Authors : Shamsuddeen Adamu Bala

Volume/Issue : Volume 10 - 2025, Issue 5 - May

Google Scholar : https://tinyurl.com/34nf2ke3

DOI : https://doi.org/10.38124/ijisrt/25may1424

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Traditionally, the oil and gas industry has been victim to the hazards of operation, environmental challenges, and an unstable market condition. Artificial intelligence (AI), and more specifically predictive analytics and machine learning (ML), have taken the industry by storm in the last decade by creating opportunities for proactive and sustainable decision- making. This journal discusses the application, benefits, and challenges associated with the integration of predictive analytics and ML within oil and gas operations. Through the examination of different cases and real data sets, the paper emphasizes how safety and environmental hazards reduction are aspects that can be improved by operational improvements through these technologies. Later, the implementation challenges relating to data quality, infrastructure, and workforce readiness are discussed. The paper ends with some recommendations concerning industry-wide implementations and the future of AI and sustainability in oil and gas.

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