machine learning and data management are positioned as crucial for operational efficiency amid rapidly growing data volumes, while recognizing inherent limitations versus traditional empirical methods.","Received 13 December 2023, accepted 22 December 2023, date of publication 1 February 2024, date of current version 8 February 2024. Digital Object Identifier 10.1109/ACCESS.2023.3349216  \nMachine Learning in Oil and Gas Exploration: A Review  \nAHMAD LAWAL1, YINGJIE YANG1, HONGMEI HE2, AND NATHANAEL L. BAISA 1  \n1 School of Computer Science and Informatics, De Montfort University, LE1 9BH Leicester, U.K.  \n2 School of Science, Engineering and Environment, University of Salford, M5 4WT Manchester, U.K.  \nCorresponding author: Ahmad Lawal ([ahmad.lawal@dmu.ac.uk](ahmad.lawal@dmu.ac.uk))  \nABSTRACT A comprehensive assessment of machine learning applications is conducted to identify the developing trends for Artificial Intelligence (AI) applications in the oil and gas sector, specifically focusing on geological and geophysical exploration and reservoir characterization. Critical areas, such as seismic data processing, facies and lithofacies classification, and the prediction of essential petrophysical properties (e.g., porosity, permeability, and water saturation), are explored. Despite the vital role of these properties in resource assessment, accurate prediction remains challenging. This paper offers a detailed overview of machine learning’s involvement in seismic data processing, facies classification, and reservoir property prediction. It highlights its potential to address various oil and gas exploration challenges, including predictive modelling, classification, and clustering tasks. Furthermore, the review identifies unique barriers hindering the widespread application of machine learning in the exploration, including uncertainties in subsurface parameters, scale discrepancies, and handling temporal and spatial data complexity. It proposes potential solutions, identifies practices contributing to achieving optimal accuracy, and outlines future research directions, providing a nuanced understanding of the field’s dynamics. Adopting machine learning and robust data management methods is crucial for enhancing operational efficiency in an era marked by extensive data generation. While acknowledging the inherent limitations of these approaches, they surpass the constraints of traditional empirical and analytical methods, establishing themselves as versatile tools for addressing industrial challenges. This comprehensive review serves as an invaluable resource for researchers venturing into less-charted territories in this evolving field, offering valuable insights and guidance for future research.  \nINDEX TERMS Oil and gas exploration, machine learning, petrophysical properties prediction, facies and lithofacies classification, seismic data processing.  \nI. INTRODUCTION  \nThe oil and gas industry is a sophisticated sector that combines many complex activities in its value chain broadly segmented into Upstream, Midstream, and Downstream, as illustrated in Fig. 1. In any industry operation, an unprecedented amount of data can be generated from the equipment involved and routine human logs. The Upstream segment, which concerns the exploration and production of oil and natural gas, produces data such as geological surveys, well logs, and readings from drilling equipment. This segment  \nThe associate editor coordinating the review of this manuscript and approving it for publication was Sotirios Goudos .  \nis also expected to generate significantly higher volumes of data with improvements in seismic acquisition devices, channel counting, and fluid front monitoring geophones [1] . The midstream segment involves transporting and storing crude oil and natural gas using pipelines and their associated infrastructure such as pumping stations and, tank trucks, etc. All these enable the generation of large volumes of data. The downstream segment involves turning crude oil and natural gas into finished products and marketing them accordingly. This involves generating and analyzing large amounts of data for competitive advantage and cost reductio","cbCais05gfnOjgzx","https://ap.wps.com/l/cbCais05gfnOjgzx","pdf",2589449,1,24,"English","en",105,"# Introduction\n## Data generation across upstream, midstream, and downstream\n## Oil and gas exploration objectives and common procedures\n## Seismic surveys and subsurface interpretation","[{\"question\":\"Which parts of the oil and gas exploration pipeline does the review focus on?\",\"answer\":\"The review focuses on geological and geophysical exploration and reservoir characterization, highlighting tasks such as seismic data processing, facies/lithofacies classification, and reservoir property prediction.\"},{\"question\":\"What petrophysical properties are discussed for machine learning prediction?\",\"answer\":\"It discusses predicting essential petrophysical properties including porosity, permeability, and water saturation.\"},{\"question\":\"Why is widespread machine learning adoption in exploration still challenging?\",\"answer\":\"Barriers include uncertainties in subsurface parameters, discrepancies in scale, and the complexity of handling temporal and spatial data.\"}]","Machine Learning in Oil and Gas Exploration - 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