In recent years, small-borehole wireline coring has become increasingly dependent on trajectory control, downhole condition sensing, and real-time directional decision-making. However, under low-flow conditions, conventional MWD pulser heads tend to generate relatively small pulse amplitudes; as hole depth increases, the pressure signal undergoes stronger attenuation and becomes more susceptible to noise interference, making it difficult to sustain a decodable amplitude under a limited pump-pressure budget. Existing studies have mainly focused on surface decoding and signal processing and therefore do not improve, at the source-structure level, the amplitude output and tool-section incremental pressure loss in low-flow operation. Accordingly, this study develops a compact small-diameter mud-pulse pulser head for 1–3 L/s operation and evaluates its performance through an integrated workflow combining theoretical screening, numerical simulation, and shallow-hole field testing focused on hydraulic pulse generation and surface detectability. The results show that, after selecting appropriate bypass-orifice sets, the proposed pulser head produces stable pressure pulses meeting the surface decoding threshold across 1–3 L/s, while maintaining the tool-section incremental pressure loss within 0.5 MPa over the main operating range. The findings further indicate that, in low-flow regimes, the achievable decoding margin and the incremental-loss ceiling are primarily governed by the upstream hydraulic architecture. This work provides a practical basis for reliable low-flow mud-pulse telemetry in small-borehole wireline coring.

Accurate wellbore trajectory prediction is of great significance for enhancing the efficiency and safety of directional drilling in coal mines. However, traditional mechanical analysis methods have high computational complexity, and the existing data-driven models cannot fully integrate non-sequential features such as stratum lithology. To solve these problems, this study proposes a parallel input gated recurrent unit (Pi-GRU) model based on the TensorFlow framework. The GRU network captures the temporal dependencies of sequence data (such as dip angle and azimuth angle), while the BP neural network extracts deep correlations from non-sequence features (such as stratum lithology), thereby achieving multi-source data fusion modeling. Orthogonal experimental design was adopted to optimize the model hyperparameters, and the ablation experiment confirmed the necessity of the parallel architecture. The experimental results obtained based on the data of a certain coal mine in Shanxi Province show that the mean square errors (MSE) of the azimuth and dip angle angles of the Pi-GRU model are 0.06° and 0.01°, respectively. Compared with the emerging CNN-BiLSTM model, they are reduced by 66.67% and 76.92%, respectively. To evaluate the generalization performance of the model, we conducted cross-scenario validation on the dataset of the Dehong Coal Mine. The results showed that even under unknown geological conditions, the Pi-GRU model could still maintain high-precision predictions. The Pi-GRU model not only outperforms existing methods in terms of prediction accuracy, with an inference delay of only 0.21 milliseconds, but also requires much less computing power for training and inference than the maximum computing power of the Jetson TX2 hardware. This proves that the model has good practicability and deployability in the engineering field. It provides a new idea for real-time wellbore trajectory correction in intelligent drilling systems and shows strong application potential in engineering applications.