The Role of Artificial Intelligence (AI) and Machine Learning (Ml) in the Oil and Gas Industry

Kadugala Aniceto ()

Journal of Technology and Systems, 2025, vol. 7, issue 1, 6 - 27

Abstract: Purpose: This study focused on the relevance of AI and ML in revolutionizing the Oil & Gas sector by innovation and rehabilitation. It investigated the role of AI and ML technologies in improving production efficiency, reducing environmental impact, and managing costs. Several end users noted considerable advancements in operation efficiency; Predictive analytics and real-time monitoring systems assisted in enhancing the effectiveness of predictive maintenance by as much as 40% lapse time. Thus, the digital twin technologies were discussed in the context of enhancing the design of production planning, as well as promoting more effective use of resources and their recycling. Methodology: The research applied integration of systematic qualitative methodologies to collect, analyze, and synthesize data from prior investigations. This methodology was designed to encompass the alignment of results, analysis, and conclusions within the literature findings. The application of Systematic Literature Review (SLR), Content Analysis, and Meta-Analysis of Qualitative Evidence effectively grounded the study objectives Findings: The Significance of AI is captured in the environmental management aspect of this study, whereby several companies’ emission control systems recorded a 30% improvement of Green House Gas (GHG), and the accuracy of compliance. Some of those that are of more economic advantage are the reduced cost of maintenance, low energy utilization and minimal wastage of resources. However, shortcomings like high implementation cost, integration difficulties, and infrastructural limitations remain some of the biggest threats to its adoption. Unique Contribution to Theory, Policy, and Practice: In addressing these challenges, this research suggests the promotion of partnerships, creating efficient innovations and establishing sustainable development initiatives. Thus, it offers valuable recommendations for policymakers, researchers, and other interested parties, stressing that AI and ML adoption demonstrate the potential to support operational excellence, environmentally sustainable practices, and increased profitability in the Oil and Gas industry.

Keywords: Artificial Intelligence; Machine Learning; Green House Gas; Internet of Things; Support Vector Machines; Carbon Capture (search for similar items in EconPapers)
Date: 2025
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