Research on data completion and generation of downhole drilling tool attitude based on Long Short-Term Memory neural networks

Abstract This study addresses the critical challenge of incomplete downhole drilling tool attitude data in rotary steerable drilling systems, a significant issue impacting drilling trajectory control, efficiency, and safety. Attitude data loss often occurs due to sensor malfunctions, communication errors, and harsh drilling environments, posing risks to accurate geological modeling and decision-making. Traditional interpolation methods struggle with the nonlinear, time-dependent nature of drilling data, resulting in poor accuracy and reliability. To overcome these limitations, a Long Short-Term Memory (LSTM) model, a type of deep learning algorithm known for capturing complex time-series dependencies, is proposed for data completion. This model was trained and validated using a dataset collected from a rotary steerable system laboratory test platform, including acceleration, magnetic flux, and Hall sensor pulse signals under varying inclination and rotational speeds. Experimental results show that the LSTM model achieves superior performance, with a coefficient of determination (R2) exceeding 0.95 and significantly lower mean squared errors compared to Fully Connected Neural Networks (R2 = 0.88) and other regression-based methods. The completed attitude data closely replicates original signal trends, accurately reconstructing gravitational acceleration, magnetic flux, and TICK signals despite noise and data loss. This work goes beyond previous studies by applying deep learning specifically to downhole drilling tool attitude data completion, a novel approach not previously explored in petroleum exploration literature. The demonstrated accuracy and robustness of the LSTM model provide a reliable, data-driven solution for enhancing drilling path optimization, reducing non-productive time, and minimizing operational risks. This research contributes a scalable method to improve drilling efficiency and resource recovery rates, with potential applicability to broader data-driven drilling automation systems.