Study on evaluation method for hydromechanical properties of hard and brittle shale based on indentation hardness

Wellbore instability in deep hard-brittle shale formations, primarily induced by hydration-driven strength degradation upon interaction with water-based fluids, poses a critical challenge to hydrocarbon extraction. Conventional triaxial testing for assessing shale hydration behavior is often constrained by substantial sample requirements, extended duration, and high operational costs. In response, this study develops an efficient alternative approach centered on the indentation hardness method. While standard indentation tests are typically limited to hardness and plasticity coefficients, this work establishes theoretical models—based on contact mechanics, elasticity theory, and the Mohr–Coulomb criterion—to derive elastic modulus, Poisson's ratio, and uniaxial compressive strength from indentation data. Experimental analysis of homogenized Longmaxi shale revealed a dense, low-porosity microstructure dominated by non-expansive clay minerals and quartz. Freshwater immersion tests displayed a three-stage absorption trend—rapid, slow, and stable—reaching near-saturation after 72 hours. Pronounced mechanical degradation was observed within the initial 300 hours of immersion, characterized by marked reductions in compressive strength, elastic modulus, and indentation hardness, alongside a stepwise increase in Poisson's ratio; this degradation trend decelerated thereafter. Validation experiments confirmed that single-point indentation hardness measurements provide mechanical equivalence to uniaxial compression responses. As a result, indentation testing on shale chips following fluid immersion offers an efficient and reliable means of evaluating time-dependent fluid–rock interactions. The proposed methodology minimizes core material requirements, enhances operational efficiency, and mitigates the influence of heterogeneity, thereby offering considerable practical value for shale hydration assessment and wellbore stability forecasting.