A Review on AI/ML applications in Geothermal systems

Artificial Intelligence (AI) and Machine Learning (ML) techniques have emerged as powerful tools to enhance the efficiency, reliability, and sustainability of geothermal energy systems. Traditional approaches to resource exploration, reservoir modeling, system optimization, and fault detection often face challenges due to the complex, nonlinear, and uncertain nature of geothermal environments. AI and ML algorithms, such as neural networks, support vector machines, decision trees, and deep learning architectures, offer data-driven solutions for handling these complexities. Recent studies demonstrate their effectiveness in geothermal resource identification, temperature and pressure prediction, energy production forecasting, and optimization of drilling and power plant operations. Additionally, hybrid approaches combining physics-based models with AI-driven methods are proving effective in improving accuracy and reducing computational costs.