Maximizing the use of rock mechanical data through empirical correlation and data-driven analytics

Rock mechanical properties are required as an input in many petroleum engineering applications, such as borehole stability analysis, hydraulic fracturing design, and sand production prediction. Their determination is commonly from various laboratory testing performed on subsurface rock samples. Due to the scarcity of reservoir samples and test cost, rock mechanical data are always very limited. Therefore, empirical correlations are very often used to estimate the mechanical properties from downhole logging measurements. Alternatively, the data-driven analytics techniques have been developed for predicting rock properties from other formation properties that can be determined directly from logs. This paper presents a study of developing correlation equations and data-driven models that are used to predict the unconfined compressive strength (UCS) from logging data. Various rock mechanical tests including UCS, single- and multi-stage triaxial tests are performed on sandstone samples from three wells in one region. UCS values are obtained either from the UCS testing directly or from the Mohr-Coulomb failure analysis indirectly. Rock properties, such as mineralogy, porosity, grain and bulk density, ultrasonic wave velocities, are measured for each tested sample, which are used to build the correlations and data-driven analytical models for predicting UCS. Results shows that the empirical correlations are not universal and often cannot be used without some modifications, while the data-driven model is more generalized in application. In addition, data quality is very crucial for building correlations or predictive models.