A method, system, medium and device for physical property parameter inversion of a tight sandstone reservoir

Abstract

The present application relates to the field of geophysical prestack seismic inversion, and provides a tight sandstone reservoir physical parameter inversion method and system. The tight sandstone reservoir physical parameter inversion method comprises the following steps: a rock physics model considering rock consolidation degree and pore structure is established, and a consolidation parameter and a pore structure parameter are calculated by combining known logging data with the rock physics model; the consolidation parameter and the pore structure parameter are fitted with porosity and shale content in the logging data to obtain a fitting relationship; the rock physics model is brought into an Aki-Richard reflection coefficient equation, and a target function is constructed under a Bayesian framework; and the target function is optimized by adopting a method of alternately updating reservoir physical parameters, consolidation parameters and pore structure parameters under the constraint of the fitting relationship to obtain the reservoir physical parameters. The present application can realize accurate prediction of a reservoir.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of geophysical prestack seismic inversion, in particular to a method and system for physical parameter inversion of tight sandstone reservoir. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute the prior art.

[0003] With the deepening of oil and gas exploration and the rapid increase in energy demand, the exploration and development of unconventional oil and gas has become particularly important. As a typical unconventional oil and gas resource, tight sandstone oil and gas has good development potential. However, due to the low porosity and low permeability of tight sandstone reservoirs, the pore structure and rock consolidation degree are often complex, which often reduces the accuracy of reservoir prediction. At present, a reasonable rock physics model is usually constructed to predict the reservoir. The high-precision rock physics model improves the accuracy of reservoir prediction. The existing technology uses the pore aspect ratio as the basis for evaluating the pore structure, divides the pores in the rock into two categories of hard pores and soft pores, and proposes a soft pore model to predict the compressional and shear wave velocities of tight sandstone. In terms of rock consolidation degree, the consolidation parameter is currently used as a factor to indicate the rock consolidation degree. However, in actual reservoirs, due to the complex underground conditions, the rock at different depths is affected by factors such as lithology and lithofacies, which makes these single models unable to be applied to tight sandstone reservoirs.

[0004] Therefore, the existing rock physics model has certain limitations. When the pore structure of the tight sandstone reservoir is complex and the rock consolidation degree varies greatly, the existing rock model cannot be applied to the reservoir, which makes it impossible to accurately predict the reservoir. SUMMARY

[0005] In order to solve the technical problems in the background art, the present application provides a method and system for physical parameter inversion of tight sandstone reservoirs. The present application considers the influence of rock consolidation degree and pore structure on the rock physics model of tight sandstone reservoirs, so that the rock physics model of tight sandstone reservoirs is not affected by the complexity of the pore structure and the difference in rock consolidation degree of tight sandstone reservoirs, thereby realizing accurate prediction of the reservoir.

[0006] In order to achieve the above purpose, the present application adopts the following technical solutions:

[0007] The first aspect of the present application provides a method for physical parameter inversion of tight sandstone reservoirs.

[0008] A method for physical parameter inversion of tight sandstone reservoirs, comprising:

[0009] A rock physics model considering the consolidation degree and pore structure of the rock is established, and consolidation parameters and pore structure parameters are calculated by using known logging data in combination with the rock physics model;

[0010] The consolidation parameters and pore structure parameters are fitted with porosity and shale content in the logging data to obtain a fitting relationship;

[0011] The rock physics model is brought into an Aki-Richard reflection coefficient equation, and a target function is constructed under a Bayesian framework;

[0012] Under the constraint of the fitting relationship, the target function is optimized by using a method of alternately updating reservoir physical parameters, consolidation parameters and pore structure parameters, to obtain the reservoir physical parameters.

[0013] Further, the consolidation parameters and pore structure parameters calculated by using known logging data in combination with the rock physics model are expressed by the following formula:

[0014]

[0015] wherein, the consolidation parameters are represented by, and the pore structure parameters are represented by, and the P-wave and S-wave velocities calculated by the rock physics model are represented by, and the P-wave and S-wave velocities in the logging data are represented by.

