Tight sandstone reservoir physical property parameter inversion method, system, medium and equipment

Abstract

The invention relates to the field of geophysical pre-stack seismic inversion, and provides a compact sandstone reservoir physical property parameter inversion method and system. The compact sandstone reservoir physical property parameter inversion method comprises the steps that a rock physical model considering the rock consolidation degree and the pore structure is established, and consolidation parameters and pore structure parameters are calculated by combining logging data with the rock physical model; fitting the consolidation parameters and the pore structure parameters with the porosity and the shale content in the logging data to obtain a fitting relational expression; the rock physical model is substituted into an Aki-Richard reflection coefficient equation, and an objective function is constructed under a Bayesian framework; and under the constraint of the fitting relational expression, optimizing the target function by adopting a method of alternately updating the reservoir physical property parameters, the consolidation parameters and the pore structure parameters to obtain the reservoir physical property parameters. According to the invention, accurate prediction of the reservoir can be realized.

Description

Technical Field

[0001] The invention relates to the field of geophysical pre-stack seismic inversion, and in particular to a method and system for inverting physical property parameters of tight sandstone reservoirs. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute 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, since tight sandstone reservoirs not only have the characteristics of low porosity and low permeability, but also the pore structure and rock consolidation degree are often complex, the accuracy of reservoir prediction is often reduced. 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, and divides the pores in the rock into two categories: hard pores and soft pores, thereby proposing a soft pore model to predict the P- and S-wave velocities of tight sandstone. When it comes to the issue of rock consolidation, consolidation parameters are currently used as factors indicating the degree of rock consolidation. However, in actual reservoirs, due to the complex underground conditions and the gradual changes in lithology and lithofacies of rocks at different depths, these single models are not applicable to tight sandstone reservoirs.

[0004] Therefore, the existing rock physics models have certain limitations. When the pore structure of the tight sandstone reservoir is complex and the degree of rock consolidation varies greatly, the existing rock models cannot be applied to the reservoir, making it impossible to accurately predict the reservoir. Summary of the invention

[0005] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a method and system for inversion of physical property parameters of tight sandstone reservoirs. The present invention simultaneously 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 pore structure and the difference of rock consolidation degree of tight sandstone reservoirs, thereby realizing accurate prediction of reservoirs.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: A first aspect of the present invention provides a method for inversion of physical property parameters of tight sandstone reservoirs.

[0007] A method for inversion of physical property parameters of tight sandstone reservoirs, comprising: Establishing a rock physics model that takes into account the rock consolidation degree and pore structure, and calculating the consolidation parameters and pore structure parameters by combining the well logging data with the rock physics model; The consolidation parameters and pore structure parameters are fitted with the porosity and shale content in the logging data to obtain the fitting relationship; Substituting the rock physics model into the Aki-Richard reflection coefficient equation and constructing the objective function under the Bayesian framework; Under the constraint of the fitting relationship, the objective function is optimized by alternately updating the reservoir physical property parameters, consolidation parameters and pore structure parameters to obtain the reservoir physical property parameters.

[0008] Furthermore, the well logging data is combined with the rock physics model to calculate the consolidation parameters and pore structure parameters, which are expressed by the following formula:

[0009] in, represents the consolidation parameter, and are pore structure parameters. and represents the P- and S-wave velocities calculated by the rock physics model, and are the P- and S-wave velocities in the logging data.

[0010] Furthermore, the fitting relationship is expressed by the following formula:

[0011]

[0012]

[0013] in, represents the consolidation parameter, and are pore structure parameters. represents the porosity, Indicates the mud content.

[0014] Furthermore, the objective function is expressed by the following formula:

[0015] in, represents earthquake data, represents the forward model, , represents the porosity, Indicates the mud content, represents water saturation, represents the wavelet, , represents the posterior weight coefficient introduced by the prior Gaussian mixture distribution, Indicates The expected mean of the Gaussian components, Indicates The covariance of the Gaussian components.

[0016] Furthermore, the earthquake data is calculated using the following formula:

[0017] in, represents the angle of incidence, Indicates error.

