Physical property parameter prior information feature characterization method and device

Abstract

The invention provides a physical property parameter prior information feature characterization method and device. The method comprises the steps that a physical property parameter logging curve of a target well in a research area is intercepted according to preset information; analyzing characteristic parameters of the physical property parameter logging curve by adopting a histogram statistics form; constructing a prior distribution probability density function to represent the form of the histogram; using a characteristic parameter value in the total sample set as a composition parameter initial value of the prior distribution probability density function; and performing iterative calculation on the composition parameters of the prior distribution probability density function, and taking the composition parameters during convergence as final composition parameters of the prior distribution probability density function to obtain the prior distribution probability density function with a deterministic expression, and utilizing a prior distribution probability density function of a deterministic expression to represent physical property prior information characteristics. The prior distribution probability density function conforms to the actual condition of physical property parameter distribution, and a guarantee is provided for accurate prediction of physical property parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas geophysical exploration, and in particular to a method and device for characterizing characteristics of prior information of physical property parameters. Background Art

[0002] As the exploration progresses, the exploration targets of oil and gas reservoirs become more complex and hidden, making it increasingly difficult to discover new favorable oil and gas reservoirs, increasing the cost of exploration investment and the risk of drilling. Oil and gas reservoirs often show strong reflection characteristics in seismic data, such as "bright spots". This seismic response feature may be caused by high-porosity and high-hydrocarbon saturation reservoirs with great commercial value, or it may be caused by low-porosity and low-hydrocarbon saturation reservoirs with no commercial value. Reservoir physical parameters such as porosity and hydrocarbon saturation are important bases for geologists and geophysicists to evaluate reservoirs, estimate oil and gas reserves, and determine development well locations.

[0003] Among the physical property parameter prediction methods, the probability and statistics physical property parameter inversion method has become the most popular physical property parameter prediction method in this field because it has the advantages of both deterministic and statistical inversion methods. However, the probability and statistics physical property parameter inversion method needs to rely on prior information characteristics to reduce the uncertainty in the inversion process. Therefore, the correct characterization of the prior information characteristics is particularly important and is a prerequisite for the effectiveness of the probability and statistics physical property parameter inversion method. The characterization of conventional prior information characteristics is mainly based on subjective assumptions, and the prior characteristics of the physical property parameters to be inverted are assumed to be Gaussian distribution. Gaussian distribution is a distribution with a known display expression. It only needs to count the mean and variance of the prior information, and then use the specific Gaussian distribution function to participate in the probability and statistics inversion process. Summary of the invention

[0004] The inventors have found through a large number of experiments that the prior characteristics of the actual parameters to be inverted are affected by factors such as lithology, fluid, pore structure and interpretation errors, and their distribution characteristics do not completely conform to the "single-peak" Gaussian distribution. In many cases, they are "multi-peak" and "long-tail" characteristics. Therefore, if the prior characteristics of the physical property parameters to be inverted are directly assumed to be Gaussian distribution, it will bring large errors and uncertainties to the subsequent inversion of physical property parameters. Therefore, it is particularly urgent to develop a reasonable solution for characterizing the prior information characteristics.

[0005] In order to address the shortcomings of conventional methods, the purpose of the present invention is to provide a method and device for characterizing the characteristics of prior information of physical property parameters, so as to overcome the problem that the existing prior information characterization methods assume that the prior information follows a Gaussian distribution, which does not conform to the actual situation of complex oil and gas reservoirs.

[0006] A method for characterizing a priori information characteristics of physical property parameters comprises the following steps:

[0007] The physical property parameter logging curves of the target wells in the study area are intercepted according to the preset information;

[0008] The physical property parameter logging curves of all wells in the study area are taken as the total sample set, and the characteristic parameters of the physical property parameter logging curves are analyzed in the form of histogram statistics. The characteristic parameters at least include the number of peaks, the position of each peak, the ratio of the number of samples corresponding to each peak to the total number of samples, and the dispersion degree of each peak;

[0009] Construct a prior distribution probability density function to characterize the shape of the histogram;

[0010] Using the characteristic parameter values ​​in the total sample set as the initial values ​​of the component parameters of the prior distribution probability density function;

[0011] The composition parameters of the prior distribution probability density function are iteratively calculated, and the composition parameters at the time of convergence are used as the final composition parameters of the prior distribution probability density function to obtain the prior distribution probability density function with a deterministic expression, and the prior distribution probability density function with a deterministic expression is used to characterize the characteristics of the prior information of physical properties.

