review · Engineering Science & Technology Journal
The oil and gas industry plays a pivotal role in global energy supply but faces increasing pressure to enhance sustainability amidst environmental concerns and economic constraints. This comprehensive review explores the integration of artificial intelligence (AI) in optimizing oil and gas production processes to achieve sustainability goals. The paper examines various AI-driven optimization techniques, including machine learning algorithms, genetic algorithms, and neural networks, and their application in different stages of oil and gas production, such as exploration, drilling, production, and distribution. By leveraging AI, operators can improve efficiency, reduce environmental impact, and maximize resource recovery. Furthermore, the review delves into specific case studies and implementations of AI-driven optimization in real-world oil and gas operations, highlighting their efficacy in minimizing greenhouse gas emissions, optimizing water usage, and mitigating operational risks. Additionally, the paper discusses challenges and limitations associated with AI adoption in the industry, such as data availability, model interpretability, and regulatory compliance. The integration of AI-driven optimization techniques not only enhances sustainability but also contributes to cost reduction and operational excellence in oil and gas production. By optimizing production processes, operators can achieve higher yields with fewer resources, leading to increased profitability and long-term viability in a rapidly evolving energy landscape. Overall, this review provides valuable insights into the transformative potential of AI-driven optimization techniques in fostering sustainability and resilience in oil and gas production processes, paving the way for a more efficient and environmentally responsible industry.
 Keywords: AL, Oil and Gas, Production, Optimization, Sustainability, Review, Process.
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DOI: 10.51594/estj.v5i3.950
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