Rock Mechanical Parameters Prediction Based on Digital Drilling Cuttings Mineral Logging Data

With the continuous advancement of oil and gas exploration and development, unconventional resources have become a crucial component of national energy strategies. The extraction of resources such as shale oil and gas relies on technologies like horizontal drilling and multi-stage fracturing, where accurate geomechanical parameters are essential for engineering design. Conventional core-based experiments and well-log inversion methods, though reliable, are often costly, time-consuming, and limited in representativeness. Recent progress in digital cutting analysis—using scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS)—offers a fast, economical, and practical alternative. This study presents a workflow for predicting rock mechanical parameters from the mineral composition and pore structure of cuttings. Site-collected cuttings were characterized via SEM-EDS to analyze morphology, mineralogy, pore networks, and microfractures. Given the high heterogeneity and anisotropy of shale, a composite modeling approach integrating heterogeneous structure theory and equivalent medium models was applied. This included the Reuss and Voigt bounds, Voigt–Reuss–Hill average, Hashin–Shtrikman bounds, Kuster–Toksöz theory, and Gassmann fluid substitution to estimate equivalent static elastic parameters. These were then converted to dynamic parameters using linear regression to ensure consistency with logging data. Results show strong agreement between cutting-derived parameters and well-log inversions. Young’s modulus and Poisson’s ratio errors range from –12.62% to 4.03% and –10.18% to 10.47%, respectively, within acceptable limits. Although minor uncertainties arise from mineral identification and image segmentation, overall trends match well-log data closely. The introduction of a Weakness Index effectively highlights reservoir heterogeneity and correlates well with measured fracture pressures. A strong linear relationship between static and dynamic parameters, particularly Young’s modulus, supports the reliability of the regression-based conversion. This study confirms the feasibility and applicability of digital cuttings for building rock-physics models and predicting mechanical properties in unconventional reservoirs. The method not only aligns with well-log results but also better captures formation heterogeneity. More importantly, it enables real-time, cost-effective mechanical characterization from wellsite cuttings, offering a practical alternative to core- or log-dependent methods. This is particularly valuable in complex wells such as horizontals, where rapid formation evaluation and fracture design are critical.