Monigha, Adogioye (2024) AI-Driven Cost Optimization in Oil and Gas Projects. International Journal of Petroleum and Gas Engineering Research, 7 (2). pp. 17-32. ISSN ISSN 2514-9253(Print), ISSN 2514-9261 (Online)
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Abstract
The oil and gas sector, a cornerstone of the global energy supply, is poised at the brink of a technological renaissance as it embraces Artificial Intelligence (AI). This paradigm shift is driven by the industry's imperative to enhance efficiency, bolster safety, and notably, optimize costs amidst volatile market conditions and escalating operational complexities. This paper presents an in-depth exploration of the multifaceted applications of AI technologies in streamlining cost management across various phases of oil and gas projects—from exploration to distribution. At the heart of this exploration is the elucidation of AI's transformative role in predictive analytics, automation, and decision support systems. By harnessing the power of machine learning algorithms, the industry can predict equipment failures, optimize maintenance schedules, and ensure uninterrupted production, thereby significantly reducing downtime costs. Furthermore, AI-driven data analytics enable the identification of patterns and insights from vast datasets, leading to more informed and cost-effective decision-making. The paper also delves into the integration of AI in operational domains such as drilling optimization, where AI algorithms analyze geological data to determine optimal drilling locations and parameters, thus minimizing the risk of costly non-productive time. Similarly, in the realm of supply chain management, AI facilitates dynamic routing and inventory control, curtailing logistical expenses. Highlighting case studies and empirical data, the paper underscores the tangible benefits realized by early adopters of AI in the industry, showcasing a potential reduction in operational costs and an increase in efficiency that often surpasses traditional methodologies. This detailed examination not only charts a course for future AI-driven endeavors in the oil and gas sector but also serves as a clarion call for stakeholders to navigate the intricacies of digital transformation strategically. In conclusion, the paper posits that the integration of AI stands as a beacon of innovation, promising not only cost optimization but also a sustainable and resilient future for the oil and gas industry. The findings herein aim to galvanize industry leaders, policymakers, and technologists to foster a collaborative ecosystem where AI can flourish, driving the industry towards a new horizon of operational excellence.
| Item Type: | Article |
|---|---|
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Depositing User: | Professor Mark T. Owen |
| Date Deposited: | 23 May 2024 10:17 |
| Last Modified: | 23 May 2024 10:17 |
| URI: | https://tudr.org/id/eprint/3017 |
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