Strack, K.; Davydycheva, S.; Passalacqua, H.; Smirnov, M.; Xu, X. Using Cloud-Based Array Electromagnetics on the Path to Zero Carbon Footprint during the Energy Transition. J. Mar. Sci. Eng.2021, 9, 906.
Strack, K.; Davydycheva, S.; Passalacqua, H.; Smirnov, M.; Xu, X. Using Cloud-Based Array Electromagnetics on the Path to Zero Carbon Footprint during the Energy Transition. J. Mar. Sci. Eng. 2021, 9, 906.
Strack, K.; Davydycheva, S.; Passalacqua, H.; Smirnov, M.; Xu, X. Using Cloud-Based Array Electromagnetics on the Path to Zero Carbon Footprint during the Energy Transition. J. Mar. Sci. Eng.2021, 9, 906.
Strack, K.; Davydycheva, S.; Passalacqua, H.; Smirnov, M.; Xu, X. Using Cloud-Based Array Electromagnetics on the Path to Zero Carbon Footprint during the Energy Transition. J. Mar. Sci. Eng. 2021, 9, 906.
Abstract
One of the key geophysical technologies for the energy industry during energy transition to zero footprint is fluid imaging. Knowledge of fluid distribution allows better, more optimized production reducing thus CO2 footprint per barrel produced and for CO2 storage the knowledge of where stored fluids go is mandatory to monitor reservoir seals. Electromagnetic is the preferred way to image fluid due to its strong coupling to the fluid resistivity. Unfortunately, acquiring and interpreting the data takes too long to contribute significantly to field operation and cost optimization. Using artificial intelligence and Cloud based data acquisition we can reduce the operational feedback to near real time and for the interpretation to close to 24 h. This then opens new door for the usefulness of this technology from exploration, monitoring and allows the application envelope to be enlarged to much noisier environment where real time acquisition can be optimized based on the acquired data.
Keywords
CSEM; artificial intelligence; energy transition using electromagnetics; reservoir monitoring; CCUS
Subject
Environmental and Earth Sciences, Atmospheric Science and Meteorology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
