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.

Wireless short-range transmission is essential for precise wellbore trajectory control and real-time formation evaluation. Its signal propagation characteristics are influenced by multiple factors, including antenna type, drill collar, mud, and formation resistivity. Most prior studies are based on Magnetic-field Antennas (MFA) and primarily focus on the effects of formation resistivity variations, whereas the investigations on the influence of drill collars and mud resistivity are limited. In this study, a three-dimensional finite-element electromagnetic model of the “antenna–drill collar–mud–formation” system was developed to investigate wireless short-range transmission. The model was used to characterize and compare the electromagnetic field distributions of MFA and Electric-field Antennas (EFA) under in situ conditions. On this basis, a set of parametric sensitivity analyses on transmission performance was performed to quantify the effects of key factors, including drill-collar conductivity and mud resistivity. The results reveal fundamentally different electromagnetic field distributions for the two antenna types: (1) MFA is dominated by localized circumferential magnetic flux loops, whereas EFA transmits signals through axially extended eddy-current channels. (2) The drill collar exerts opposite effects on the two antennas, suppressing signal levels for MFA while significantly enhancing transmission for EFA, resulting in signal amplitudes that are ${10}^3-{10}^5$ times higher. (3) In addition, mud resistivity has little influence on MFA, whereas increasing mud resistivity leads to the pronounced attenuation of EFA signals. These findings provide a quantitative basis for antenna selection and performance optimization in wireless short-range transmission systems under different Logging-While-Drilling (LWD) conditions.

The accurate petrophysical characterization of unconventional reservoirs, particularly deeply buried fractured–vuggy carbonate systems, remains challenging due to extreme heterogeneity, ultra-low permeability, and strong scale dependency of reservoir properties. Conventional formation evaluation workflows often fail to reconcile geological observations at the core scale with log- and seismic-scale petrophysical interpretations in a physically consistent manner.In this study, we propose an integrated geological–geophysical petrophysical characterization workflow that links core and thin-section observations, electrofacies classification, and seismic data through an electrofacies-constrained seismic waveform-guided inversion framework. Rock types identified from core and petrographic analyses are translated into electrofacies at the log scale to represent pore-structure variability. Electrofacies are incorporated as conditional constraints in a Bayesian seismic waveform-guided porosity inversion framework. Electrofacies-dependent porosity priors derived from well-log statistics restrict the admissible pore-structure space, while seismic waveform similarity controls the lateral propagation of high-frequency porosity features, implicitly embedding seismic facies within the inversion.The resulting porosity volume exhibits enhanced vertical resolution and improved lateral continuity, allowing thin-layer and non-layered heterogeneities to be resolved beyond the limitations of conventional impedance-based inversion. Recognizing that permeability is not directly observable at the seismic scale, permeability is subsequently derived from the inverted porosity volume under electrofacies control, ensuring that pore connectivity and flow characteristics are consistently represented at the appropriate scale.This workflow establishes a causally consistent and scalable solution for advanced petrophysical characterization and formation evaluation of heterogeneous unconventional reservoirs. By integrating geological constraints with seismic waveform-driven inversion, the proposed method effectively bridges core-, log-, and seismic-scale information and demonstrates strong potential for application in complex carbonate systems as well as other unconventional plays.