De-risking Carbon Capture and Sequestration with Explainable CO$_2$ Leakage Detection in Time-lapse Seismic Monitoring Images

TitleDe-risking Carbon Capture and Sequestration with Explainable CO$_2$ Leakage Detection in Time-lapse Seismic Monitoring Images
Publication TypeConference
Year of Publication2022
AuthorsHuseyin Tuna Erdinc, Abhinav Prakash Gahlot, Ziyi Yin, Mathias Louboutin, Felix J. Herrmann
Conference NameAAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges
Month08
KeywordsAAAI, CAM, CCS, classification, explainability, JRM, seismic imaging
Abstract

With the growing global deployment of carbon capture and sequestration technology to combat climate change, monitoring and detection of potential CO$_2$ leakage through existing or storage induced faults are critical to the safe and long-term viability of the technology. Recent work on time-lapse seismic monitoring of CO$_2$ storage has shown promising results in its ability to monitor the growth of the CO$_2$ plume from surface recorded seismic data. However, due to the low sensitivity of seismic imaging to CO$_2$ concentration, additional developments are required to efficiently interpret the seismic images for leakage. In this work, we introduce a binary classification of time-lapse seismic images to delineate CO$_2$ plumes (leakage) using state-of-the-art deep learning models. Additionally, we localize the leakage region of CO$_2$ plumes by leveraging Class Activation Mapping (CAM) methods.

Notes

(AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges, Arlington)

URLhttps://slim.gatech.edu/Publications/Public/Conferences/AAAI/2022/erdinc2022AAAIdcc/erdinc2022AAAIdcc.pdf
Citation Keyerdinc2022AAAIdcc