Accurate pore pressure prediction is crucial for maintaining wellbore stability and preventing drilling hazards. Therefore, this research aimed to present a new empirical method derived from machine learning models, applied to two wells in South Sumatra Basin (S-3 and S-4) comprising 214 depth intervals. The method integrated geomechanics principles, statistical correlation analysis, and neural network optimization to generate an interpretable and transferable equation. The internal parameters of the trained model were extracted and reformulated into a transparent empirical expression that engineers could apply directly in practice. This was distinct from the conventional black-box artificial neural network (ANN). Model performance was rigorously validated against analytical pore pressure measurements. Additionally, the method achieved strong predictive accuracy, with coefficients of determination (R²) of 0.94 and 0.91 for S-3 and for S-4, and root mean square error (RMSE) of 115 psi and 142 psi, respectively. These values represented a significant improvement compared to traditional methods. For example ANN-derived formula reduced RMSE by 28% and 22% in contrast to Eaton’s equation and the Bowers velocity–effective stress relationship. It also outperformed Normal Compaction Trendline (NCT) method in intervals with abrupt lithological changes. The clear identification of significant predictors, namelytemperature, gamma ray, porosity, and water saturation, helped bridges the gap between machine learning accuracy and engineering usability. The results showed that converting advanced computational models into interpretable tools significantly enhanced operational safety, reduced non-productive time, and improved drilling efficiency in Indonesian most prolific hydrocarbon provinces.

This research presents a comprehensive study of 1D geomechanics modeling and wellbore stability analysis for directional wells in the S field, South Sumatra Basin, Indonesia. The primary goals include pre-drilling analysis and determining safe mud weight requirements about well inclinations. The study integrates data from nearby drilling experiences, incorporating wireline logs, FIT/LOT data, pressure readings, rock samples, drilling records, gamma ray profiles, and mud logs. Petrophysical analyses, shear wave velocity modeling, and the Mogi-Coulomb failure criterion were employed to define a safe mud weight range for distinct formations. The geomechanics model was calibrated using caliper data and drilling records to validate the breakout model. Additionally, the study transferred the earth mechanical model from wells S-3 and S-4 to S-8 and S-9, performing sensitivity analyses to refine mud weight recommendations and prevent shear failure. The study concludes that a maximum inclination of 30 degrees should be maintained for future wells to ensure wellbore stability. These findings provide critical insights for optimizing drilling operations in the South Sumatra Basin.