A combined approach for estimating horizontal principal stress magnitudes from borehole breakout data via artificial neural network and rock failure criterion

Abstract

In this paper, a newly proposed approach on horizontal stress estimation from borehole breakout data is presented. In a previous study, a machine learning model was developed, capable of estimating maximum horizontal stress (σH) from breakout data accurately. However, due to the limitation in experimental data, it was difficult to obtain the minimum horizontal stress (σh) reliably. In this study, a series of breakout tests on Hydrostone-TB specimens was carried out to investigate the influence of σh and vertical stress (σv) on breakout geometries, as these two parameters were rarely studied previously. Results revealed that both breakout angular span and depth decrease with increasing σh or σv, although the influence of σh is more significant. Based on experimental results from this paper as well as the literature, nine failure criteria were examined on the prediction accuracy of σh providing the magnitude of σH. Except for one model, all the other eight failure criteria consider the influence of σv, as indicated in experimental findings. However, none of the failure criteria yielded reasonable σh estimations. To overcome this problem, an Artificial Neural Network (ANN) model was developed from the experimental dataset. Once the model was constructed, it was examined against twenty-three field data, and yielded an acceptable average error rate of 15.88% on σh considering the easily accessible breakout data. Then a comparative analysis on σH estimation was performed via a number of approaches, namely, Kriging, ANN, and constitutive modeling. Results revealed that the use of the Mogi-Coulomb failure criterion is the most reliable approach for σH estimation, with an average error rate of 6.82%. Overall, this newly presented ‘ANN’-‘Mogi-Coulomb’ approach to horizontal stress estimation shows reasonable prediction results, which is expected to be improved in future studies by including additional data.