Logging-data-driven lithology identification of conglomerate reservoir by the assistance of integrated machine learning methods

Abstract Lithology is a key parameter in reservoir fine description and evaluation. It is difficult to identify reservoir lithology directly by single curve or conventional cross plot method, because the mud-gravel mixing in complex reservoirs. The accurate identification of conglomerate reservoir lithology has always been a high-profile issue in reservoir characterization. In this study, over 70 meters of cores were observed in detail. The conglomerate lithology after depth correction is matched with the log curves, including five log curves such as GR, DT, RHOB, TNPH, and M2R1. With the logging data as input, three machine learning models were built separately, and the prediction results were compared using a variety of methods, including accuracy analysis parameters and ROC curves. The results show that the machine learning model based on logging data has excellent performance in the lithology prediction of conglomerate reservoir, and the XGBoost model shows the best prediction results with the highest prediction accuracy of 0.902. In addition, the optimal model is interpreted by SHAP method. In different lithology prediction, the contribution of different log curves is different. On the whole, TNPH curve plays the most important role in lithology prediction. This study provides insights for lithology prediction of complex reservoirs.