Big Data

Optimizing Prediction of Reservoir Properties with Artificial Intelligence, Big Data, and the Subsurface Trend Analysis Method

Falyn Eisiminger2021-12-30T18:45:21+00:00Categories: 2020 Presentations, Geohazards and Subsurface Modeling, Presentations|Tags: Big Data, Prediction, Reservoir Properties|

Rose, K., Suhag, A., Mark-Moser, M., and Wingo, P., “Optimizing Prediction of Reservoir Properties with Artificial Intelligence, Big Data, and the Subsurface Trend Analysis Method,” invited talk, accepted at the 2020 Machine Learning in Oil & Gas Virtual Conference, November 9–11, 2020.

Deep Learning to Locate Seafloor Landslides in High Resolution Bathymetry

Falyn Eisiminger2021-12-30T18:33:53+00:00Categories: 2020 Presentations, Infrastructure and Metocean Technology, Presentations|Tags: Big Data, Gulf of Mexico, Modeling, Prediction|

Dyer, A., Zaengle, D., Mark-Moser, M., Duran, R., Bauer, J., Rose, K., 2020. “Deep Learning to Locate Seafloor Landslides in High Resolution Bathymetry,” AGU Annual Fall Meeting (Virtual), 2020. https://www.osti.gov/servlets/purl/1779617

Building Big Data Geospatial Tools for a Common Operating Platform: Cumulative Spatial Impact Layers

Falyn Eisiminger2021-12-30T18:20:47+00:00Categories: 2020 Presentations, Assessing Current and Future Infrastructure Hazards, Presentations|Tags: Big Data, Geospatial|

Romeo, L. and Barkhurst, A. Building Big Data Geospatial Tools for a Common Operating Platform: Cumulative Spatial Impact Layers. DOE GIS Users Group Meeting. September 10, 2020. Virtual. Invited presentation.

Data to Discovery – Big Data, Machine Learning Informed Tools for Offshore Spill Prevention

Falyn Eisiminger2021-12-30T18:27:45+00:00Categories: 2020 Presentations, Presentations|Tags: Big Data, Offshore, Spill Prevention|

Rose, K., and Ciferno, J., “Data to Discovery – Big Data, Machine Learning Informed Tools for Offshore Spill Prevention,” IADC ART Committee Meeting, August 31, 2020, https://www.iadc.org/committees/advanced-rig-technology/

Subsurface Trend Analysis, a Multi-Variate Geospatial Approach for Evaluation of Geologic Properties and Uncertainty Reduction, Interpretation

Jeff Kirch2019-12-19T17:39:34+00:00Categories: 2019 Publications, ORM (Offshore Risk Modeling), Publications|Tags: Big Data, Geohazard Prediction, Methods, Reservoir Properties, STA, Subsurface Modeling|

In press.
Rose, K.; Bauer, J. R.; Mark-Moser, M.

Building Data-Driven Analytical Approaches and Tools to Evaluate Offshore Infrastructure Integrity

Jeff Kirch2022-01-12T19:17:42+00:00Categories: 2019 Presentations, ORM (Offshore Risk Modeling)|Tags: Big Data, Geospatial, Infrastructure, Offshore|

Romeo, L., Wenzlick, M., Dyer, A., Sabbatino, M., P. Wingo, Nelson, J., Barkhurst, A., Bauer, J., and Rose, K. 2019. Building Data-Driven Analytical Approaches and Tools to Evaluate Offshore Infrastructure Integrity. Addressing the nation’s energy needs through technology innovation – 2019 carbon capture, utilization, storage, and oil and gas technologies integrated review meeting, Pittsburgh, PA, August 26–30, 2019

NETL-TRS-13-2018; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Albany, OR, 2018; p 108. DOI: 10.18141/1461471.
Mark-Moser, M.; Miller, R.; Rose, K.; Bauer, J.; Disenhof, C.
July 2018
https://edx.netl.doe.gov/dataset/detailed-analysis-of-geospatial-trends-of-hydrocarbon-accumulations-offshore-gulf-of-mexico/resource_download/bbc09d03-004d-45dd-98ca-37a1352b0485

Extracting quasi-Steady Lagrangian transport patterns from the ocean circulation: An application to the Gulf of Mexico

Jeff Kirch2019-12-06T18:15:41+00:00Categories: 2018 Publications, ORM (Offshore Risk Modeling)|Tags: Big Data, CIAM, Metocean, Modeling, Prediction|

Scientific Reports 2018, 8, 10. DOI:10.1038/s41598-018-23121-y.
Duran, R.; Beron-Vera, F. J.; Olascoaga, M. J.
March 2018
https://www.nature.com/articles/s41598-018-23121-y