Sarcouncil Journal of Multidisciplinary

Abstract: The contemporary oil and gas industry faces critical challenges balancing operational efficiency with environmental compliance while maintaining equipment reliability across extensive infrastructure networks. Traditional maintenance approaches and emissions monitoring systems demonstrate significant limitations in predicting failure patterns and detecting environmental incidents, creating persistent gaps between regulatory expectations and operational capabilities. Advanced artificial intelligence technologies, computer vision systems, and Internet of Things sensor networks present transformative opportunities for addressing these challenges through integrated monitoring and analytics platforms. Computer vision algorithms enable automated pipeline integrity assessment and leak detection. At the same time, machine learning models provide predictive capabilities for drilling equipment failure prevention and thermal imaging analysis for continuous equipment condition monitoring. Multi-modal data fusion techniques combine thermal imaging, acoustic monitoring, and satellite imagery to create comprehensive operational awareness systems that simultaneously optimize predictive maintenance strategies and emissions reduction objectives. Integration with existing SCADA infrastructure and regulatory compliance frameworks enables automated workflows for emissions tracking and real-time alert systems that provide immediate situational awareness for operational personnel. Economic evaluation demonstrates the viability of artificial intelligence implementation through quantified benefits, including reduced operational downtime, optimized maintenance scheduling, and enhanced regulatory compliance performance. The convergence of these technologies creates intelligent systems that transform industrial monitoring paradigms while establishing frameworks for sustainable energy operations that balance operational excellence with environmental stewardship requirements.