[0016] Further, the fitting relationship is expressed by the following formula:

[0017]

[0018]

[0019]

[0020] wherein, the consolidation parameters are represented by, and the pore structure parameters are represented by, the porosity is represented by, and the shale content is represented by.

[0021] Further, the target function is expressed by the following formula:

[0022]

[0023] wherein, the seismic data are represented by, denotes a forward model, , denotes porosity, denotes shale content, denotes water saturation, denotes a wavelet, , denotes a posterior weight coefficient introduced by a prior Gaussian mixture distribution, denotes an expected mean value of the th Gaussian component, denotes a covariance of the th Gaussian component.

[0024] Further, the seismic data is calculated, and is expressed by the following formula:

[0025]

[0026] wherein, denotes an incident angle, denotes an error.

[0027] Further, the reservoir physical property parameters include porosity, shale content and water saturation.

[0028] The second aspect of the present application provides a tight sandstone reservoir physical property parameter inversion system.

[0029] A tight sandstone reservoir physical property parameter inversion system comprises:

[0030] a model construction module configured to establish a rock physics model considering rock consolidation degree and pore structure, and to calculate consolidation parameters and pore structure parameters by using known logging data in combination with the rock physics model;

[0031] a fitting module configured to fit the consolidation parameters and the pore structure parameters with porosity and shale content in the logging data to obtain a fitting relationship;

[0032] a target function construction module configured to bring the rock physics model into an Aki-Richard reflection coefficient equation, and to construct a target function under a Bayesian framework;

[0033] an inversion module configured to optimize the target function by using a method of alternately updating reservoir physical property parameters and consolidation parameters and pore structure parameters under the constraint of the fitting relationship to obtain the reservoir physical property parameters.

[0034] The third aspect of the present application provides a computer readable storage medium.

[0035] A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of inversion of petrophysical parameters of tight sandstone reservoirs according to the first aspect described above.

[0036] A fourth aspect of the present application provides a computer device.

[0037] A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the method of inversion of petrophysical parameters of tight sandstone reservoirs according to the first aspect described above when executing the program.

[0038] A fifth aspect of the present application provides a computer program product or computer program.

[0039] The present application provides a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the steps of the method of inversion of petrophysical parameters of tight sandstone reservoirs according to the first aspect described above.

[0040] Compared with the prior art, the present application has the beneficial effects that:

[0041] The present application provides a method and system for inversion of petrophysical parameters of tight sandstone reservoirs. A tight sandstone rock physics model considering complex pore structure and rock consolidation degree is established, and pore structure parameters and consolidation parameters are taken as variables, so that they have the characteristics of changing with depth, thereby improving the accuracy of the tight sandstone reservoir rock physics model. Then, the porosity and shale content in the logging curve are fitted with the pore structure parameters and the consolidation parameters to obtain the relationship between the consolidation parameters, the pore structure parameters, the porosity and the shale content. The fitting relationship is taken as prior information, combined with the fast simulated annealing algorithm, and the pore structure parameters and the consolidation parameters are alternately updated with the reservoir physical parameters in the iterative optimization to improve the accuracy of reservoir prediction. BRIEF DESCRIPTION OF DRAWINGS

[0042] The accompanying drawings, which form a part of this description, are included to provide a further understanding of the application and are incorporated in and constitute a part of this application. The embodiments of the application illustrated in the drawings are intended to explain the aspects of the present application and are not intended to limit the present application in any way.

[0043] Figure 1 is a flow chart of the tight sandstone reservoir rock physics modeling process shown in the present application;

[0044] Figure 2 is a data schematic diagram of test well A shown in the present application;

[0045] Figure 3 This is a schematic diagram illustrating the prediction results of a rock physics model in well A using fixed consolidation parameters and pore structure parameters, and a rock physics model considering variations in consolidation parameters and pore structure parameters, as shown in this invention.

[0046] Figure 4 This is a schematic diagram showing the data from the verification well illustrated in this invention, as well as the prediction results of a rock physics model using fixed consolidation parameters and pore structure parameters versus a rock physics model considering variations in consolidation parameters and pore structure parameters.

[0047] Figure 5(a) is a fitted surface diagram showing the relationship between consolidation parameters and porosity and clay content as illustrated in this invention.

