Geophysical Geothermal Reservoir Exploration, Monitoring, and Development – Volume II
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "J: Thermal Management".
Deadline for manuscript submissions: closed (25 July 2024) | Viewed by 4673
Special Issue Editors
Interests: combined near-surface geophysical exploration imaging and geothermal reservoir monitoring
Special Issues, Collections and Topics in MDPI journals
Interests: geothermal monitoring with geophysics and machine learning methods
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
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Hot dry rock (HDR) geothermal or supercritical geothermal systems are a clean renewable energy source of great developmental value. Geophysical methods, such as magnetotelluric (MT), distributed acoustic sensing (DAS), and gravitational, active, and passive seismic methods, are important technical means in the exploration, development, and monitoring of HDR reservoirs based on the differences in 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 in 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 geophysical methods to monitor reservoir parameter variations and build a dynamic reservoir model from different scales and parameters. The machine learning (ML) method is used to organize and classify geophysical data and to correct and calculate the reservoir dynamic model to predict the variation in reservoir intrinsic parameters. In this Special Issue, we want to present papers on geothermal resource exploration, monitoring, and development for HDR or deep supercritical geothermal systems. We also would like to address geothermal resource/reserve classifications and their mutual relations. We also invite authors specializing in technological novelties in geothermal exploration, monitoring, and development. 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 and geothermal well development;
- Supercritical geothermal systems.
Prof. Dr. Jing Li
Dr. Kai Gao
Prof. Dr. Zhaofa Zeng
Guest Editors
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