Hole structure parameter inversion method and device, electronic equipment and medium

Abstract

The invention relates to the technical field of oil and gas exploration, and discloses a hole structure parameter inversion method and device, electronic equipment and a medium. The method comprises the following steps: acquiring key rock physical parameters; processing logging data of the typical well, and calculating a hole structure parameter curve of the typical well; mathematical optimization is carried out, and coefficients of hole structure parameter constraint conditions are determined; pre-stack elastic parameter inversion is used to acquire a longitudinal wave impedance data body and a transverse wave impedance data body of the work area to be inverted; based on hole structure parameter constraint conditions, establishing a nonlinear mapping relation of longitudinal wave impedance and transverse wave impedance about the porosity, and solving a porosity inversion result by using a golden section algorithm; and calculating inversion results of the volume modulus hole structure parameters and the shear modulus hole structure parameters. According to the technical scheme, density is not used, inversion of hole structure parameters is achieved only through longitudinal wave impedance and transverse wave impedance, and input data are more stable; the use conditions of hole structure parameter inversion are broadened, and the stability and precision of hole structure parameter inversion are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of seismic data interpretation and inversion in oil and gas exploration, and particularly relates to a pore configuration parameter inversion method and device, electronic equipment and a medium. BACKGROUND

[0002] Pore configuration parameters can measure the shape of rock pores and are a key bridge connecting elastic parameters and porosity and porosity and permeability. The prediction accuracy of pore configuration parameters seriously restricts the prediction accuracy of subsequent porosity and permeability and other physical parameters.

[0003] Through a rock physics model, a mapping relationship between pore configuration parameters, porosity and elastic parameters can be established, so the pore configuration parameter inversion based on seismic technology usually consists of two parts. First, pre-stack elastic parameter inversion is used to obtain an elastic parameter data volume, and then a rock physics model established based on logging is used to obtain a pore configuration parameter data volume by pore configuration parameter inversion. Therefore, there are three main factors affecting the prediction accuracy of pore configuration parameters: first, the accuracy of elastic parameter inversion; second, the accuracy of the rock physics model; and finally, the stability of the pore configuration parameter inversion optimization algorithm.

[0004] In the prior art, Sun Yafeng (2004) first proposed a Sun model rock physics model, which uses the flexibility factor of rock skeleton to represent the pore configuration parameters of rock, and developed a two-parameter model to establish the relationship between porosity, pore configuration parameters and velocity. Based on the Sun model, Sun Yafeng (2010) and Dou Qifeng (2011) developed a pore configuration parameter prediction technology based on seismic technology. In this technology, the bulk modulus of the rock saturated with fluid is assumed to be approximately equal to the bulk modulus of the dry skeleton rock, thereby ignoring the influence of pore fluid on elastic parameters. In addition, the input of this technology is the bulk modulus and shear modulus, which does not directly include the density term, but the density term is indirectly used in the process of calculating the bulk modulus and shear modulus. In view of the above problems, Zhang Tingting (2018) made corresponding improvements to the methods of Sun Yafeng and Dou Qifeng. First, in order to further consider the influence of pore fluid on elastic parameters, Gassmann equation is further combined with Sun model. Due to the introduction of Gassmann equation, the nonlinear relationship between porosity and elastic parameters is further enhanced, so it cannot be directly solved. In view of this problem, Zhang Tingting uses Taylor series to expand it, only taking the first-order approximation, and uses iteration to gradually approach. In terms of the calculation of bulk modulus and shear modulus, the longitudinal wave impedance, transverse wave impedance and elastic impedance are used to approximate the calculation. Chen Tiansheng (2014) further developed pore configuration parameter inversion by combining Sun model, Gassmann equation and the bulk average formula of density, but the input data of this method is longitudinal wave velocity, transverse wave velocity and density.

[0005] It is evident that the input data for conventional pore parameter inversion are the bulk modulus, shear modulus, and density obtained from pre-stack elastic parameter inversion. However, currently, all the aforementioned pore parameter inversion methods require the direct or indirect use of density. Due to the inherent high uncertainty of density in pre-stack elastic parameter inversion, the stability of the density inversion results is significantly reduced, and its accuracy is affected by numerous factors (including burial depth, offset range, signal-to-noise ratio, dynamic stretching, and formation anisotropy), which in turn seriously affects the accuracy and reliability of subsequent pore parameter inversion. Summary of the Invention

[0006] This invention provides a method, apparatus, electronic device, and medium for pore structure parameter inversion, addressing the aforementioned technical problem where the accuracy and reliability of pore structure parameter inversion are affected in existing technologies. This invention achieves pore structure parameter inversion using only longitudinal wave impedance and transverse wave impedance, without using density.

