A Novel Fuzzy Logic Geospatial Approach to Characterize and Predict Subsurface Structural Complexity
Presented by NETL at AGU Fall Meeting, 12/19
Justman, Devin M.↗
Engineering topics
Bauer, Jennifer
Publications and source records attributed to Bauer, Jennifer.
Putting Data to Work: Transforming Disparate Open-Source Data for Engineered-Natural Systems and Models
Presented by NETL at American Geophysical Union Fall Meeting 2019, 12/19
Creason, Christopher↗
Cumulative Spatial Impact Layers: A Novel Multivariate Spatio-Temporal Analytical Summarization Tool
Scientific inquiry often requires analysis of multiple spatio-temporal datasets, ranging in type and size, using complex multi-step processes demanding an understanding of GIS theory and software. Cumulative Spatial Impact Layers (CSIL) is a GIS-based tool that summarizes spatio-temporal datasets based on overlapping features and attributes. Leveraging a recursive quadtree method, and applying multiple additive frameworks, the CSIL tool allows users to analyze raster and vector datasets by calculating data, record, or attribute density. Providing an efficient and robust method for summarizing disparate, multi-format, multi-source geospatial data, CSIL addresses the need for a new integration approach and resulting geospatial product. The built-in flexibility of the CSIL tool allows users to answer a range of spatially-driven questions. Example applications are provided in this paper to illustrate the versatility and variety of uses for this CSIL tool and method. Use cases include addressing regulatory decision-making needs, economic modeling, and resource management. Performance reviews for each use case are also presented, demonstrating how CSILs provides a more efficient and robust approach to assess a range of multivariate spatial data for a variety of uses.
58 GEOSCIENCES↗
Simulation of the 2003 Foss Barge - Point Wells Oil Spill: A Comparison between BLOSOM and GNOME Oil Spill Models
The Department of Energy’s (DOE’s) National Energy Technology Laboratory’s (NETL’s) Blowout and Spill Occurrence Model (BLOSOM), and the National Oceanic and Atmospheric Administration’s (NOAA’s) General NOAA Operational Modeling Environment (GNOME) are compared. Increasingly complex simulations are used to assess similarities and differences between the two models’ components. The simulations presented here are forced by ocean currents from a Finite Volume Community Ocean Model (FVCOM) implementation that has excellent skill in representing tidal motion, and with observed wind data that compensates for a coarse vertical ocean model resolution. The comprehensive comparison between GNOME and BLOSOM presented here, should aid modelers in interpreting their results. Beyond many similarities, aspects where both models are distinct are highlighted. Some suggestions for improvement are included, e.g., the inclusion of temporal interpolation of the forcing fields (BLOSOM) or the inclusion of a deflection angle option when parameterizing wind-driven processes (GNOME). Overall, GNOME and BLOSOM perform similarly, and are found to be complementary oil spill models. This paper also sheds light on what drove the historical Point Wells spill, and serves the additional purpose of being a learning resource for those interested in oil spill modeling. The increasingly complex approach used for the comparison is also used, in parallel, to illustrate the approach an oil spill modeler would typically follow when trying to hindcast or forecast an oil spill, including detailed technical information on basic aspects, like choosing a computational time step. We discuss our successful hindcast of the 2003 Point Wells oil spill that, to our knowledge, had remained unexplained. The oil spill models’ solutions are compared to the historical Point Wells’ oil trajectory, in time and space, as determined from overflight information. Our hindcast broadly replicates the correct locations at the correct times, using accurate tide and wind forcing. While the choice of wind coefficient we use is unconventional, a simplified analytic model supported by observations, suggests that it is justified under this study’s circumstances. We highlight some of the key oceanographic findings as they may relate to other oil spills, and to the regional oceanography of the Salish Sea, including recommendations for future studies.
42 ENGINEERING↗
Oklahoma Wellbore Rasters
Seven raster surfaces generated from wellbore information that are thought to influence fluid and or gas migration in the subsurface. Wellbore information was originally retrieved from the IHS Energy Group (current as of April 2015), which is a proprietary, commercial database containing information for most oil and gas wells in the U.S. Processing of wellbore information into gridded cells was performed to provide a graphic solution to overcome the problem of displaying proprietary well data. No proprietary data are displayed or included in the cell maps. These rasters were developed to serve as inputs to the SIMPA (Spatially Weighted Multivariate Probabilistic Assessment) tool. Each raster surface is a derivative product of wellbore dataset. Each raster represents a single wellbore attribute or combination of attributes that speak to the following wellbore characteristics: number of recompletions, year spud, year abandoned, year completed, number of abandoned wells, hole direction, switch from production to injection. The original wellbore information, retrieved from the IHS Energy Group, are in the form of points – these derivative rasters aggregate point attributes to approximately 1,000-meter resolution grid cells. Each raster only includes cells that contain wellbores with the characteristics represented in each surface (i.e. if the wellbores in a given cell have no year abandoned data, the cell is not included in the raster). Since the raster cells combine data from multiple points in many cases, methods such as the average, minimum, and count are used to summarize the information in each cell. The specific operations used to develop each raster are detailed in its metadata.
Abandoned↗
Global Oil & Gas Infrastructure Features Database EDX Spatial Web Map
This submission offers a link to a web mapping application hosted instance of the Global Oil & Gas Features Database (GOGI), via EDX Spatial. This offers users with the ability to visualize, interact, and create maps with data of their choice, as well as download specific attributes or fields of view from the database. This data can also be downloaded as a File Geodatabse from EDX at https://edx.netl.doe.gov/dataset/global-oil-gas-features-database. Access the technical report describing how this database was produced using the following link: https://edx.netl.doe.gov/dataset/development-of-an-open-global-oil-and-gas-infrastructure-inventory-and-geodatabase” This data was developed using a combination of big data computing, custom search and data integration algorithms, and expert driven search to collect open oil and gas data resources worldwide. This approach identified over 380 data sets and integrated more than 4.8 million features into the GOGI database. Acknowledgements: This work was funded under the Climate and Clean Air Coalition (CCAC) Oil and Gas Methane Science Studies. The studies are managed by United Nations Environment in collaboration with the Office of the Chief Scientist, Steven Hamburg of the Environmental Defense Fund. Funding was provided by the Environmental Defense Fund, OGCI Companies (Shell, BP, ENI, Petrobras, Repsol, Total, Equinor, CNPC, Saudi Aramco, Exxon, Oxy, Chevron, Pemex) and CCAC.
gas↗