A porosity prediction method, device, electronic equipment and medium

Abstract

The application discloses a porosity prediction method, device, electronic equipment and medium, and the method comprises the following steps: acquiring logging data, logging porosity interpretation results and prestack seismic inversion results; acquiring elastic parameters based on the logging data; performing cross analysis on the elastic parameters and the logging porosity interpretation results to obtain sensitive elastic parameters of reservoir porosity; acquiring function coefficients and standard deviations by using a correlation vector machine based on the sensitive elastic parameters of reservoir porosity and the logging porosity interpretation results; calculating the sensitive elastic parameters of reservoir porosity by using the prestack seismic inversion results, and acquiring a reservoir porosity probability data body based on base functions, the function coefficients, the standard deviations and the calculated sensitive elastic parameters of reservoir porosity. The application can at least finely depict the longitudinal and lateral changes of reservoir porosity, so that the spatial distribution law of reservoir porosity is obtained.

Description

Technical Field

[0001] This invention belongs to the field of seismic exploration of oil and gas and coalbed methane, and more specifically, relates to a porosity prediction method, device, electronic equipment and medium. Background Technology

[0002] In the exploration and development of underground sedimentary minerals such as oil and coal, porosity is one of the key parameters of the reservoir, and its study is of great significance.

[0003] The most direct method for obtaining reservoir porosity is core analysis. However, drilling and coring are generally not continuous operations, and the total core recovery rate of an exploratory well is often only a few percent to a dozen percent, which poses a significant challenge to the study of the spatial distribution of reservoir porosity. In addition, drilling and coring are very expensive and can only be performed after drilling.

[0004] In practice, the most widespread and common method for obtaining porosity is using well logging data. Well logging data can provide a continuous interpretation of porosity across all logging intervals, offering high vertical resolution. However, because well locations are often sparsely and unevenly distributed in space, the information reflected by well logging data is also sparse and uneven in space, failing to accurately depict the spatial distribution of reservoir porosity. Further understanding the spatial distribution characteristics of reservoir porosity requires a large number of sufficiently dense boreholes, which is difficult to achieve during the exploration phase. Moreover, porosity calculations based on well logging data are performed after drilling is completed, and drilling costs are high. Furthermore, well location needs to be determined before drilling, meaning reservoir evaluation or prediction, including porosity prediction, needs to be conducted pre-drilling. Therefore, there is an urgent need for a means or method that can effectively grasp the planar variation characteristics of reservoir porosity using only a small number of boreholes.

[0005] Seismic reservoir porosity prediction arose precisely to meet these urgent needs. In oil exploration and certain coalfield and salt mine exploration, seismic exploration data is an indispensable and crucial foundational resource. This data is generally available in the early stages of exploration and typically covers the entire basin, containing extremely rich stratigraphic, structural, and sedimentary facies information; therefore, it is invaluable basic data for subsurface geological analysis.

[0006] Traditional seismic reservoir porosity prediction is based on seismic data, guided by geological principles, and constrained by drilling and logging data to study the spatial variation characteristics of porosity in oil and gas reservoirs. There are two main approaches: one is based on seismic inversion, using cross-analysis of logging data to select sensitive elastic properties that are sensitive to the porosity of the target reservoir section, and establishing the relationship between these sensitive parameters and porosity (function type and parameters); then, the sensitive elastic parameters are calculated using the seismic inversion results, and the spatial distribution characteristics of reservoir porosity are obtained using the relationship. This method requires determining the porosity sensitive parameters and their relationships during the cross-analysis stage. The other approach uses reservoir porosity as the inversion object or parameter, performing model-based seismic inversion to directly obtain the porosity. Due to the inherent ambiguity of inversion, and the even greater uncertainty in multi-parameter inversion including porosity, the former method is the most widely used in practice. However, in practical applications, the former method always employs a deterministic approach, meaning that the coefficients or parameters of the function determined during the function relationship establishment stage are fixed, resulting in a unique prediction outcome. Summary of the Invention

[0007] In view of this, the present disclosure provides a method, apparatus, electronic device and medium for porosity prediction, which can at least accurately characterize the longitudinal and lateral variations of reservoir porosity, thereby obtaining the spatial distribution law of reservoir porosity.