[0007] According to a first aspect of the present invention, a method for inverting pore structure parameters is provided, comprising the following steps:

[0008] S1. Based on typical wells, rock physics modeling, and rock physics analysis, key rock physics parameters are obtained; the key rock physics parameters include the elastic modulus and density of the rock matrix, as well as the elastic modulus and density of the mixed fluid;

[0009] S2. Process the logging data of the typical well, calculate the pore structure parameter curve of the typical well, and determine the extreme values ​​of pore structure parameter and porosity based on the pore structure parameter curve and the porosity curve;

[0010] S3. Based on the pore structure parameter curve, mathematically optimize and determine the coefficients of the pore structure parameter constraint conditions;

[0011] S4. Use pre-stack elastic parameters to invert and obtain the longitudinal wave impedance data volume and transverse wave impedance data volume of the work area to be inverted;

[0012] S5. Based on the aforementioned pore structure parameter constraints, establish a nonlinear mapping relationship between longitudinal wave impedance and transverse wave impedance with respect to porosity, and use the golden section algorithm to obtain the porosity inversion result.

[0013] S6. Calculate the inversion results of bulk modulus pore parameters and shear modulus pore parameters.

[0014] Preferably, in step S2, the pore structure parameter curve of the typical well is calculated based on the well curve coarsening. And based on the pore structure curves of the typical well And porosity curve φ W Determine the minimum value of the pore structure parameters. and maximum value Determine the minimum porosity φmin and maximum value φ max The calculation formula is:

[0015]

[0016] In the formula,

[0017] K m is the average bulk modulus of the rock matrix,

[0018] μ m is the average shear modulus of the rock matrix,

[0019] K f is the average bulk modulus of the mixed fluid,

[0020] γ K is the bulk modulus pore structure parameter,

[0021] γ μ is the shear modulus pore structure parameter,

[0022] φ is the porosity,

[0023] ρ W and <φ W > respectively represent the coarse longitudinal wave velocity curve, the transverse wave velocity curve, the density curve and the porosity curve, <μ W > represents the coarse shear modulus curve,

[0024] K, μ, f and F K are all intermediate variables for calculation.

[0025] Preferably, in step S3, the coefficients C and D of the pore structure parameter constraint condition are determined by using a least square algorithm based on the pore structure parameter curve

[0026] The objective function argmin: is optimized by using a least square algorithm, wherein M W represents the number of logging sampling points, represents the pore structure parameter of the i-th sampling point.

[0027] Preferably, in step S5, the establishment of the nonlinear mapping relationship between the longitudinal wave impedance and the transverse wave impedance with respect to the porosity is based on the Sun model formula, the Gassmann equation and the volume average formula of the density.

[0028] The Sun model formula is:

[0029]

[0030] The Gassmann equation is:​

[0031]

[0032] The volume average equation of density is:

[0033] ρ sat = (1- φ) ρ m + φ ρ f (6)

[0034] The constraint condition of pore structure parameter is:

[0035] γ μ = C γ K + D (7)

[0036] Wherein, the conversion relationship between elastic parameters is considered as follows:

[0037]

[0038] In the above formula, K d , μ d , K sat , μ sat and ρ sat respectively represent the bulk modulus of rock skeleton, the shear modulus of rock skeleton, the bulk modulus of fluid-saturated rock, the shear modulus of fluid-saturated rock and the density of fluid-saturated rock,

[0039] I P , I S respectively represent the longitudinal wave impedance and the transverse wave impedance,

[0040] ρ m is the average density of rock matrix,

[0041] ρ f is the average density of mixed fluid;

[0042] The formula (4) to formula (8) are arranged to establish the three nonlinear equations (9) of longitudinal wave impedance, transverse wave impedance about porosity and pore structure parameter to solve φ, γ μ and γ K ;

[0043]

[0044] Further arranging the formula (9), the nonlinear equation of longitudinal wave impedance, transverse wave impedance about porosity can be obtained:

[0045]

[0046] Wherein,

[0047]

[0048] And / or,

[0049] In step S5, based on the determined minimum porosity φ min and maximum value φ max The porosity value range is set to [φ]. min ,φ max The porosity φ was calculated using the golden section algorithm.

[0050] Preferably, in step S6, the shear modulus pore structure parameter γ is calculated based on the porosity results. μ ,have:

[0051]

[0052] Based on the shear modulus pore parameter γ μ Calculate the bulk modulus and pore structure parameter γ K ,have:

[0053]

[0054] Furthermore, based on the minimum value of the pore structure parameter and maximum value Boundary constraints are applied to the pore parameter inversion results to ensure the bulk modulus and pore parameter γ K exist Within the range, ensure the shear modulus pore structure parameter γ μ exist Within the range.

