Working Smarter Not Harder - Developing a Virtual Subsurface Data Framework for U.S. Energy R&D
Abstract
The data revolution has resulted in a proliferation of resources that span beyond commercial and social networking domains. Research, scientific, and engineering data resources, including subsurface characterization, modeling, and analytical datasets, are increasingly available through online portals, warehouses, and systems. Data for subsurface systems is still challenging to access, discontinuous, and varies in resolution. However, with the proliferation of online data there are significant opportunities to advance access and knowledge of subsurface systems. The Energy Data eXchange (EDX) is an online platform designed to address research data needs by improving access to energy R&D products through advanced search capabilities. In addition, EDX hosts private, virtualized computational workspaces in support of multi-organizational R&D. These collaborative workspaces allow teams to share working data resources and connect to a growing number of analytical tools to support research efforts. One recent application, a team digital data notebook tool, called DataBook, was introduced within EDX workspaces to allow teams to capture contextual and structured data resources. Starting with DOE's subsurface R&D community, the EDX team has been developing DataBook to support scientists and engineers working on subsurface energy research, allowing them to contribute and curate both structured and unstructured data and knowledge about subsurface systems. These resources span petrophysical, geologic, engineering, geophysical, interpretations, models, and analyses associated with carbon storage, water, oil, gas, geothermal, induced seismicity and other subsurface systems to support the development of a virtual subsurface data framework. The integration of EDX and DataBook allows for these systems to leverage each other's best features, such as the ability to interact with other systems (Earthcube, OpenEI.net, NGDS, etc.) and leverage custom machine learning algorithms and capabilities to enhance user experience, make access and connection to relevant subsurface data resources more efficient for research teams to use, analyze and draw insights. Ultimately, the public and private resources in EDX seek to make subsurface energy research more efficient, reduce redundancy, and drive innovation.
- Publication:
-
AGU Fall Meeting Abstracts
- Pub Date:
- December 2017
- Bibcode:
- 2017AGUFMIN21C0050R
- Keywords:
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- 0525 Data management;
- COMPUTATIONAL GEOPHYSICS;
- 1847 Modeling;
- HYDROLOGY;
- 5112 Microstructure;
- PHYSICAL PROPERTIES OF ROCKS;
- 5194 Instruments and techniques;
- PHYSICAL PROPERTIES OF ROCKS