[0008] In a first aspect, embodiments of this disclosure provide a method for predicting porosity, including:

[0009] S1: Acquire well logging data, well logging porosity interpretation results, and pre-stack seismic inversion results;

[0010] S2: Obtain elastic parameters based on the well logging data;

[0011] S3: Perform cross-analysis between the elastic parameters and the well logging porosity interpretation results to obtain reservoir porosity-sensitive elastic parameters;

[0012] S4: Based on the reservoir porosity-sensitive elastic parameters and the well logging porosity interpretation results, the function coefficients and standard deviations are obtained using a correlation vector machine;

[0013] S5: Calculate the reservoir porosity-sensitive elastic parameters using the pre-stack seismic inversion results, and obtain the reservoir porosity probability data volume based on the basis functions, the function coefficients, the standard deviation, and the calculated reservoir porosity-sensitive elastic parameters.

[0014] As one specific implementation of this disclosure, the logging data includes porosity, P-wave velocity, S-wave velocity, and density.

[0015] As a specific implementation of this disclosure, the elastic parameters include longitudinal wave impedance, transverse wave impedance, longitudinal-to-transverse wave velocity ratio, Poisson's ratio, bulk modulus, shear modulus, Young's modulus, and elastic impedance.

[0016] As one specific implementation of this disclosure, in step S4...

[0017] The expression for the function coefficients is:

[0018]

[0019]

[0020] In the formula, P(W) is the prior distribution of parameter W, P(D) is the prior distribution of parameter D, and P(D|W) represents the likelihood function of coefficient W given data D(Y,X), where Y is the interpretation result of well logging porosity, X is the reservoir porosity-sensitive elastic parameter, and σ ε Let W be the standard deviation and W be the function coefficients.

[0021] As one specific implementation of this disclosure, in step S4...

[0022] In step S4, the method for obtaining the function coefficients is as follows: based on the fact that each component of the function coefficients is controlled by a hyperparameter, the process of obtaining the function coefficients is converted into an expression for obtaining the hyperparameters.

[0023] P(W, α, σ) ε |D)=P(W|D,α,σ ε )·P(α,σ ε |D)

[0024]

[0025]

[0026]

[0027] In the formula, A has diagonal elements α i The diagonal matrix; σ ε σ is the standard deviation; W is the function coefficient; α is the hyperparameter controlling the coefficients of the function; P(α, σ) ε |D) represents α and σ given data D. ε The joint probability distribution of ; Y is porosity.

[0028] Secondly, embodiments of this disclosure also provide an apparatus for predicting porosity, comprising:

[0029] The acquisition module is used to acquire well logging data, well logging porosity interpretation results, and pre-stack seismic inversion results.

[0030] The elastic parameter module is used to obtain elastic parameters using the well logging data;

[0031] The sensitive elastic parameter module is used to perform cross-analysis with the well logging porosity interpretation results to obtain reservoir porosity sensitive elastic parameters.

[0032] The function coefficient and variance module is used to obtain the function coefficients and standard variance using the reservoir porosity-sensitive elastic parameters and the well logging porosity interpretation results, and employing a correlation vector machine.

[0033] Probabilistic data volume module: used to calculate the reservoir porosity-sensitive elastic parameters using the pre-stack seismic inversion results, and to obtain the reservoir porosity probabilistic data volume using the basis functions, the function coefficients, the standard deviation, and the calculated reservoir porosity-sensitive elastic parameters.

[0034] As one specific implementation of this disclosure, the logging data includes porosity, P-wave velocity, S-wave velocity, and density.

[0035] As a specific implementation of this disclosure, the elastic parameters include longitudinal wave impedance, transverse wave impedance, longitudinal-to-transverse wave velocity ratio, Poisson's ratio, bulk modulus, shear modulus, Young's modulus, and elastic impedance.

[0036] Thirdly, embodiments of this disclosure also provide an electronic device, the electronic device comprising:

[0037] At least one processor; and,

[0038] A memory communicatively connected to the at least one processor; wherein,

[0039] The memory stores instructions executable by the at least one processor, which, when executed, enable the at least one processor to perform the porosity prediction method described in any of the first aspects.

[0040] Fourthly, embodiments of this disclosure also provide a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the porosity prediction method described in any of the first aspects.

