Artificial Intelligence in the Oil and Gas Industry: Applications, Challenges, and Future Directions
Source abstract
Abstract
This study aims to provide a comprehensive overview of the application of artificial intelligence (AI) methods to solve real-world problems in the oil and gas sector. The methodology involved a two-step process for analyzing AI applications. In the first step, an initial exploration of scientific articles in the Scopus database was conducted using keywords related to AI and computational intelligence, resulting in a total of 11,296 articles. The bibliometric analysis conducted using VOS Viewer version 1.6.15 software revealed an average annual growth of approximately 15% in the number of publications related to AI in the sector between 2015 and 2024, indicating the growing importance of this technology. In the second step, the research focused on the OnePetro database, widely used by the oil industry, selecting articles with terms associated with production and drilling, such as “production system”, “hydrate formation”, “machine learning”, “real-time”, and “neural network”. The results highlight the transformative impact of AI on production operations, with key applications including optimizing operations through real-time data analysis, predictive maintenance to anticipate failures, advanced reservoir management through improved modeling, image and video analysis for continuous equipment monitoring, and enhanced safety through immediate risk detection. The bibliometric analysis identified a significant concentration of publications at Society of Petroleum Engineers (SPE) events, which accounted for approximately 40% of the selected articles. Overall, the integration of AI into production operations has driven significant improvements in efficiency and safety, and its continued evolution is expected to advance industry practices further and address emerging challenges.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Reservoir Engineering and Simulation Methods · Oil and Gas Production Techniques · Drilling and Well Engineering
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Engineering Department, Universidade Federal Fluminense, Campus Praia Vermelha, R. Passo da Pátria, 152-470—São Domingos, Niterói 24210-240, RJ, Brazil
Engineering Department, Universidade Federal do Rio de Janeiro, R. Antônio Barros de Castro, 119—Cidade Universitária, Rio de Janeiro 21941-853, RJ, Brazil
Engineering Department, Centro Universitário de Valença, R. Srg. Vitor Hugo, 161—Fatima, Valença 27600-000, RJ, Brazil
Engineering Department, Centro Universitário Augusto Motta, Av. Paris, 84—Bonsucesso, Rio de Janeiro 21041-020, RJ, Brazil
Funding
Brazilian National Council for Scientific and Technological Development (CNPq) · Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)
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