Reservoir Discrimination Based on Physic-Informed Semi-Supervised Learning

Accurate and stable identification of oil and gas reservoirs based on seismic data can effectively improve exploration success rates, enhance production efficiency, and reduce exploration and development costs. Limited by uncertainties in seismic data and inadequate label samples, problems of overfitting and instability generally exist in current deep-learning reservoir discrimination studies. A semi-supervised physics-informed workflow for reservoir discrimination is herein proposed. The approach synthesizes rock physics theory, elastic forward modeling, prior geological information, and deep learning algorithms. Furthermore, to establish a connection between seismic data and reservoir types, a geofluid parameter is employed, selected for its sensitivity to oil and gas reservoirs and its reliable extraction from seismic data. Accordingly, the reservoir classification network, geofluid inversion network, and elastic forward network are designed to complete the reservoir prediction cooperatively with a task-decomposed strategy. Finally, the established networks are optimized based on the constructed "seismic-geofluid-reservoir" training dataset with the proposed multi-step cooperative semi-supervised training strategy, which can improve the learning ability of the model by capturing explicit physics knowledge from labeled data, mining implicit knowledge from massive unlabeled data, and incorporating geophysics domain knowledge simultaneously. The proposed reservoir discrimination workflow is successfully applied to a field survey. The precision, recall, and f1-score of the predicted gas reservoirs can reach about 55%, 84%, and 67%.