[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128716-en":3,"doc-seo-128716-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"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},128716,1099523882182,"Eliana","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","Applications of machine learning and artificial intelligence in the oil and gas industry - a study of keywords and research results","The integration of advanced machine learning (ML) and artificial intelligence (AI) techniques in the oil and gas industry is rapidly evolving, targeting improved operational predictions and optimization. The study examines the frequency and interconnectedness of key technology-related terms using network diagrams and academic research outcomes. A broad Scopus database search identifies significant trends, with forecasting and optimization highlighted as central themes. The work also addresses interdisciplinary collaboration needs, along with data quality and infrastructure constraints, while emphasizing benefits for efficiency, cost reduction, and safety, supported by case studies.","Applications of machine learning and artificial intelligence in the oil and gas industry: a study of keywords and research results  \nAplicações de aprendizado de máquina e inteligência artificial no setor de petróleo egás: um estudo de palavras-chave e resultados de pesquisa  \nAplicaciones del aprendizaje automático y la inteligencia artificial en la industriadel petróleo y el gas: un estudio de palabras clave y resultados de investigación  \nDOI: 10.34140/bjbv6n4-066  \nSubmetido: 01/08/2024  \nAprovado: 30/09/2024  \nMarcelo dos Santos Povoas  \nDoutorando em Gestão de Processos de Engenharia  \nUniversidade Federal Fluminense  \nNiterói, RJ. Brasil  \n[marcelopovoas@id.uff.br](marcelopovoas@id.uff.br)  \nJéssica Freire Moreira  \nMestrado em Gestão de Processos de Engenharia  \nUniversidade Federal Fluminense  \nNiterói, RJ. Brasil  \n[jessicafreiremoreira@id.uff.br](jessicafreiremoreira@id.uff.br)  \nGilson Brito Alves Lima  \nDoutor  \nUniversidade Federal Fluminense  \nNiterói, RJ. Brasil  \n[glima@id.uff.br](glima@id.uff.br)  \nSeverino Virgínio Martins Neto  \nGraduando em Engenharia de Produção  \nUniversidade Federal do Rio de Janeiro  \nRio de Janeiro, RJ. Brasil  \n[severino_virginio@poli.ufrj.br](severino_virginio@poli.ufrj.br)  \nABSTRACT  \nThe integration of advanced machine learning (ML) and artificial intelligence (AI) techniques in the oil and gas industry is rapidly evolving, focusing on improving operational predictions and optimizations. This study investigates the frequency and interconnectedness of key terms associated with these technologies through network diagrams and academic research results. An extensive search in the Scopus database revealed significant trends and patterns, highlighting the central role of forecasting and optimization within the industry. The interdisciplinary nature of the research underscores the need for collaboration across various fields to tackle complex challenges. While data quality and infrastructure pose challenges, the potential for enhanced efficiency, reduced costs, and increased safety through AI and ML applications is substantial. Case studies demonstrate the practical benefits, and future advancements promise deeper integration of these technologies into industry operations.  \nKeywords: machine learning, artificial intelligence, oil and gas industry and environment.  \nRESUMO  \nA integração de técnicas avançadas de aprendizado de máquina (ML) e inteligência artificial (IA) no setorde petróleo e gás está evoluindo rapidamente, com foco no aprimoramento das previsões e otimizações operacionais. Este estudo investiga a frequência e a interconexão dos principais termos associados a essas tecnologias por meio de diagramas de rede e resultados de pesquisas acadêmicas. Uma extensa pesquisano banco de dados Scopus revelou tendências e padrões significativos, destacando o papel central da previsão e da otimização no setor. A natureza interdisciplinar da pesquisa ressalta a necessidade decolaboração entre vários campos para enfrentar desafios complexos. Embora a qualidade dos dados e ainfraestrutura representem desafios, o potencial para aumentar a eficiência, reduzir os custos e aumentara segurança por meio de aplicativos de IA e ML é substancial. Os estudos de caso demonstram os benefícios práticos, e os avanços futuros prometem uma integração mais profunda dessas tecnologias nas operações do setor.  \nPalavras-chave: aprendizado de máquina, inteligência artificial, setor de petróleo e gás e meio ambiente.  \nRESUMEN  \nLa integración de técnicas avanzadas de aprendizaje automático (AM) e inteligencia artificial (IA) en laindustria del petróleo y el gas está evolucionando rápidamente, centrándose en la mejora de las predicciones y optimizaciones operativas. Este estudio investiga la frecuencia e interconexión de términos clave asociados a estas tecnologías mediante diagramas de red y resultados de investigaciones académicas. Una búsqueda exhaustiva en la base de datos Scopus reveló tendencias y patro","cbCairsNPnpYN8Mv","https://ap.wps.com/l/cbCairsNPnpYN8Mv","pdf",523951,1,13,"English","en",105,"# Introduction\n## Background and prior studies\n# ABSTRACT\n## Objectives and method\n# Keywords\n## Key terms and themes","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To investigate the frequency and interconnectedness of key terms related to ML and AI in the oil and gas industry, supported by research results and network diagrams.\"},{\"question\":\"How were research trends identified?\",\"answer\":\"An extensive search in the Scopus database was conducted to reveal significant patterns, with forecasting and optimization emphasized as central roles.\"},{\"question\":\"What challenges and benefits does the study discuss?\",\"answer\":\"Challenges include data quality and infrastructure, while the potential benefits include improved efficiency, reduced costs, enhanced safety, and practical gains shown through case studies.\"}]","Applications of machine learning and artificial intelligence in the oil and gas industry - 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