Unsupervised deep learning framework for early detection of wellbore trajectory deviation in drilling operations

Wellbore trajectory deviation remains one of the major operational challenges encountered during directional and extended-reach drilling because even small departures from the planned well path can lead to poor reservoir placement, wellbore instability, increased non-productive time, and significant drilling costs. In most field operations, trajectory monitoring depends on periodic directional surveys together with threshold-based diagnostics. Although these methods are widely used, they often identify deviations only after they have become operationally noticeable, limiting the opportunity for timely corrective action. To overcome the limitations of conventional monitoring, the present study proposes an unsupervised deep learning framework capable of identifying the early onset of trajectory deviation by analyzing integrated well-log and geo-mechanical data without relying on labeled deviation events. The proposed framework combines Depth, Gamma Ray (GR), Shale Volume (Vsh), Resistivity, Sonic Transit Time (ΔT), P-wave Velocity (Vp), S-wave Velocity (Vs), Bulk Density, Calculated Density, Neutron Porosity (NPHI), Density Porosity (DPHI), and Poisson's Ratio to capture the lithological and mechanical characteristics that influence drilling behavior and trajectory stability. An LSTM Autoencoder (LSTM-AE) is employed to learn the normal temporal evolution of drilling parameters and identify anomalous behavior through reconstruction error. To complement the sequential learning capability of the autoencoder, a Graph Neural Network (GNN) is developed to represent the physical and geological relationships among the measured parameters, allowing complex multivariate interactions to be analyzed without requiring labeled datasets. The performance of both models is evaluated by comparing their ability to distinguish normal drilling behavior from progressively unstable operating conditions. The obtained results demonstrate that the LSTM-AE effectively learns the sequential characteristics of stable drilling and provides reliable early warning through changes in reconstruction error, whereas the GNN offers improved discrimination of trajectory-related anomalies by modeling the underlying relationships between geological and geo-mechanical variables. Collectively, these complementary approaches enableearlier recognition of developing trajectory deviations while reducing false alarms compared with conventional monitoring techniques. The findings demonstrate that integrating temporal sequence modeling with graph-based relational learning provides a practical and scalable solution for intelligent wellbore trajectory monitoring, supporting improved wellbore stability assessment, safer drilling operations, and more informed decision-making in complex subsurface environments.