[0012] Furthermore, the physical property parameter logging curve of the target well in the study area is intercepted according to preset information, including:

[0013] The reservoir and non-reservoir sections of all lithology-fluid types of the physical property parameter logging curves of the target wells in the study area are intercepted.

[0014] Furthermore, constructing a priori distribution probability density function includes:

[0015] Construct the prior distribution probability density function according to the following equation:

[0016]

[0017] Where x represents the value of the physical property parameter; M represents the total number of lithology-fluid types in the study area; k represents one of the lithology-fluid types in the study area; β k represents the weight of type k; μ k represents the sample mean of the physical property parameters belonging to lithology-fluid type k; γ k represents the sample variance of the physical property parameters belonging to lithology-fluid type k; π is pi.

[0018] Furthermore, the use of the characteristic parameter values ​​in the total sample set as the initial values ​​of the constituent parameters of the prior distribution probability density function includes:

[0019] The number of peaks is taken as the initial value of the number of types of lithology-fluid types, the position of each peak is taken as the initial value of the mean of the sample values ​​of each lithology-fluid physical property parameter, the ratio of the number of samples corresponding to each peak to the total number of samples is taken as the initial value of the weight of each lithology-fluid type, and the dispersion degree of each peak is taken as the initial value of the variance of the sample values ​​of each lithology-fluid physical property parameter.

[0020] Furthermore, the iterative calculation of the constituent parameters of the prior distribution probability density function and taking the constituent parameters at the time of convergence as the final constituent parameters of the prior distribution probability density function include:

[0021] Obtaining the logarithm of the prior distribution probability density function;

[0022] Calculating the probability density using the obtained initial values ​​of the constituent parameters of the prior distribution probability density function;

[0023] According to the probability density, the constituent parameters of the prior distribution probability density function are recalculated according to a preset iterative formula until the logarithm of the prior distribution probability density function converges, and the constituent parameters at the time of convergence are used as the final constituent parameters of the prior distribution probability density function.

[0024] Furthermore, the iterative calculation of the constituent parameters of the prior distribution probability density function and taking the constituent parameters at the time of convergence as the final constituent parameters of the prior distribution probability density function include:

[0025] The logarithm of the prior distribution probability density function is obtained as:

[0026]

[0027] Introducing an M-dimensional implicit variable z, the probability density is calculated using the initial values ​​of the constituent parameters of the prior distribution probability density function:

[0028]

[0029] Among them, z k (1≤k≤M) takes the value of 0 or 1, z k = 1 indicates that the distribution of the kth lithology-fluid type Select, z k =0 means not selected;

[0030] According to the probability density, the composition parameters are recalculated according to the following iterative formula:

[0031]

[0032]

[0033]

[0034] Where: N represents the total number of samples, N k represents the number of samples belonging to lithology-fluid type k;

[0035] Calculate whether lnP(x) converges. If not, re-iterate and calculate the composition parameters until convergence, and use the composition parameters at the time of convergence as the final composition parameters of the prior distribution probability density function.

[0036] The present invention also provides a physical property parameter prior information characterization device to overcome the problem that the prior information characterization method of existing physical property prior characteristics is inaccurate. The device includes a physical property parameter logging curve interception module, a physical property parameter logging curve characteristic analysis module, a prior distribution probability density function construction module, a composition parameter initial value determination module, and a physical property prior information characteristic characterization module, wherein:

[0037] The physical property parameter logging curve interception module is used to intercept the physical property parameter logging curve of the target well in the study area according to the preset information;

[0038] The physical property parameter logging curve characteristic analysis module is used to take the physical property parameter logging curves of all wells in the study area as the total sample set, and analyze the characteristic parameters of the physical property parameter logging curves in the form of histogram statistics for the total sample set, wherein the characteristic parameters at least include the number of peaks, the position of each peak, the ratio of the number of samples corresponding to each peak to the total number of samples, and the dispersion degree of each peak;

