Reservoir physical property parameter quantitative prediction method, device, equipment, medium and program

Abstract

This invention discloses a method, apparatus, equipment, medium, and program for quantitative prediction of reservoir physical parameters. The method includes: constructing a target rock physics model based on well logging correlation parameters from known wells; using a target inversion algorithm to invert the target inversion model correction reference parameters of the target rock physics model to obtain model correction reference parameters; correcting the target rock physics model based on the model correction reference parameters to obtain a corrected rock physics model; and using the target inversion algorithm based on the corrected rock physics model to invert and predict the reservoir physical parameters of the formation target area data volume. The technical solution of this invention can improve the accuracy and scientific validity of reservoir physical parameter prediction.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of geophysical exploration technology, and in particular to a method, apparatus, electronic device, storage medium and program for quantitative prediction of reservoir physical parameters. Background Technology

[0002] In seismic rock physics, the elastic properties of rocks are typically described by parameters such as P-wave velocity, S-wave velocity, and density, as well as the bulk modulus and shear modulus calculated based on these three parameters. These parameters characterizing the elastic properties of rocks are called elastic parameters. The physical properties of rocks are typically described by parameters such as porosity, clay content, and water saturation. These parameters characterizing the physical properties of rocks are called physical property parameters.

[0003] Rock physics models can establish a mapping relationship between elastic parameters and reservoir physical properties. In related technologies, well logging data is typically used as a basis to build a sandstone rock physics model that considers pore shape for predicting physical properties. Then, based on the established rock physics model, an objective function is constructed to predict reservoir physical properties using seismic elastic parameters obtained from pre-stack seismic inversion. Therefore, the core of rock physics model-driven reservoir physical property prediction lies in the mapping relationship between seismic elastic parameters and reservoir physical properties established by the rock physics model.

[0004] In the process of realizing this invention, the inventors discovered the following defects in the prior art: In the process of modeling rock physics models, the elastic modulus and pore shape parameters of matrix minerals are usually selected from empirical values. These empirical values ​​are data obtained through a large amount of statistics, and may be inapplicable due to different regions. Therefore, they will cause errors in the prediction of reservoir physical parameters driven by rock physics models. Summary of the Invention

[0005] This invention provides a method, apparatus, electronic device, storage medium, and program for quantitative prediction of reservoir physical parameters, which can improve the accuracy and scientific validity of reservoir physical parameter prediction.

[0006] According to one aspect of the present invention, a method for quantitatively predicting reservoir physical parameters is provided, comprising:

[0007] Construct a target rock physics model based on known well logging correlation parameters;

[0008] The target inversion algorithm is used to invert the target inversion model correction reference parameters of the target rock physics model to obtain the model correction reference parameters.

[0009] The target rock physics model is modified according to the model correction reference parameters to obtain the modified rock physics model;

[0010] Based on the modified rock physics model, the target inversion algorithm is used to invert and predict the reservoir physical parameters of the target area data volume of the formation.

[0011] According to another aspect of the present invention, a reservoir physical property parameter quantitative prediction device is provided, comprising:

[0012] The rock physics model building module is used to build a target rock physics model based on known well logging correlation parameters;

[0013] The model correction reference parameter acquisition module is used to invert the target inversion model correction reference parameters of the target rock physics model using a target inversion algorithm to obtain the model correction reference parameters.

[0014] A rock physics model correction module is used to correct the target rock physics model according to the model correction reference parameters to obtain a corrected rock physics model.

[0015] The reservoir physical property parameter prediction module is used to predict the reservoir physical property parameters of the target area data volume of the formation based on the modified rock physics model and the target inversion algorithm.

[0016] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0017] At least one processor; and

[0018] A memory communicatively connected to the at least one processor; wherein,

[0019] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the quantitative prediction method for reservoir physical parameters according to any embodiment of the present invention.

[0020] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the method for quantitative prediction of reservoir physical parameters according to any embodiment of the present invention.

[0021] According to another aspect of the present invention, a computer program product is also provided, comprising a computer program that, when executed by a processor, implements the method for quantitative prediction of reservoir physical parameters as described in any embodiment of the present invention.