[0055] Preferably, the pore structure parameter inversion method includes the following steps:

[0056] S01. Based on the type, quality, and length of the logging curves, select typical wells from the work area to be inverted, wherein the logging curves of the typical wells are complete in type, reliable in quality, and their length covers the entire target inversion layer.

[0057] According to a second aspect of the present invention, a pore structure parameter inversion apparatus is provided, comprising:

[0058] The key parameter acquisition module is used to acquire key rock physical parameters based on typical wells, rock physical modeling, and rock physical analysis; the key rock physical parameters include the elastic modulus and density of the rock matrix, as well as the elastic modulus and density of the mixed fluid;

[0059] The extreme value determination module is used to process the logging data of the typical well, calculate the pore structure parameter curve of the typical well, and determine the extreme values ​​of the pore structure parameter and the porosity based on the pore structure parameter curve and the porosity curve.

[0060] The constraint determination module is used to mathematically optimize and determine the coefficients of the pore parameter constraint conditions based on the pore parameter curve.

[0061] The wave impedance acquisition module is used to invert the pre-stack elastic parameters and obtain the longitudinal wave impedance data volume and transverse wave impedance data volume of the work area to be inverted.

[0062] The first calculation module is used to establish a nonlinear mapping relationship between longitudinal wave impedance and transverse wave impedance with respect to porosity based on the porosity parameter constraints, and to obtain the porosity inversion result using the golden section algorithm; and

[0063] The second calculation module is used to calculate the inversion results of bulk modulus pore parameters and shear modulus pore parameters;

[0064] Preferably, the pore structure parameter inversion device includes:

[0065] The typical well screening module is used to screen typical wells from the work area to be inverted based on the type, quality, and length of the logging curves. The typical wells have complete well curve types, reliable quality, and the length covers the entire inversion target layer.

[0066] According to a third aspect of the present invention, an electronic device is provided, comprising:

[0067] Memory; and

[0068] processor;

[0069] The memory is used to store one or more computer instructions; the one or more computer instructions are executed by the processor to implement the method described in any of the above.

[0070] According to a fourth aspect of the present invention, a readable storage medium is provided, wherein computer instructions are stored thereon; wherein, when executed by a processor, the computer instructions implement the method described in any of the preceding claims.

[0071] The technical solution of this invention achieves pore parameter inversion without relying on density, using more stable P-wave impedance and S-wave impedance as input data; it broadens the application conditions of pore parameter inversion and improves its stability and accuracy. Specifically, based on rock physical modeling and analysis, key rock physical parameters are determined, and nonlinear equations for P-wave impedance and S-wave impedance with respect to porosity are established. Using the P-wave impedance and S-wave impedance data obtained from pre-stack elastic parameter inversion as input, porosity and pore structure parameters are calculated.

[0072] The technical solution of this invention fully considers the influence of pore fluid by introducing the Gassmann equation; and instead of approximating the Gassmann equation by performing a first-order Taylor expansion, it chooses the golden section algorithm, which is more advantageous for solving univariate nonlinear equations, by combining the characteristics of the finite range of porosity and the monotonically decreasing characteristic of the Gassmann equation with porosity; due to the limitation of the finite range and the fact that it does not use derivatives, the golden section algorithm has very high stability, which improves the stability and accuracy of pore parameter inversion. Attached Figure Description

[0073] Figure 1 This is a flowchart of one form of the pore parameter inversion method in one embodiment;

[0074] Figure 2 This is a schematic diagram of a typical well selected in one embodiment;

[0075] Figure 3 This is a schematic diagram of the pore structure parameter curve in one embodiment;

[0076] Figure 4 One embodiment is a cross-plot of pore structure parameters;

[0077] Figure 5 In one embodiment, longitudinal wave impedance inversion results and transverse wave impedance inversion results are obtained based on pre-stack elastic parameter inversion.

[0078] Figure 6 This is an example of an inversion result of porosity, bulk modulus pore structure parameters, and shear modulus pore structure parameters obtained based on the method of the present invention;

[0079] Figure 7 This is a schematic diagram comparing the well bypass inversion results with the measured curves in one embodiment;

[0080] Figure 8 This is a flowchart of another form of the pore parameter inversion method in one embodiment;

[0081] Figure 9 , Figure 10 This is a structural diagram of a pore parameter inversion device in one embodiment. Detailed Implementation

[0082] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0083] To enable those skilled in the art to better understand the present invention, the technical solution of one embodiment will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some, not all, embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort should fall within the scope of protection of the present invention.

[0084] It should be noted that the terms "first," "second," etc., used in this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be used interchangeably where appropriate for the embodiments of the invention described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0085] It should be understood that when an element (such as a layer, film, region, or substrate) is described as being "on" another element, the element may be directly on the other element, or there may be an intermediate element present. Moreover, in this invention, when an element is described as being "connected" to another element, the element may be "directly connected" to the other element, or "connected" to the other element via a third element.

