Geothermal 4.0: AI-enabled geothermal reservoir development- current status, potentials, limitations, and ways forward

Muther, Temoor; Syed, Fahad I.; Lancaster, Austin T.; Salsabila, Farah D.; Dahaghi, Amirmasoud Kalantari; Negahban, Shahin

doi:10.1016/j.geothermics.2022.102348

Cited by 35 publications

(19 citation statements)

References 41 publications

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“…Because of the great advances of these three forces, plenty of signi cant progress has been made to resurge the breakthrough of AI in the recent decade. In the present study, we rst created a new image dataset, equipped a suitable experimental environment, and then selected empirically four models to perform comparative analysis to push forward the advance of geothermal AI, so-called Geothermal 4.0 [6]. The present study is a challenge for DL in the geothermics domain because there is a lack of a suitable dataset with high quality to train and develop a DL model for the recognition of the GSMs.…”

Section: Limitations and Future Workmentioning

confidence: 99%

“…Together with other new energies such as solar, wind, and biomass, it can play an essential role in energy-saving and emission reduction, coping with energy shortages, environmental and climate change, and realizing green and sustainable development that the world is facing [2][3][4][5]. However, the development of geothermal resources has been slowly progressing, although the proven long-term sustainability of geothermal energy has remained one of the attractions for further exploration and exploitation [6]. Geothermal resource exploration is the basis for its development and utilization, which need rst make geothermal potential mapping and evaluation.…”

Section: Introductionmentioning

confidence: 99%

“…Many GSMs, especially warm and hot springs, geysers, and fumaroles, have drawn plenty of attention from the geothermal community in recent decades. Many researchers investigated geothermal anomalies related to GSMs such as hot springs using geophysics [9][10][11], geochemistry [12][13][14], remote sensing [15][16][17][18][19], Geographic Information System (GIS) [20], statistical modeling [21] and conventional Machine Learning (ML) [6,22,23]. For example, Gentana et al (2019) demonstrated that the fault system is correlated with the appearances of the GSMs in the Indonesia volcanic zone [24]; Freski et al (2021) tested the effects of alteration degree, moisture, and temperature on laser return intensity for the GSMs.…”

Section: Introductionmentioning

confidence: 99%

“…Most of these works revealed the distribution and formation of geothermal resources by integrating multisource data with traditional approaches [25]. Compared to the previous ML models, Arti cial Neural Network (ANN) is getting more and more attention nowadays [6]. Dramsch (2020) made an overview of the development of ML in geoscience in the recent 70 years with an emphasis on technical explanations of some popularly used ML models [26].…”

Section: Introductionmentioning

confidence: 99%

“…Dramsch (2020) made an overview of the development of ML in geoscience in the recent 70 years with an emphasis on technical explanations of some popularly used ML models [26]. Muther et al (2022) outlined Arti cial Intelligence (AI) technology integration with geothermal reservoir characterization and management, discussed its potentials, limitations, and ways forward, compared different statistical, numerical, and AI/ML methods, and put forward a concept of Geothermal 4.0 stemmed from the concept of Industry 4.0 [6].…”

Section: Introductionmentioning

confidence: 99%

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Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning

Xiong

Zhu

et al. 2022

Preprint

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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 and the biggest OF for both validation and test, and the fastest CT for both validation and test. On the contrary, the three selected ML classifiers perform poorly for this task due to their low OA, small OF, and long CT. It 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.

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Section: Limitations and Future Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning

Xiong

Zhu

et al. 2022

Preprint

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Application of machine learning and deep learning in geothermal resource development: Trends and perspectives

Al‐Fakih,

Abdulraheem,

Kaka

2024

Deep Underground Science and Engineering

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This study delves into the latest advancements in machine learning and deep learning applications in geothermal resource development, extending the analysis up to 2024. It focuses on artificial intelligence's transformative role in the geothermal industry, analyzing recent literature from Scopus and Google Scholar to identify emerging trends, challenges, and future opportunities. The results reveal a marked increase in artificial intelligence (AI) applications, particularly in reservoir engineering, with significant advancements observed post‐2019. This study highlights AI's potential in enhancing drilling and exploration, emphasizing the integration of detailed case studies and practical applications. It also underscores the importance of ongoing research and tailored AI applications, in light of the rapid technological advancements and future trends in the field.

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Applications of artificial intelligence in geothermal resource exploration: A review

AlGaiar,

Hossain,

Petrovski

et al. 2024

Deep Underground Science and Engineering

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Artificial intelligence (AI) has become increasingly important in geothermal exploration, significantly improving the efficiency of resource identification. This review examines current AI applications, focusing on the algorithms used, the challenges addressed, and the opportunities created. In addition, the review highlights the growth of machine learning applications in geothermal exploration over the past decade, demonstrating how AI has improved the analysis of subsurface data to identify potential resources. AI techniques such as neural networks, support vector machines, and decision trees are used to estimate subsurface temperatures, predict rock and fluid properties, and identify optimal drilling locations. In particular, neural networks are the most widely used technique, further contributing to improved exploration efficiency. However, the widespread adoption of AI in geothermal exploration is hindered by challenges, such as data accessibility, data quality, and the need for tailored data science training for industry professionals. Furthermore, the review emphasizes the importance of data engineering methodologies, data scaling, and standardization to enable the development of accurate and generalizable AI models for geothermal exploration. It is concluded that the integration of AI into geothermal exploration holds great promise for accelerating the development of geothermal energy resources. By effectively addressing key challenges and leveraging AI technologies, the geothermal industry can unlock cost‐effective and sustainable power generation opportunities.

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Geothermal 4.0: AI-enabled geothermal reservoir development- current status, potentials, limitations, and ways forward

Cited by 35 publications

References 41 publications

Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning

Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning

Application of machine learning and deep learning in geothermal resource development: Trends and perspectives

Applications of artificial intelligence in geothermal resource exploration: A review

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