A shale-oil reservoir multi-mineral inversion method based on well-logging data using an improved random-forest approach: methodology and application

High-precision quantitative characterisation of reservoir mineral composition is central to hydrocarbon exploration and development. However, integrated mineral–organic evaluation in organic-rich shale reservoirs remains technically challenging, owing to the high cost and limited scalability of elemental logging, and the susceptibility of conventional logs to organic-matter effects. The core innovation of this study is the construction of a comprehensive training dataset that explicitly decouples organic and mineral signals—achieved by integrating elemental logging, conventional logs, and core measurements. This dataset serves as high-fidelity labels, enabling a machine learning model to accurately predict mineral and kerogen contents using only conventional logs. Taking the Chang 7 Member shale-oil reservoir in the Ordos Basin as a case study, the workflow comprises three steps. First, inorganic mineral fractions are accurately inverted from elemental-logging data and used as baseline constraints. Second, a kerogen-content inversion model is calibrated by integrating conventional logs, elemental-logging results, and core measurements, enabling a robust separation of organic matter from the mineral matrix and yielding a complete mineral–organic reservoir-parameter dataset. Third, an improved Random Forest (RF) model optimised using the Sparrow–Bald Eagle Optimisation Algorithm (SBOA) is established, with conventional logs and derived total organic carbon (TOC) curves as input features to simultaneously predict the contents of five mineral groups and kerogen. Application results demonstrate that the SBOA–RF model achieves high predictive accuracy, with a mean relative error (MRE) of 6.71% for clay minerals and an MRE of 0.92, outperforming back-propagation neural networks (BPNN), gradient boosting decision trees (GBDT), and conventional approaches; moreover, SBOA is computationally more efficient than random search for hyperparameter optimisation. Porosity computed with a variable dry-rock skeleton model yields an average MRE of 9.06%, corroborating the reliability of the predicted mineral and organic contents. The model further exhibits strong generalisation in blind wells not used for training, with inversion results in good agreement with elemental-logging outputs and core X-ray diffraction (XRD) data. By reducing reliance on elemental logging, the proposed method provides a robust data foundation for reservoir-parameter evaluation and lithofacies classification in organic-rich shale intervals where elemental logs are unavailable, with substantial engineering relevance.