抄録
As an important component of unconventional oil and gas resources, the efficient development of shale oil has long been constrained by key challenges such as strong reservoir heterogeneity and difficulties in identifying effective oil-bearing zones. The sweet spot, as a high-quality target in shale oil exploration and development, plays a critical role in enhancing productivity, optimizing development strategies, and reducing costs, making its accurate prediction highly significant. In recent years, with the rapid advancement of big data analytics and artificial intelligence, machine learning has achieved remarkable progress in sweet spot prediction, covering aspects such as multi-source geological and engineering data integration, feature extraction and selection, as well as model construction and optimization. This paper systematically reviews the applications of supervised learning, unsupervised learning, deep learning, and hybrid approaches in sweet spot prediction, summarizes the main challenges in data quality control, model generalization, and result interpretability, and analyzes the current bottlenecks limiting technological advancement, including insufficient geological consistency constraints, inadequate handling of imbalanced samples, and the lack of spatiotemporal coupling mechanisms. Furthermore, this paper explores frontier directions such as the deep integration of artificial intelligence with geological prior knowledge, cross-regional model development driven by federated learning, and the construction of end-to-end intelligent prediction systems. The aim is to provide a systematic review and future development reference for shale oil sweet spot prediction research, thereby promoting theoretical innovation and practical applications in this field.
