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Time-Lapse Geophysical Geothermal Reservoir Monitoring and Prediction by Deep Learning
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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 12034

Special Issue Editors

Prof. Dr. Jing Li

E-Mail Website
Guest Editor
College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China
Interests: combined near-surface geophysical exploration imaging and geothermal reservoir monitoring
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhaofa Zeng

E-Mail Website
Guest Editor
1. College of Geo-exploration Science and Technology, Jilin University, Changchun 130026, China
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 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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Related Special Issue

Published Papers (4 papers)

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Research

16 pages, 4149 KiB  
Article
3-D Inversion of Gravity Data of the Central and Eastern Gonghe Basin for Geothermal Exploration
by Jianwei Zhao, Zhaofa Zeng, Shuai Zhou, Jiahe Yan and Baizhou An
Energies 2023, 16(5), 2277; https://doi.org/10.3390/en16052277 - 27 Feb 2023
Cited by 4 | Viewed by 2102
Abstract
The Gonghe Basin is one of the most important regions for the exploration and development of hot dry rock geothermal resources in China. However, there is still some controversy about the main heat source of hot dry rock geothermal resources in the Gonghe [...] Read more.
The Gonghe Basin is one of the most important regions for the exploration and development of hot dry rock geothermal resources in China. However, there is still some controversy about the main heat source of hot dry rock geothermal resources in the Gonghe Basin. Combined with previous research results including three-dimensional magnetotelluric imaging and linear inversion of Rayleigh wave group and phase velocity result, we obtained a high-resolution underground spatial density distribution model of the Gonghe Basin based on satellite gravity data by using 3-D gravity focusing inversion method. According to the results, there are widely distributed low density anomalies relative to surrounding rock in the middle crust of the study area. The low-density layer is speculated to be a low-velocity, high-conductivity partial melting layer in the crust of the Gonghe Basin. The inversion result confirms for the first time the existence of a partial melt layer from the gravity point of view, and this high temperature melt layer may be the main heat source of the hot dry rock geothermal resources in the Gonghe Basin. It can provide a new basis for further research on the genesis of the hot dry rock geothermal system in the Gonghe Basin. Full article
(This article belongs to the Special Issue Time-Lapse Geophysical Geothermal Reservoir Monitoring and Prediction by Deep Learning)
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17 pages, 8563 KiB  
Article
UNet–Based Temperature Simulation of Hot Dry Rock in the Gonghe Basin
by Wanli Gao, Jingtao Zhao and Suping Peng
Energies 2022, 15(17), 6162; https://doi.org/10.3390/en15176162 - 25 Aug 2022
Cited by 6 | Viewed by 2214
Abstract
Hot dry rock (HDR) geothermal energy, as a clean and renewable energy, has potential value in meeting the rapid demand of the social economy. Predicting the temperature distribution of a subsurface target zone is a fundamental issue for the exploration and evaluation of [...] Read more.
Hot dry rock (HDR) geothermal energy, as a clean and renewable energy, has potential value in meeting the rapid demand of the social economy. Predicting the temperature distribution of a subsurface target zone is a fundamental issue for the exploration and evaluation of hot dry rock. Numerical finite–element simulation is currently the mainstream method used to study the variation in underground temperature fields. However, it has difficulty in dealing with multiple geological elements of deep and complex hot dry rock models. A Unity networking for hot dry rock temperature (HDRT–UNet) is proposed in this study that incorporates the matrix rock temperature field equation for relating the three parameters of density, specific heat capacity and thermal conductivity. According to the numerical geological structures and rock parameters of cap rocks, faults and magma intrusions, a new dataset simulated by the finite element method was created for training the HDRT–UNet. The temperature simulation results in the Gonghe basin show that the predicted temperatures within faults and granites were higher than their surrounding rocks, while a lower thermal conductivity of the cap rocks caused the temperature of overlying strata to be smaller than their surrounding temperature field. The simulation results also prove that our proposed HDRT–UNet can provide a certain evolutionary knowledge for the prediction and development of geothermal reserves. Full article
(This article belongs to the Special Issue Time-Lapse Geophysical Geothermal Reservoir Monitoring and Prediction by Deep Learning)
