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The Risk Assessment technology research effort focuses on the development and validation of effective quantitative risk assessment tools validated in field projects. These tools are integrated with other tools developed under the Carbon Transport & Storage Program to predict a storage system’s response to large-scale carbon dioxide (CO2) injection, understand site performance, identify potential site problems, and develop mitigation procedures to quickly and effectively address any unwanted condition, should one arise.
The Science-Informed Machine Learning for Accelerating Real-Time Decisions in Subsurface Application (SMART) Initiative is a 10-year, multi-organizational effort with the goal of transforming interactions within the subsurface and significantly improving efficiency and effectiveness of field-scale carbon storage and unconventional oil and gas operations through the use of data analytics such as science-based machine learning (ML).
Energy Data eXchange (EDX) provides a platform for the integration of tools by supporting private collaboration for ongoing research efforts and tech transfer of finalized United States (U.S.) Department of Energy (DOE) National Energy Technology Laboratory (NETL) research products.
The National Risk Assessment Partnership’s (NRAP) is a collaboration of five U.S. national laboratories focused on quantifying and managing subsurface environmental risks to support implementation of safe and secure large-scale geologic carbon storage. NRAP is focused on developing and demonstrating science-based methods, computational tools, workflows, and protocols to quantitatively assess and manage environmental risks at geologic carbon storage sites.
This project focused on developing an integrated modeling and data system from the subsurface to the shore, including evaluation of potential risks and identification of knowledge and technology gaps to inform offshore spill prevention efforts. It has continued into Phase 2 as Geohazard and Subsurface Uncertainty Modeling. NETL’s Offshore Risk Modeling (ORM) suite resulted in a flexible set of custom data, tools, and models that integrate innovative spatio-temporal analytics, machine learning, big data, and advanced visualization technologies to support DOE’s offshore spill prevention, operational efficiency, and safety goals. NETL has demonstrated how the ORM suite can be used to help improve reserves estimates, increase profitability, guide safety and maintenance decisions, forecast risks, and optimize well/facilities designs.
The focus of this research is to produce programmatically relevant tools, technologies, and analyses that directly address CCUS challenges. The portfolio leverages advanced computer approaches coupled with critical data analysis representative of real carbon storage scenarios. The projects will focus on: onshore and offshore CO2 storage cost analysis for saline reservoirs, economics of CCUS technology and deployment, and life cycle analyses.
