Estimation of in-situ horizontal stresses based on multiscale borehole breakout data via machine learning: model development, validation and application

Borehole breakout (BO) has increasingly been utilized to estimate in-situ stress magnitudes given the importance of the stress field in subsurface activities and the limitations of conventional stress measurement techniques. In this study, a new backpropagation neural network model is developed to estimate both maximum and minimum horizontal stress magnitudes from multiscale BO data. A total of 150 experimental data points from pre-stressed true-triaxial laboratory tests and 44 field data from a mine site in Australia and the literature are collected and employed for model development and validation. Compared to previous studies, the collected data set is significantly enhanced in both quantity and quality. To address discrepancies in stress magnitudes between experimental and field data, the three principal stresses are normalized by borehole wall strength (BWS). Overall, the model achieves mean absolute percentage errors of below 8 per cent for the maximum horizontal stress and below 20 per cent for the minimum horizontal stress, significantly outperforming the previous model developed for this purpose. Furthermore, these error rates fall within the typical error range (10–20 per cent) of conventional stress measurement techniques, indicating the model's sufficient accuracy for practical applications. Moreover, the effectiveness and generalizability of the model are verified using 166 additional BOs from two mine sites, which are independent of those used in model development. Continuous and detailed stress profiles are established based on these BOs, covering greater depth intervals than the stress measurements from the overcoring method. The results of this study demonstrate that the proposed model can provide reliable and accurate stress estimation, utilizing input parameters that can be readily obtained from borehole geophysical logs.