Clastic reservoirs exhibit complex and diverse lithologies. Some lithological heterogeneities, occurring as thin but effectively low-permeability units, have pronounced impact on CO₂ flooding schemes and oil recovery. Thin low-permeability units within permeable sandbodies typically exhibit weak well-log responses, and are therefore of difficult recognition using conventional well-log analysis methods. To address this challenge, a hierarchical method is proposed for interpreting thin lithological heterogeneities by integrating wavelet transform and machine learning. The discrete wavelet transform enhances well-log responses of thin heterogeneities. An automated machine-learning framework is designed, which integrates multiple algorithms and achieves automated parameter optimization. This machine-learning method is then applied to well logs to establish a nonlinear mapping model between lithology and well-log responses. Additionally, the hierarchical nature of the workflow highlights lithological contrasts, facilitating a more accurate lithological differentiation by dividing the recognition of thin heterogeneities into three levels. Benefiting from these three advantages, the proposed method offers potential to significantly enhance the accuracy of well-log interpretations. The results demonstrate that this method yields accurate identification of lithological units as thin as 0.2 m for muddy beds and 0.3 m for diagenetic units, achieving a recognition accuracy exceeding the conventional well-log interpretations. This method also shows significant potential for broader applications, including the identification of other types of geological entities of limited thickness, and determination of reservoir parameters at fine scales.

The applicability of sequence stratigraphic models to continental fluvial successions has long been topic for debate. To improve our understanding of how fluvial architectures record responses to changes in the ratio between accommodation rate and sediment-supply rate (A/S), two case studies are analyzed, including a densely drilled subsurface fluvial reservoir imaged with a seismic cube, and an outcropping fluvial succession. The subsurface dataset provides a larger, three-dimensional perspective, whereas the outcrop dataset enables observation at higher resolution. On the basis of both datasets, channel-body density, channel-body stacking patterns and their formative river types are interpreted at different scales, and how these may reflect responses to A/S change (the rate of accommodation creation relative to the rate of sediment supply) are discussed. The results indicate that (ⅰ) channel-body stacking patterns undergo four evolutionary stages along with the A/S increase, i.e., multi-story, mixed multi- and two-story, two-story, and isolated patterns; (ⅱ) channel-body density decreases along with the channel-body stacking patterns varying from multi-story to isolated; (ⅲ) formative rivers types are interpreted as evolving from braided planforms to braided-meandering planforms and then to meandering ones, with the increase of A/S.