Rock strength evaluation is critical in oil and gas exploration, but traditional methods, such as empirical formulas, laboratory tests, and numerical simulations, often struggle with accuracy, generalizability, and alignment with field conditions. This study proposes the use of Random Forest and Transformer algorithms to predict rock strength from Elemental Capture Spectroscopy (ECS) logs. By utilizing the dry weight of minerals as input, the model predicts key mechanical properties, including Young's modulus, Poisson's ratio, bulk modulus, shear modulus, and uniaxial compressive strength. The findings demonstrate that mineral compositions, such as clay, quartz-feldspar-mica, carbonate, anhydrite, and pyrite, significantly influence rock strength. Specifically, clay content impacts Young's modulus, bulk modulus, and shear modulus, while quartz-feldspar-mica affects Poisson's ratio, and anhydrite is the primary factor influencing compressive strength. Positive correlations were observed between rock strength and the dry weight of anhydrite and carbonate minerals, while negative correlations emerged with clay, pyrite, and quartz-feldspar-mica. The Random Forest model outperformed the Transformer model in terms of predictive accuracy and computational efficiency. Its training time is only one three hundredth of the latter and its prediction time is just one tenth of the later, making it highly suitable for well-logging interpretation. Although the Transformer model was less computationally efficient, it exhibited strengths in predicting subsurface strength parameters, particularly in capturing spatial variations and forecasting these parameters across different spatial locations. This study introduces a novel AI-driven approach to rock strength evaluation, bridging the gap between mineral composition and mechanical properties, with significant implications for resource extraction and reservoir management.

Figuring out rock strength plays essential roles in the sub ground mining activities, such as oil and gas well drilling and hydraulic fracturing, coal mining, tunneling, and other civil engineering scenarios. To help understand the effects of the mineralogical composition on evaluating the rock strength, this research tries to establish indirect prediction models of rock strength by specific input mineral contents for common sedimentary rocks. Using rock samples collected from the outcrops in the Sichuan Basin, uniaxial compression tests have been conducted to sandstone, carbonate, and shale cores. Combining with statistical analysis, the experimental data prove it true that the mineralogical composition can be utilized to predict the rock strength under specific conditions but the effects of mineralogical composition on the rock strength highly depend on the rock lithologies. According to the statistical analysis results, the predicted values of rock strengths by the mineral contents can get high accuracies in sandstone and carbonate rocks while no evidences can be found in shale rocks. The best indicator for predicting rock strength should be the quartz content for the sandstone rocks and the dolomite content for the carbonate rocks. Especially, to improve the evaluation accuracy, the rock strengths of sandstones can be obtained by substituting the mineral contents of quartz and clays, and those of carbonates can be calculated by the mineral contents of dolomite and calcite. Noticeably, the research data point out a significant contrast of quartz content in evaluating the rock strength of the sandstone rocks and the carbonate rocks. Increasing quartz content helps increase the sandstone strength but decrease the carbonate strength. As for shale rocks, no relationship exists between the rock strength and the mineralogical composition (e.g., the clay fractions). To provide more evidences, detailed discussion also provides the readers more glances into the framework of the rock matrix, which can be further studied in the future. These findings can help understand the effects of mineralogical composition on the rock strengths, explain the contrasts in the rock strength of the responses to the same mineral content (e.g., the quartz content), and provide another indirect method for evaluating the rock strength of common sedimentary rocks.