Electromagnetic look-ahead logging while drilling instruments detect the electrical characteristics of undrilled formations, enabling proactive decision-making. Real-time geological insight ahead of the drill bit is critical for effective geosteering. This study introduces a multi-task U-net neural network that simultaneously inverts multiple formation parameters real-time. Six datasets, each corresponding to different electromagnetic components, were used to train six specialized neural networks. All networks exhibited rapid convergence and successfully inverted 60,000 sample in 15 s, satisfying real-time requirements. Residual and relative error analyses reveal that the multi-component network delivers the highest accuracy. Sensitivity analysis shows that coaxial and coplanar components are more sensitive to conductivity variations, whereas coaxial and cross-components excel at resolving interface positions. The yy component displays the strongest sensitivity to anisotropy. Compared with the traditional Levenberg-Marquardt algorithm, the proposed method demonstrates improved accuracy and efficiency. Moreover, the Levenberg-Marquardt inversion with the neural network output as initial models further enhances accuracy. Benchmark comparisons reveal that the multi-task U-net outperforms various mainstream machine learning and deep learning models, including LSTM, FCN, ResNet, and XGBoost, in both inversion accuracy and generalization. Moreover, sensitivity analyses to noise and near-bit geological complexity reveal that, while the proposed model experiences some performance degradation under high noise levels or highly heterogeneous backgrounds, it maintains strong robustness under moderate noise conditions and achieves reliable inversion results in two-layer geological settings. These results establish the multi-task U-net as a fast, accurate, and robust tool for real-time electromagnetic look-ahead inversion in geosteering applications.

Electromagnetic technology used in logging while drilling (LWD) provides the resistivity distribution around a borehole within a range of several tens of meters. However, a blind zone appears in front of the drill bit when operating in high-angle wells, limiting the ability to detect formations ahead of the drill bit. Look-ahead technology addresses this issue and substantially enhances the proactive capability of geological directional drilling. In this study, we examine the detection capabilities of various component combinations of magnetic dipole antenna. Based on the sensitivity of each component to the axial information, a coaxial component is selected as a boundary indicator. We investigate the impact of various factors, such as frequency and transmitter and receiver (TR) distance, under different geological models. This study proposes 5 and 20 kHz as appropriate frequencies, and 10–14 and 12–17 m as suitable TR distance combinations. The accuracy of the numerical calculation results is verified via air-sea testing, confirming the instrument's detection capability. A test model that eliminated the influence of environmental factors and seawater depth is developed. The results have demonstrated that the tool can recognize the interface between layers up to 21.6 m ahead. It provides a validation idea for the design of new instruments as well as the validation of detection capabilities.