The SMART Initiative: Applying Machine Learning to Enable Efficient and Effective Real-Time Decisions for Geological Carbon Storage Operations

This paper provides an overview of the SMART Initiative – Science-Informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Applications – that seeks to transform our interactions within and understanding of the subsurface, and significantly improve efficiency and effectiveness of field-scale carbon storage and unconventional oil and gas operations. The accomplishments of the recently concluded “proof-of-concept” Phase 1 are described. Goals for the “field deployement” of ML-assisted tools and workflows for both greenfield (i.e., pre-injection permitting) and brownfield (i.e., active injection operational control and post-injection site care) applications in Phase 2 of SMART are also presented.