[0039] A prior distribution probability density function construction module is used to construct a prior distribution probability density function to characterize the shape of the histogram;

[0040] A component parameter initial value determination module, used to use the characteristic parameter values ​​in the total sample set as the component parameter initial values ​​of the prior distribution probability density function;

[0041] The physical property prior information feature characterization module is used to iteratively calculate the composition parameters of the prior distribution probability density function, use the composition parameters at the time of convergence as the final composition parameters of the prior distribution probability density function, obtain the prior distribution probability density function with a deterministic expression, and use the prior distribution probability density function with a deterministic expression to characterize the physical property prior information characteristics.

[0042] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for characterizing prior information of physical property parameters when executing the computer program.

[0043] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for executing the above-mentioned method for characterizing prior information of gas physical property parameters.

[0044] The present invention also provides a method for predicting oil and gas reservoirs, which uses the prior distribution probability density function with a deterministic expression obtained by the above method as a function to characterize the prior information of physical property parameters, participates in the probability statistics type physical property parameter inversion, and obtains the physical property parameter inversion result to predict oil and gas reservoirs.

[0045] Compared with the prior art, the beneficial effect of the present invention is that: considering that the prior distribution of actual physical property parameters is not a "single-peak" Gaussian distribution, but a "multi-peak" and "long-tail" characteristic, the present invention constructs a new prior distribution probability density function with "multi-peak" and "long-tail" characteristics. The component parameters of the function are determined by feature statistics and iterative updates, and a method and device for characterizing the characteristics of prior information of physical property parameters are developed to obtain prior feature characterization results that are more in line with the actual distribution of physical property parameters, providing a guarantee for the accurate prediction of physical property parameters. The new prior distribution probability density function with "multi-peak" and "long-tail" characteristics constructed by the present invention can accurately describe the "non-Gaussian" phenomenon presented by the actual physical property parameters affected by factors such as lithology, fluid, pore structure, and interpretation errors. By using feature statistics as the initial value and iteratively determining the component parameters of the function, the subjective error problem caused by artificial statistical sample characteristics can be effectively avoided. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0047] Figure 1 It is a flow chart of a method for characterizing prior information of physical property parameters in an embodiment of the present invention;

[0048] Figure 2 It is a statistical characteristic diagram of the gas saturation histogram in the embodiment of the present invention;

[0049] Figure 3 Schematic diagram of the distribution function obtained from the gas saturation experiment in the embodiment of the present invention;

[0050] Figure 4 is a flowchart of step S105 in an embodiment of the present invention;

[0051] Figure 5 Schematic diagram of the structure of a device for characterizing prior information of physical property parameters in an embodiment of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] This embodiment provides a method for characterizing the characteristics of physical property parameter prior information, such as Figure 1 As shown, the specific steps include:

[0054] Step S101, intercepting the physical property parameter logging curve of the target well in the study area according to preset information.

[0055] If there are multiple wells in the study area, the physical parameter logging curves of the multiple wells are intercepted by preset information. The preset information interception can include reservoir and non-reservoir sections of all lithology-fluid types in the study area, such as hydrocarbon-bearing sections, aquifer sections, and mudstone sections. Because the original logging curve extends from the ground to deep underground, it is necessary to intercept the curve of the section where the research target is located, and other sections that are not of concern are invalid information.

[0056] Step S102, taking the intercepted physical property parameter logging curves of all wells in the study area as the total sample set, and analyzing the characteristic parameters of the physical property parameter logging curves in the form of histogram statistics for the total sample set, wherein the characteristic parameters include at least the number of peaks, the position of each peak, the ratio of the number of samples corresponding to each peak to the total number of samples, and the dispersion degree of each peak.