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26 pages, 35132 KiB  
Article
Multiscale Full-Waveform Inversion with Land Seismic Field Data: A Case Study from the Jizhong Depression, Middle Eastern China
by Kai Wang, Xuan Feng, Alison Malcolm, Christopher Williams, Xiaojiang Wang, Kai Zhang, Baowei Zhang and Hangyu Yue
Energies 2022, 15(9), 3223; https://doi.org/10.3390/en15093223 - 28 Apr 2022
Cited by 5 | Viewed by 2735
Abstract
The Jizhong depression contains several geothermal reservoirs that are characterized by localized low-velocity anomalies. In this article, full-waveform inversion (FWI) is used to characterize these anomalies and determine their extent. This is a challenging problem because the reservoirs are quite small and the [...] Read more.
The Jizhong depression contains several geothermal reservoirs that are characterized by localized low-velocity anomalies. In this article, full-waveform inversion (FWI) is used to characterize these anomalies and determine their extent. This is a challenging problem because the reservoirs are quite small and the available data have usable frequencies only down to 5 Hz. An accurate-enough starting model is carefully built by using an iterative travel time tomography method combined with a cycle-skipping assessment method to begin the inversion at 5 Hz. A multiscale Laplace–Fourier-domain FWI with a layer-stripping approach is implemented on the starting model by gradually increasing the maximum offset. The result of overlapping the recovered velocity model on the migrated seismic profile shows a good correlation between the two results. The recovered model is assessed by ray tracing, synthetic seismogram modeling, checkerboard testing and comparisons with nearby borehole data. These tests indicate that low-velocity anomalies down to a size of 0.3 km × 0.3 km at a maximum depth of 2 km can be recovered. Combined with the well log data, the resulting velocity model allows us to delineate two potential geothermal resources, one of which was previously unknown. Full article
(This article belongs to the Special Issue Time-Lapse Geophysical Geothermal Reservoir Monitoring and Prediction by Deep Learning)
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29 pages, 5333 KiB  
Article
Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning
by Yongzhu Xiong, Mingyong Zhu, Yongyi Li, Kekun Huang, Yankui Chen and Jingqing Liao
Energies 2022, 15(8), 2913; https://doi.org/10.3390/en15082913 - 15 Apr 2022
Cited by 9 | Viewed by 3478
Abstract
Geothermal surface manifestations (GSMs) are direct clues towards hydrothermal activities of a geothermal system in the subsurface and significant indications for geothermal resource exploration. It is essential to recognize various GSMs for potential geothermal energy exploration. However, there is a lack of work [...] Read more.
Geothermal surface manifestations (GSMs) are direct clues towards hydrothermal activities of a geothermal system in the subsurface and significant indications for geothermal resource exploration. It is essential to recognize various GSMs for potential geothermal energy exploration. However, there is a lack of work to fulfill this task using deep learning (DL), which has achieved unprecedented successes in computer vision and image interpretation. This study aims to explore the feasibility of using a DL model to fulfill the recognition of GSMs with photographs. A new image dataset was created for the GSM recognition by preprocessing and visual interpretation with expert knowledge and a high-quality check after downloading images from the Internet. The dataset consists of seven GSM types, i.e., warm spring, hot spring, geyser, fumarole, mud pot, hydrothermal alteration, crater lake, and one type of none GSM, including 500 images of different photographs for each type. The recognition results of the GoogLeNet model were compared with those of three machine learning (ML) algorithms, i.e., Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN), by using the assessment metrics of overall accuracy (OA), overall F1 score (OF), and computational time (CT) for training and testing the models via cross-validation. The results show that the retrained GoogLeNet model using transfer learning has significant advantages of accuracies and performances over the three ML classifiers, with the highest OA, the biggest OF, and the fastest CT for both the validation and test. Correspondingly, the three selected ML classifiers perform poorly for this task due to their low OA, small OF, and long CT. This suggests that transfer learning with a pretrained network be a feasible method to fulfill the recognition of the GSMs. Hopefully, this study provides a reference paradigm to help promote further research on the application of state-of-the-art DL in the geothermics domain. Full article
(This article belongs to the Special Issue Time-Lapse Geophysical Geothermal Reservoir Monitoring and Prediction by Deep Learning)
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