Method for identifying stratum lithology parameters based on multi-kernel ensemble learning

An integrated learning and stratum lithology technology, applied in character and pattern recognition, instruments, computer components, etc., can solve the problems of low judgment accuracy and low logging data utilization, and achieve high judgment accuracy and improve The effect of utilization

Active Publication Date: 2020-04-28
NORTHEAST GASOLINEEUM UNIV
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Problems solved by technology

[0007] The present invention provides a method for identifying stratum lithology parameters based on multi-core integrated learning to overcome the defec

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  • Method for identifying stratum lithology parameters based on multi-kernel ensemble learning
  • Method for identifying stratum lithology parameters based on multi-kernel ensemble learning
  • Method for identifying stratum lithology parameters based on multi-kernel ensemble learning

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Embodiment Construction

[0042] The present invention will be described in detail below in conjunction with the accompanying drawings and embodiments.

[0043] Such as figure 1 As shown, the embodiment of the present invention provides a method for identifying formation lithology parameters based on multi-core integrated learning, which specifically includes:

[0044] Step S1, dividing M different sample sets according to the characteristics of logging parameters,

[0045] S 1 ={(x 11 ,y 11 ),(x 12 ,y 12 ),…,(x 1n ,y 1n )};

[0046] S 2 ={(x 21 ,y 21 ),(x 22 ,y 22 ),…,(x 2n ,y 2n )};...;

[0047] S m ={(x m1 ,y m1 ),(x m2 ,y m2 ),…,(x mn ,y mn )}

[0048] in

[0049] Step S2, according to the predicted lithological parameter characteristics, in the case of maintaining the same ratio, each sample set is divided into the training sample set S according to a certain ratio mtrain and test sample set S mtest , the number of samples in the training sample set is n mtrain , the ...

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Abstract

The invention relates to the technical field of reservoir lithology identification, in particular to a method for identifying stratum lithology parameters based on multi-kernel ensemble learning. Themethod comprises the steps of dividing different sample sets according to logging parameter characteristics; dividing a training sample set and a test sample set; establishing a strong classifier forlithology parameter characteristics, judging test samples in the test sample set, and obtaining lithology parameters by adopting an average method; establishing a strong classifier for the lithology parameter characteristics according to a prediction result and reconstructed sample data; forming a strong classifier by utilizing the strong classifier; judging the samples, and determining a final stratum lithology category by adopting a voting mode; adopting an absolute majority voting method, if a certain lithology is marked to obtain a half vote, prediciting the lithology, and otherwise, refusing prediction. Characteristics of a multi-kernel ensemble learning algorithm are applied, a plurality of classifiers are combined, the classification error rate is minimized, the logging data utilization rate is improved, and the judgment accuracy is high.

Description

technical field [0001] The invention relates to the technical field of reservoir lithology identification, in particular to a method for identifying formation lithology parameters based on multi-core integrated learning. Background technique [0002] Reservoir lithology identification is a very important part of reservoir evaluation. Only after accurately understanding the actual situation of the stratum can a development plan be formulated to promote high and stable production in the oil field. Coring and crossplot methods are routinely used in the process of lithology identification , Statistical analysis method, not only the workload is heavy, but also the recognition accuracy is affected by professional knowledge and human factors. The relationship between logging parameters and lithology is intricate, and the identification results are affected by various logging methods. The accuracy of using single-kernel learning to identify different lithologies is unstable, and dif...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/24G06F18/214
Inventor 王梅杨二龙戚开元李董李东旭薛成龙
Owner NORTHEAST GASOLINEEUM UNIV
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