Classification of shale lithofacies with minimal data: application to the Early Permian shales in the Ordos Basin, China

[Display omitted] •XGBoost machine learning model predicts shale lithofacies from conventional logs and few XRD data.•Gamma ray, neutron porosity, and density logs control the model predictions and are known to be sensitive to clay content.•Microstructural images and gas adsorption measurements acquired on various shale facies support the machine learning results. Shale lithofacies classification is one of the key components of shale reservoir evaluation. Typically, a significant amount of laboratory X-ray diffraction (XRD) data acquired on many shale samples collected in a large number of boreholes is required to constrain the mineral composition that enables classifying shale lithofacies at the basin scale. This procedure is costly and time consuming. Here, we propose a supervised machine learning method to predict the mineral composition of shale samples, including the clay and silicate content. The main advantage of our approach is that it only uses conventional logging data and a small number of XRD measurements of core samples, combined with XGBoost algorithm, to predict shale lithofacies, which can reduce the cost and improve the efficiency of reservoir evaluation. We apply our method on the early Permian shales in the Ordos Basin, China, because these shale rocks have a high potential of gas production. However, these formations are also highly heterogeneous, making them challenging to explore and exploit. Therefore, it is critical to perform detailed lithofacies classification analysis at the basin scale before gas production. Our result show that the gamma ray, neutron porosity, and density measurements are the critical logging data that control the model predictions. These parameters are known to be sensitive to clay content, thereby supporting the robustness of model predictions. Applying the model to different wells to classify shale lithofacies, results that these shale formations are dominated by three types of lithofacies. We characterize the different shale lithofacies by microscopy images and gas adsorption measurements, and demonstrate that our results are consistent with previous studies, verifying the accuracy and applicability of our machine learning method to classify shale lithofacies.