Complex lithologic identification method based on decision tree data mining algorithm and complex lithologic identification system thereof

Abstract

The invention relates to a complex lithology identification method and system based on a decision tree data mining algorithm. It relates to the technical field of oil exploration reservoir evaluation. The method includes: establishing a lithology profile of a closed coring well section, forming a lithology identification data set according to the lithology profile; performing data preprocessing on the lithology identification data set; and using different logging parameters to correspond to lithology Identify different lithologies in the data set; perform data mining on the lithology in the preprocessed lithology identification data set to form a tree identification model, so that complex Lithology is identified. The present invention forms the lithology identification data set through the lithology profile, performs unified lithology identification, and corresponds to different logging parameters according to the characteristics of lithology, and forms a clear and clear tree after data mining on the lithology identification data set The identification model can perform accurate lithology identification under complex lithology conditions.

Description

technical field [0001] The invention relates to the technical field of petroleum exploration reservoir evaluation, in particular to a complex lithology identification method and system based on a decision tree data mining algorithm. Background technique [0002] With the expansion of the breadth and depth of oil exploration and development, on the one hand, oil data is continuously accumulating at a rapid rate. If the massive oil data is used as a carrier of wealth, if it is fully utilized, it can bring additional benefits to oil exploration and development. Traditional Geophysical exploration methods based on petrophysics, mathematics, statistics, and petroleum exploration theory are somewhat powerless to deepen the utilization of these massive data; on the other hand, various unconventional oil and gas reservoirs are becoming the main exploration targets. However, conventional methods such as crossplot, linear regression, and multivariate discriminant analysis cannot effec...

AI Technical Summary

Problems solved by technology

But one of the biggest disadvantages of neural network is that it is easy to "over-learn" the training samples. The method has too many variable parameters. If the algorithm is given enough time, it can "remember" almost anything, and the model established in this way will be out of control. The geological background has no practical application value; in addition, neural networks, support vector machines, and Bayesian networks have a common shortcoming that the predicted models are all "black boxes", and how the sample data and attributes are related to each other Relationship fitting is invisible

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