New Empirical Correlations to Estimate the Least Principal Stresses Using Conventional Logging Data

The maximum (Sh_max) and minimum (Sh_min) horizontal stresses are essential parameters for the well planning and hydraulic fracturing design. These stresses can be accurately measured using field tests such as the leak-off test, step-rate test, and so forth, or approximated using physics-based equations. These equations require measuring some in situ geomechanical parameters such as the static Poisson ratio and static elastic modulus via experimental tests on retrieved core samples. However, such measurements are not usually accessible for all drilled wells. In addition, the recently proposed machine learning (ML) models are based on expensive and destructive tests. Therefore, this study aims at developing a new approach to predict the least principal stresses in a time- and cost-effective way. New models have been developed using ML approaches, that is, artificial neural network (ANN) and support vector machine (SVM), to predict Sh_min and Sh_max gradients (outputs) from well-log data (inputs). A wide-ranged set of actual field data were collected and extensively analyzed before being fed to the algorithms to train the models. The developed ANN-based models outperformed the SVM-based ones with a mean absolute average error (MAPE) not exceeding 0.30% between the actual and predicted output values. Besides, new equations have been developed to mimic the processing of the optimized networks. The new empirical equations were verified by another unseen data set, resulting in a remarkably matched actual stress-gradient values, confirmed by a prediction accuracy exceeding 90% in addition to an MAPE of 0.43%. The results' statistics confirmed the robustness of the developed equations to predict the Sh_min and Sh_max gradients with a high degree of accuracy whenever the logging data are available.