[0018] Furthermore, the reservoir physical property parameters include porosity, mud content and water saturation.

[0019] A second aspect of the present invention provides a tight sandstone reservoir physical property parameter inversion system.

[0020] A tight sandstone reservoir physical property parameter inversion system, comprising: A model building module is configured to: establish a rock physics model that takes into account the rock consolidation degree and pore structure, so as to calculate consolidation parameters and pore structure parameters by combining the well logging data with the rock physics model; A fitting module is configured to: fit the consolidation parameters and pore structure parameters with the porosity and shale content in the well logging data to obtain a fitting relationship; An objective function building module is configured to: bring the rock physics model into the Aki-Richard reflection coefficient equation and build an objective function under a Bayesian framework; The inversion module is configured to optimize the objective function by alternately updating the reservoir physical property parameters, consolidation parameters and pore structure parameters under the constraint of the fitting relationship to obtain the reservoir physical property parameters.

[0021] A third aspect of the present invention provides a computer-readable storage medium.

[0022] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps in the method for inverting physical property parameters of tight sandstone reservoirs as described in the first aspect above.

[0023] A fourth aspect of the present invention provides a computer device.

[0024] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the method for inverting physical property parameters of tight sandstone reservoirs as described in the first aspect above are implemented.

[0025] A fifth aspect of the present invention provides a computer program product or a computer program.

[0026] The present invention 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 the steps in the method for inverting physical property parameters of a tight sandstone reservoir as described in the first aspect above.

[0027] Compared with the prior art, the present invention has the following beneficial effects: The present invention provides a method and system for inversion of physical property parameters of tight sandstone reservoirs. By establishing a tight sandstone rock physical model that simultaneously considers the complex pore structure and the degree of rock consolidation, and taking the pore structure parameters and consolidation parameters as variables, the model has the characteristics of varying with depth, thereby improving the accuracy of the tight sandstone reservoir rock physical model. Then, the porosity and mud content in the logging curve are fitted with the pore structure parameters and consolidation parameters to obtain the relationship between the consolidation parameters and the pore structure parameters and the porosity and mud content. The fitting relationship is used as prior information, combined with a fast simulated annealing algorithm, and the pore structure parameters and consolidation parameters are alternately updated with the reservoir physical property parameters in iterative optimization to improve the accuracy of reservoir prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0029] Figure 1 is a flow chart of rock physics modeling of tight sandstone reservoirs shown in the present invention; Figure 2 It is a data schematic diagram of the test well A shown in the present invention; Figure 3 It is a schematic diagram of prediction results of a rock physics model using fixed consolidation parameters and pore structure parameters and a rock physics model considering changes in consolidation parameters and pore structure parameters in Well A shown in the present invention; Figure 4The present invention shows the data of the verification well, and a schematic diagram of the prediction results of the rock physics model using fixed consolidation parameters and pore structure parameters and the rock physics model considering the changes of consolidation parameters and pore structure parameters; FIG5( a ) is a fitting surface diagram showing the relationship between the consolidation parameters and the porosity and the shale content according to the present invention; Figure 5(b) shows the pore structure parameters of the present invention. Fitted surface plots with porosity and shale content; Figure 5 (c) shows the pore structure parameters of the present invention. Fitted surface plots with porosity and shale content; Figure 6 It is a schematic diagram of model test results of different inversion methods shown in the present invention; FIG. 7 (a) is a cross-sectional view of seismic data when the angle is 10° shown in the present invention; FIG7( b ) is a cross-sectional view of seismic data when the angle is 20° as shown in the present invention; FIG7 (c) is a cross-sectional view of seismic data when the angle is 30° shown in the present invention; FIG8 (a) is a porosity inversion profile diagram shown in the present invention without considering the rock consolidation degree and the changes in the complex pore structure; FIG8( b ) is an inversion profile of the mud content without considering the rock consolidation degree and the changes in the complex pore structure shown in the present invention; FIG8( c ) is a cross-sectional view of water saturation inversion without considering the rock consolidation degree and the changes in the complex pore structure shown in the present invention; FIG9( a ) is a cross-sectional view of porosity inversion considering changes in rock consolidation degree and complex pore structure according to the present invention; FIG9( b ) is an inversion profile of the mud content considering the rock consolidation degree and the changes in the complex pore structure shown in the present invention; FIG9( c ) is a cross-sectional diagram of water saturation inversion according to the present invention, which takes into account the changes in rock consolidation degree and complex pore structure. DETAILED DESCRIPTION

