Lithology identification method and system

A technology for lithology identification and lithology, applied in the field of deep learning, it can solve the problems of algorithm performance impact, separation from geological background, and many variable parameters, and achieve the effect of improving rock lithology identification rate.

Inactive Publication Date: 2021-05-07
中国地质调查局成都地质调查中心
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AI Technical Summary

Problems solved by technology

However, one of the biggest disadvantages of BP 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 Without the geological background, there is 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 the relationship between sample data and attributes and relational fitting is not visible
[0003] Similarly, in the existing lithology identification, the KNN algorithm is mostly used to determine the rock sample type, but KNN may require a large amount of memory or space to store all the data, and the measurement method using distance or proximity may be difficult in crashes in very high dimensionality (with many input variables), which can negatively affect the performance of the algorithm on your problem

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  • Lithology identification method and system
  • Lithology identification method and system

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Embodiment 1

[0029] see figure 1 , figure 1 Shown is a schematic diagram of the steps of a lithology identification method provided in the embodiment of the present application, which is as follows:

[0030] Step S100, establishing a closed lithology profile, and classifying and judging the rock data samples according to the number of rock data samples in the lithology identification data set formed by the lithology profile;

[0031] In some embodiments, before forming the lithology identification data set, combined with the geological background and rock formation characteristics of the research area, and based on the overall requirements of reservoir evaluation, the lithology of the research area is determined from two aspects: the description of the sealed core sample and the naming of the cast thin section. Due to the strong heterogeneity of complex reservoirs and the drastic changes in lithology, the local cast thin slices of cores cannot accurately reflect the real lithology of form...

Embodiment 2

[0043] see figure 2 , figure 2 A schematic diagram of the detailed steps of a lithology identification method provided by an embodiment of the present invention is as follows:

[0044] In step S200, after the closed lithology section is established, the response values ​​of different lithology sections are read according to the reading principles of the rock formation and lithology transition zone, and the corresponding relationship between lithology and parameters is established to form a lithology identification data set. The ANN algorithm determines the type of rock data samples, so as to achieve the purpose of distinguishing the types of rock data samples.

[0045] In step S210, when the rock data sample type is determined to be an outlier, it is eliminated as a noise point or an error point.

[0046] Step S220, judging the true and false outliers includes determining the size Sn of the similarity threshold in the range (0,1); calculating the values ​​of all rock data ...

Embodiment 3

[0074] see image 3 , image 3 A schematic diagram of a lithology identification system module provided by an embodiment of the present invention is as follows:

[0075] The classification and determination module 10 is used to establish a closed lithology profile, and classify and determine the rock data samples according to the number of rock data samples in the lithology identification data set formed by the lithology profile;

[0076] The stripping module 20 is used for performing data preprocessing according to the rock data samples, removing true outliers therefrom, oversampling the rock data sample types marked as effective samples, and obtaining a balanced stratum rock data set;

[0077] The identification and classification module 30 is used to normalize the rock data set, normalize the value of the element content of the rock data sample, and input it into the trained residual shrinkage network to classify the lithology of the formation rock data sample .

[0078]...

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Abstract

The invention provides a lithology identification method and system, and relates to the field of deep learning. The lithology identification method comprises the following steps: establishing a closed lithology section, and classifying and judging rock data samples according to different numbers of the rock data samples of a lithology identification data set formed by the lithology section; carrying out data preprocessing according to the rock data samples, removing true outliers from the rock data samples, carrying out oversampling on the samples with the rock data sample types marked as effective, and obtaining a balanced stratum rock data set. According to the method and system, a lithology identification data set can be formed through a lithology section, unified lithology identification is carried out, different parameters correspond to the characteristics of lithology, and after data mining is carried out on the lithology identification data set, a clear identification model is formed. In addition, the invention further provides a lithology identification system which comprises a classification and judgment module, a stripping module and an identification and classification module.

Description

technical field [0001] The present invention relates to the field of deep learning, in particular to a method and system for identifying lithology. Background technique [0002] Data mining is the process of extracting implicit, previously unknown, but potentially useful information and knowledge from a large number of incomplete, noisy, fuzzy, and random data. Data mining tasks are divided into two categories: description and prediction. The former derives a generalized pattern of potential relationships in the data, and the latter infers the current data to make predictions. The main methods are neural networks, support vector machines, Bayesian networks, and decision-making. Among them, BP neural network has been widely used in lithology identification, sedimentary facies division, permeability prediction, oil, gas and water layer identification, etc. Practice has proved that when dealing with complex geological problems affected by multiple factors, nonlinear neural netw...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/241G06F18/214
Inventor 郝明王东辉
Owner 中国地质调查局成都地质调查中心
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