Machine Learning in Automating Carbon Sequestration Site Assessment (Proposals Track)

Jay Chen (Shell); Ligang Lu (Shell); Mohamed Sidahmed (Shell); Taixu Bai (Shell); Ilyana Folmar (Shell); Puneet Seth (Shell); Manoj Sarfare (Shell); Duane Mikulencak (Shell); Ihab Akil (Shell)



Carbon capture and sequestration are viewed as an indispensable component to achieve the Paris Agreement climate goal, i.e., keep the global warming within 2 degrees Celsius from pre-industrial levels. Once captured, most CO2 needs to be stored securely for at least decades, preferably in deep underground geological formations. It is economical to inject and store CO2 near/around a depleted gas/oil reservoir or well, where a geological trap for CO2 with good sealing properties and some minimum infrastructure exist. In this proposal, with our preliminary work, it is shown that Machine Learning tools like Optical Character Recognition and Natural Language Processing can aid in screening and selection of injection sites for CO2 storage, facilitate identification of possible CO2 leakage paths in the subsurface, and assist in locating a depleted gas/oil well suitable for CO2 injection and long-term storage. The automated process based on ML tools can also drastically decrease the decision-making cycle time in site selection and assessment phase by reducing human effort. In the longer term, we expect ML tools like Deep Neural Networks to be utilized in CO2 storage monitoring, injection optimization etc. By injecting CO2 into a trapping geological underground formation in a safe and sustainable manner, the Energy industry can contribute substantially to reducing global warming and achieving the goals of the Paris Agreement by the end of this century.