[0030] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0031] It should be noted that the following detailed descriptions are all illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0032] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0033] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the methods and systems according to various embodiments of the present disclosure. It should be noted that each box in the flowchart or block diagram can represent a module, a program segment, or a part of a code, and the module, program segment, or a part of a code may include one or more executable instructions for implementing the logical functions specified in each embodiment. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the flowchart and / or block diagram, and the combination of boxes in the flowchart and / or block diagram can be implemented using a dedicated hardware-based system that performs a specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0034] Embodiment 1 The present embodiment provides a method for inversion of physical property parameters of tight sandstone reservoirs. The present embodiment uses the method applied to a server as an example for illustration. It is understandable that the method can also be applied to a terminal, and can also be applied to a terminal, a server, and a system, and is implemented through the interaction between the terminal and the server. The server can be an independent physical server, or a server cluster or a distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communications, middleware services, domain name services, security services CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, a smart speaker, a smart watch, etc., but is not limited thereto. The terminal and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in this application. In the present embodiment, the method includes the following steps: Establishing a rock physics model that takes into account the rock consolidation degree and pore structure, and calculating the consolidation parameters and pore structure parameters by combining the well logging data with the rock physics model; The consolidation parameters and pore structure parameters are fitted with the porosity and shale content in the logging data to obtain the fitting relationship; Substituting the rock physics model into the Aki-Richard reflection coefficient equation and constructing the objective function under the Bayesian framework; Under the constraint of the fitting relationship, the objective function is optimized by alternately updating the reservoir physical property parameters, consolidation parameters and pore structure parameters to obtain the reservoir physical property parameters.

[0035] The present embodiment is described in detail below with reference to the accompanying drawings: Step 1: First, establish a rock physics model that takes into account both the rock consolidation degree and pore structure, and calculate the consolidation parameters and pore structure parameters. The specific process is as follows: Figure 1 As shown: The present invention adopts the Xu-White model, which mainly includes three steps, namely: calculating the elastic modulus of rock matrix and pore fluid, calculating the elastic modulus of dry rock skeleton and calculating the elastic modulus of saturated fluid rock.

[0036] (1) Calculation of rock matrix and pore fluid moduli The present invention uses the Voigt-Reuss-Hill average method to calculate the elastic modulus of the rock matrix: (1) (2) (3) in, Indicates The volume fraction and elastic modulus of the mineral components. , , They represent the Voigt upper limit and Reuss lower limit of rock matrix elastic modulus, respectively. is the matrix elastic modulus, ie, the bulk modulus and shear modulus of the matrix of the present invention.

[0037] Calculate the product modulus of the pore fluid using Wood's equation : (4) in, and Refers to the bulk modulus of water and air respectively; is the water saturation.

[0038] (2) Calculation of elastic modulus of dry rock skeleton Dry rock bulk modulus including pore structure parameters and shear modulus The expression is: (5) (6) in, and denote the bulk modulus and shear modulus of the rock matrix, respectively, Indicates porosity. and is a pore structure parameter, which indicates the flexibility of the rock skeleton when the rock is compressed and sheared. When the porosity, pressure and temperature are constant, the smaller the value, the harder the rock is, the better the internal coupling is, and the worse the pore connectivity is; the larger the value, the less contact and coupling between rock particles, the more flexible the skeleton is, and the better the pore connectivity is.

[0039] Considering the effect of consolidation diagenesis on the dry rock modulus, the consolidation parameter Introduced into the dry rock model: (7) (8) The size of the consolidation parameter is not only related to the degree of rock compaction, but also to the mineral content of the rock. When the consolidation parameter of sandstone is between 2 and 20, the larger the consolidation parameter, the worse the rock consolidation degree, and the smaller the consolidation parameter, the better the rock consolidation degree. This model takes into account the influence of diagenesis on the modulus of the rock skeleton, but does not consider the influence of pore structure on the rock skeleton.

