Advancements in predictive maintenance in the oil and gas industry: A review of AI and data science applications

Abstract

This study provides a comprehensive review of the advancements in predictive maintenance within the oil and gas industry, focusing on the integration and impact of Artificial Intelligence (AI) and Data Science. The primary objective was to evaluate how AI and data science have transformed maintenance practices from traditional methods to more advanced, predictive approaches. The methodology involved a systematic literature review, utilizing databases such as IEEE Xplore, ScienceDirect, SpringerLink, and Web of Science. The search strategy was centered around keywords related to AI, data science, and predictive maintenance in the oil and gas sector, with a focus on literature published from 2010 onwards. The findings reveal that AI and data science significantly enhance predictive maintenance strategies. AI algorithms and data analytics have enabled more accurate predictions of equipment failures and optimized maintenance scheduling, leading to reduced downtime and operational costs. The study also identifies challenges, including the complexity of data management and the need for high-quality, real-time data. Opportunities for future advancements lie in developing more robust AI models capable of adapting to the industry's dynamic environment. The study recommends that industry stakeholders invest in workforce training for AI-based systems and that policymakers develop frameworks supporting ethical AI use. Future research directions include exploring the integration of AI with other emerging technologies and developing sustainable maintenance practices. The study concludes that AI's continuous evolution will play a crucial role in shaping the future of maintenance strategies in the oil and gas industry.