Motivation for this Phase 1 project stemmed from the advancement of offshore oil and gas operations into extreme offshore environments that are often remote, environmentally sensitive, and economically challenging. The lessons learned from Hurricanes Rita and Katrina and the 2010 Deepwater Horizon oil spill in the Gulf of Mexico were also impetuses for the development of this project. These events highlighted the need for improved models, data, and tools to identify knowledge and technology gaps with offshore hydrocarbon exploration and production systems to help prevent future spills and provide predictions for a range of end-users. 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.


This project provided data, tools, and techniques to evaluate the potential risks and identify possible knowledge and technology gaps in the offshore system using science-based, data-driven assessments. The tools and techniques leveraged advanced big data science and computing to innovate and advance the understanding of spatial and temporal behaviors and relationships for engineered-natural, multi-variate systems. These products can support the analysis of subsurface, wellbore, and water column to evaluate relationships, trends, risks of offshore spills, and uncertainty.

Researchers developed a new multi-component model, the Offshore Risk Model (ORM), which ties the subsurface, wellbore, and water column into an integrated assessment model. ORM relies on the synthesis of data—from the subsurface to the shore—to develop innovative tools and approaches to drive analyses that effectively evaluate and reduce risks associated with extreme offshore hydrocarbon development.

Data have been collected and integrated into NETL’s Energy Data eXchange (EDX), providing a single point of discovery and access. Since the start of this project, over 6 terabytes of data have been collected and incorporated into EDX. Along with discovery and access to these data via EDX, users can visualize data using GeoCube, a custom web-based mapping application. GeoCube supports basic spatial and temporal analysis, allowing users to quickly identify overall trends and patterns in the data, as well as share these discoveries with others using various export functions (print, snapshot, and extract data). In addition, these data have been used to develop five science-based, data-driven tools and models for the ORM to support the evaluation and reduction of risks and uncertainty associated with extreme offshore hydrocarbon development. These five tools and models address concerns from the subsurface, wellbore, and water column to evaluate relationships, trends, risks of offshore spills, and uncertainty. The tools are:

  • Subsurface Trend Analysis (STA)—a data-driven approach for improving geologic knowledge and reducing subsurface uncertainty;
  • Blowout and Spill Occurrence Model (BLOSOM)—a 4D fate and transport model for simulating oil spills to support spill prevention and provide a greater understanding of how hydrocarbon leaks from all sources are transported throughout offshore systems;
  • Cumulative Spatial Impact Layers (CSIL)—a spatial-temporal approach for rapidly quantifying potential impacts;
  • Spatially Weighted Impact Model (SWIM)—a decision support tool driven by multi-variate relationship models and user-defined weights; and
  • Variable Grid Method (VGM)—an approach for quantifying and visualizing uncertainty associated with spatial data.  The VGM is pictured below.


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. Five years of development produced terabytes of new data and seven trademarked or copyrighted tools built into the ORM suite that can be used independently or in combination to support data-driven analytics for offshore systems to improve global energy, environmental, and economic conditions. 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. These pioneering applications of the ORM suite, and the data science innovations driving them, have garnered national and global attention. This has translated into millions of dollars of funding from DOE-FE and external stakeholders for new projects that apply the ORM suite to address additional energy systems and help inform a range of industry and regulatory decisions. To date, the ORM suite has been adapted to address energy infrastructure, carbon storage, geothermal, rare-earth element, induced seismicity, energy materials, and other oil and gas system needs.

Research Products

Highlighted Research Products

Blowout and Spill Occurrence Model (BLOSOM)

Variable Grid Method (VGM) U.S. 14/619,501.

*Image Source: NETL, 2018

Infographic on critical components and value added from the Offshore Risk Modeling Suite.

*Image Source: NETL

The Offshore Risk Modeling (ORM) suite offers tools that address the entire offshore system (from the subsurface and wellbore, through water column, to the sea surface and coastline). To date, the ORM also provides terabytes of data for federal waters in the Gulf of Mexico, and offshore California and Alaska to drive all the ORM tools, models, and analytics.

*Image Source: NETL

Example oil spill forecast (dark gray- black tones) and possible socio-economic and environmental impacts (green – red tones) outputs from both data and tools, specifically the BLOSOM, CSIL, and SWIM models, that make up the Offshore Risk Modeling suite. These integrated outputs offer critical data reduce risks and cost associated with offshore exploration and production.

*Image Source: NETL

Subsurface pressure gradient, developed using data and the STA and VGM from the Offshore Risk Modeling Suite. Together, STA and VGM offer and improved prediction of subsurface pressure gradients in the offshore Gulf of Mexico, include for regions with little to no data; provide novel insights that can improve resource estimates, increase profitability, and reduce geohazards.


Jennifer Bauer
Co-Principal Investigator