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.

Reservoir characterization (identification and parameter estimation) is critical for gas hydrate exploration and development. While machine learning techniques excel at capturing complex relationships in well-logging reservoir characterization, existing research mainly focuses on end-to-end supervised learning approaches relying on costly labeled data and lacking multi-task learning capabilities. In this paper, we introduce a self-supervised learning (SSL) framework to learn general representations of large-scale unlabeled well-logging intervals from sandy, silty, and clayey hydrate reservoirs via masked autoencoders (WirMAE). By incorporating channel-based attention mechanisms and a masked reconstruction pre-training strategy for variable tokens, WirMAE effectively extracts intrinsic multivariate correlations within logging data, enabling the generation of various missing log curves. The model is validated on data from globally distributed hydrate with diverse accumulation patterns. Compared to supervised deep learning methods and classical machine learning models, Fine-turned WirMAE achieves superior reservoir identification accuracy (average F1-score: 0.864) using only 1% labeled data in complex geological settings. Combined with domain expertise, WirMAE yields precise estimation of key reservoir parameters such as hydrate saturation and permeability, outperforming conventional petrophysical methods. Additionally, embedding visualizations and attention analyses reveal the inner workings of the model and its consistency with expert-driven geological interpretations. Our findings highlight the potential of SSL for advancing more accurate and transparent intelligent reservoir characterization using well-log data, indicating that the application of WirMAE could be extended to broader hydrocarbon reservoirs in the future.

“Thin-bed” reservoirs have become important targets of seismic exploration and exploitation. However, traditional amplitude versus offset/amplitude versus angle (AVO/AVA) technologies, for example, those based on Zoeppritz equations and their approximations for a single interface, are not sufficiently accurate for thin-bed stratigraphy. Analytic solutions of thin-bed reflectivity may become practical for thin-bed AVO analysis and inversion. Therefore, a linear analytic approximation of thin-bed P-wave reflectivity is developed under small-incidence and thin-bed assumptions. Numerical simulations show that the amplitude approximation errors are usually smaller than 10% for incidence angles less than 20 degrees, and the thin-bed thicknesses are less than one-tenth of the P-wave wavelength. Based on the least-squares approach, the inversion strategy is proposed using the approximate formula. A synthetic data test shows that the proposed inversion method can produce more accurate thin-bed properties than that based on the Zoeppritz equations, which reveals the potential of the inversion method based on the linear analytic approximate formula in the fine characterization of thin reservoirs.