Comparison of Mathematical and Intelligent Prediction Models of Directional Wellbore Collapse

Given the great burial depth, ancient depositional age, and multi-phase tectonic evolution of deep formations, drilling operations are highly susceptible to wellbore instability. The design and deployment of directional wells further exacerbate this risk, underscoring the need for quantitative risk assessments for directional drilling operations. Based on linear poroelasticity theory, a mechanical model for directional wellbore stability is established to enable wellbore stability evaluation and trajectory optimization design. Furthermore, an intelligent prediction method for collapse pressure is proposed using the XGBoost algorithm. The results indicate that the prediction accuracy of collapse pressure reaches 93%. Under strike-slip in situ stress regimes, wellbore stability is most critical for vertical wells, whereas horizontal and directional wells exhibit lower collapse pressure. The optimal wellbore trajectory is determined to be a horizontal well with an azimuth approximately 36° deviated from the maximum horizontal principal stress direction. The intelligent prediction results show a 98% goodness-of-fit with theoretical calculations, reducing the calculation time from hours to seconds. This study provides a novel approach for wellbore stability analysis and offers a practical tool for the rapid risk assessment of wellbore collapse during directional drilling operations.