A Novel Fuzzy Logic Geospatial Approach to Characterize and Predict Subsurface Structural Complexity
Presented by NETL at AGU Fall Meeting, 12/19
Engineering topics
Publications and source records attributed to Rose, Kelly.
Presented by NETL at AGU Fall Meeting, 12/19
Presented by NETL at ESRI User Conference, 07/19
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.
A collection of open and public data that contains REE and Coal data that covers the US coal basins.
The REE and Coal Open Database is an online collection of subsurface contextual data from publicly available geological, geochemical, and geospatial resources. These data align to and support execution of NETL’s REE coal assessment method. The database includes basin- and national-level spatial datasets, in addition to other non-spatial data that support the assessment approach. Data in this collection are sourced from a range of authoritative, public sources, including NETL, U.S. Geological Survey (USGS), Energy Information Administration (EIA), and state geological surveys.
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.
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.
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.
Presented by NETL at the Groundwater Quality and Unconventional Oil and Gas Development: Current Understanding and Science Needs (#224); Columbus, OH; April 25-26, 2017
Natural gas storage facilities are a critical component of our energy supply and distribution chain, allowing elasticity in gas supply to accommodate daily to seasonal demand fluctuations. As has been made evident by the recent Aliso Canyon Gas Storage facility incident, a loss of well integrity may result in significant consequences, including the prolonged shutdown of an entire facility. The Aliso Canyon gas well blowout emitted approximately 100,000 tonnes of natural gas (mostly methane) over 4 months and displaced thousands of nearby residents from their homes. The high visibility of the event has led to increased scrutiny of the safety of natural gas storage at the Aliso Canyon facility, led to questions about energy reliability, and raised broader concerns for natural gas storage integrity throughout the country.
An ArcGIS Online Story Map that reviews the geothermal potential exploration and modeling of Newberry Volcano. The Story Map focuses on the subsurface model built by an NETL team for the DOE 4D EGS Geothermal Monitoring project.
Presented by NETL at the Stanford Geothermal Workshop; Stanford, CA; February 13-14, 2017
Introduction Motivation The 2015-2016 Aliso Canyon/Porter Ranch natural gas well blowout emitted approximately 100,000 tonnes of natural gas (mostly methane, CH 4 ) over four months. The blowout impacted thousands of nearby residents, who were displaced from their homes. The high visibility of the event has led to increased scrutiny of the safety of natural gas storage at the Aliso Canyon facility, as well as broader concern for natural gas storage integrity throughout the country. Federal Review of Well Integrity In April of 2016, the U.S. Department of Energy (DOE), in conjunction with the U.S. Department of Transportation (DOT) through the Pipeline and Hazardous Materials Safety Administration (PHMSA), announced the formation of a new Interagency Task Force on Natural Gas Storage Safety. The Task Force enlisted a group of scientists and engineers at the DOE National Laboratories to review the state of well integrity in natural gas storage in the U.S. The overarching objective of the review is to gather, analyze, catalogue, and disseminate information and findings that can lead to improved natural gas storage safety and security and thus reduce the risk of future events. The “Protecting our Infrastructure of Pipelines and Enhancing Safety Act of 2016’’ or the ‘‘PIPES Act of 2016,’’which was signed into law on June 22, 2016, created an Aliso Canyon Natural Gas Leak Task Force led by the Secretary of Energy and consisting of representatives from the DOT, Environmental Protection Agency (EPA), Department of Health and Human Services, Federal Energy Regulatory Commission (FERC), Department of Commerce and the Department of Interior. The Task Force was asked to perform an analysis of the Aliso Canyon event and make recommendations on preventing similar incidents in the future. The PIPES Act also required that DOT/PHMSA promulgate minimum safety standards for underground storage that would take effect within two years. Background on the DOE National Laboratories Well Integrity Work Group One of the primary areas that the Task Force is studying is integrity of natural gas wells at storage facilities. The DOE Office of Fossil Energy (FE) took the lead in this area and asked scientists and engineers from the National Energy Technology Laboratory (NETL), Lawrence Livermore National Laboratory (LLNL), Sandia National Laboratories (SNL), and Lawrence Berkeley National Laboratory (LBNL)) to form a Work Group to address this area. This Work Group is an expansion of the original “Lab Team” comprising scientists and engineers from SNL, LLNL, and LBNL which was formed to support the State of California’s response to the Aliso Canyon incident and operated under the Governor of California’s Aliso Canyon Emergency Order (1/6/2016). The Lab Team played a key role in advising the State of California’s Department of Conservation (DOC) in its oversight of SoCalGas during and after the incident.
The 2015-2016 Aliso Canyon/Porter Ranch natural gas well blowout emitted approximately 100,000 tonnes of natural gas (mostly methane, CH 4 ) over four months. The blowout impacted thousands of nearby residents, who were displaced from their homes. The high visibility of the event has led to increased scrutiny of the safety of natural gas storage at the Aliso Canyon facility, as well as broader concern for natural gas storage integrity throughout the country. This report presents the findings of the DOE National Laboratories Well Integrity Work Group efforts in the four tasks. In addition to documenting the work of the Work Group, this report presents high priority recommendations to improve well integrity and reduce the likelihood and consequences of subsurface natural gas leaks.
Formation fluid influxes (i.e. kicks) pose persistent challenges and operational costs during drilling operations. Implications of kicks range in scale but cumulatively result in substantial costs that affect drilling safety, environment, schedule, and infrastructure. Early kick detection presents a low-cost, easily adopted solution for avoiding well control challenges associated with kicks near the bit. Borehole geophysical tools used during the drilling process as part of the logging-while-drilling (LWD) and measurement-while-drilling (MWD) provide the advantage of offering real-time downhole data. LWD/MWD collect data on both the annulus and borehole wall. The annular data are normally treated as background, and are filtered out to isolate the formation measurements. Because kicks will change the local physical properties of annular fluids, bottom-hole measurements are among the first indicators that a formation fluid has invaded the wellbore. This report describes and validates a technique for using the annular portion of LWD/MWD data to facilitate early kick detection using first order principles. The detection technique leverages data from standard and cost-effective technologies that are typically implemented during well drilling, such as MWD/LWD data in combination with mud-pulse telemetry for data transmission.
Research references to literature about the Newberry geothermal area, Oregon.
Datasets and information used to characterize the subsurface of Newberry and support modeling efforts. Includes sources for well logs, earthquakes, maps & cross-sections, and LiDAR/DEM