Carbonate lithology Identification Based on Genetic Algorithm Adaptive LightGBM

Abstract

Lithology identification utilizing logging data stands as a pivotal research area within reservoir prediction, integral to the broader context of oil and gas exploration and development. In order to solve the problem that the traditional logging lithology identification method and machine learning method do not have high accuracy for the lithology identification of complex carbonate rocks. In view of the powerful performance of deep learning methods in feature extraction and data analysis, this paper proposes a lithology recognition method based on the Genetic Algorithm-adaptive LightGBM model. Due to the small number of features of the original logging data, the feature shift method is first used to expand the features of each data. Subsequently, to optimize the classification performance of our model, we leverage the Genetic Algorithm (GA) to fine-tune the hyperparameters of the LightGBM model. With the optimal hyperparameter configuration in place, we evaluate the model's performance through rigorous training with logging data. The adaptive LightGBM lithology recognition model based on GA is compared with the lithology identification results of GBDT, Bayes, DNN and DT, and the overall prediction performance is evaluated by using the accuracy and its macroscopic mean. Through the test and verification of the actual well data, the proposed method has achieved very considerable results in lithology identification, showing the broad prospect of LightGBM technology in the field of geophysics.