[0048] Figure 5(b) shows the pore structure parameters illustrated in this invention. Fitted surface plot of porosity and clay content;

[0049] Figure 5(c) shows the pore structure parameters illustrated in this invention. Fitted surface plot of porosity and clay content;

[0050] Figure 6 This is a schematic diagram of the test results of different inversion method models shown in this invention;

[0051] Figure 7(a) is a seismic data profile at an angle of 10° as shown in this invention;

[0052] Figure 7(b) is a seismic data profile at an angle of 20° as shown in this invention;

[0053] Figure 7(c) is a seismic data profile at an angle of 30° as shown in this invention;

[0054] Figure 8(a) is a porosity inversion profile shown in this invention without considering the degree of rock consolidation and the changes in complex pore structure;

[0055] Figure 8(b) is a cross-sectional view of mud content inversion without considering the degree of rock consolidation and the changes in complex pore structure as shown in this invention;

[0056] Figure 8(c) is a water saturation inversion profile shown in this invention without considering the degree of rock consolidation and the changes in complex pore structure;

[0057] Figure 9(a) is a porosity inversion profile showing the changes in rock consolidation degree and complex pore structure as illustrated in this invention.

[0058] Figure 9(b) is a cross-sectional view of mudstone content inversion considering the degree of rock consolidation and the variation of complex pore structure as shown in this invention.

[0059] Figure 9(c) is a water saturation inversion profile considering the degree of rock consolidation and the change in complex pore structure, according to the present disclosure. DETAILED DESCRIPTION

[0060] The present application is further described in connection with the accompanying drawings and examples.

[0061] It should be noted that the following detailed description is exemplary in nature and is intended to provide further description of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0062] It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting, as the scope of the exemplary embodiments of this application will be limited only by the appended claims. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. Unless otherwise required by context, singular terms shall include pluralities and vice versa. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups thereof.

[0063] It should be noted that the flow diagrams and block diagrams in the drawings are representative of the architectural, functional, and operational aspects of possible implementations of the methods and systems according to various embodiments of the present disclosure. It should be noted that each block in the flow diagrams and block diagrams can represent a module, a segment, or a portion of code, which includes one or more executable instructions for implementing the specified logical functions (s). It should also be noted that in some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks noted in succession can in fact be executed substantially concurrently or can sometimes be executed in reverse order, depending on the functionality involved. It will also be noted that each block of the flow diagrams and / or block diagrams and combinations of blocks in the flow diagrams and / or block diagrams can be implemented by special purpose hardware-based systems that perform the specified functions or operations, or combinations of special purpose hardware and computer instructions.

[0064] Embodiment One

[0065] The embodiment provides a method for inverting physical parameters of a tight sandstone reservoir, and the method is applied to a server for example, and it can be understood that the method can also be applied to a terminal, and can also be applied to a system including a terminal and a server and is realized through interaction of the terminal and the server. The server can be a physical server, a server cluster composed of multiple physical servers or a distributed system, or a cloud server providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communication, middleware services, domain name services, security services CDN, and basic cloud computing services such as big data and artificial intelligence platforms. The terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch and the like, but is not limited to this. The terminal and the server can be directly or indirectly connected through wired or wireless communication, which is not limited in the application. In the embodiment, the method comprises the following steps:

[0066] A rock physics model considering the consolidation degree and pore structure of rock is established, and the consolidation parameters and pore structure parameters are calculated by using known logging data in combination with the rock physics model;

[0067] The consolidation parameters and pore structure parameters are fitted with the porosity and shale content in the logging data to obtain a fitting relationship;

[0068] The rock physics model is brought into the Aki-Richard reflection coefficient equation, and a target function is constructed under a Bayesian framework;

[0069] Under the constraint of the fitting relationship, the target function is optimized by using a method of alternately updating the reservoir physical parameters, the consolidation parameters and the pore structure parameters to obtain the reservoir physical parameters.

