Unsupervised learning for real-time detection of pre-bit pressure variations in drilling operations

Pressure variations around the near-bit regions represent significant analytical challenges during drilling operations with polycrystalline diamond compact bits, especially in offshore environments. Undetected pressure fluctuations can disrupt drilling process stability, feasibility identification, and operational safety; therefore, prior detection is essential for supporting optimum performance and minimising risks during the operation. This study provides a data-driven predictive framework to understand and measure the near-bit pressure behaviour using key drilling parameters, including depth, rate of penetration, weight on bit, rotational speed, torque, and derived pressure-related signals representing near-bit pressure behaviour. A Long Short-Term Memory-Autoencoder (LSTM-AE) model is employed to capture sequential drilling behaviour and detect pressure deviations, while a Graph Neural Network (GNN) framework is introduced to model structured relationships among input variables, addressing strong multivariate interdependencies beyond temporal correlations. Comparative assessment indicates that the LSTM-AE effectively captures time-based patterns, while the GNN model demonstrates superior anomaly discrimination capability, achieving a detection accuracy of 91.7% against 84.3% for LSTM-AE, with a lower false alarm rate (9.3% vs. 15.7%) and an earlier detection lead-time (7.8 ft vs. 4.2 ft). These findings underline the potential of graph-based deep learning models for enhanced near-real-time monitoring and early identification of operationally significant pressure deviations during polycrystalline diamond compact drilling operations.