Application of Machine Learning to Predict the Failure Parameters from Conventional Well Logs

Prediction of the failure parameters, cohesion (C) and friction angle (phi), is essential for drilling operation, field development, and hydraulic fracturing design. Several case studies have been conducted to predict these parameters. In all cases, empirical correlations were recommended based on experimental data where the issue of measurements' uncertainties remained a common factor in all of them. Hence, in this study, the artificial neural network (ANN) is employed to predict these parameters from readily available logging data of two wells. A sensitivity analysis showed a high correlation between bulk density (ROHB), compressional time (DTC), and neutron porosity (NPHI) with the friction angle and cohesion analysis. Hence, ROHB, DTC, and NPHI were selected as inputs for the ANN model. Comparing the measured cored data of cohesion and friction angle with those predicted from the ANN model, the results indicated a model error less than 3% average absolute percentage error (AAPE) and a correlation coefficient greater than 0.95. Moreover, an empirical correlation was developed and validated with another set of data, with AAPE less than 3% and R greater than 0.95. Comparison with the literature shows the better accuracy of the ANN in predicting cohesion and friction angle. The model is several times faster than the existing methods in-field application with high reliability and accuracy.