[0070] The embodiment will be described in detail below with reference to the drawings:

[0071] Step 1: First, a rock physics model considering the consolidation degree and pore structure of rock is established, and the consolidation parameters and pore structure parameters are calculated, and the specific process is as shown in Figure 1

[0072] The Xu-White model is used in the application, and the model mainly comprises three steps, that is, calculating the elastic modulus of rock matrix and pore fluid, calculating the dry rock skeleton elastic modulus and calculating the saturated fluid rock elastic modulus.

[0073] (1) Calculate the rock matrix and pore fluid modulus

[0074] The Voigt-Reuss-Hill average method is used to calculate the elastic modulus of the rock matrix:

[0075] ​ (1)

[0076] (2)

[0077] (3)

[0078] where, is the volume fraction of the th mineral component, the elastic modulus, , , are the upper Voigt bound and the lower Reuss bound of the elastic modulus of the rock matrix, respectively, is the matrix elastic modulus, i.e., the bulk modulus and the shear modulus of the matrix of the present application.

[0079] The bulk modulus of the pore fluid is calculated using the wood equation :

[0080] (4)

[0081] where, and are the bulk modulus of water and gas, respectively; is the water saturation.

[0082] (2) Calculation of the elastic modulus of the dry rock skeleton

[0083] The expressions of the bulk modulus and the shear modulus of the dry rock containing the pore structure parameters:

[0084] (5)

[0085] (6)

[0086] where, and are the bulk modulus and the shear modulus of the rock matrix, respectively, is the porosity. and are the pore structure parameters, representing the flexibility of the rock skeleton when the rock is compressed and sheared, respectively. When the porosity, pressure and temperature are constant, the smaller the value is, the harder the rock is formed, the better the internal coupling is, and the worse the pore connectivity is; the larger the value is, the less the contact and coupling of the rock particles are, the more flexible the skeleton is, and it means that the pore connectivity is better.

[0087] Considering the effect of the cementation diagenesis on the modulus of the dry rock, the cementation parameter is introduced into the dry rock model: ​​

[0088] (7)

[0089] (8)

[0090] The size of the consolidation parameter is not only related to the compaction degree of the rock, but also related to the mineral content of the rock. When the consolidation parameter of the sandstone is between 2 and 20, the larger the consolidation parameter represents the worse the rock consolidation degree, and the smaller the consolidation parameter represents the better the rock consolidation degree. The model considers the influence of diagenesis on the modulus of the rock skeleton, but does not consider the influence of the pore structure on the rock skeleton.

[0091] Therefore, the model is combined with the model considering the pore structure to establish a dry rock skeleton model considering the consolidation and diagenesis and the pore structure.

[0092] (9)

[0093] (10)

[0094] (3) calculating the elastic modulus of the saturated fluid rock

[0095] The present application uses Gassmann equation to calculate the bulk modulus of the saturated rock And shear modulus Namely:

[0096] (11)

[0097] (12)

[0098] The density of the saturated fluid rock Is:

[0099] (13)

[0100] Wherein, The fluid density, Is the rock matrix density.

[0101] Therefore, the longitudinal wave velocity of the saturated fluid rock And the transverse wave velocity Are:

[0102] (14)

[0103] (15)

[0104] (4) calculating the pore structure parameter and the consolidation parameter

[0105] Due to the complexity of geological conditions in practical applications, the pore structure parameters and the consolidation parameters are difficult to determine, and therefore, the pore structure parameters and the consolidation parameters are calculated by the following formula.

[0106]

[0107] In the formula, and denote the P and S wave velocities calculated by the rock physics model, and are the P and S wave velocities in the logging data.

[0108] The rock physics model is verified by the real logging data well A ( Figure 2 ) and the subsequent single well model test, Figure 3 the rock physics model (green curve) with fixed consolidation parameters ( ) and pore structure parameters ( ) and the model (red) of the present application with the change of the pore structure parameters and the consolidation parameters with depth, and the results show that the rock physics model considering the change of the consolidation parameters and the pore structure parameters with depth has higher accuracy. Well B ( Figure 4 ), the pore structure parameters and the consolidation parameters with depth also help to improve the accuracy of the rock physics model, and since the well passes through the seismic profile, the well is used as a verification well to verify the accuracy of the seismic inversion results.

