Optimizing Carbon Capture Efficiency through AI-Driven Process Automation for Enhancing Predictive Maintenance and CO2 Sequestration in Oil and Gas Facilities

The increasing worldwide focus, on cutting down carbon emissions has heightened the need for cutting edge carbon capture and storage (CCUS or CCSU) in the oil and gas industry sector. This examination delves into how AI powered automation processes can boost the effectiveness of carbon capture systems and improve maintenance practices, in oil and gas installations. Combining intelligence (AI) with procedures and systems in place to predict outcomes accurately can enhance the dependability and effectiveness of CCS technologies by tackling essential issues like constant monitoring in real-time and identifying faults for system optimization purposes efficiently. AI-powered automation processes implemented by facilities have the potential to boost the rates of CO2 sequestration while minimizing interruptions, resulting in a more effective carbon capture infrastructure. The methodology involves a systematic review of existing literature, peer-reviewed articles, case studies, and industry reports on AI techniques, such as machine learning and neural networks, in CCS. Databases like Google Scholar and IEEE Xplore were used, focusing on keywords like “AI in CCS” and “predictive maintenance. The analysis also explores real-life examples from oil and gas firms that have effectively integrated AI solutions into their carbon capture and storage endeavors, hence shedding light on strategies, hurdles, and upcoming developments in the field. The evaluation highlights how AI-driven automation processes significantly improve the efficiency and environmental sustainability of oil and gas facilities.