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Integrating AI and CCUS for Decarbonizing the Oil and Gas Industry: Challenges and Opportunities
Subject area: Science,Engineering and Technology · Area of research: Carbon capture, utilization, and storage
Abstract
The intersection of artificial intelligence (AI) and carbon capture, utilization, and storage (CCUS) technologies represents a transformative opportunity for decarbonizing the oil and gas industry, a sector responsible for a significant share of global greenhouse gas emissions. This article investigates the potential of AI to address critical challenges in CCUS, including high implementation costs, inefficiencies in carbon capture rates, and storage risks. Leveraging advanced AI techniques such as predictive analytics, real-time monitoring, and machine learning, the integration of these technologies promises improved operational efficiency, reduced costs, and enhanced storage safety. Case studies illustrate both the opportunities and obstacles in implementing AI-driven CCUS solutions. Key findings emphasize that while AI can revolutionize CCUS technologies, significant barriers remain. These include technical hurdles like computational demands and data inconsistencies, regulatory challenges stemming from unclear guidelines, and financial constraints linked to the high upfront adoption costs. This article calls for targeted investments, interdisciplinary collaboration, and supportive policy measures to overcome these obstacles and realize the large-scale integration of AI-CCUS systems. By bridging technical innovation with policy and societal acceptance, this article shows the critical role of AI-CCUS in achieving global decarbonization goals and advancement in the future of sustainable energy.
Keywords
AI-CCUS Integration, Carbon Capture Technologies, Oil and Gas Decarbonization, Predictive Analytics, Real-Time Monitoring, Gorgon Project, Boundary Dam Power Station, Regulatory Barriers, Technical Challenges, Sustainable Energy.
How to cite this paper
@article{1706950,
author = {Angela Ndalaku Ibemenem},
title = {Integrating AI and CCUS for Decarbonizing the Oil and Gas Industry: Challenges and Opportunities},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {7},
pages = {382-395},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1706950.pdf},
abstract = {The intersection of artificial intelligence (AI) and carbon capture, utilization, and storage (CCUS) technologies represents a transformative opportunity for decarbonizing the oil and gas industry, a sector responsible for a significant share of global greenhouse gas emissions. This article investigates the potential of AI to address critical challenges in CCUS, including high implementation costs, inefficiencies in carbon capture rates, and storage risks. Leveraging advanced AI techniques such as predictive analytics, real-time monitoring, and machine learning, the integration of these technologies promises improved operational efficiency, reduced costs, and enhanced storage safety. Case studies illustrate both the opportunities and obstacles in implementing AI-driven CCUS solutions. Key findings emphasize that while AI can revolutionize CCUS technologies, significant barriers remain. These include technical hurdles like computational demands and data inconsistencies, regulatory challenges stemming from unclear guidelines, and financial constraints linked to the high upfront adoption costs. This article calls for targeted investments, interdisciplinary collaboration, and supportive policy measures to overcome these obstacles and realize the large-scale integration of AI-CCUS systems. By bridging technical innovation with policy and societal acceptance, this article shows the critical role of AI-CCUS in achieving global decarbonization goals and advancement in the future of sustainable energy.},
keywords = {AI-CCUS Integration, Carbon Capture Technologies, Oil and Gas Decarbonization, Predictive Analytics, Real-Time Monitoring, Gorgon Project, Boundary Dam Power Station, Regulatory Barriers, Technical Challenges, Sustainable Energy.},
month = {January},
}