[0057] Specifically, the physical property parameter logging curves of all wells in the study area are taken as the total sample set, and the peak number, peak position and dispersion of the samples are automatically recorded. The characteristics of the physical property parameter samples in the total sample set are analyzed in the form of histogram statistics. Figure 2 The histogram statistics show that the distribution characteristics of the physical property parameters are "multi-peak" characteristics, with 2 peaks (a common statistical term, that is, there are multiple obvious protrusions in the histogram). Figure 2 The "long tail" (a common term in statistics) feature refers to the length of the samples that are far away from the "peak" end and close to 0 on the abscissa of the histogram.

[0058] Step S103, constructing a priori distribution probability density function to characterize the shape of the histogram.

[0059] Construct the prior distribution probability density function according to the following equation:

[0060]

[0061] in,

[0062] x represents the value of the physical property parameter, and the dimension depends on the specific physical property parameter that needs to be characterized;

[0063] M represents the total number of lithology-fluid types in the study area, dimensionless;

[0064] k represents one of the lithology-fluid types in the study area and is dimensionless;

[0065] β k represents the weight of type k;

[0066] μ k represents the sample mean of the physical property parameter belonging to lithology-fluid type k, and its dimension depends on the specific physical property parameter to be characterized;

[0067] γ k represents the sample variance of the physical property parameter belonging to lithology-fluid type k, dimensionless;

[0068] π is the dimensionless value of circumference.

[0069] Step S104: using the characteristic parameter values ​​in the total sample set as initial values ​​of the constituent parameters of the prior distribution probability density function.

[0070] As can be seen from step S103, the parameters of the prior distribution probability density function include: the number of lithofacies-fluid types M, the weight β of each lithofacies-fluid type k , the mean μk of each lithology-fluid property parameter sample value, the variance γ of each lithology-fluid property parameter sample value k The statistical results obtained in step S102 are used as the initial values ​​of the parameters constituting the probability density function of the prior distribution. The number of peaks is used as the initial value of the number of types of lithology-fluid types, the position of each peak is used as the initial value of the mean of the sample values ​​of each lithology-fluid property parameter, the ratio of the number of samples corresponding to each peak to the total number of samples is used as the initial value of the weight of each lithology-fluid type, and the dispersion degree of each peak is used as the initial value of the variance of the sample values ​​of each lithology-fluid property parameter.

[0071] Step S105, iteratively calculate the composition parameters of the prior distribution probability density function, use the composition parameters at the time of convergence as the final composition parameters of the prior distribution probability density function, obtain the prior distribution probability density function with a deterministic expression, and use the prior distribution probability density function with a deterministic expression to characterize the characteristics of the prior information of physical properties.

[0072] like Figure 4 As shown, this step S105 includes sub-steps S1051-S1053:

[0073] Step S1051, obtaining the logarithm of the prior distribution probability density function.

[0074]

[0075] Step S1052, calculating the probability density using the obtained initial values ​​of the constituent parameters of the prior distribution probability density function.

[0076] Introduce an M-dimensional implicit variable z, and calculate the probability density based on the initial values ​​of the parameters of the prior distribution probability density function obtained in step S104:

[0077]

[0078] Among them, z k (1≤k≤M) takes the value of 0 or 1. k =1 indicates

[0079] The distribution of the kth lithology-fluid type Selected, 0 means not selected.

[0080] Step S1053, based on the probability density, according to the preset iterative formula, recalculate the component parameters of the prior distribution probability density function until the logarithm of the prior distribution probability density function converges, and use the component parameters at the time of convergence as the final component parameters of the prior distribution probability density function.

[0081] According to P(z k |x), recalculate the composition parameters according to the following iterative formula:

[0082]

[0083]

[0084]

[0085] in,

[0086] N represents the total sample size.

[0087] N k represents the number of samples belonging to lithology-fluid type k.

[0088] Calculate whether lnP(x) converges. If not, re-iterate and calculate the composition parameters until convergence, and use the composition parameters at the time of convergence as the final composition parameters of the prior distribution probability density function.

[0089] This step uses the maximum likelihood solution theory to fit the unknown parameters in the known expression, takes the composition parameters at the time of convergence as the final composition parameters of the prior distribution probability density function, and brings the final composition parameters into the prior distribution probability density function to obtain a prior distribution probability density function with a deterministic expression.

