Deep-learning-based dual model with an iterative prediction process for the improvement of missing well-log predictions

Traditional well-log analysis often involves incomplete datasets, which reduces the accuracy of petrophysical assessments. This study thus introduces an innovative dual-model approach that integrates a conditional variational autoencoder (CVAE) with a long short-term memory (LSTM) to predict missing shear-slowness (DTS) data and other well-log data. Utilizing well-logs and the corresponding lithological sequence from the Volve oil field in the North Sea, the proposed model demonstrates excellent prediction capabilities when facing multiple types of missing well-logs. Our findings reveal that the CVAE-LSTM model not only enhances DTS prediction accuracy but also adapts to the inherent variability of geological formations. It outperforms traditional autoencoder and standalone LSTM models across a range of metrics, including correlation coefficients, the root mean squared error, and Kolmogorov–Smirnov statistics, validating the predictive accuracy of the proposed model and the alignment of the statistical distributions for predicted and actual logs. The robustness of the proposed model is further highlighted by its ability to maintain its high performance despite the absence of key well-log data such as compressional slowness and the neutron porosity index. This study demonstrates the effectiveness of advanced machine-learning techniques in overcoming the limitations associated with incomplete well-log datasets.