[0086] Example 1

[0087] The input data for conventional pore parameter inversion must include density data. However, the density obtained based on pre-stack elastic inversion is difficult to obtain accurately due to the influence of its inherent properties, which greatly reduces the accuracy and stability of subsequent pore parameter inversion.

[0088] In view of this, please refer to Figure 1 , Figure 2 , Figure 8 This embodiment provides a pore structure parameter inversion method that does not depend on density for pore structure parameter inversion. The inversion only uses longitudinal wave impedance and transverse wave impedance. The method includes steps S01 and S1 to S6, and the specific steps are as follows:

[0089] S01. Based on the type, quality, and length of the logging curves, select typical wells from the work area to be inverted. The typical wells are characterized by having complete types of logging curves, reliable quality, and a length that includes the entire target layer for inversion.

[0090] S1. Based on typical well and rock physics modeling and analysis, obtain key rock physics parameters. These parameters include the elastic modulus and density of the rock matrix, as well as the elastic modulus and density of the mixed fluid. Specifically, step S1 requires determining the average bulk modulus K of the rock matrix. m Mean shear modulus μ m and average density ρ m It is also necessary to determine the average bulk modulus K of the mixed fluid. f and average density ρ f .

[0091] S2. Process the logging data of the typical well, calculate the pore structure parameter curve of the typical well, and determine the extreme values ​​of the pore structure parameter and the porosity based on the pore structure parameter curve and the porosity curve.

[0092] In one embodiment, in step S2, processing the logging data of the typical well is preferably well curve coarsening. Based on the typical well, and on the basis of well curve coarsening, the pore structure parameter curve of the typical well is calculated using formulas (1) to (3). And based on the pore structure curves of the typical well And porosity curve φ W Determine the minimum value of the pore structure parameters. and maximum value Determine the minimum porosity φ min and maximum value φ max The calculation formulas are:

[0093]

[0094] In the formula,

[0095] K m It is the average bulk modulus of the rock matrix.

[0096] μ m It is the average shear modulus of the rock matrix.

[0097] K f It is the average bulk modulus of the mixed fluid.

[0098] γ K These are bulk modulus and pore structure parameters.

[0099] γ μ These are shear modulus and pore structure parameters.

[0100] φ is porosity.

[0101] ρ W > and <φ W> These represent the coarsened P-wave velocity curve, S-wave velocity curve, density curve, and porosity curve, respectively. <μ W > represents the shear modulus curve after coarsening.

[0102] K, μ, f, and F K All of these are intermediate variables used in the calculation.

[0103] S3. Based on the pore parameter curve, mathematically optimize and determine the coefficients of the pore parameter constraint conditions.

[0104] In one embodiment, step S3 includes the following steps:

[0105] S31. Based on pore parameter curves The coefficients C and D of the pore structure parameter constraints are determined using the least squares algorithm.

[0106] S32. Optimize the objective function argmin using the least squares algorithm: Among them, M W Indicates the number of well logging sampling points. This represents the pore structure parameter of the i-th sampling point.

[0107] S4. Using pre-stack elastic parameter inversion, obtain the longitudinal wave impedance data volume and transverse wave impedance data volume of the work area to be inverted, and use them as input data. That is, the input data of the pore parameter inversion method of the present invention are the longitudinal wave impedance and transverse wave impedance obtained by pre-stack elastic parameter inversion.

[0108] S5. Based on the aforementioned pore structure parameter constraints, establish a nonlinear mapping relationship between longitudinal wave impedance and transverse wave impedance with respect to porosity, and use the golden section algorithm to obtain the porosity inversion result.

[0109] In one embodiment, step S5 includes the following steps:

[0110] S51. Based on the aforementioned pore structure parameter constraints, and based on the Sun model formula, Gassmann equation, and volume average formula for density, establish a nonlinear mapping relationship between longitudinal wave impedance and transverse wave impedance with respect to porosity.

[0111] The Sun model formula is:

[0112]

[0113] The Gassmann equation is:

[0114]

[0115] The volume-average equation for density is:

[0116] ρ sat=(1-φ)ρ m +φρ f (6)

[0117] The constraints on the pore structure parameters are as follows:

[0118] γ μ =Cγ K +D (7)

[0119] Among them, the following transformation relationship exists between the elastic parameters:

[0120]

[0121] In the above formula, K d μ d K sat μ sat and ρ sat Let represent the bulk modulus of the rock skeleton, the shear modulus of the rock skeleton, the bulk modulus of the fluid-saturated rock, the shear modulus of the fluid-saturated rock, and the density of the fluid-saturated rock, respectively.

[0122] I P I S These represent the longitudinal wave impedance and the transverse wave impedance, respectively.

