Facing engineering challenges of real-time and high-precision recognition of strongly heterogeneous formations during directional drilling, it is crucial to address the issues of sparse lithology geological labels and multi-source lithology identification from LWD data. This paper proposes a real-time intelligent recognition method for strongly heterogeneous formations based on multi-parameter logging while drilling and drilling engineering, which can effectively guide directional drilling operations. Traditional supervised learning methods rely heavily on extensive lithology labels and rich wireline logging data. However, in LWD applications, challenges such as limited sample sizes and stringent real-time requirements make it difficult to achieve the accuracy needed for effective geosteering in strongly heterogeneous reservoirs, thereby impacting the reservoir penetration rate. In this study, we comprehensively utilize LWD parameters (six types, including natural gamma and electrical resistivity, etc.) and drilling engineering parameters (four types, including drilling rate and weight on bit, etc.) from offset wells. The UMAP algorithm is employed for nonlinear dimensionality reduction, which not only integrates the dynamic response characteristics of drilling parameters but also preserves the sensitivity of logging data to lithological variations. The K-means clustering algorithm is employed to extract the deep geo-engineering characteristics from multi-source LWD data, thereby constructing a lithology label library and categorizing the training and testing datasets. The optimized CatBoost machine learning model is subsequently utilized for lithology classification, enabling real-time and high-precision geological evaluation during directional drilling. In the Hugin Formation of the Volve field in the Norwegian North Sea, the application of UMAP demonstrates superior data separability compared with PCA and t-SNE, effectively distinguishing thin reservoirs with strong heterogeneity. The CatBoost model achieves a balanced accuracy of 92.7% and an F1-score of 89.3% in six lithology classifications. This approach delivers high-precision geo-engineering decision support for the real-time control of horizontal well trajectories, which holds significant implications for the precision drilling of strongly heterogeneous reservoirs.

This study developed a bio-based fluid loss reducer based on itaconic acid-grafted corn starch (IACS) for water-based drilling fluid systems. The product was synthesized through free radical graft copolymerization and characterized by FTIR, TGA, and SEM. In bentonite-based mud systems, IACS demonstrated excellent filtration control performance significantly superior to that of conventional fluid loss reducers such as PAM, CMC, and PAC. IACS exhibited outstanding temperature resistance, salt tolerance, and calcium contamination resistance. Particle size analysis revealed that IACS effectively dispersed bentonite particles to the nanoscale at elevated temperatures, preventing thermal aggregation. Mechanistic studies indicated that carboxyl groups introduced by the grafting reaction endowed IACS with strong adsorption capacity and hydration ability, forming a dense polymer network layer on clay particle surfaces. Environmental evaluation confirmed that IACS possessed moderate biodegradability and extremely low toxicity, meeting green drilling fluid additive requirements. This study provides new insights for developing high-performance, environmentally friendly fluid loss reducers.