A Machine Learning Approach for the Clustering and Classification of Geothermal Reservoirs in the Ying-Qiong Basin

The exploration and development of marine geothermal energy is a field with significant potential, but it is also one that presents considerable challenges and costs. The assessment of marine geothermal reservoir potential is currently based on subjective analysis, and this study proposes an innovative clustering-based method to classify marine geothermal reservoirs systematically. The Yingqiong Basin was analysed to develop a machine learning framework to predict the potential of marine geothermal reservoirs (CPPOGR). The study integrated eight key geothermal features into a unified dataset, employing dimensionality reduction techniques (principal component analysis and sparse autoencoder) and SMOTE to balance the sample size. Machine learning classifiers, including XGBoost, BP Neural Networks, Support Vector Machines, K-Nearest Neighbours, and Random Forests, were utilised for prediction. The experimental results demonstrate that XGBoost is the most suitable classifier, achieving an excellent performance of 0.96 precision, 0.9556 recall, 0.9528 F1 score, and 0.9623 accuracy. These results demonstrate the effectiveness of the proposed CPPOGR in accurately classifying marine geothermal reservoirs based on intrinsic features. This study underscores the potential of integrating cluster analysis with machine learning for efficient reservoir characterisation, thereby offering a novel approach for marine geothermal resource assessment.