[0123] ρ m It is the average density of the rock matrix.

[0124] ρ f It is the average density of the mixed fluid;

[0125] Arrange formulas (4) to (8), establish a three-dimensional nonlinear equation system (9) for longitudinal wave impedance and transverse wave impedance with respect to porosity and pore structure parameters, and solve for φ and γ. μ and γ K ;

[0126]

[0127] In formula (9), besides φ and γ μ and γ K Apart from the given information, all other variables are known, so φ and γ can be solved using this system of equations. μ and γ K ;

[0128] Further refining formula (9), we can obtain the nonlinear equations for longitudinal wave impedance and transverse wave impedance with respect to porosity:

[0129]

[0130] in,

[0131]

[0132] S52. In obtaining the porosity inversion result using the golden section algorithm, based on the determined minimum porosity value φ... min and maximum value φ max The porosity value range is set to [φ]. min ,φ max The porosity φ was calculated using the golden section algorithm. The minimum porosity φ was determined. min and maximum value φ max The calculation was performed in step S2.

[0133] S6. Calculate the inversion results of bulk modulus pore parameters and shear modulus pore parameters.

[0134] In one embodiment, step S6 includes the following steps:

[0135] S61. Calculate the shear modulus and pore structure parameter γ based on the porosity results. μ ,have:

[0136]

[0137] S62. Based on the shear modulus and pore structure parameter γ μ Calculate the bulk modulus and pore structure parameter γ K ,have:

[0138]

[0139] S63. Based on the minimum value of the pore structure parameter and maximum value Boundary constraints are applied to the pore parameter inversion results to ensure the bulk modulus and pore parameter γ K exist Within the range, ensure the shear modulus pore structure parameter γ μ exist Within the range.

[0140] The above description of the pore parameter inversion method is quite detailed; in short, as follows: Figure 1 It mainly consists of 7 steps:

[0141] Step 1: Select typical wells based on the type, quality, and length of the logging curves;

[0142] Step 2: Based on typical wells, integrate rock physics modeling and rock physics analysis to determine the average bulk modulus, average shear modulus, and average density of the rock matrix, and determine the average shear modulus and mixing density of the mixed fluid.

[0143] Step 3: Based on a typical well, coarsen the logging curves, calculate the bulk modulus pore structure parameter curves and the shear modulus pore structure parameter curves, and determine the minimum and maximum values ​​of porosity, bulk modulus pore structure parameter, and shear modulus pore structure parameter.

[0144] Step 4: Based on the bulk modulus pore structure parameter curves and shear modulus pore structure parameter curves calculated in Step 3, the coefficients C and D of the pore structure parameter constraint conditions are determined using the least squares algorithm.

[0145] Step 5: Based on the pre-stack elastic parameter inversion, obtain the longitudinal wave impedance and transverse wave impedance data volumes;

[0146] Step 6: By combining the Sun model, Gassmann equation, volume average formula of density, and pore parameter constraints, nonlinear equations for longitudinal wave impedance and transverse wave impedance with respect to porosity are established, and the golden section algorithm is used to solve them, thereby obtaining the porosity inversion results.

[0147] Step 7: Based on the porosity inversion results, calculate the inversion results of the bulk modulus pore structure parameters and the inversion results of the shear modulus pore structure parameters.

[0148] The pore parameter inversion method of this invention, based on rock physical modeling and analysis, determines key rock physical parameters such as the elastic modulus and density of the rock matrix and the elastic modulus and density of the mixed fluid. It integrates the Sunmodel formula, the Gassmann equation, and the volume-averaged density formula, and selects appropriate pore parameter constraints to establish nonlinear equations for P-wave impedance and S-wave impedance with respect to porosity φ. Using the P-wave impedance and S-wave impedance data obtained from pre-stack elastic parameter inversion as input, the porosity φ is calculated using the golden section algorithm, and finally, the pore structure parameter γ is calculated. K and γ μ This method is a density-independent pore parameter inversion technique, which broadens the application conditions of pore parameter inversion and improves the stability and accuracy of pore parameter inversion.

[0149] Through the Figure 2 The calculation of actual data verified that the pore structure parameters and porosity obtained by the method of the present invention have high stability and accuracy.

[0150] Example 2

[0151] Please refer to Figure 9 , Figure 10 One embodiment provides a pore structure parameter inversion device that achieves pore structure parameter inversion using only longitudinal wave impedance and transverse wave impedance, without using density. This pore structure parameter inversion device takes the following form:

[0152] 1. Key Parameter Acquisition Module

[0153] The key parameter acquisition module 10 is used to acquire key rock physical parameters based on typical wells, rock physical modeling, and rock physical analysis; the key rock physical parameters include the elastic modulus and density of the rock matrix, as well as the elastic modulus and density of the mixed fluid;

[0154] 2. Extreme Value Determination Module

[0155] The extreme value determination module 20 is used to process the logging data of the typical well, calculate the pore structure parameter curve of the typical well, and determine the extreme values ​​of pore structure parameter and porosity based on the pore structure parameter curve and the porosity curve.

