Abstract
The accurate determination of in-situ stress is crucial for various geological and engineering applications. Hydraulic fracturing (HF) and hydraulic testing of pre-existing fractures (HTPF) are established methods for in-situ stress measurement, and the Integrated Stress Determination Method (ISDM) has emerged as a powerful technique for analysing these measurements. ISDM utilizes the Generalized Least Squares (GLS) iteration, which relies on a priori estimates to initiate and run the solution until convergence is achieved. The quality of the individual measurements and the a priori estimates significantly impacts the convergence rate and the accuracy of the in-situ stress results, as ISDM is highly sensitive to these. Typically, a priori estimates are derived from prior knowledge of the in-situ stress conditions. However, in scenarios where such prior knowledge is unavailable, defining suitable a priori estimates for the GLS iteration becomes challenging. To address this challenge and mitigate the sensitivity of the ISDM to the a priori estimates and problematic measurements, we present an approach that combines ISDM with statistical analysis. By generating a statistical distribution for the in-situ stresses, this method allows for the identification of the best estimate results from a probabilistic perspective. We demonstrate the effectiveness of this combined approach using test data from the literature. Our results show that integrating ISDM with statistical analysis provides a robust framework for reliable in-situ stress determination.