Naive Bayes lithofacies classification ensemble learning method and device based on characteristic randomness

Abstract

The invention provides a naive Bayes lithofacies classification ensemble learning method and device based on characteristic randomness. The method comprises steps of multiple kinds of logging data ofa target work area being acquired and preprocessed; randomly sampling a plurality of preprocessed logging data into a training set and a test set according to a proportion; randomly generating a plurality of training subsets according to the feature combinations randomly selected from the training set and the component number of the feature combinations; training a plurality of first base classifiers in parallel by using a plurality of training subsets to obtain a plurality of second base classifiers and performance index values thereof; wherein the first base classifier is a naive Bayes classifier; determining a voting weight of each second base classifier according to the performance index value of each second base classifier; performing parallel lithofacies classification on the test set by utilizing a plurality of second base classifiers to obtain a classification sub-result of each second base classifier; and voting and combining the classification sub-results according to the voting weight to obtain a lithofacies classification result. According to the method, classification accuracy and learning efficiency of the lithofacies classifier based on naive Bayes can be improved.

Description

technical field [0001] This specification relates to the technical field of oil and gas exploration and development, in particular to an integrated learning method and device for naive Bayesian lithofacies classification based on random features. Background technique [0002] Lithofacies classification is not only an important work in stratum evaluation and geological analysis, but also has great significance for reserve prediction and reservoir description in the field of oil and gas exploration and development. At present, lithofacies classification is usually carried out by experts analyzing cuttings and cores of exploratory wells to determine lithofacies. The process is not only time-consuming, labor-intensive, expensive, but also has a lot of human factors. [0003] For this reason, the Naive Bayes (NB) classification method for lithofacies has appeared. In practical applications, in order to simplify the calculation of the conditional probability of joint classes, the...

AI Technical Summary

Problems solved by technology

However, the real distribution of well logging data is often complex and diverse, which makes the fitting effect of Gaussian distribution poor

In addition, the assumption of feature condition independence is often not true in practical tasks, making the classification accuracy of the lithofacies classifier based on Naive Bayesian not high

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