Estimation of in-situ maximum horizontal principal stress magnitudes from borehole breakout data using machine learning

This paper presents a technique for in-situ horizontal stress prediction in vertical boreholes based on borehole breakout data using a machine learning-based meta-modelling technique, i.e. Kriging. This model is generated based on the deterministic mean function and a stationary Gaussian process function derived from the 106 data points from published experimental studies. To examine the reliability of the model, a leave-one-out cross-validation process is conducted against the published experimental results. The Kriging prediction gives an average error of 10.59% on maximum horizontal principal stress (σH). A total of 23 field data were also extracted from both literature and mine site A with stress measurements conducted at the similar depth. The model yields an average prediction error of 8.4% in comparison with field stress measurement results on σH, which is remarkable considering its simplicity, reliability and low cost. This study limits the scope to σH estimation only due to the nature of the experimental setup, in which minimum horizontal principal stress (σh) values are often kept constant while changing σH. This results in a lot of repetition σh values in training data and it makes the prediction of σh unreliable. By collecting a broader range of data on breakout geometries, stress magnitudes and the rock strength, it is expected that the accuracy and the parameter coverage of this technique can be further improved in practical conditions.