[0022] This invention constructs a target rock physics model based on known well logging correlation parameters, and uses a target inversion algorithm to invert the target inversion model correction reference parameters of the target rock physics model to obtain model correction reference parameters. The target rock physics model is then corrected based on these model correction reference parameters to obtain a corrected rock physics model. Finally, reservoir physical parameters of the target formation data volume are predicted based on the corrected rock physics model. Because the target rock physics model is corrected using model correction reference parameters, which are obtained by inverting reference parameter data from the actual model, the accuracy of the corrected rock physics model is improved. This solves the problem of prediction errors that exist when constructing sandstone rock physics models based on empirical data and predicting reservoir physical parameters, thus improving the accuracy and scientific validity of reservoir physical parameter prediction.

[0023] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a flowchart of a method for quantitative prediction of reservoir physical parameters provided in an embodiment of the present invention;

[0026] Figure 2 This is a flowchart of a method for quantitative prediction of reservoir physical parameters provided in an embodiment of the present invention;

[0027] Figure 3 This is a flowchart of a method for quantitative prediction of reservoir physical parameters provided in an embodiment of the present invention;

[0028] Figure 4 This is a flowchart illustrating the iterative process of an adaptive step-size stochastic inversion algorithm provided in an embodiment of the present invention;

[0029] Figure 5 This is a schematic diagram of the data distribution of known well logging data provided in an embodiment of the present invention;

[0030] Figure 6 This is a comparison chart of P-wave velocity, S-wave velocity, and density calculated based on a modified rock physics model and actual well logging P-wave velocity, S-wave velocity, and density provided by an embodiment of the present invention.

[0031] Figure 7 This is a flowchart of a method for quantitative prediction of reservoir physical parameters provided in an embodiment of the present invention;

[0032] Figure 8 This is a schematic diagram illustrating the effect of pre-stack inversion profile of elastic parameters of a stratigraphic target area data volume provided in an embodiment of the present invention;

[0033] Figure 9 This is a flowchart illustrating the iterative process of another adaptive step-size stochastic inversion algorithm provided in an embodiment of the present invention;

[0034] Figure 10 This is a schematic diagram showing the comparison between reservoir properties of a formation target area data volume obtained by inversion based on a variable sampling space random inversion algorithm and reservoir properties of actual well logging in the formation target area data volume, according to an embodiment of the present invention.

[0035] Figure 11 This is a schematic diagram of the predicted profile effect of reservoir physical property parameters of a stratigraphic target area data volume provided in an embodiment of the present invention;

[0036] Figure 12 This is a schematic diagram of a reservoir physical parameter quantitative prediction device provided in an embodiment of the present invention;

[0037] Figure 13 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0038] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0039] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of 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 interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or 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.

[0040] Figure 1 This is a flowchart of a method for quantitative prediction of reservoir physical parameters provided by an embodiment of the present invention. This embodiment is applicable to the situation where reservoir physical parameters of a target area of ​​formation are predicted by inversion based on a modified sandstone petrophysical model. This method can be executed by a reservoir physical parameter quantitative prediction device, which can be implemented by software and / or hardware, and is generally integrated into an electronic device. This electronic device can be a terminal device or a server device, as long as it can execute the reservoir physical parameter quantitative prediction method. The present invention does not limit the specific type of electronic device. Accordingly, as... Figure 1 As shown, the method includes the following operations:

[0041] S110. Construct a target rock physics model based on the known well logging correlation parameters.

[0042] Well logging, also known as geophysical logging or mine geophysics, is a method of measuring geophysical parameters by utilizing the electrochemical, electrical, acoustic, and radioactive properties of rock formations. It belongs to the category of applied geophysical methods. In short, well logging is the measurement of the physical parameters of geological formations and rocks.

[0043] In this embodiment of the invention, optionally, there is a correlation between the known logging data (i.e., the logging correlation parameters of the known logging) and the formation target area data volume for which reservoir parameters need to be predicted. The formation target area data volume can be a data volume of a specific region within the target stratigraphic segment corresponding to the known logging data. That is, the known logging data is a small subset of the formation target area data volume, and the known logging data has a high accuracy rate.

