Lithology identification based on interpretability integration learning

Abstract

Abstract A lithology intelligent identification interpretability model is proposed based on Ensemble Learning Stacking, Permutation Importance (PI) and Local Interpretable Model-agnostic Explanations (LIME). The method aiming to provide more accurate geological information and more scientific theoretical support for oil and gas resource exploration. Two logging datasets from the public domain were used as experiments, and support vector machine (SVM), random forest (RF) and naive bayes (NB) were used as primary learners, and SVM as secondary learners, to classify lithology through stacking algorithm. Then, the evaluation indexes such as Area Under Curve (AUC), precision, recall and F1-score were used to verify its accuracy, and PI and LIME were used to explain the lithology identification model. The study shows that the results of the stacking algorithm have the best indexes and the highest prediction accuracy. In terms of overall interpretation, PHIND, GR and RT have the most influence on lithology identification of a natural gas protection area in the United States; DEN, CAL and PEF have the most influence on lithology identification in Daqing Oilfield in China. Interpreted from the perspective of a single sample, the LIME algorithm is able to give a quantitative prediction probability and the degree of influence of the characteristic variables.