[0041] The beneficial effects of this invention are:

[0042] This invention calculates various elastic parameters using the P-wave and S-wave velocities and densities of reservoir sections from well logging data. Then, it performs cross-analysis using these elastic parameters and well logging porosity interpretation results to obtain reservoir porosity-sensitive elastic parameters. Next, using these parameters and the well logging interpretation results as training data, it calculates the function coefficients and standard deviation using a correlation vector machine. Simultaneously, it calculates the reservoir porosity-sensitive elastic parameters using pre-stack seismic inversion results. Finally, it uses the calculated reservoir porosity-sensitive elastic parameters, basis functions, function coefficients, and standard deviation to predict the porosity probability distribution, achieving a detailed characterization of the longitudinal and transverse variations of reservoir porosity and revealing the spatial distribution law of reservoir porosity.

[0043] Other features and advantages of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0044] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments of the invention.

[0045] Figure 1 A flowchart of a porosity prediction method according to an embodiment of the present invention is shown.

[0046] Figure 2 A structural block diagram of a porosity prediction method according to an embodiment of the present invention is shown. Detailed Implementation

[0047] Preferred embodiments of the invention will now be described in more detail. While preferred embodiments of the invention are described below, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0048] Example 1:

[0049] See Figure 1 and Figure 2 This disclosure provides a method for predicting porosity. It includes the following steps:

[0050] S1: Acquire well logging data, well logging porosity interpretation results, and pre-stack seismic inversion results;

[0051] S2: Obtain elastic parameters based on the well logging data;

[0052] S3: Perform cross-analysis between the elastic parameters and the well logging porosity interpretation results to obtain reservoir porosity-sensitive elastic parameters;

[0053] S4: Based on the reservoir porosity-sensitive elastic parameters and the well logging porosity interpretation results, the function coefficients and standard deviations are obtained using a correlation vector machine;

[0054] S5: Calculate the reservoir porosity-sensitive elastic parameters using the pre-stack seismic inversion results, and obtain the reservoir porosity probability data volume based on the basis functions, the function coefficients, the standard deviation, and the calculated reservoir porosity-sensitive elastic parameters.

[0055] The following provides a detailed explanation of each step.

[0056] Perform step S1 to obtain well logging data, well logging porosity interpretation results, and pre-stack seismic inversion results.

[0057] In this embodiment, the logging data mainly consists of porosity φ and P-wave velocity V. p Shear wave velocity V s Density ρ.

[0058] Perform step S2 to obtain elastic parameters based on the well logging data.

[0059] In this embodiment, the elastic parameters mainly include the following parameters:

[0060] Longitudinal wave impedance: I p =V p ·ρ

[0061] Transverse wave impedance: I s =V s ·ρ

[0062] P-wave to S-wave velocity ratio:

[0063] λρ:

[0064] μρ:

[0065] Poisson's ratio:

[0066] Bulk modulus:

[0067] Shear modulus:

[0068] Young's modulus: E = 3·K·(1-2·σ)

[0069] Elastic resistance: Among them

[0070] You can also define some calculation parameters yourself, for example:

[0071] Steps S3 and S4 are executed to perform cross-analysis of the elastic parameters and the well logging porosity interpretation results to obtain reservoir porosity-sensitive elastic parameters; based on the reservoir porosity-sensitive elastic parameters and the well logging porosity interpretation results, the function coefficients and standard deviations are obtained using a correlation vector machine.

[0072] In this embodiment, assuming the reservoir porosity-sensitive elastic parameter obtained in step S3 is X, and the well logging porosity interpretation result is represented by Y, then the training data obtained from the well logging data is represented as D(X, Y); the relationship between Y and X is obtained through the training data: The residual is ε, and f(X, W) can be linear or other functions. For abstract functions f(X, W), the computation needs to be specific; a common approach is to introduce basis functions. Then f(X, W) is considered as being composed of Obtained by linear combination:

[0073] Conventional deterministic methods ignore residuals and then determine the parameters W. The correlation vector machine, however, within a Bayesian framework, treats the coefficients W and residuals ε as random variables following a certain probability distribution. It assumes that the residuals ε have a mean of 0 and a variance of σ. ε Gaussian noise, i.e.

