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CN-115964604-B - Expansion method, device, equipment and storage medium of logging data

CN115964604BCN 115964604 BCN115964604 BCN 115964604BCN-115964604-B

Abstract

The application provides a method, a device, equipment and a storage medium for expanding logging data, and relates to the technical field of petroleum exploration. The expansion method of the logging data comprises the steps of obtaining original logging data, and obtaining new data which accords with the rule of the original logging data as expansion data by adopting a random sampling method. The application utilizes the law among the existing logging data, adopts the Monte Carlo random sampling method to obtain a large amount of new data which accords with the law of the original logging data, and can be further applied to various inversion and reservoir prediction method technologies to improve the certainty of oil and gas exploration and development.

Inventors

  • LI JINGNAN
  • ZHU TONG
  • LV WEI

Assignees

  • 中国石油化工股份有限公司
  • 中国石油化工股份有限公司石油物探技术研究院

Dates

Publication Date
20260512
Application Date
20211012

Claims (9)

  1. 1. An extension method of logging data, comprising: Acquiring original logging data; the method for acquiring new data conforming to the original logging data rule by adopting the Monte Carlo random sampling method is used as expansion data, and specifically comprises the following steps: S1, fitting a relation between a first parameter and a second parameter according to a statistical rule of the measured value of the first parameter and the measured value of the second parameter in the original logging data to obtain a fitting relation; S2, substituting the measured value of the first parameter in the original logging data into the fitting relation, calculating a fitting result of the second parameter, and calculating a standard deviation between the fitting result of the second parameter and an actual measured value; s3, counting the cumulative probability distribution of the first parameter in the original logging data; S4, generating random numbers according to the cumulative probability distribution of the first parameters; s5, calculating a first expansion numerical value of the first parameter corresponding to the random number according to the generated random number and the cumulative probability distribution of the first parameter; S6, acquiring a fitting value of the second parameter according to the expansion value of the first parameter and the fitting relation; s7, calculating a second expansion numerical value of the second parameter according to the standard deviation and the fitting value of the second parameter; and respectively taking the first expansion value of the first parameter and the second expansion value of the second parameter as expansion data of the first parameter and the second parameter.
  2. 2. The expansion method according to claim 1, wherein calculating a standard deviation of a fitting result from an actual measurement value according to the fitting relation comprises: calculating fitting values of the corresponding second parameters according to the fitting relation for all the sample points of the first parameters; a standard deviation std of the difference between the true value of the second parameter and the calculated fitting value is then calculated.
  3. 3. The expansion method according to claim 1, wherein the generating random numbers according to the cumulative probability distribution of the first parameter specifically includes: generating a random number which accords with uniform distribution in the range of 0-1.
  4. 4. The expansion method according to claim 1, wherein the fitting relation obtained in step S1 is a linear fitting relation y=a×x+b, where x is the first parameter, y is the second parameter, and a and b are constant coefficients; the fitting value of the second parameter obtained in the step S6 is par 2=a×par1+b, wherein par1 is a first expansion value of the first parameter, and par2 is a fitting value of the second parameter; The second extended value of the second parameter calculated in step S7 is par2' =par 2+std randn (1), where randn (1) represents a value that is randomly generated and conforms to a normal distribution with a mean of 0 and a variance of 1.
  5. 5. The expansion method according to any one of claims 1 to 4, wherein the obtaining new data conforming to the original log data rule by using a monte carlo random sampling method further comprises: repeating the steps S4-S7N times to obtain N groups of logging data of random simulation of the first parameter and the second parameter as expansion data, wherein N is a natural number larger than zero.
  6. 6. The expansion method of any one of claims 1 to 4, wherein the first parameter and the second parameter are the porosity and the argillaceous content of the same target region, respectively.
  7. 7. An expansion device for logging data, comprising: the acquisition module is used for acquiring original logging data; The expansion module is used for obtaining new data conforming to the original logging data rule by adopting a Monte Carlo random sampling method, and specifically comprises a fitting module, a calculation module and a calculation module, wherein the fitting module is used for fitting the relation between a first parameter and a second parameter according to the statistical rule of the measured value of the first parameter and the measured value of the second parameter in the original logging data to obtain a fitting relation; The standard deviation calculation module is used for substituting the measured value of the first parameter in the original logging data into the fitting relation, calculating the fitting result of the second parameter, and calculating the standard deviation between the fitting result of the second parameter and the actual measured value; The probability distribution module is used for counting the cumulative probability distribution of the first parameter in the original logging data; the random number module is used for generating random numbers according to the cumulative probability distribution of the first parameters; A first parameter expansion module, configured to calculate a first expansion value of the first parameter corresponding to the random number according to the generated random number and the cumulative probability distribution of the first parameter; The fitting value calculation module is used for obtaining a fitting value of the second parameter according to the expansion numerical value of the first parameter and the fitting relation; The second parameter expansion module is used for calculating a second expansion value of the second parameter according to the standard deviation and the fitting value of the second parameter; Wherein the first expansion value of the first parameter and the second expansion value of the second parameter are respectively used as expansion data of the first parameter and the second parameter.
  8. 8. An expansion device for logging data, comprising a memory and a processor, wherein the memory has stored thereon a computer program which, when executed by the processor, performs the method for expanding logging data according to any one of claims 1 to 6.
  9. 9. A storage medium storing a computer program executable by one or more processors for implementing the method of augmenting logging data according to any one of claims 1 to 6.