[0040] Therefore, it is combined with the model considering pore structure to establish a dry rock skeleton model that considers both consolidation diagenesis and pore structure.

[0041] (9) (10) (3) Calculation of elastic modulus of saturated fluid rock The present invention uses the Gassmann equation to calculate the bulk modulus of saturated rock and shear modulus ,Right now: (11) (12) Fluid-saturated rock density for: (13) in, is the fluid density, is the rock matrix density.

[0042] So the longitudinal wave velocity of the saturated fluid rock and shear wave velocity for: (14) (15) (4) Calculation of pore structure parameters and consolidation parameters Since in practical applications, the geological conditions are complex and the pore structure parameters and consolidation parameters are difficult to determine, the present invention uses the following formulas to obtain the pore structure parameters and consolidation parameters.

[0043]

[0044] In the formula, and represents the P- and S-wave velocities calculated by the rock physics model, and are the P- and S-wave velocities in the logging data.

[0045] The present invention uses real logging data well A ( Figure 2 ) to verify the rock physics model and the subsequent single well model test, Figure 3 The fixed consolidation parameters are shown ( ) and pore structure parameters ( ) The rock physics model (green curve) and the model of the pore structure parameters and consolidation parameters that vary with depth (red) of the present invention predict the results. The results show that the rock physics model that considers the changes in consolidation parameters and pore structure parameters that vary with depth has higher accuracy. Well B ( Figure 4 ), the pore structure parameters and consolidation parameters that also vary with depth are helpful to improve the accuracy of the rock physics model. Since the well passes through the seismic profile, it is used as a verification well to verify the accuracy of the seismic inversion results.

[0046] Step 2: Fitting the relationship between consolidation parameters and pore structure parameters and porosity and shale content The pore structure parameters and consolidation parameters were compared with the porosity in the well logging data (well A). , mud content The fitting relationship is: (17) (18) (19) Figures 5(a), 5(b) and 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.

[0047] Step 3: Construct the objective function The present invention adopts the Aki-Richard reflection coefficient equation, which is expressed as follows: (20) in, represents the reflection coefficient, is the angle of incidence, , and They represent the density difference, longitudinal wave velocity difference and shear wave velocity difference on both sides of the interface respectively.

[0048] Substitute equations (13), (14), and (15) into the reflection coefficient equation (20) and perform convolution operation with the wavelet to obtain the prestack seismic data .

[0049] (twenty one) in, ; is the convolution operator; is a wavelet; It is a forward model; It's an error.

[0050] Since formula (21) is highly nonlinear, in order to reduce the ill-posedness of multi-parameter inversion, the present invention obtains the reservoir physical property parameters under the Bayesian framework. The posterior probability distribution of .

[0051] (twenty two) in, is earthquake data, is the likelihood function, is the prior probability distribution.

[0052] Gaussian mixture model is used to represent the prior distribution of reservoir parameters.

[0053] (twenty three) in, is the expected mean and covariance No. Gaussian components, It is The weights of the Gaussian components (whose sum is equal to 1), is the total number of components, Indicates The expected mean of the Gaussian components, Indicates The covariance of the Gaussian components.

[0054] Substituting formulas (21) and (22) into (23), for the sake of simplicity, it is assumed that there are only two lithofacies: mudstone and sandstone, that is, , the objective function is: (twenty four) in, , is the posterior weight coefficient introduced by the prior Gaussian mixture distribution.

[0055] Step 4: Inversion of physical properties The fast simulated annealing algorithm has the advantages of global optimization capability, strong adaptability, and the ability to handle nonlinear problems. Therefore, the present invention uses the fast simulated annealing (Ryden and Park) method to solve the objective function. Its expression is: (25) (26) in, is a random number between 0 and 1; and are the current temperature and the initial temperature respectively; is the objective function.

