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

A classification integration and learning method technology, applied in the direction of integrated learning, computer components, special data processing applications, etc., can solve the problem of poor fitting effect of Gaussian distribution, complex and diverse real distribution of logging data, and accurate classification of lithofacies classifiers problems such as low accuracy, achieve good generalization performance, improve classification accuracy, and avoid time-consuming and labor-intensive effects

Pending Publication Date: 2021-02-05
CHINA UNIV OF PETROLEUM (BEIJING)
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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

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

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[0048] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described The embodiments are only some of the embodiments in this specification, not all of the embodiments. Based on the implementations in this specification, all other implementations obtained by persons of ordinary skill in the art without creative efforts shall fall within the protection scope of this specification.

[0049] refer to figure 1 As shown, in some embodiments of this specification, the feature random-based naive Bayesian lithofacies classification ensemble learning method may include the following steps:

[0050] S101. Obtain various logging data of a target work area and perform preprocessing on them.

[0051] In the embodiment of this spec...

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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...

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Application Information

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IPC IPC(8): G06K9/62G06N20/20G06F16/25
CPCG06N20/20G06F16/254G06F18/214G06F18/29
Inventor 玉龙飞雪宋先知李根生黄中伟田守嶒肖立志廖广志
Owner CHINA UNIV OF PETROLEUM (BEIJING)
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