Description

Expansion method, device, equipment and storage medium of logging data Technical Field The present application relates to the field of petroleum exploration, and in particular, to a method, apparatus, device, and storage medium for expanding logging data. Background Logging is a method of measuring geophysical parameters using the geophysical properties of the formation, such as electrochemical properties, conductive properties, acoustic properties, radioactivity, etc. The logging data is a reaction to the lithology of the actual underground structure, and by using sufficient logging data, petrophysical modeling can be performed, so that uncertainty of reservoir prediction technologies such as seismic inversion can be reduced. Logging data plays an important role in oil and gas exploration and development, but in the initial stage of oil and gas exploration and development, logging and other data are usually less, and seismic data are often the only guiding information for developing reservoir property prediction outside well positions. Because of limitations of seismic data in terms of signal-to-noise ratio, resolution, and the like, developing reservoir descriptions based solely on seismic data often has a strong uncertainty, which increases the risk of later reservoir development. Disclosure of Invention In view of the above problems, the present application provides a method, apparatus, device and storage medium for expanding logging data, which can solve the problem of lack of logging data in the initial stage of oil and gas exploration and development. The application provides an expansion method of logging data, which comprises the steps of obtaining original logging data, and obtaining new data conforming to the rule of the original logging data as expansion data by adopting a random sampling method. In some embodiments, a Monte Carlo random sampling method is used to obtain new data that conforms to the rules of the original log data as extended data. In some embodiments, the obtaining new data conforming to the original log data rule by using a monte carlo random sampling method specifically includes: s1, fitting the relation between the first parameter and the second parameter to obtain a fitting relation; s2, calculating standard deviation of a fitting result and an actual measured value according to the fitting relation; s3, counting the cumulative probability distribution of the first parameter in the original logging data; S4, generating random numbers according to the cumulative probability distribution of the first parameters; s5, calculating a first expansion numerical value of the first parameter corresponding to the random number according to the generated random number and the cumulative probability distribution of the first parameter; S6, acquiring a fitting value of the second parameter according to the expansion value of the first parameter and the fitting relation; And S7, calculating a first expansion numerical value of the second parameter according to the standard deviation and the fitting value of the second parameter. In some embodiments, said calculating a standard deviation of the fitting result from the actual measurement from the fitting relation comprises: Calculating fitting values of the corresponding second parameters according to the fitting relation for all the sample points of the first parameters; a standard deviation std of the difference between the true value of the second parameter and the calculated fitting value is then calculated. In some embodiments, the generating the random number according to the cumulative probability distribution of the first parameter specifically includes generating a random number conforming to a uniform distribution within a range of 0-1. In some embodiments, the fitting relation obtained in step S1 is a linear fitting relation y=a×x+b, where x is the first parameter, y is the second parameter, and a and b are constant coefficients; the fitting value of the second parameter obtained in the step S6 is par 2=a×par1+b, wherein par1 is a first expansion value of the first parameter, and par2 is a fitting value of the second parameter; the first extended value of the second parameter calculated in step S7 is par2' =par 2+std randn (1), where randn (1) represents a value that is randomly generated and conforms to a normal distribution with a mean of 0 and a variance of 1. In some embodiments, the method for obtaining new data conforming to the rule of the original logging data by adopting the Monte Carlo random sampling method further comprises repeatedly executing steps S4-S7N times to obtain N groups of logging data of random simulation of the first parameter and the second parameter as expansion data, wherein N is a natural number greater than zero. In some embodiments, the first parameter and the second parameter are the porosity and the argillaceous content, respectively, of the same target region. The embodiment of the application a