Artificial Intelligence for predictive analysis, efficiency improvement and reduction in carbon footprint during decommissioning and site remediation in oil and gas fields

Egbuna, Ifeanyi Kingsley and Agboro, Harrison and Nwachukwu, Ogechi Olive and George, Freda Ekpenyong and Asere, Joshua Babatunde and Ogunkanmi, Shola Abayomi (2025) Artificial Intelligence for predictive analysis, efficiency improvement and reduction in carbon footprint during decommissioning and site remediation in oil and gas fields. World Journal of Advanced Research and Reviews, 26 (2). pp. 3394-3405. ISSN 2581-9615

This piece discusses how Artificial Intelligence facilitates oil and gas decommissioning and site renewal to allow for sustainability of the environment. With well over twenty peer-reviewed articles attested, the review describes how digital twin, machine learning, predictive analytics, and remote sensing technologies revolutionize back-end decommissioning to proactive and data-informed practices. Observations from empirical studies record Artificial Intelligence implementation reduces decommissioning expense by as much as 35%, flare volumes and fugitive methane by 40% minimum and remediation efficiency by 60% under ground and water pollution conditions. Decreases in emission by 20 metric tonnes of CO₂ equivalent per well and downtime by 25 to 40% were similarly recorded from case studies. This study credits Artificial Intelligence with empowering oil and gas operations with environment, social, and government considerations; as well as technical, economic, and ecological optimization at the oil and gas industry's end-of-life phase.