NETL geo-data scientist Jennifer Bauer will discuss the Lab’s research activities focused on legacy oil and gas infrastructure — retired pipelines, platforms and other structures that remain in place after abandonment or end of use — at the Gulf Offshore Energy Safety Informational Webinar to be held from 11 a.m. to noon ET Thursday, Nov. 3, 2022.
The University of North Dakota’s Energy & Environmental Research Center’s (EERC) Produced Water Management Through Geologic Homogenization, Conditioning and Reuse (GHCR) project — funded by NETL and developed in partnership with the North Dakota Industrial Commission Oil and Gas Research Program and Nuverra Environmental Solutions — is a finalist in the Oil and Gas category of the 2022 Institution of Chemical Engineers (IChemE) Global Awards.
Barbara Kutchko, a senior researcher at NETL who develops advanced cement systems to reduce the environmental footprint of drilling operations, will serve as a key organizer and presenter at Cementing in an Unconventional World, a workshop presented by the Society of Petroleum Engineers (SPE) Sept. 27-29, 2022, in Galveston, Texas.
NETL’s Advanced Infrastructure Integrity Model (AIIM) is a science-based, artificial intelligence, big data, and big data computing informed approach to assessing offshore energy infrastructure integrity for safe future use and repurposing. AIIM is the latest advancement in the 2019 R&D 100 award-winning Offshore Risk Modeling suite, and was recognized as a top-ranked innovation and selected as a TechConnect National Innovation Awardee. Additional information on NETL’s participation at TechConnect was featured as an NETL story here: NETL Connects with Industry at National Innovation Symposium | netl.doe.gov.
NETL tool is being used to monitor the Great Pacific Garbage Patch for cleanup activities. A digital tool developed by NETL that helps examine ocean currents and wind patterns to predict where oil and other particles in the ocean are likely to travel in the event of an oil spill is being used for a range of non-energy related uses: like keeping track of the Great Pacific Garbage Patch for cleanup activities. NETL’s Kelly Rose, Ph.D., explained that Climatological and Instantaneous Isolation and Attraction Model (CIIAM) was developed within NETL’s Advanced Offshore Research portfolio (AOR) as one of several projects initiated because of lessons learned following the 2010 Deepwater Horizon oil spill in the Gulf of Mexico. That event highlighted the need for improved models, data and tools to prevent future events, and improve response preparedness.
Hall, D.M., Lvov, S.N, Gamwo, I.K, Prediction of Barium Sulfate Deposition in Petroleum and Hydrothermal Systems, Solid–Liquid Separation Technologies: Applications for Produced Water, CRC Press Taylor & Francis, in press, 2022.
Duran R., S. X. Liang, M. E. Allende Arandia and C. M. Appendini 2021. “On the Predictability of Loop Current Eddy Shedding Events and Unexpected Links to the Brazil and Guiana Currents,” AGU Fall Meeting 2021, Dec. 13-17, New Orleans, LA/Virtual.
Mark-Moser, M., Wingo, P., Duran, R., Dyer, A., Zaengle, D., Suhag, A., Hoover, B., Pantaleone, S., Shay, J., Bauer, J., Rose, K. “AI/ML Integration for Accelerated Analysis and Forecast of Offshore Hazards”, AGU Fall Meeting 2021, Dec. 13-17, New Orleans, LA/Virtual. Session: EP027 -Proven AI/ML applications in the Earth Sciences
Rose, K., Mark-Moser, M., Suhag, A., and Bauer, J. 2021. Improving prediction of subsurface properties using a geoscience informed, multi-technique, artificial intelligence approach (Invited). AGU Fall Meeting 2021, Dec. 13-17, New Orleans, LA/Virtual. Session H33C – Application of Multimodal Physics-Informed Machine Learning/Deep Learning in Subsurface Flow and Transport Modeling.
Dyer, A., Zaengle, D., Duran, R., Nelson, J., Wenzlick, M., Wingo, P., Bauer, J., Rose, K., and L. Romeo. (In Review, 2021). Applied Machine Learning Model Comparison: Predicting Offshore Platform Integrity with Gradient Boosting Algorithms and Neural Networks. Marine Structures.