1 Department of Management Science, Ghana Institute of Management and Public Administration, Ghana.
2 Department of Mechanical Engineering, University of Portharcourt, Rivers State, Nigeria.
3 Department of Systems Engineering, University of Lagos, Lagos, Nigeria.
4 Department of Geology, Federal Univeristy of Technology Owerri, Imo State, Nigeria.
5 Department of Computer Science, Ebonyi State University, Abakaliki, Ebonyi State, Nigeria.
6 Department of Mechanical Engineering, University of Benin, Edo State, Nigeria.
7 Department of Renewable Energy Engineering, Henriot Watt University, Edinburgh, United Kingdom.
World Journal of Advanced Research and Reviews, 2025, 25(02), 1029-1038
Article DOI: 10.30574/wjarr.2025.25.2.0436
Received on 30 December 2024; revised on 07 February 2025; accepted on 10 February 2025
AI is increasingly being used in drilling operations, redefining efficiency, cost-effectiveness, and safety within the oil and gas industry. Traditional drilling operations are usually plagued by inefficiencies, high NPT, and suboptimal well placement due to over-reliance on manual decisions and conventional geological interpretation. AI-driven automation uses machine learning, IoT devices, real-time data analytics, and predictive maintenance to provide improved drilling precision, better placement of wells, and reduced operational risks. Industry leaders have shown that the gains in efficiency are huge; Chevron recorded a 30% increase in drilling speed, with a corresponding 25% reduction in operational costs, resulting from AI-driven automated drilling. Shell reported 130% gains in drilling efficiency due to AI-enhanced optimization models. BP and ExxonMobil implemented AI predictive maintenance, realizing a 20% reduction in maintenance costs, with a resulting 15% increase in equipment uptime. Saudi Aramco optimized well placement, leading to a 35% increase in production and reduced drilling time. This review critically assesses such AI applications in drilling automation with regard to operational efficiency, cost reduction, and sustainability. While a game-changing technology, several barriers to widespread diffusion exist: integration of data, which is highly complex; costs of implementation, which are relatively high; and skilled people are required. The ability to remove these barriers through technological development and strategic collaboration by the industry will be key in maximizing the full benefits of AI in drilling automation.
Artificial Intelligence; Drilling Automation; Well Placement Optimization; Predictive Maintenance; Cost Efficiency
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Kofi Yeboah Adjei, Godwin Ekunke Odor, Sharafadeen Ashafe Nurein, Peace Chinaza Ogu-Opara, Onyedioranma Collins Ugwu, Ikechukwu Bismarck Owunna and Ekunke Onyeka Virginia. Optimizing well placement and reducing costs using AI-driven automation in drilling operations. World Journal of Advanced Research and Reviews, 2025, 25(02), 1029-1038. Article DOI: https://doi.org/10.30574/wjarr.2025.25.2.0436.
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