[0074]

[0075] According to Bayesian box theory, the posterior distribution of parameter W given data D(Y,X) can be expressed as:

[0076]

[0077] Where P(W) is the prior distribution of parameter W, P(D) is the prior distribution of parameter D, and P(D|W) represents the likelihood function of coefficient W given data D(Y, X), and it is assumed that P(D|W) is normally distributed: i.e.

[0078]

[0079] For P(W), it is also assumed to have a mean of 0 and a variance of σ. w It follows a Gaussian distribution. Finding the parameter W can be considered as finding P(W). Assume each component w in the coefficient W... i It is determined by a hyperparameter α i Control, thereby transforming the distribution P(W) of coefficient W into:

[0080]

[0081] In other words, finding the parameter W is transformed into finding the hyperparameter α. According to Bayesian theory, the problem is transformed into the following form:

[0082] P(W, α, σ) ε |D)=P(W|D,α,σ ε )·P(α,σ ε |D),

[0083] The likelihood is:

[0084]

[0085] Where A is a diagonal element α i The diagonal matrix; σ ε σ is the standard deviation; W is the function coefficient; α is the hyperparameter controlling the coefficients of the function; P(α, σ) ε |D) represents α and σ given data D. ε The joint probability distribution of ; Y is porosity.

[0086] P(α,σ ε |D)∝P(D|α,σ ε )·P(α)·P(σ ε )

[0087]

[0088]

[0089]

[0090] Where Γ(a)=∫0 ∞ t a-1 ·e -t ·dt is the gamma function.

[0091] The parameters a, b, c, and d can be obtained using data and optimization methods. Alternatively, a simple approximation method can be used, where they are set to very small numbers, for example, a = b = c = d = 10. -6 .

[0092] In this way, P(W, α, σ) can be obtained from the data D(Y, X). ε |D).

[0093] Execute step S5, calculate the reservoir porosity-sensitive elastic parameter using the pre-stack seismic inversion results, and obtain the reservoir porosity probability data volume based on the basis function, the function coefficients, the standard deviation, and the calculated reservoir porosity-sensitive elastic parameter.

[0094] In this embodiment, for the sensitive elastic property data obtained from seismic inversion, the trained correlation vector machine described above is applied to obtain the probability of the corresponding porosity, that is, given a new value... Seeking probability distribution:

[0095] Example 2:

[0096] This disclosure provides an apparatus for predicting porosity, comprising:

[0097] The acquisition module is used to acquire well logging data, well logging porosity interpretation results, and pre-stack seismic inversion results.

[0098] The elastic parameter module is used to obtain elastic parameters using the well logging data;

[0099] The sensitive elastic parameter module is used to perform cross-analysis with the well logging porosity interpretation results to obtain reservoir porosity sensitive elastic parameters.

[0100] The function distribution and variance module is used to obtain function coefficients and standard deviations using the reservoir porosity-sensitive elastic parameters and the well logging porosity interpretation results, and employing a correlation vector machine.

[0101] Probabilistic data volume module: used to calculate the reservoir porosity-sensitive elastic parameters using the pre-stack seismic inversion results, and to obtain the reservoir porosity probabilistic data volume using the basis functions, the function coefficients, the standard deviation, and the calculated reservoir porosity-sensitive elastic parameters.

[0102] In this embodiment, the logging data includes porosity, P-wave velocity, S-wave velocity, and density.

[0103] In this embodiment, the elastic parameters include longitudinal wave impedance, transverse wave impedance, longitudinal wave velocity ratio, Poisson's ratio, bulk modulus, shear modulus, Young's modulus, and elastic impedance.

[0104] Example 3:

[0105] This disclosure also provides an electronic device, which includes:

[0106] At least one processor; and,

[0107] A memory communicatively connected to the at least one processor; wherein,

[0108] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the porosity prediction method in Embodiment 1.

[0109] An electronic device according to embodiments of the present disclosure includes a memory and a processor. The memory is used to store non-transitory computer-readable instructions. Specifically, the memory may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM) and / or cache memory. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, etc.

[0110] The processor may be a central processing unit (CPU) or other form of processing unit with data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device to perform desired functions. In one embodiment of this disclosure, the processor is used to execute computer-readable instructions stored in the memory.