[0109] Step 2: fitting the relationship between the consolidation parameters and the pore structure parameters and the porosity and the shale content

[0110] The fitting relationship is as follows:

[0111] (17)

[0112] (18)

[0113] (19)

[0114] Fig. 5 (a), Fig. 5 (b) and Fig. 5 (c) verify the accuracy of the fitting relationship. The data points on the surface are the projections of the logging data on the surface, indicating the accuracy of the fitting relationship.

[0115] Step 3: constructing the objective function

[0116] The present application adopts the Aki-Richard reflection coefficient equation, and the expression is as follows: ​​

[0117] (20)

[0118] in, Represents the reflection coefficient. It is the angle of incidence. , and These represent the density difference, longitudinal wave velocity difference, and transverse wave velocity difference on both sides of the interface, respectively.

[0119] Substituting equations (13), (14), and (15) into the reflection coefficient equation (20) and performing a convolution operation with the wavelet, we obtain pre-stack seismic data. .

[0120] (twenty one)

[0121] in, ; It is a convolution operator; It is a wavelet; It is a forward model; It's an error.

[0122] Since formula (21) is highly nonlinear, in order to reduce the ill-posedness of multi-parameter inversion, this invention obtains reservoir physical property parameters within a Bayesian framework. posterior probability distribution .

[0123] (twenty two)

[0124] in, It's earthquake data. It is the likelihood function. It is a prior probability distribution.

[0125] A Gaussian mixture model is used to represent the prior distribution of reservoir parameters.

[0126] (twenty three)

[0127] in, It is the expected mean Covariance The Gaussian components It is the first The weights of the Gaussian components (whose sum equals 1), The total number of all components. Indicates the first The expected mean of each Gaussian component. Indicates the first The covariance of the Gaussian components.

[0128] Substitute formula (21) and (22) into (23), for the sake of simplicity, assume that there are only two lithofacies, i.e., mudstone and sandstone, and the objective function is:

[0129] (24)

[0130] wherein, is the posterior weight coefficient introduced by the prior Gaussian mixture distribution.

[0131] Step 4: Physical property parameter inversion

[0132] The rapid simulated annealing algorithm has the advantages of global optimization ability, strong adaptability, and can handle nonlinear problems. Therefore, the rapid simulated annealing (Ryden and Park) method is used to solve the objective function. The expression is:

[0133] (25)

[0134] (26)

[0135] wherein, is a random number between 0 and 1; and are the current temperature and the initial temperature, respectively; is the objective function.

[0136] In this method, the pore structure parameters and the consolidation parameters are taken as internal variables, and are iteratively updated synchronously with the reservoir physical property parameters to meet the characteristics of the change with depth. In order to stabilize the optimization process of multiple parameters, the fitting relationships (17), (18) and (19) between the pore structure parameters and the consolidation parameters and the porosity and the shale content obtained by statistical analysis are used as prior information to constrain the iterative updating process of the consolidation parameters and the pore structure parameters, and the updating equation is as follows:

[0137] (27)

[0138] (28)

[0139] (29)

[0140] wherein,

[0141] (30)

[0142] (31)

[0143] (32)​​

[0144] wherein, 、 and represent the fitting coefficients between the consolidation parameters and the porosity structure parameters and the porosity, the shale content, 、 and are the fitting deviation values; 、 and are the perturbation intervals determined by the standard deviations of the porosity structure parameters and the consolidation parameters.

[0145] Figure 6 The inversion results using the depth-variable consolidation parameters and porosity structure parameters (red curves) and the inversion results using the fixed consolidation parameters and porosity structure parameters (green curves) are shown. The results show that the inversion results using the depth-variable consolidation parameters and porosity structure parameters are closer to the logging values (black curves), and the inversion results of the water saturation and the shale content are greatly improved.

