How much stress data do we need for a robust geomechanical integrity assessment?

Geomechanical-numerical modeling aims to provide a continuous description of the stress tensor within rock volumes by leveraging localized stress magnitude data for model calibration. This calibration process involves optimizing displacement boundary conditions to achieve the best possible alignment with in-situ stress measurements obtained from microhydraulic fracturing (MHF) and sleeve reopening (SR) test in boreholes providing magnitudes of the minimum and maximum horizontal stresses. However, the high cost associated with acquiring stress magnitude data often results in sparse and incomplete datasets, which can potentially hinder a robust model calibration.

We investigate the relationship between calibration dataset size and stress field prediction quality by leveraging an exceptionally high-quality set of 45 in-situ stress magnitude data from MHF and SR tests performed in two boreholes in the Zürich Nordost region, a potential deep geological repository location in northern Switzerland. We present a statistical framework that analytically evaluates extensive model ensembles, where each realization uses a distinct subset of the entire stress magnitude dataset for the model calibration. This methodology quantifies the effect of data quantity on modeled stress distributions in lithological units with different elastic properties by efficiently computing stress states for multiple data combinations.

By systematically comparing modeled stress magnitudes derived from incrementally larger calibration datasets, we identify the minimum number of stress magnitude data records needed to achieve a modeled stress range narrower than the predicted stress range resulting from rock stiffness variability. For Zürich Nordost, approximately 15 stress magnitude data records are sufficient to reach this threshold, beyond which additional data provides only minimal improvement to model predictions.

Examination of the dataset revealed one measurement that deviated from the expected model, due to localized variations in the mechanical properties of the rock. Although this observation is geologically valid, it substantially distorts predictions when there are few calibration points available. However, its distorting effect weakens progressively as the size of the calibration dataset increases. Our analytical framework also enables the systematic detection of such anomalous records, which in turn affects the minimum number of data points required for model calibration.

Our findings emphasize the importance of dataset size and composition in reducing uncertainties and help establishing a practical methodology for efficient data acquisition planning. These insights are particularly valuable for subsurface engineering applications that require reliable stress estimates, such as geothermal systems, carbon storage facilities, radioactive waste repositories and underground energy infrastructure.