Comprehensive geological borehole logging lithology identification method

Abstract

The invention provides a comprehensive geological borehole logging lithology identification method, which comprises the following steps of: refining borehole logging data to obtain a refined data set,including missing value filling, equalization processing and data set normalization processing on the borehole logging data; enabling refined data set to be subjected to dimensionality reduction processing according to a tSNE algorithm, improving and optimizing a BP neural network according to a PSO algorithm, obtaining the optimal initialization weight and threshold value of the network, establishing a network model, and carrying out training learning on the dimensionality reduction data set through the established network model.According to the method, drilling and logging data is refined,the problem that the final recognition rate is too low due to the fact that acquired drilling and logging data are missing, data sets are unbalanced and training data are not in a unified dimension range is solved, dimension reduction processing is conducted on the drilling and logging data sets according to the tSNE algorithm, data are simplified accordingly, a common BP neural network in the prior art is optimized through the PSO algorithm, and the identification accuracy and the identification rate are improved.

Description

technical field [0001] The invention relates to a comprehensive geological drilling logging lithology identification method, in particular to a comprehensive drilling logging lithology identification method based on t-SNE and PSO to improve BP neural network. Background technique [0002] The rapid development of the economy makes people's demand for resources and energy continuously increase, which puts forward higher requirements for geological exploration. The composition, structure and physical and chemical properties of rocks and all the attributes that can reflect the characteristics of rocks are the lithology of rocks. Because lithology controls the distribution of petrophysical properties such as porosity and permeability, an understanding of spatial variation in lithology is important for the exploration of subsurface deposits. [0003] Lithology identification is mainly obtained by analyzing drilling cores and logging data, but it is difficult to fully describe th...

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Problems solved by technology

[0005] In order to improve the recognition rate, speed up the recognition rate, and avoid the error caused by human factors in the method of manual analysis of logging curves to identify lithology, BP neural network technology has been used to identify lithology from logging data. Logging parameters with uneven distribution and ambiguity will have a serious impact on the speed of logging recognition, and it is easy to fall into a local minimum, and the BP neural network is generally based on experience to set the initial network. The required parameters, and then adjust the parameters according to the feedback of the error rate of the experimental results, repeated iterations in the parameter adjustment stage, the workload is heavy, and the optimal weight and threshold may not be found, so the currently used in lithology identification According to the identification data obtained by the BP neural network learning algorithm, the identification accuracy rate is low in lithology identification

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