Comparative study of intelligent lithology identification models based on factor analysis

Abstract:

Lithology identification is a very critical process in geological exploration and development. With the development of the geological exploration industry， the traditional lithology identification is limited by time and space， and can no longer meet the increasing data scale and dimensionality. Therefore， there is an urgent need for in-depth research on the intelligent prediction of lithology identification， so as to promote the development of lithology identification in the direction of digitalization， intelligence and timeliness. In this paper， with the goal of realizing intelligent lithology identification and prediction based on drilling parameters， five algorithms are used to establish intelligent lithology identification and prediction models， including Logistic Regression （LR）， Support Vector Machine （SVM）， K-Nearest Neighbor （KNN） algorithm， Random Forest （RF）， and Neural Network （NN）. They are designed to realize the intelligent prediction of drilling encounters. The main research contents are as follows： A regional stratigraphic identification model based on drilling data was designed. Through the data preprocessing of the original data， and then the factor analysis of the formation parameters and other parameters were reduced by factor analysis and modeled by five machine algorithms. An intelligent identification model was established with the drilling parameter matrix as the input， the lithology classification identification and formation parameter prediction as the output. Experiments show that for most of the five algorithms， the lithology identification accuracy and F1-score on the test set exceed 70%， and the individual accuracy is low but also about 60%， and most of the MAE and RMSE predicted by the stratigraphic parameters of the five models are below 1， and the identification and prediction are accurate.