[0056] In this method, the pore structure parameters and consolidation parameters are taken as internal variables and iteratively updated synchronously with the reservoir physical property parameters to meet their variation characteristics with depth. In order to stabilize the multi-parameter optimization process, the fitting relationships between the pore structure parameters and consolidation parameters and the porosity and shale content obtained by statistical analysis (17), (18) and (19) are used as prior information to constrain the iterative update process of the consolidation parameters and pore structure parameters. The update equation is as follows: (27) (28) (29) in, (30) (31) (32) in, , and They represent the fitting coefficients between consolidation parameters and pore structure parameters and porosity and shale content, respectively. , and is the fitting deviation value; , and It is the disturbance interval determined by the standard deviation of the pore structure parameter and the consolidation parameter.

[0057] Figure 6 The inversion results of the consolidation parameters and pore structure parameters that vary with depth (red curve) and the inversion results of the pore structure parameters that are fixed (green curve) are shown. The results show that the inversion results of the consolidation parameters and pore structure parameters that vary with depth are closer to the well logging values ​​(black curve), especially the inversion results of water saturation and shale content have been greatly improved.

[0058] The method was verified using actual seismic data. Figures 7 (a), 7 (b), and 7 (c) are seismic data at different angles, and Figures 8 (a), 8 (b), and 8 (c) are the inversion results of fixed consolidation parameters and fixed pore structure parameters. Figures 9 (a), 9 (b), and 9 (c) are the inversion results of variable consolidation parameters and variable pore structure parameters. From the inversion results of the two methods, it can be seen that the results based on the inversion of variable consolidation parameters and variable pore structure parameters can better indicate the tight sandstone gas-bearing reservoir (arrows in Figure 9), which has the characteristics of high porosity, low mud content and water saturation in the reservoir, which is consistent with the geological understanding of the study area. In addition, this method also reduces the abnormal values ​​of water saturation in non-reservoir areas (circles in Figure 9 (c)), and the inversion results are more continuous in the horizontal direction, which is more conducive to identifying the location of the reservoir. Verification well B is located in channel 65. The correlation coefficient between the inversion results of the wellside channel and verification well B is shown in Table 1. It can be seen that the correlation coefficient of the inversion results using variable consolidation parameters and variable pore structure parameters is larger, indicating that this method can better improve the accuracy of the inversion results.

[0059] Table 1 Correlation coefficients between well bypass inversion results and verification well B data

[0060] Embodiment 2 This embodiment provides a tight sandstone reservoir physical property parameter inversion system, including: A tight sandstone reservoir physical property parameter inversion system, comprising: A model building module is configured to: establish a rock physics model that takes into account the rock consolidation degree and pore structure, so as to calculate consolidation parameters and pore structure parameters by combining the well logging data with the rock physics model; A fitting module is configured to: fit the consolidation parameters and pore structure parameters with the porosity and shale content in the well logging data to obtain a fitting relationship; An objective function building module is configured to: bring the rock physics model into the Aki-Richard reflection coefficient equation and build an objective function under a Bayesian framework; The inversion module is configured to optimize the objective function by alternately updating the reservoir physical property parameters, consolidation parameters and pore structure parameters under the constraint of the fitting relationship to obtain the reservoir physical property parameters.

[0061] In some embodiments, the well logging data is combined with the rock physics model to calculate the consolidation parameters and pore structure parameters, which are expressed by the following formula:

[0062] in, represents the consolidation parameter, and are pore structure parameters. and represents the P- and S-wave velocities calculated by the rock physics model, and are the P- and S-wave velocities in the logging data.

[0063] In some embodiments, the fitting relationship is expressed by the following formula:

[0064]

[0065]

[0066] in, represents the consolidation parameter, and are pore structure parameters. represents the porosity, Indicates the mud content.

[0067] In some embodiments, the objective function is expressed by the following formula:

[0068] in, represents earthquake data, represents the forward model, , represents the porosity, Indicates the mud content, represents water saturation, represents the wavelet, , represents the posterior weight coefficient introduced by the prior Gaussian mixture distribution, Indicates The expected mean of the Gaussian components, Indicates The covariance of the Gaussian components.

