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Accurate and Timely Forecasts of Geologic Carbon Storage using Machine Learning Methods...

by Dan Lu, Scott L Painter
Publication Type
Conference Paper
Journal Name
Conference on Neural Information Processing Systems
Book Title
NeurIPS 2021 Workshop: Tackling Climate Change with Machine Learning
Publication Date
Page Numbers
1 to 8
Publisher Location
United States of America
Conference Name
35th Conference on Neural Information Processing Systems (NeurIPS 2021)
Conference Location
Virtual conference, California, United States of America
Conference Sponsor
multiple institutions
Conference Date

Carbon capture and storage is one strategy to reduce greenhouse gas emissions. One approach to storing the captured CO2 is to inject it into deep saline aquifers. However, dynamics of the injected CO2 plume is uncertain and the potential for leakage back to the atmosphere must be assessed. Thus, accurate and timely forecasts of CO2 storage via real-time measurements integration becomes very crucial. This study proposes a learning-based, inverse-free prediction method that can accurately and rapidly forecast CO2 movement and distribution with uncertainty quantification based on limited simulation and observation data. The machine learning techniques include dimension reduction, multivariate data analysis, and Bayesian learning. The outcome is expected to provide CO2 storage site operators with an effective tool for real-time decision making.