[0090] The inventors have found through a large number of experiments that the prior distribution characteristics of physical property parameters are the "multi-peak" and "long-tail" shapes shown in the histogram. The present invention intends to construct a function that can fit this shape through mathematical theoretical experiments. The shape of this function is consistent with the shape of the histogram and can "wrap" the histogram characteristics, indicating that it accurately characterizes the shape of the histogram. The inventors conducted experiments using gas saturation in physical property parameters as an example. The distribution function obtained in the experiment is as follows: Figure 3 As shown by the grey dotted line in , it can be seen that the distribution function can accurately characterize the "multi-peak" and "long-tail" characteristics of the physical property parameters. The prior distribution probability density function constructed by the present invention is different from any previously known distribution function.

[0091] In practical applications, the prior distribution probability density function with a deterministic expression can be used as a function to characterize the prior information of physical property parameters, and can be involved in conventional probability statistics physical property parameter inversion methods to obtain physical property parameter inversion results as a basis for favorable oil and gas reservoir prediction.

[0092] Based on a large number of experiments, the present invention found that the prior distribution of physical property parameters is not a "single-peak" Gaussian distribution, but a "multi-peak" and "long-tail" characteristic, so a new prior distribution probability density function with "multi-peak" and "long-tail" characteristics is constructed. The component parameters of the function are determined by characteristic statistics and iterative updates, and a prior distribution probability density function with a deterministic expression is obtained. The prior distribution probability density function conforms to the actual distribution of physical property parameters and provides a guarantee for the accurate prediction of physical property parameters. The new prior distribution probability density function with "multi-peak" and "long-tail" characteristics constructed by the present invention can accurately describe the "non-Gaussian" phenomenon presented by the actual physical property parameters affected by factors such as lithology, fluid, pore structure, and interpretation errors. Using characteristic statistics as the initial value and iteratively determining the component parameters of the function can effectively avoid the subjective error problem caused by artificial statistical sample characteristics.

[0093] On the other hand, the embodiment of the present invention also provides a physical property parameter prior information feature characterization device, the structure of the device is as follows Figure 5 As shown, it includes: a physical property parameter logging curve interception module 10, a physical property parameter logging curve feature analysis module 20, a prior distribution probability density function construction module 30, a composition parameter initial value determination module 40, and a physical property prior information feature characterization module 50, wherein:

[0094] The physical property parameter logging curve interception module 10 is used to intercept the physical property parameter logging curve of the target well in the study area according to preset information.

[0095] If there are multiple wells in the study area, the physical parameter logging curves of the multiple wells are intercepted by preset information. The preset information interception can include reservoir and non-reservoir sections of all lithology-fluid types in the study area, such as hydrocarbon-bearing sections, aquifer sections, and mudstone sections. Because the original logging curve extends from the ground to deep underground, it is necessary to intercept the curve of the section where the research target is located, and other sections that are not of concern are invalid information.

[0096] The physical property parameter logging curve characteristic analysis module 20 is used to take the physical property parameter logging curves of all wells in the study area as the total sample set, and analyze the characteristic parameters of the physical property parameter logging curves in the form of histogram statistics for the total sample set. The characteristic parameters at least include the number of peaks, the position of each peak, the ratio of the number of samples corresponding to each peak to the total number of samples, and the dispersion degree of each peak.

[0097] Specifically, the physical property parameter logging curves of all wells in the study area are taken as the total sample set, and the peak number, peak position and dispersion of the samples are automatically recorded. The characteristics of the physical property parameter samples in the total sample set are analyzed in the form of histogram statistics.

[0098] The prior distribution probability density function construction module 30 is used to construct a prior distribution probability density function to characterize the shape of the histogram.

[0099] Construct the prior distribution probability density function according to the following equation:

[0100]

[0101] in,

[0102] x represents the value of the physical property parameter, and the dimension depends on the specific physical property parameter that needs to be characterized;

[0103] M represents the total number of lithology-fluid types in the study area, dimensionless;

[0104] k represents one of the lithology-fluid types in the study area and is dimensionless;

[0105] β k represents the weight of type k;

[0106] μ k represents the sample mean of the physical property parameter belonging to lithology-fluid type k, and its dimension depends on the specific physical property parameter to be characterized;

[0107] γ k represents the sample variance of the physical property parameter belonging to lithology-fluid type k, dimensionless;

[0108] π is the dimensionless value of circumference.

