This project focuses on using and enhancing components of NETL’s offshore risk modeling (ORM) platform to improve and conduct geohazard and subsurface uncertainty modeling.
ORM is a multi-component platform built from simulating and predicting the behavior of engineered-natural systems from 2011 through 2016—incorporating lessons learned from previous deleterious events and spanning natural and anthropogenic offshore hydrocarbon activities. The platform provides data, tools, and technologies to assist with evaluating potential risks and identifying possible technology gaps using science-based, data-driven assessments. The modules comprising the ORM support analysis of subsurface, wellbore, and water column to evaluate relationships, trends, risks of offshore spills, and uncertainty.
Much of the previous work on the ORM platform focused on developing data and tools that can be used to characterize and map various geohazards and reduce subsurface uncertainty. This project will enhance ORM’s subsurface trends analysis (STA) methodology, which couples expert geologic knowledge and geostatistics to improve the characterization of subsurface properties. These enhancements will incorporate additional datasets and start developing a workflow for using the STA to support additional application for assessing other geologic properties and reducing geologic uncertainty in additional offshore areas.
The goal of this three-year project is to identify potential subsurface hazards and innovate new, advanced data computing methods to improve prediction of subsurface properties to inform resource, environmental, and operational needs. The work uses data and models from the ORM with intelligent databases, machine learning, big data, and other advanced computing technologies to address subsurface industry challenges to help characterize and map geologic hazards, improve safety and reliability, and reduce costs.
In the first year, the project will initiate development of the STA method into a tool to make application and iteration of STA analyses more efficient. This will open the potential for integration of real-time data. Strategies will be developed for enhancing the approach into a more robust 3D implementation and visualization, which will more realistically and accurately capture and constrain subsurface hazards and resource predictions. The project will evaluate the potential of using STA to improve prediction of subsurface properties in the central Gulf of Mexico (GOM), which may include carbon dioxide and/or hydrogen sulfide occurrence, fracture and fault distributions, and reservoir thickness. Additionally, the team will combine STA with logging while drilling (LWD)/seismic while drilling (SWD) data streams for real time subsurface prediction and uncertainty reduction. The team will evaluate the feasibility of generating on-demand, real-time updates to STA-based analyses to provide near-well forecasts of geohazards and inform resource evaluations. The goal is to leverage the larger scale, big data framework offered by STA to inform local, well, and reservoir-scale forecasts.
In the second year, the strategy for 3D STA application and visualization should be completed and will be assessed for integration into the STA tool. The analysis of advanced subsurface properties in the central GOM will continue with the new 3D approach and visualization techniques. Application of STA analysis of subsurface for a region other than the GOM will be conducted, if appropriate. The team will conduct real-time subsurface prediction and uncertainty reduction by combining STA with LWD/SWD data streams.
In the third year, the STA tool for 2D analytics will be released. Integration of 3D analytical and visualization logic into the tool will be initiated. The analysis of advanced subsurface properties in the central GOM will continue, as will real-time subsurface prediction and uncertainty reduction. The STA tool will be copyrighted and available for licensing and use either through open-source and/or commercial agreements. The analyses will be available via Geocube.