[0146] The method is verified using actual seismic data. FIGS. 7(a), 7(b), and 7(c) are seismic data at different angles, FIGS. 8(a), 8(b), and 8(c) are inversion results of fixed consolidation parameters and fixed porosity structure parameters. FIGS. 9(a), 9(b), and 9(c) are inversion results of variable consolidation parameters and variable porosity structure parameters. From the inversion results of the two methods, it can be seen that the inversion results based on the variable consolidation parameters and the variable porosity structure parameters can better indicate the tight sandstone gas-bearing reservoir (arrow in FIG. 9), which has the characteristics of high porosity, low shale content and low water saturation at the reservoir, which is consistent with the geological understanding of the research area. In addition, the method also reduces the abnormal values of the water saturation at the non-reservoir (circled in FIG. 9(c)), and the inversion results are more continuous in the horizontal direction, which is more conducive to identifying the location of the reservoir. Well B is located at 65 channels, and the correlation coefficients between the inversion results of the well flanking channels and well B are shown in Table 1. It can be seen that the correlation coefficients of the inversion results using the variable consolidation parameters and the variable porosity structure parameters are larger, indicating that the method can better improve the accuracy of the inversion results.

[0147] Table 1 Correlation coefficients between the inversion results of the well flanking channels and the data of well B

[0148]

[0149] Example Two

[0150] The embodiment provides a tight sandstone reservoir physical property parameter inversion system, comprising:

[0151] A tight sandstone reservoir physical property parameter inversion system comprises:

[0152] a model building module configured to: establish a rock physics model considering rock consolidation degree and pore structure, and calculate consolidation parameters and pore structure parameters by using known logging data in combination with the rock physics model;

[0153] a fitting module configured to: fit the consolidation parameters and the pore structure parameters with porosity and shale content in the logging data to obtain a fitting relationship;

[0154] a target function building module configured to: bring the rock physics model into an Aki-Richard reflection coefficient equation, and build a target function under a Bayesian framework;

[0155] an inversion module configured to: optimize the target function by using a method of alternately updating reservoir physical parameters and consolidation parameters and pore structure parameters under the constraint of the fitting relationship, to obtain the reservoir physical parameters.

[0156] In some embodiments, the calculation of the consolidation parameters and the pore structure parameters by using the known logging data in combination with the rock physics model is represented by the following formula:

[0157]

[0158] wherein, represents the consolidation parameters, and both represent the pore structure parameters, represents the porosity, and represents the P-wave and S-wave velocity calculated by the rock physics model, and are the P-wave and S-wave velocity in the logging data.

[0159] In some embodiments, the fitting relationship is represented by the following formula:

[0160]

[0161]

[0162]

[0163] wherein, represents the consolidation parameters, and both represent the pore structure parameters, represents the porosity, and represents the shale content.

[0164] In some embodiments, the target function is represented by the following formula:

[0165]

[0166] wherein, denotes seismic data, denotes a forward model, , denotes porosity, denotes shale content, denotes water saturation, denotes wavelet, , denotes a posterior weight coefficient introduced by a prior Gaussian mixture distribution, denotes an expected mean value of the th Gaussian component, denotes a covariance of the th Gaussian component.

[0167] In some embodiments, the seismic data is calculated, and is represented by the following formula:

[0168]

[0169] wherein, denotes an incident angle, denotes an error.

[0170] In some embodiments, the reservoir physical property parameters include porosity, shale content, and water saturation.

[0171] Embodiment Three

[0172] The embodiment provides a computer readable storage medium, which stores a computer program, and the program is executed by a processor to implement steps in the tight sandstone reservoir physical property parameter inversion method according to the above-mentioned embodiment one.

[0173] Embodiment Four

[0174] The embodiment provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements steps in the tight sandstone reservoir physical property parameter inversion method according to the above-mentioned embodiment one when executing the program.

[0175] Embodiment Five

[0176] The embodiment provides a computer program product or a computer program, which includes computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device executes steps in the tight sandstone reservoir physical property parameter inversion method according to the above-mentioned embodiment one.

[0177] Those skilled in the art will appreciate that embodiments of the present application can be readily used as a method, a system or a computer program product. Accordingly, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage and so forth) embodying computer-readable program code.

[0178] The present application is described in reference to the flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 means for carrying out each of the one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks.

[0179] These computer program instructions can also be stored in a computer- readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 means for carrying out each of the one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks.