[0109] The inventors have found through a large number of experiments that the prior distribution characteristics of physical property parameters are the "multi-peak" and "long-tail" forms shown in the histogram. The present invention intends to construct a function that can fit this form through mathematical theoretical experiments. The form of this function is consistent with the form of the histogram. Figure 3 We can see that the distribution of this function ( Figure 3 The gray dashed line in the middle can "enclose" the histogram feature, indicating that the shape of the histogram is accurately characterized. The prior distribution probability density function constructed by the present invention is different from any previously known distribution function.

[0110] The component parameter initial value determination module 40 is used to use the characteristic parameter values ​​in the total sample set as the component parameter initial values ​​of the prior distribution probability density function.

[0111] The statistical results obtained by the physical property parameter logging curve characteristic analysis module 20 are used as the initial values ​​of the parameters of the prior distribution probability density function. Among them, the number of peaks is used as the initial value of the number of types of lithofacies-fluid types, and the positions of each peak are used as the mean μ of the sample values ​​of each lithofacies-fluid physical property parameter. k The initial value and each peak position are used as the variance γ of the sample values ​​of each lithology-fluid physical property parameter k Initial value.

[0112] The physical property prior information feature characterization module 50 is used to iteratively calculate the composition parameters of the prior distribution probability density function, use the composition parameters at the time of convergence as the final composition parameters of the prior distribution probability density function, obtain the prior distribution probability density function with a deterministic expression, and use the prior distribution probability density function with a deterministic expression to characterize the physical property prior information characteristics.

[0113] Specifically, taking the logarithm of the prior distribution probability density function, we get:

[0114]

[0115] Introduce an M-dimensional implicit variable z, and calculate the probability density based on the initial values ​​of the parameters of the prior distribution probability density function obtained in step S104:

[0116]

[0117] Among them, z k (1≤k≤M) takes the value of 0 or 1. k = 1 indicates that the distribution of the kth lithology-fluid type Select, z k =0 means no selection. Count the number of selected items N k, represents the number of samples belonging to lithofacies-fluid type k.

[0118] According to P(z k |x), recalculate the composition parameters according to the following iterative formula:

[0119]

[0120]

[0121]

[0122] in,

[0123] N represents the total sample size.

[0124] N k represents the number of samples belonging to lithology-fluid type k.

[0125] Calculate whether lnP(x) converges. If not, re-iterate and calculate the composition parameters until convergence, and use the composition parameters at the time of convergence as the final composition parameters of the prior distribution probability density function.

[0126] This step uses the maximum likelihood solution theory to fit the unknown parameters in the known expression, takes the composition parameters at the time of convergence as the final composition parameters of the prior distribution probability density function, and brings the final composition parameters into the prior distribution probability density function to obtain a prior distribution probability density function with a deterministic expression.

[0127] Based on a large number of experiments, the present invention found that the prior distribution of physical property parameters is not a "single-peak" Gaussian distribution, but a "multi-peak" and "long-tail" characteristic, so a new prior distribution probability density function with "multi-peak" and "long-tail" characteristics is constructed. The component parameters of the function are determined by characteristic statistics and iterative updates, and a prior distribution probability density function with a deterministic expression is obtained. The prior distribution probability density function conforms to the actual distribution of physical property parameters and provides a guarantee for the accurate prediction of physical property parameters. The new prior distribution probability density function with "multi-peak" and "long-tail" characteristics constructed by the present invention can accurately describe the "non-Gaussian" phenomenon presented by the actual physical property parameters affected by factors such as lithology, fluid, pore structure, and interpretation errors. Using characteristic statistics as the initial value and iteratively determining the component parameters of the function can effectively avoid the subjective error problem caused by artificial statistical sample characteristics.

[0128] The present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for characterizing prior information of physical property parameters when executing the computer program.

[0129] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for executing the above-mentioned method for characterizing prior information of gas physical property parameters.

