Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Applications of artificial intelligence in geothermal resource exploration: A review
Abstract 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.
Applications of artificial intelligence in geothermal resource exploration: A review
Abstract 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.
Applications of artificial intelligence in geothermal resource exploration: A review
Mahmoud AlGaiar (Autor:in) / Mamdud Hossain (Autor:in) / Andrei Petrovski (Autor:in) / Aref Lashin (Autor:in) / Nadimul Faisal (Autor:in)
2024
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
Applications of artificial intelligence in geothermal resource exploration: A review
| Wiley | 2024
Applications of artificial intelligence in geothermal resource exploration: A review
| Wiley | 2024
Geologic remote sensing for geothermal exploration: A review
| Online Contents | 2014