AI-Powered Workforce Planning and Skills Management for the Digital Transformation of Oil and Gas Projects

Suresh Chikurumilli

doi:10.55524/ijirem.2026.13.1.3

Abstract

Upstream oil and gas industry is very complex and capital intensive so for safety, productivity as well as profitability project management should be done well. With technology development, AI is now being considered as a force that can revolutionize project planning, scheduling, and management in managing costs without altering risk or safety supervision. Generic AI tools such as automation, machine learning, predictive analytics, and NLP will help progress project lifecycles by reducing operating costs thus improve the overall efficiency. The above also results in lower downtime and operational risk caused by real-time monitoring systems predictive maintenance smarter resource allocations and better decision-making. In this paper, the application of AI in controlling of petroleum projects is analyzed using an Industrial Use Case for AI. In fact, artificial Intelligence has definitely had a higher commodity impact throughout the organization and added value both on upstream and downstream, from targeting during exploration to reservoir modelling (e.g., advanced surface tracking), optimizing drilling operations or increasing production efficiency. We introduced AI to project management and have seen tangible declines in instances ineffectuality’s that make our business more sustainable as well as greener. There are some hitches to deploying AI, however. Companies do have high up-front costs, a lack of standards, and cyber-security risks as well as conventional resistance from staff. Solving these problems will take planning, investment in personnel and the willingness to modernize outdated systems.

Keywords

Artificial Intelligence, Oil and Gas, Project Management, Risk Mitigation, Efficiency

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Cites this article as

S. Chikurumilli "AI-Powered Workforce Planning and Skills Management for the Digital Transformation of Oil and Gas Projects", International Journal of Innovative Research in Engineering and Management (IJIREM), Vol-13, Issue-1, Page No-23-32, 2026. Available from: https://doi.org/10.55524/ijirem.2026.13.1.3