[0130] The present invention also provides a method for predicting oil and gas reservoirs, which uses the prior distribution probability density function with a deterministic expression obtained by the above method as a function to characterize the prior information of physical property parameters, participates in the probability statistics type physical property parameter inversion, and obtains the physical property parameter inversion result to predict oil and gas reservoirs.

[0131] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0132] The above embodiments illustrate that the device for characterizing physical property parameter prior information characteristics of the present invention has flexible scalability and portability.

[0133] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions.

[0134] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing the present application, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0135] It can be known from the description of the above implementation mode that a person skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a software product. In a typical configuration, the computing device includes one or more processors (CPU), an input / output interface, a network interface and a memory. The computer software product may include several instructions to enable a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application or some parts of the embodiments. The computer software product may be stored in a memory, which may include a non-permanent memory in a computer-readable medium, a random access memory (RAM) and / or a non-volatile memory in the form of a read-only memory (ROM) or a flash memory (flash RAM). The memory is an example of a computer-readable medium. Computer-readable media including permanent and non-permanent, removable and non-removable media can be implemented by any method or technology to store information. The information may be a computer-readable instruction, a data structure, a module of a program or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0136] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0137] The present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0138] Although the present application has been described through embodiments, persons skilled in the art will appreciate that there are many modifications and variations of the present application without departing from the spirit of the present application. It is intended that the appended claims include these modifications and variations without departing from the spirit of the present application.

Claims

1. A method for characterizing the characteristics of prior information of physical property parameters, characterized in that: The following steps are involved: The physical property parameter logging curves of the target wells in the study area are intercepted according to the preset information; The physical property parameter logging curves of all wells in the study area are taken as the total sample set, and the characteristic parameters of the physical property parameter logging curves are analyzed in the form of histogram statistics. The characteristic parameters at least include the number of peaks, the position of each peak, the ratio of the number of samples corresponding to each peak to the total number of samples, and the dispersion degree of each peak; Construct a prior distribution probability density function to characterize the shape of the histogram; Using the characteristic parameter values ​​in the total sample set as the initial values ​​of the component parameters of the prior distribution probability density function; The composition parameters of the prior distribution probability density function are iteratively calculated, and the composition parameters at the time of convergence are used as the final composition parameters of the prior distribution probability density function to obtain the prior distribution probability density function with a deterministic expression, and the prior distribution probability density function with a deterministic expression is used to characterize the characteristics of the prior information of physical properties.

2. The method according to claim 1, characterized in that The physical property parameter logging curve of the target well in the study area is intercepted according to the preset information, including: The reservoir and non-reservoir sections of all lithology-fluid types of the physical property parameter logging curves of the target wells in the study area are intercepted.

3. The method according to claim 1, characterized in that The constructing of a priori distribution probability density function comprises: Construct the prior distribution probability density function according to the following equation: Where x represents the value of the physical property parameter; M represents the total number of lithology-fluid types in the study area; k represents one of the lithology-fluid types in the study area; β k represents the weight of type k; μ k represents the sample mean of the physical property parameters belonging to lithology-fluid type k; γ k represents the sample variance of the physical property parameters belonging to lithology-fluid type k; π is pi.

4. The method according to claim 1, characterized in that The method of using the characteristic parameter values ​​in the total sample set as the initial values ​​of the constituent parameters of the prior distribution probability density function includes: The number of peaks is taken as the initial value of the number of types of lithology-fluid types, the position of each peak is taken as the initial value of the mean of the sample values ​​of each lithology-fluid physical property parameter, the ratio of the number of samples corresponding to each peak to the total number of samples is taken as the initial value of the weight of each lithology-fluid type, and the dispersion degree of each peak is taken as the initial value of the variance of the sample values ​​of each lithology-fluid physical property parameter.

5. The method according to claim 1, characterized in that The iterative calculation of the constituent parameters of the prior distribution probability density function and taking the constituent parameters at the time of convergence as the final constituent parameters of the prior distribution probability density function include: Obtaining the logarithm of the prior distribution probability density function; Calculating the probability density using the obtained initial values ​​of the constituent parameters of the prior distribution probability density function; According to the probability density, the constituent parameters of the prior distribution probability density function are recalculated according to a preset iterative formula until the logarithm of the prior distribution probability density function converges, and the constituent parameters at the time of convergence are used as the final constituent parameters of the prior distribution probability density function.

