Toward smart correlations for predicting in-situ stress: Application to evaluating subsurface energy structures

Abstract

The precise calculation of the in-situ stress tensor is a crucial factor in addressing the challenges associated with the development of subsurface energy structures. To establish a consistent relationship between breakout shapes and the in-situ stress using nonlinear or coupled process assumptions through numerical methods, effective techniques are required. In this regard, three white box algorithms, namely gene expression programming (GEP), genetic programming (GP), and the group method of data handling (GMDH), were developed to predict the maximum horizontal stress. To develop a robust correlation using the white box algorithms, 662 data points were obtained from numerical analysis using an elastoplastic model across a wide range of wellbore pressures. Input parameters were breakout width and depth, wellbore pressure, and minimum horizontal stress. The study results indicated that the GP algorithm demonstrates higher accuracy compared to the GEP and GMDH, with a root mean square error (RMSE) of 0.9977 and a determination coefficient (R2) of 0.97564. Additionally, both SHAP values (SHapley Additive exPlanations) and sensitivity analysis were employed. The sensitivity analysis revealed that breakout width has a greater influence on predicting the maximum in-situ stress compared to other parameters. Furthermore, the Leverage technique indicated that the GP model can be considered a reliable tool for accurately estimating the in-situ stress, making it suitable for use in the subsurface energy structures.