Category-Wise Sequence Correlation Filter for Lithology Identification With Well Logs

Well log data play a crucial role in lithology identification, given their nature as petrophysical measurements of stratigraphy that reflect lithological successions and depositional processes. Extracting recognizable features from well logs has become a focal point in lithology identification tasks. Recent advancements in machine learning (ML) have improved feature extraction by incorporating neighborhood information, particularly through filtering techniques that integrate sequence dependencies from well logs. However, traditional filters are often constrained by predefined theoretical models, posing adaptability challenges across varied reservoir conditions. To address these limitations, we propose a novel category-wise sequence correlation (CSC) filter for lithology identification, which is data-driven and model-free. Notably, the CSC filter captures the sequence structural variations arising from category heterogeneity by utilizing labeling information derived from well logs. Building on this, the CSC filter banks (CSCFBs) model is developed. It uses a series of CSCFBs as 1-D convolution templates acting on each logging curve to extract discriminative features associated with lithological categories. Application studies on several oil and gas fields demonstrate that the CSCFB model outperforms competitors in classification performance, proving its effectiveness and utility across diverse reservoir types.