Enhanced Geothermal Systems: Potential for Geothermal Energy Production
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "J: Thermal Management".
Deadline for manuscript submissions: 25 September 2025 | Viewed by 42
Special Issue Editors
Interests: data mining (modeling/development); evolutionary algorithms; computer vision; hybrid modeling; complex computational approaches; optimization techniques
Special Issues, Collections and Topics in MDPI journals
Interests: exploration geophysics; geology; remote sensing; spatial analysis; spatial statistics; geostatistical analysis; applied artificial intelligence; data science; image analysis; data mining
Special Issue Information
Dear Colleagues,
Enhanced geothermal systems (EGSs) represent a game-changing breakthrough in geothermal energy exploration and production, allowing for higher heat extraction from dry or low-permeability rock formations. Such a process aims to stimulate the reservoirs by injection and creating artificial fractures to gain higher productivity. Therefore, EGS can harness geothermal energy in regions previously considered unsuitable for geothermal power generation. This technology has the potential to significantly expand the global availability of clean and renewable energy, reduce greenhouse gas emissions, and provide a reliable baseload power source. However, EGSs face several challenges, including high upfront costs, subsurface uncertainty, and a risk of induced seismicity. Addressing these challenges requires innovative approaches to reservoir characterization, fracture network modeling, and real-time monitoring. This is where deep learning (DL) can play a transformative role and what the current Special Issue will be focused on. Therefore, the integration of DL into EGS research holds immense promise for overcoming technical and economic barriers, unlocking the potential of geothermal energy. By advancing DL-based solutions for reservoir characterization, fracture modeling, real-time monitoring, and sustainability assessment, researchers can accelerate the deployment of EGSs and contribute to a cleaner, more sustainable energy future.
Topics of interest for this SI include, but are not limited to, the following:
- Geothermal modeling exploration.
- Integrated numerical and DL approaches for geothermal reservoir characterization/ fracture network predictions.
- DL-based integrated multisource and multimodal data (e.g., seismic, geophysical, well-log, etc.)/(e.g., seismic, thermal, and geochemical) in subsurface imaging.
- Prediction of rock properties, fracture networks, and thermal gradients using DL models like CNN or RNN.
- Fracture network modeling using hybrid visualized DL-based approaches.
- Simulation of fracture propagation/optimize injection strategies using developed DL and specifically generative adversarial networks (GANs).
- Real-time monitoring and analysis of microseismic data to monitor reservoir behavior and mitigate induced seismicity.
- Application of reinforcement learning (RL) to optimize fluid injection and production rates dynamically.
- Identifying high-potential EGS sites using DL-based assessment approaches (application of satellite data, geological maps, and historical drilling data).
- Predictive modeling of long-term reservoir performance and energy output.
- Uncertainty quantification of subsurface resources using advanced automated DL/probabilistic DL techniques.
- Developing DL models to assess the environmental impact of EGS operations, including water usage and carbon footprint.
- Optimizing EGS designs for minimal environmental disruption and maximum energy efficiency.
Dr. Abbas Abbaszadeh Shahri
Dr. Fardad Maghsoudi Moud
Guest Editors
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Keywords
- geothermal exploration
- deep learning
- uncertainty quantification
- real-time monitoring
- enhanced geothermal systems
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