[0111] Those skilled in the art will understand that, in order to solve the technical problem of how to achieve a good user experience, this embodiment may also include well-known structures such as communication buses and interfaces, and these well-known structures should also be included within the protection scope of this disclosure.

[0112] For a detailed description of this embodiment, please refer to the corresponding descriptions in the foregoing embodiments, which will not be repeated here.

[0113] Example 4:

[0114] This disclosure provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the porosity prediction method of Embodiment 1.

[0115] A computer-readable storage medium according to embodiments of the present disclosure stores non-transitory computer-readable instructions. When these non-transitory computer-readable instructions are executed by a processor, all or part of the steps of the methods described in the foregoing embodiments of the present disclosure are performed.

[0116] The aforementioned computer-readable storage media include, but are not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or portable hard drive), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).

[0117] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. A method for predicting porosity, characterized in that, include: S1: Acquire well logging data, well logging porosity interpretation results, and pre-stack seismic inversion results; S2: Obtain elastic parameters based on the well logging data; S3: Perform cross-analysis between the elastic parameters and the well logging porosity interpretation results to obtain reservoir porosity-sensitive elastic parameters; S4: Based on the reservoir porosity-sensitive elastic parameters and the well logging porosity interpretation results, the function coefficients and standard deviations are obtained using a correlation vector machine; S5: Calculate the reservoir porosity-sensitive elastic parameters using the pre-stack seismic inversion results, and obtain the reservoir porosity probability data volume based on the basis functions, the function coefficients, the standard deviation, and the calculated reservoir porosity-sensitive elastic parameters.

2. The method according to claim 1, characterized in that, The logging data includes porosity, P-wave velocity, S-wave velocity, and density.

3. The method according to claim 1, characterized in that, The elastic parameters include longitudinal wave impedance, transverse wave impedance, longitudinal wave velocity ratio, Poisson's ratio, bulk modulus, shear modulus, Young's modulus, and elastic impedance.

4. The method according to claim 1, characterized in that, In step S4, the expression for the function coefficients is: In the formula, It is a parameter The prior distribution of parameter D is P(D), which is the prior distribution of parameter D. Represents given data coefficient The probability distribution function is given by Y, where Y is the interpretation result of well logging porosity, and X is the reservoir porosity-sensitive elastic parameter. Let W be the standard deviation and W be the function coefficients.

5. The method according to claim 1, characterized in that, In step S4, the method for obtaining the function coefficients is as follows: based on the fact that each component of the function coefficients is controlled by a hyperparameter, the process of obtaining the function coefficients is converted into an expression for obtaining the hyperparameters. In the formula, diagonal elements are a diagonal matrix; denoted as standard deviation; W represents the function coefficients. These are hyperparameters that control the coefficients of the control function; This indicates that given data D, and The joint probability distribution of ; Y is porosity.

6. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the reservoir porosity prediction method according to any one of claims 1-5.

7. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions for causing a computer to perform the method for predicting reservoir porosity as described in any one of claims 1-5.

8. A device for predicting porosity, characterized in that, include: The acquisition module is used to acquire well logging data, well logging porosity interpretation results, and pre-stack seismic inversion results. The elastic parameter module is used to obtain elastic parameters using the well logging data; The sensitive elastic parameter module is used to perform cross-analysis with the well logging porosity interpretation results to obtain reservoir porosity sensitive elastic parameters. The function distribution and variance module is used to obtain function coefficients and standard deviations using the reservoir porosity-sensitive elastic parameters and the well logging porosity interpretation results, and employing a correlation vector machine. Probabilistic data volume module: used to calculate the reservoir porosity-sensitive elastic parameters using the pre-stack seismic inversion results, and to obtain the reservoir porosity probabilistic data volume using the basis functions, the function coefficients, the standard deviation, and the calculated reservoir porosity-sensitive elastic parameters.

9. The apparatus according to claim 8, characterized in that, The logging data includes porosity, P-wave velocity, S-wave velocity, and density.

10. The apparatus according to claim 8, characterized in that, The elastic parameters include longitudinal wave impedance, transverse wave impedance, longitudinal wave velocity ratio, Poisson's ratio, bulk modulus, shear modulus, Young's modulus, and elastic impedance.

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