[0180] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the flowchart illustrations and / or block diagrams block or blocks. Figure 1 one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks. Figure 1 means for carrying out each of the one or more functions specified in the flowchart illustrations and / or block diagrams block or blocks.

[0181] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing relevant hardware, and the program can be stored in a computer readable storage medium. When the program is executed, the program can include the processes of the above-mentioned embodiment methods. The storage medium can be a magnetic disc, an optical disc, a read-only memory (ROM), a random access memory (RAM), or the like.

[0182] The above only describes the preferred embodiments of the present application and is not used to limit the present application. For those skilled in the art, the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method of petrophysical parameter inversion for tight sand reservoirs, characterized in that, The method comprises: a rock physics model considering rock consolidation degree and pore structure is established, consolidation parameters and pore structure parameters are calculated by combining the rock physics model with known logging data, and the following formula is used to represent the consolidation parameters and the pore structure parameters: wherein, represents a consolidation parameter, and both represent a pore structure parameter, and represent the P and S wave velocities calculated from the rock physics model, and are the P and S wave velocities from the well log data; the consolidation parameters and the pore structure parameters are fitted with porosity and shale content in the logging data to obtain a fitting formula; the rock physics model is brought into an Aki-Richard reflectivity equation, and a target function is constructed under a Bayesian framework, the target function is represented by the following formula: wherein, d denotes seismic data, denotes a forward model, , denotes porosity, denotes shale content, denotes water saturation, denotes wavelet, , denotes a posterior weight coefficient introduced by the prior Gaussian mixture distribution, μ denotes an expected mean value of the k th Gaussian component, denotes a covariance of the k th Gaussian component; under the constraint of the fitting formula, the target function is optimized by alternately updating reservoir physical property parameters and the consolidation parameters and the pore structure parameters to obtain the reservoir physical property parameters.

2. The method of tight sand reservoir petrophysical parameter inversion of claim 1, characterized in that, the fitting formula is represented by the following formula: wherein, represents a consolidation parameter, and both represent a pore structure parameter, represents a porosity, represents a shale content.

3. The method of claim 1, wherein, the seismic data are calculated by the following formula: wherein, θ denotes the angle of incidence, e denotes the error.

4. The method of claim 1, wherein, the reservoir physical property parameters comprise porosity, shale content and water saturation.

5. A system for tight sand reservoir petrophysical parameter inversion, characterized in that, The method comprises: a model construction module configured to establish a rock physics model considering rock consolidation degree and pore structure, and to calculate consolidation parameters and pore structure parameters by combining the rock physics model with known logging data, and the following formula is used to represent the consolidation parameters and the pore structure parameters: wherein, represents a consolidation parameter, and both represent a pore structure parameter, and represents a P-wave and S-wave velocity calculated from a rock physics model, and are P-wave and S-wave velocities in the well logging data; a fitting module configured to fit the consolidation parameters and the pore structure parameters with porosity and shale content in the logging data to obtain a fitting formula; a target function construction module configured to bring the rock physics model into an Aki-Richard reflectivity equation, and to construct a target function under a Bayesian framework, and the target function is represented by the following formula: wherein, d denotes seismic data, denotes a forward model, , denotes porosity, denotes shale content, denotes water saturation, denotes a wavelet, , denotes a posterior weight coefficient introduced by the prior Gaussian mixture distribution, μ denotes an expected mean of the k th Gaussian component, denotes a covariance of the k th Gaussian component; an inversion module configured to optimize the target function by alternately updating reservoir physical property parameters and the consolidation parameters and the pore structure parameters under the constraint of the fitting formula to obtain the reservoir physical property parameters.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The program is executed by the processor to implement the steps in the tight sandstone reservoir physical property parameter inversion method according to any one of claims 1-4.

7. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the program to implement the steps in the tight sandstone reservoir physical property parameter inversion method according to any one of claims 1-4.

8. A computer program product, characterised in that, The computer program product comprises a computer program, and the computer program is executed by the processor to implement the steps in the tight sandstone reservoir physical property parameter inversion method according to any one of claims 1-4.

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