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Identification of Shale Lithofacies by Well Logs Based on Random Forest Algorithm
Wang Min, Yang Jinlu, Wang Xin, Li Jinbu, Xu Liang, Yan Yu
PDF(6142 KB)
PDF(6142 KB)
Identification of Shale Lithofacies by Well Logs Based on Random Forest Algorithm
Shale lithofacies identification is an important task in the spatial distribution of shale oil and exploration target prediction, but it is difficult to identify lithofacies based on logging response equations due to the formation heterogeneity and redundancy of logging information. In this paper, a lithofacies identification model based on random forest algorithm is proposed, which uses the SHAP method to quantify the contribution of logging parameters. The results show that the random forest algorithm can identify shale lithofacies well, and its accuracy is higher than support vector machine, k-nearest neighbors and XGBoost; SP, CAL and AC contribute the most to the model’s identification of lithofacies. The model can quickly identify the lithofacies of a single well, and determine the favorable lithofacies by combining total porosity, free hydrocarbon S 1, TOC, etc., and then determine the distribution of favorable lithofacies in the whole area, providing a basis for subsequent “sweet spot” prediction.
random forest / machine learning / logging / lithofacies identification / shale
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