6. The method according to claim 3, characterized in that The iterative calculation of the constituent parameters of the prior distribution probability density function and taking the constituent parameters at the time of convergence as the final constituent parameters of the prior distribution probability density function include: The logarithm of the prior distribution probability density function is obtained as: Introducing an M-dimensional implicit variable z, the probability density is calculated using the initial values ​​of the constituent parameters of the prior distribution probability density function: Among them, z k (1≤k≤M) takes the value of 0 or 1, z k = 1 indicates that the distribution of the kth lithology-fluid type Select, z k =0 means not selected; According to the probability density, the composition parameters are recalculated according to the following iterative formula: Where: N represents the total number of samples, N k represents the number of samples belonging to lithology-fluid type k; Calculate whether lnP(x) converges. If not, re-iterate and calculate the composition parameters until convergence, and use the composition parameters at the time of convergence as the final composition parameters of the prior distribution probability density function.

7. A device for characterizing prior information of physical property parameters, characterized in that: It includes physical property parameter logging curve interception module, physical property parameter logging curve feature analysis module, prior distribution probability density function construction module, composition parameter initial value determination module, physical property prior information feature characterization module, among which: The physical property parameter logging curve interception module is used to intercept the physical property parameter logging curve of the target well in the study area according to preset information; The physical property parameter logging curve characteristic analysis module is used to take the physical property parameter logging curves of all wells in the study area as the total sample set, and analyze the characteristic parameters of the physical property parameter logging curves in the form of histogram statistics for the total sample set, wherein the characteristic parameters at least include the number of peaks, the position of each peak, the ratio of the number of samples corresponding to each peak to the total number of samples, and the dispersion degree of each peak; The prior distribution probability density function construction module is used to construct a prior distribution probability density function to characterize the shape of the histogram; The component parameter initial value determination module is used to use the characteristic parameter values ​​in the total sample set as the component parameter initial values ​​of the prior distribution probability density function; The physical property prior information feature characterization module is used to iteratively calculate the composition parameters of the prior distribution probability density function, use the composition parameters at the time of convergence as the final composition parameters of the prior distribution probability density function, obtain the prior distribution probability density function with a deterministic expression, and use the prior distribution probability density function with a deterministic expression to characterize the physical property prior information characteristics.

8. The device according to claim 7, characterized in that The prior distribution probability density function construction module constructs the prior distribution probability density function according to the following equation: Where x represents the value of the physical property parameter; M represents the total number of lithology-fluid types in the study area; k represents one of the lithology-fluid types in the study area; β k represents the weight of type k; μ k represents the sample mean of the physical property parameters belonging to lithology-fluid type k; γ k represents the sample variance of the physical property parameters belonging to lithology-fluid type k; π is pi.

9. The device according to claim 7, characterized in that The physical property prior information feature characterization module iteratively calculates the composition parameters of the prior distribution probability density function, and uses the composition parameters at the time of convergence as the final composition parameters of the prior distribution probability density function, specifically for obtaining the logarithm of the prior distribution probability density function; and calculates the probability density using the obtained initial values ​​of the composition parameters of the prior distribution probability density function; According to the probability density, the constituent parameters of the prior distribution probability density function are recalculated according to a preset iterative formula until the logarithm of the prior distribution probability density function converges, and the constituent parameters at the time of convergence are used as the final constituent parameters of the prior distribution probability density function.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method of any one of claims 1 to 6 is implemented.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program for executing the method according to any one of claims 1 to 6.

12. A method for predicting oil and gas reservoirs, characterized in that: The prior distribution probability density function with a deterministic expression obtained by any of the methods described in claims 1 to 6 is used as a function to characterize the prior information of physical property parameters, and is involved in the probabilistic and statistical physical property parameter inversion to obtain the physical property parameter inversion results to predict oil and gas reservoirs.

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