[0156] 3. Constraint Determination Module

[0157] The constraint determination module 30 is used to mathematically optimize and determine the coefficients of the pore parameter constraint conditions based on the pore parameter curve.

[0158] 4. Wave Impedance Acquisition Module

[0159] The wave impedance acquisition module 40 is used to invert the pre-stack elastic parameters to obtain the longitudinal wave impedance data volume and the transverse wave impedance data volume of the work area to be inverted.

[0160] 5. First Calculation Module

[0161] The first calculation module 50 is used to establish a nonlinear mapping relationship between longitudinal wave impedance and transverse wave impedance with respect to porosity based on the porosity parameter constraints, and to obtain the porosity inversion result using the golden section algorithm.

[0162] 6. Second Calculation Module

[0163] The second calculation module 60 is used to calculate the inversion results of bulk modulus pore parameters and shear modulus pore parameters.

[0164] In one embodiment, the pore structure parameter inversion device further includes a typical well screening module 01. The typical well screening module 01 is used to screen typical wells from the work area to be inverted based on the type, quality, and length of the logging curves, wherein the typical wells have complete well curve types, reliable quality, and their length covers the entire inversion target layer.

[0165] It should be noted that the apparatus of the present invention is used to implement the methods in the above embodiments, and each module in the apparatus corresponds to each step in the method.

[0166] Example 3

[0167] Based on the same inventive concept, one embodiment of the present invention provides an electronic device, including: a memory and a processor; wherein the memory is used to store one or more computer instructions; the one or more computer instructions are executed by the processor using any of the methods described in the above embodiments.

[0168] Example 4

[0169] Based on the same inventive concept, one embodiment of the present invention provides a readable storage medium storing computer instructions; wherein, when the computer instructions are executed by a processor, they implement the method of any one of the above embodiments.

[0170] One or more of the aforementioned computer instructions can form a program.

[0171] The aforementioned program can run on a processor or be stored in memory (or computer-readable medium). Computer-readable medium includes both permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, program modules, 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 technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable medium does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0172] These computer programs may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes can be implemented using different modules, and different steps can be implemented using different modules.

[0173] Example 5

[0174] Please refer to Figures 1-8This embodiment elaborates on the pore structure parameter inversion method of the present invention with a specific case. The method does not use density, but only uses longitudinal wave impedance and transverse wave impedance to realize the inversion of pore structure parameters.

[0175] Specifically, Figure 1 The flowchart of the pore structure parameter inversion method is given, including the following steps:

[0176] Step 1: Select typical wells from the work area to be inverted. Typical wells are characterized by complete well curve types, reliable quality, and length that can cover the entire target layer for inversion.

[0177] Step 2: Based on typical wells, determine the average bulk modulus K of the rock matrix by integrating rock physical modeling and rock physical analysis. m Mean shear modulus μ m and average density ρ m Determine the average bulk modulus K of the mixed fluid. f and average density ρ f Key rock physical parameters;

[0178] Step 3: Based on a typical well, and after coarsening the well curve, calculate the pore structure parameter curve of the well using formulas (1) to (3). And based on the porosity curve φ of the well W and pore curve Determine the minimum porosity φ min and maximum value φ max and the minimum value of pore structure parameters and maximum value

[0179]

[0180] In the above formula, ρ W > and <φ W > These represent the coarsened P-wave velocity curve, S-wave velocity curve, density curve, and porosity curve, respectively. <μ W > represents the coarsened shear modulus curve, K, μ, f, and F. K All of these are intermediate variables in the calculation;

[0181] Step 4: Based on the pore structure parameter curve calculated in Step 3 The coefficients C and D of the pore structure parameter constraints are determined using the least squares algorithm.

[0182] Step 5: Use pre-stack elastic parameter inversion to obtain the longitudinal wave impedance and transverse wave impedance data of the work area to be inverted;

[0183] Step 6: By combining the Sun model, Gassmann equation, volume average formula of density, and constraints of pore parameters, establish nonlinear equations for longitudinal wave impedance and transverse wave impedance with respect to porosity, and use the golden section algorithm to obtain the porosity inversion results.

