De-risking Carbon Capture and Sequestration with Explainable CO$_2$ Leakage Detection in Time-lapse Seismic Monitoring Images
Title | De-risking Carbon Capture and Sequestration with Explainable CO$_2$ Leakage Detection in Time-lapse Seismic Monitoring Images |
Publication Type | Conference |
Year of Publication | 2022 |
Authors | Huseyin Tuna Erdinc, Abhinav Prakash Gahlot, Ziyi Yin, Mathias Louboutin, Felix J. Herrmann |
Conference Name | AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges |
Month | 11 |
Keywords | AAAI, 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) |
URL | https://slim.gatech.edu/Publications/Public/Conferences/AAAI/2022/erdinc2022AAAIdcc/erdinc2022AAAIdcc.pdf |
Presentation | |
Citation Key | erdinc2022AAAIdcc |