Time-Lapse Geophysical Geothermal Reservoir Monitoring and Prediction by Deep Learning
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "J: Thermal Management".
Deadline for manuscript submissions: closed (10 April 2023) | Viewed by 11815
Special Issue Editors
Interests: combined near-surface geophysical exploration imaging and geothermal reservoir monitoring
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2. Key Laboratory of Applied Geophysics, Ministry of Natural Resources of PRC, Changchun 130026, China
3. Ministry of Land and Resources Key Laboratory of Applied Geophysics, Jilin University, Changchun 130026, China
Interests: electromagnetic inversion and geological interpretation
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Special Issue Information
Dear Colleagues,
Hot dry rock (HDR) geothermal is considered to be a clean renewable energy source of great developmental value. Geophysical methods, such as low-frequency electromagnetic, gravitational, and seismic, are important technical means in the exploration, development, and monitoring of HDR reservoirs based on the differences of reservoir physics parameters. The conventional geothermal-geophysical methods focus on the reservoir interpretation and evaluation of the HDR target site. This does not provide details about the formation mechanisms of HDR thermal storage and the temporal and spatial variation of the geothermal heat flux, especially for the monitoring of reservoir intrinsic parameters before and after artificial fracturing, such as the extension of fractures in the reservoir, the distribution of fluid migration, and reservoir permeability. Based on the gravitational anomaly, electrical parameters (resistivity, impedance phase), and reservoir velocity changes, we combine different time-lapse geophysical methods to monitor reservoir parameter variations and build a dynamic reservoir model from different scales and different parameters. The machine learning (ML) method is used to organize and classify the time-lapse geophysical data and to correct and calculate the reservoir dynamic model to predict the variation of reservoir intrinsic parameters. In this Special Issue, we would like to present papers on geothermal resource exploration and monitoring for shallow, deep, and HDR structures. We also would like to address geothermal resource/reserve classifications and their mutual relations. We also invite authors specializing in technological novelties of geothermal time-lapse monitoring and prediction. This Special Issue calls for theoretical and empirical papers focusing on the following topics:
- Geothermal reservoir monitoring by geophysics methods;
- Geothermal reservoir prediction by deep learning;
- Geothermal reservoir modeling and simulation;
- Geothermal multi-field coupling.
Prof. Dr. Jing Li
Prof. Dr. Zhaofa Zeng
Guest Editors
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Keywords
In this Special Issue, we would like to present papers on geothermal resource exploration and monitoring for shallow, deep, and HDR structures. We also would like to address geothermal resource/reserve classifications and their mutual relations. We also invite authors specializing in technological novelties of geothermal time-lapse monitoring and prediction. This Special Issue calls for theoretical and empirical papers focusing on the following topics:
- Geothermal reservoir monitoring by geophysics methods;
- Geothermal reservoir prediction by deep learning;
- Geothermal reservoir modeling and simulation;
- Geothermal multi-field coupling.
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