[0069] In some embodiments, the seismic data is calculated using the following formula:

[0070] in, represents the angle of incidence, Indicates error.

[0071] In some embodiments, the reservoir physical property parameters include porosity, mud content and water saturation.

[0072] Embodiment 3 This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the method for inverting physical property parameters of tight sandstone reservoirs as described in the first embodiment above are implemented.

[0073] Embodiment 4 This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps in the method for inverting physical property parameters of tight sandstone reservoirs as described in the first embodiment are implemented.

[0074] Embodiment 5 This 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 the steps in the method for inverting physical property parameters of tight sandstone reservoirs described in the first embodiment.

[0075] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0076] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0077] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0078] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0079] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. The storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc.

[0080] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for inversion of physical property parameters of tight sandstone reservoirs, characterized in that: include: Establishing a rock physics model that takes into account the rock consolidation degree and pore structure, and calculating the consolidation parameters and pore structure parameters by combining the well logging data with the rock physics model; The consolidation parameters and pore structure parameters are fitted with the porosity and shale content in the logging data to obtain the fitting relationship; Substituting the rock physics model into the Aki-Richard reflection coefficient equation and constructing the objective function under the Bayesian framework; Under the constraint of the fitting relationship, the objective function is optimized by alternately updating the reservoir physical property parameters, consolidation parameters and pore structure parameters to obtain the reservoir physical property parameters.

2. The method for inversion of physical property parameters of tight sandstone reservoirs according to claim 1, characterized in that: The well logging data is combined with the rock physics model to calculate the consolidation parameters and pore structure parameters, which are expressed by the following formula: f(C,γ k ,γ u )=argmin(||V p-est -V p-log ||2+||V s-est -V s-log ||2) Where C represents the consolidation parameter, γ k and γ u Both represent pore structure parameters, V P-est and V S-est represents the P- and S-wave velocities calculated by the rock physics model, V p-log and V S-log are the P- and S-wave velocities in the well logging data.

3. The method for inversion of tight sandstone reservoir physical property parameters according to claim 1, characterized in that: The fitting relationship is expressed by the following formula: Where C represents the consolidation parameter, γ k and γ u are pore structure parameters. Represents porosity, V cl Indicates the mud content.

4. The method for inversion of tight sandstone reservoir physical property parameters according to claim 1, characterized in that: The objective function is expressed by the following formula: Where, d represents seismic data, f(.) represents the forward model, Represents porosity, V cl Indicates the mud content, S w represents water saturation, w represents wavelet, π1 and π2 represent the posterior weight coefficients introduced by the prior Gaussian mixture distribution, represents the expected mean of the kth Gaussian component, represents the covariance of the kth Gaussian component.

5. The method for inversion of tight sandstone reservoir physical property parameters according to claim 4, characterized in that: The earthquake data is calculated using the following formula: d(θ)=f(m)*w+e Where θ is the incident angle and e is the error.

6. The method for inversion of tight sandstone reservoir physical property parameters according to claim 1, characterized in that: The reservoir physical property parameters include porosity, mud content and water saturation.

7. A tight sandstone reservoir physical property parameter inversion system, characterized in that: include: A model building module is configured to: establish a rock physics model that takes into account the rock consolidation degree and pore structure, so as to calculate consolidation parameters and pore structure parameters by combining the well logging data with the rock physics model; A fitting module is configured to: fit the consolidation parameters and pore structure parameters with the porosity and shale content in the well logging data to obtain a fitting relationship; An objective function building module is configured to: bring the rock physics model into the Aki-Richard reflection coefficient equation and build an objective function under a Bayesian framework; The inversion module is configured to optimize the objective function by alternately updating the reservoir physical property parameters, consolidation parameters and pore structure parameters under the constraint of the fitting relationship to obtain the reservoir physical property parameters.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps in the method for inverting physical property parameters of tight sandstone reservoirs as described in any one of claims 1 to 6 are implemented.

9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the method for inverting physical property parameters of tight sandstone reservoirs as described in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps in the method for inverting physical property parameters of tight sandstone reservoirs as described in any one of claims 1 to 6 are implemented.

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