[0184] Among them, the nonlinear equations for longitudinal wave impedance and transverse wave impedance with respect to porosity are:

[0185]

[0186] in,

[0187]

[0188] Based on the minimum porosity φ determined in step 3 min and maximum value φ max The porosity value range is set to [φ]. min ,φ max Then, the porosity φ is calculated using the golden section algorithm;

[0189] Step 7: Calculate the bulk modulus pore structure parameter γ based on the porosity inversion results. K and shear modulus pore structure parameter γ μ ;

[0190] Using formula (6), the shear modulus pore structure parameter γ is calculated based on the porosity results. μ ,have:

[0191]

[0192] Using formula (7), based on the shear modulus pore structure parameter γ μ Calculate the bulk modulus and pore structure parameter γ K ,have:

[0193]

[0194] Based on the minimum value of the pore structure parameter and maximum value Boundary constraints are applied to the pore parameter inversion results to ensure the bulk modulus and pore parameter γ K exist Within the range, ensure the shear modulus pore structure parameter γ μ exist Within the range.

[0195] Referring to the attached diagram, Figure 2 The typical wells selected to verify the method of the present invention are, from left to right, P-wave velocity, S-wave velocity, density and porosity. The light-colored thin lines in the figure represent the measured well curves, and the dark-colored thick lines represent the curves after scale coarsening.

[0196] Figure 3 The pore structure parameter curves are calculated based on the coarsened elasticity curve and porosity curve, according to formulas (1) to (3).

[0197] Figure 4 This is a cross-plot of pore parameters. The light-colored dots in the plot represent pore parameters. The cross-plot shows that the bulk modulus pore parameters and the shear modulus pore parameters have a good linear relationship. The dark, thick lines in the plot represent the pore parameter constraints obtained using the least squares algorithm.

[0198] Figure 5 To obtain the P-wave impedance inversion results (top figure) and S-wave impedance inversion results (bottom figure) based on pre-stack elastic parameter inversion, well A is used as a verification well.

[0199] Figure 6 The porosity inversion results (top figure), bulk modulus pore structure parameter inversion results (middle figure), and shear modulus pore structure parameter inversion results (bottom figure) obtained based on the method of this invention show that the inversion results of these three are stable and have good lateral continuity.

[0200] Figure 7 The figure shows a comparison between the well bypass inversion results and the measured curves. The light-colored thin line represents the measured curve, and the dark-colored thick line represents the well bypass inversion results. From left to right, these represent porosity, bulk modulus pore structure parameters, and shear modulus pore structure parameters. The comparison shows that the well bypass inversion results and the measured curves have a high degree of agreement, verifying that the porosity and pore structure parameters obtained by this invention have high inversion accuracy.

[0201] In summary, this invention selects more stable P-wave impedance and S-wave impedance obtained from pre-stack elastic parameter inversion as inputs for pore parameter inversion. Key rock physics parameters are obtained through rock physics modeling and analysis. By integrating the Sun model formula, Gassmann equation, volume-averaged density formula, and pore parameter constraints, a nonlinear mapping relationship between P-wave impedance, S-wave impedance, porosity, and pore parameters is directly established. Finally, the golden section algorithm is used for simultaneous inversion of porosity and pore parameters, achieving efficient and stable solutions. This develops a density-independent pore parameter inversion technique, broadening the application conditions of pore parameter inversion and improving its stability and accuracy.

[0202] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for inverting pore structure parameters, characterized in that, Includes the following steps: S1. Based on typical wells, rock physics modeling, and rock physics analysis, key rock physics parameters are obtained; the key rock physics parameters include the elastic modulus and density of the rock matrix, as well as the elastic modulus and density of the mixed fluid; S2. Process the logging data of the typical well, calculate the pore structure parameter curve of the typical well, and determine the extreme values ​​of pore structure parameter and porosity based on the pore structure parameter curve and the porosity curve; S3. Based on the pore structure parameter curve, mathematically optimize and determine the coefficients of the pore structure parameter constraint conditions; S4. Use pre-stack elastic parameters to invert and obtain the longitudinal wave impedance data volume and transverse wave impedance data volume of the work area to be inverted; S5. Based on the aforementioned pore structure parameter constraints, establish a nonlinear mapping relationship between longitudinal wave impedance and transverse wave impedance with respect to porosity, and use the golden section algorithm to obtain the porosity inversion result. S6. Calculate the inversion results of bulk modulus pore parameters and shear modulus pore parameters.

2. The pore structure parameter inversion method according to claim 1, characterized in that, In step S2, based on well curve coarsening, the pore structure parameter curve of the typical well is calculated. And based on the pore structure curves of the typical well And porosity curve φ W Determine the minimum value of the pore structure parameters. and maximum value Determine the minimum porosity φ min and maximum value φ max ; The calculation formulas are: In the formula, K m It is the average bulk modulus of the rock matrix. μ m It is the average shear modulus of the rock matrix. K f It is the average bulk modulus of the mixed fluid. γ K These are bulk modulus and pore structure parameters. γ μ These are shear modulus and pore structure parameters. φ is porosity. <ρ W > and <φ W > These represent the coarsened P-wave velocity curve, S-wave velocity curve, density curve, and porosity curve, respectively. <μ W > represents the shear modulus curve after coarsening. K, μ, f, and F K All of these are intermediate variables used in the calculation.

