Missing Sonic Logs Generation for Gas Hydrate-Bearing Sediments via Hybrid Networks Combining Deep Learning With Rock Physics Modeling
Zikun Li, Jialong Xia, Zhichao Liu, Gang Lei, Kyungbook Lee, Fulong Ning
Abstract
Logging-while-drilling (LWD) sonic data is critical for marine gas hydrate reservoir evaluation and production prediction. However, acquiring complete acoustic logs, particularly shear-wave, poses significant challenges and incurs high costs. To tackle this issue, we develop a two-branch hybrid framework for predicting LWD sonic logs of hydrate-bearing sediments from existing logging data. One branch based on a rock physics model is utilized to generate background (no-hydrate/no-gas) elastic wave velocity profiles, while the other deep learning branch (DLB) compensates for the residuals between actual observations and the outputs of the first branch. The state-of-the-art Transformer encoder block is employed in the DLB to extract potentially intricate patterns within logging sequences. Such a scientific knowledge-guided network architecture with additional physics-based feature construction provides an explainable process that improves the physical consistency of predictions. Our method is tested with two publicly available datasets from the Cascadia continental margin. The hybrid model greatly enhances the predictive accuracy of the physical process model (with a minimum mean absolute percentage error of 0.73% and 4.33% for P- and S-wave velocities, respectively) and demonstrates outstanding generalization performance compared to pure data-driven approaches. The well-trained model offers impressive extrapolation beyond observed conditions for unmeasured high hydrate saturation (>40%) intervals in the region.