Transverse wave time difference prediction method and apparatus

Patent metadata

Field	Value
Publication number	US-2026072190-A1
Application number	US-202219105841-A
Country	US
Kind code	A1
Filing date	Dec 14, 2022
Priority date	Aug 26, 2022
Publication date	Mar 12, 2026
Grant date	—

Abstract

A shear wave time difference prediction method and apparatus, relating to the technical field of petroleum exploration and development. The method comprises: acquiring well logging sample data as a training data set of a prediction model ( 101 ); preprocessing the training data set, performing data screening on the basis of importance analysis, and grouping data on the basis of the kurtosis and the skewness to obtain a processed training data set ( 102 ); respectively inputting into a neural network constructed by mixing a CNN and an LSTM the processed training data set for training to obtain a shear wave time difference prediction model ( 103 ); acquiring well logging data of a shear wave time difference to be predicted ( 104 ); preprocessing the well logging data, and grouping the well logging data on the basis of the kurtosis and the skewness to obtain processed well logging data ( 105 ); and respectively using the processed well logging data as an input of the shear wave time difference prediction model to obtain a shear wave time difference ( 106 ). The method and the apparatus have the advantages of high processing efficiency, high prediction precision, and strong regional applicability.

First claim

1 . A method for predicting a shear wave time difference, comprising:
obtaining well logging sample data as a training data set of a prediction model; performing the following data processing to obtain a processed training data set:
performing data cleansing, data filtering and normalization on the training data set to obtain first training data;
screening out, from the first training data, data with a correlation coefficient between the data and the shear wave time difference being greater than a first preset coefficient value as second training data;
respectively calculating a correlation coefficient between two different types of data in the second training data;
screening out, in a case that two different types of data with the correlation coefficient being greater than a second preset coefficient value exist, any one type of data from the two different types of data, and using the any one type of data and the rest of different types of data with the correlation coefficient being less than or equal to the second preset coefficient value in the second training data as third training data; and
dividing the third training data into at least two groups of well logging data on the basis of a preset kurtosis coefficient and a preset skewness coefficient as the processed training data set;
respectively inputting the processed training data set into a neural network constructed by mixing a CNN and an LSTM for training to obtain a shear wave time difference prediction model; obtaining well logging data of a shear wave time difference to be predicted; preprocessing the well logging data, and grouping the well logging data on the basis of the kurtosis and the skewness to obtain processed well logging data; and respectively using the processed well logging data as an input of the shear wave time difference prediction model to obtain a shear wave time difference.

2 . The method according to claim 1 , wherein the CNN and the LSTM neural network in the shear wave time difference prediction model are connected through a Dropout layer.

3 . The method according to claim 1 , wherein the well logging data includes: gamma ray, caliper, spontaneous potential, resistivity, neutron, sonic and density.

4 . The method according to claim 1 , wherein the correlation coefficient is calculated by using a Pearson's correlation coefficient computational formula.

5 . An apparatus for predicting a shear wave time difference, comprising:
a training data obtaining module, configured to obtain well logging sample data as a training data set of a prediction model; a first data processing module, configured to perform the following data processing to obtain a processed training data set:
performing data cleansing, data filtering and normalization on the training data set to obtain first training data;
screening out, from the first training data, data with a correlation coefficient between the data and the shear wave time difference being greater than a first preset coefficient value as second training data;
respectively calculating a correlation coefficient between two different types of data in the second training data;
screening out, in a case that two different types of data with the correlation coefficient being greater than a second preset coefficient value exist, any one type of data from the two different types of data, and use the any one type of data and the rest of different types of data with the correlation coefficient being less than or equal to the second preset coefficient value in the second training data as third training data; and
dividing the third training data into at least two groups of well logging data on the basis of a preset kurtosis coefficient and a preset skewness coefficient as the processed training data set;
a model training model, configured to respectively input the processed training data set into a neural network constructed by mixing a CNN and an LSTM for training to obtain a shear wave time difference prediction model; an inputted data obtaining module, configured to obtain well logging data of a shear wave time difference to be predicted; a second data processing module, configured to preprocess the well logging data, and group the well logging data on the basis of the kurtosis and the skewness to obtain processed well logging data; and a result output module, configured to respectively use the processed well logging data as an input of the shear wave time difference prediction model to obtain a shear wave time difference.

6 . The apparatus according to claim 5 , wherein the CNN and the LSTM neural network in the shear wave time difference prediction model are connected through a Dropout layer.

7 . The apparatus according to claim 5 , wherein the well logging data comprises: gamma ray, caliper, spontaneous potential, resistivity, neutron, sonic and density.

8 . The apparatus according to claim 5 , wherein the correlation coefficient is calculated by using a Pearson's correlation coefficient computational formula.

9 . An electronic device, comprising a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor, when performing the computer program, implements the following steps:
obtaining well logging sample data as a training data set of a prediction model; performing the following data processing to obtain a processed training data set:
performing data cleansing, data filtering and normalization on the training data set to obtain first training data;
screening out, from the first training data, data with a correlation coefficient between the data and the shear wave time difference being greater than a first preset coefficient value as second training data;
respectively calculating a correlation coefficient between two different types of data in the second training data;
screening out, in a case that two different types of data with the correlation coefficient being greater than a second preset coefficient value exist, any one type of data from the two different types of data, and using the any one type of data and the rest of different types of data with the correlation coefficient being less than or equal to the second preset coefficient value in the second training data as third training data; and
dividing the third training data into at least two groups of well logging data on the basis of a preset kurtosis coefficient and a preset skewness coefficient as the processed training data set;
respectively inputting the processed training data set into a neural network constructed by mixing a CNN and an LSTM for training to obtain a shear wave time difference prediction model; obtaining well logging data of a shear wave time difference to be predicted; preprocessing the well logging data, and grouping the well logging data on the basis of the kurtosis and the skewness to obtain processed well logging data; and respectively using the processed well logging data as an input of the shear wave time difference prediction model to obtain a shear wave time difference.

10 . (canceled)

11 . The method according to claim 1 , wherein the method is performed by a processor.

12 . The method according to claim 1 , wherein the obtained shear wave time difference is used for rock physics analysis, lithology identification, rock elastic mechanics parameter calculation, reservoir description and/or fluid identification.

13 . The apparatus according to claim 5 , wherein the apparatus is implemented as a processor.

14 . The apparatus according to claim 5 , wherein the obtained shear wave time difference is used for rock physics analysis,

Assignees

• Petrochina Co Ltd

Inventors

• Song Lianteng
• Liu Zhonghua
• Li Chaoliu
• Yuan Chao
• Ning Congqian

Classifications

• G01V1/50

  Analysing data · CPC title

• G06N3/08

  Learning methods · CPC title

• G01V1/307 Primary

  for determining seismic attributes, e.g. amplitude, instantaneous phase or frequency, reflection strength or polarity · CPC title

• G06N3/045

  Combinations of networks · CPC title

• Y02A90/30

  Assessment of water resources · CPC title