3. The pore structure parameter inversion method according to claim 1, characterized in that, In step S3, based on the pore structure parameter curve The coefficients C and D of the pore structure parameter constraints are determined using the least squares algorithm. Optimize the objective function using the least squares algorithm Among them, M W Indicates the number of well logging sampling points. This represents the pore structure parameter of the i-th sampling point.

4. The pore structure parameter inversion method according to claim 1, characterized in that, In step S5, the establishment of the nonlinear mapping relationship between longitudinal wave impedance and transverse wave impedance with respect to porosity is based on the Sun model formula, the Gassmann equation, and the volume average formula of density. The Sun model formula is: The Gassmann equation is: The volume-average equation for density is: r sat =(1-φ)ρ m +fr f (6) The constraints on the pore structure parameters are as follows: c μ =Cγ K +D (7) Among them, the following transformation relationship exists between the elastic parameters: In the above formula, K d μ d K sat μ sat and ρ sat Let represent the bulk modulus of the rock skeleton, the shear modulus of the rock skeleton, the bulk modulus of the fluid-saturated rock, the shear modulus of the fluid-saturated rock, and the density of the fluid-saturated rock, respectively. I P I S These represent the longitudinal wave impedance and the transverse wave impedance, respectively. ρ m It is the average density of the rock matrix. ρ f It is the average density of the mixed fluid; Arrange formulas (4) to (8), establish a three-dimensional nonlinear equation system (9) for longitudinal wave impedance and transverse wave impedance with respect to porosity and pore structure parameters, and solve for φ and γ. μ and γ K ; Further refining formula (9), we can obtain the nonlinear equations for longitudinal wave impedance and transverse wave impedance with respect to porosity: in, And / or, In step S5, based on the determined minimum porosity φ min and maximum value φ max The porosity value range is set to [φ]. min ,φ max The porosity φ was calculated using the golden section algorithm.

5. The pore structure parameter inversion method according to claim 1, characterized in that, In step S6, the shear modulus pore structure parameter γ is calculated based on the porosity results. μ ,have: Based on the shear modulus pore parameter γ μ Calculate the bulk modulus and pore structure parameter γ K ,have: Furthermore, based on the minimum value of the pore structure parameter and maximum value Boundary constraints are applied to the pore parameter inversion results to ensure the bulk modulus and pore parameter γ K exist Within the range, ensure the shear modulus pore structure parameter γ μ exist Within the range.

6. The pore structure parameter inversion method according to claim 1, characterized in that, The pore parameter inversion method includes the following steps: S01. Based on the type, quality, and length of the logging curves, select typical wells from the work area to be inverted, wherein the logging curves of the typical wells are complete in type, reliable in quality, and their length covers the entire target inversion layer.

7. A pore structure parameter inversion device, characterized in that, include: The key parameter acquisition module is used to acquire key rock physical parameters based on typical wells, rock physical modeling, and rock physical analysis; the key rock physical parameters include the elastic modulus and density of the rock matrix, as well as the elastic modulus and density of the mixed fluid; The extreme value determination module is used to process the logging data of the typical well, calculate the pore structure parameter curve of the typical well, and determine the extreme values ​​of the pore structure parameter and the porosity based on the pore structure parameter curve and the porosity curve. The constraint determination module is used to mathematically optimize and determine the coefficients of the pore parameter constraint conditions based on the pore parameter curve. The wave impedance acquisition module is used to invert the pre-stack elastic parameters and obtain the longitudinal wave impedance data volume and transverse wave impedance data volume of the work area to be inverted. The first calculation module is used to establish a nonlinear mapping relationship between longitudinal wave impedance and transverse wave impedance with respect to porosity based on the porosity parameter constraints, and to obtain the porosity inversion result using the golden section algorithm. and The second calculation module is used to calculate the inversion results of bulk modulus pore parameters and shear modulus pore parameters.

8. The pore structure parameter inversion device according to claim 7, characterized in that, The pore parameter inversion device includes: The typical well screening module is used to screen typical wells from the work area to be inverted based on the type, quality, and length of the logging curves. The typical wells have complete well curve types, reliable quality, and the length covers the entire inversion target layer.

9. An electronic device, characterized in that, include: Memory; and processor; The memory is used to store one or more computer instructions; the one or more computer instructions are executed by the processor to implement the method according to any one of claims 1 to 7.

10. A readable storage medium, characterized in that, The readable storage medium stores computer instructions; wherein, when executed by a processor, the computer instructions implement the method described in any one of claims 1 to 7.

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