Machine Learning-Based Mineral Quantification from Lower Cambrian Shale in the Sichuan Basin: Implications for Reservoir Quality
2025
Xin Ye | Yan Liu | Tianyu Huang | Ting Chen | Chenglin Liu | Sibing Liu | Siding Jin
In this study, cores from Well S1 in the Sichuan Basin were investigated to quantify mineral composition. A neural network analysis was employed to apply machine learning to X-ray fluorescence (XRF) datasets for predicting the mineralogical characteristics of Well S1. A total of 77 sample points were divided into training, validation, and test sets at a ratio of 6:2:2. After training and fine-tuning the model using the training and validation sets, the performance of the neural network model was evaluated with the test set. The best result was achieved for calcite prediction, reaching an R-squared (R2) value of 95%. Predictions for the seven minerals, except quartz, all exhibited R2 values of 80% or higher. Quantitative laboratory-measured X-ray diffraction (XRD) mineralogy was used for training to develop a high-resolution semi-quantitative model, and the resulting mineralogical model shows promising potential. The modeled mineralogy represents continuous relative abundance, which provides more meaningful insights compared to discrete single-point XRD measurements. The significance of this research lies in its ability to utilize relatively inexpensive and non-destructive XRF logging analysis, requiring minimal sample preparation, to construct high-resolution mineral abundance profiles. With modern technological advancements, operators can adopt the proposed method to build semi-quantitative mineralogical models for evaluating potential lateral drilling intervals and designing completion strategies accordingly.
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