Identifying lithofacies types by boosting algorithm and resampling technique: a case study of deep-water submarine fans in an oil field in West Africa

AbstractThe continuous discovery of giant oil and gas fields in deep-water low stand fans has made deep-water submarine fan reservoirs with huge oil and gas potential important targets for oil and gas exploration and development. Nowadays, machine learning algorithm has been proven to be an effective method to classify various rock types from geophysical logging data, but rarely has there been focus on predicting deep-water submarine fans in previous studies. In this paper, we utilized five classical Boosting machine learning algorithms, namely GBDT, XGBoost, LightGBM, CatBoost, and LogitBoost, to identify 14 deep-water submarine fan lithofacies types from 7 wells in a West African oilfield. To address the sample non-balance problem, we employed SMOTE and MAHAKIL oversampling techniques and optimized the hyperparameters of the model using Genetic Algorithm. The experimental results show that the model performance is improved by using oversampling technologies and hyperparameter optimization. The proposed MAHAKIL-GA-GBDT algorithm is the most effective in identifying the lithofacies of deep-water submarine fans, with an accuracy of 0.986. This study provides a new approach for identifying deep-water submarine fan lithofacies and highlights the potential of machine learning algorithms in this field.Keywords: Boostingdeep-water submarine fansgenetic algorithmlithofacies identificationmachine learningoversampling Author contributionsYan Zhen: conceptualization, methodology, investigation, writing-original, writing-reviewing and editing. Yifei Xiao: methodology, programming, validation, visualization, writing-original. Xiaoming Zhao: conceptualization, methodology, writing-reviewing and editing, validation, investigation. Xiaoya Lu: writing-reviewing and editing, validation. Junyi Fang: visualization, investigation, programming. Jintao Kang: visualization, programming. Liang Liu: visualization, investigation, data curation.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Natural Science Foundation of China (Nos. 42072183 and 41902124).