Tertiary oil recovery layer series combination screening method based on improved LDA method and related device

Through the improved LDA method, the scientificity and accuracy of the three oil layer screening methods were solved, efficient and accurate layer system combination screening was achieved, and the efficiency and resource utilization of the three oil layer production were improved.

CN120541422APending Publication Date: 2025-08-26NORTHEAST GASOLINEEUM UNIV
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Patent Information

Application Number
CN202510639454.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-16
Publication Date
2025-08-26

AI Technical Summary

Technical Problem

In the prior art, the combined screening method of the three-class oil production layer system of the three-class oil layer lacks scientificity and accuracy, resulting in waste of resources and increased mining costs, making it difficult to meet the needs of refined management of oil field development.

Method used

Using the improved LDA method, by obtaining the feature data set, preprocessing and calculating the global mean, obtaining the intra-class and inter-class dispersion matrix, calculating the optimal projection direction, combining feature weights and comprehensive evaluation coefficients, a combination of three oil production layers suitable for the three types of oil layers was selected.

Benefits of technology

The classification accuracy and scientificity of the three-time oil recovery layer combinations have been improved, the calculation complexity has been reduced, the accuracy and efficiency of the screening results have been ensured, and resource waste and mining costs have been reduced.

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Abstract

The invention discloses a tertiary oil recovery layer series combination screening method based on an improved LDA method and a related device, and relates to the technical field of tertiary oil recovery, and the method comprises the steps: carrying out the preprocessing of original values of evaluation parameters in all feature data sets, and obtaining an evaluation matrix; calculating the evaluation matrix by using a global mean value method to obtain an overall mean value vector, and calculating to obtain intra-class and inter-class scatter matrixes based on the evaluation matrix and the overall mean value vector; calculating to obtain an optimal projection direction based on intra-class and inter-class scatter matrixes; based on the evaluation matrix and the optimal projection direction, calculating to obtain a comprehensive projection score corresponding to each tertiary oil recovery layer series combination; obtaining a feature weight corresponding to each evaluation parameter based on the feature data set; and on the basis of the comprehensive projection score, the evaluation matrix and the feature weight, calculating to obtain a comprehensive evaluation coefficient corresponding to each tertiary oil recovery layer series combination so as to screen out the layer series combination suitable for tertiary oil recovery of the three types of oil layers.
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