[0044] The well logging correlation parameters can be relevant parameters from known well logging data, such as, but not limited to, P-wave velocity, S-wave velocity, density, porosity, water saturation, and clay content, as long as they are related to known well logging data. This embodiment of the invention does not limit the type or number of well logging correlation parameters. The target rock physics model can be a certain type of rock physics model. For example, the target rock physics model can be a sandstone rock physics model considering pore shape, or it can also be a sandstone rock physics model considering pore fluid interaction, etc., as long as it can accurately reflect the physical properties of the rock. This embodiment of the invention does not limit the type of target rock physics model.

[0045] To predict reservoir physical parameters, relevant well logging correlation parameters from known wells can be obtained first, and a target rock physics model of a certain type can be constructed based on these parameters. It is understood that the target rock physics model can reflect the mapping relationship between the elastic parameters of known wells and the reservoir physical parameters.

[0046] S120. The target inversion algorithm is used to invert the target inversion model correction reference parameters of the target rock physics model to obtain the model correction reference parameters.

[0047] The target inversion algorithm can be an algorithm used to invert and extrapolate certain parameters in a target rock physics model. As long as it has inversion and extrapolation capabilities, it is acceptable; this embodiment of the invention does not limit the specific algorithm type of the target inversion algorithm. The target inversion model correction reference parameters can be parameter types for which specific values ​​need to be inverted using the target inversion algorithm. The target inversion model correction reference parameters can be some unknown parameter types in the target rock physics model; this embodiment of the invention does not limit the specific parameter types of the target inversion model correction reference parameters. It is understood that different types of target rock physics models may have different parameter types for their corresponding target inversion model correction reference parameters. The model correction reference parameters can be parameter types obtained after correcting the target inversion model correction reference parameters.

[0048] Accordingly, after constructing the target rock physics model, some unknown parameters in the target rock physics model can be used as reference parameters for correcting the target inversion model. A suitable inversion algorithm is selected as the target inversion algorithm, and then the target inversion algorithm is used to invert the reference parameters for correcting the target inversion model, determining their calculated values, and thus obtaining the corresponding model correction reference parameters. It is understandable that the target inversion model correction reference parameters and the model correction reference parameters have the same parameter type; the only difference is that the target inversion model correction reference parameters are unknowns, while the model correction reference parameters are knowns.

[0049] In a specific example, assuming the target rock physics model adopts a sandstone rock physics model that considers pore shape, the target inversion model correction reference parameters corresponding to the sandstone rock physics model considering pore shape can include the elastic moduli of sandy and argillaceous minerals, such as mineral bulk modulus and shear modulus, as well as pore shape parameters such as equivalent pore aspect ratio. By reflecting the above target inversion model correction reference parameters through the target inversion algorithm, the calculated values ​​of the elastic modulus of sandy and argillaceous minerals and the pore shape parameters can be obtained, thus obtaining the model correction reference parameters.

[0050] S130. Correct the target rock physics model according to the model correction reference parameters to obtain the corrected rock physics model.

[0051] Among them, the modified rock physics model is the rock physics model obtained by modifying some unknown parameters in the target rock physics model.

[0052] In existing technologies, the final rock physics model is obtained by directly modifying the empirical values ​​of the reference parameters of the target inversion model to predict reservoir physical parameters. However, due to the differences in formation parameters across different regions, this can lead to prediction errors.

[0053] This invention employs an inversion algorithm to invert the target inversion model correction reference parameters for unknown quantities in the target rock physics model, obtaining the corresponding model correction reference parameters. During the inversion process, the algorithm can refer to well logging correlation parameters from known wells for scientific estimation. Since these well logging correlation parameters represent some of the actual measurement data from the formation target area data volume, the obtained model correction reference parameters are closer to the actual reservoir characteristics of the target stratigraphic segment in the area where the known well logging data is located. In other words, the model correction reference parameters are more accurate and scientifically sound than empirical values. After obtaining the model correction reference parameters, the target rock physics model is corrected based on these parameters. The resulting corrected rock physics model reduces the modeling error of the target rock physics model.

[0054] In an optional embodiment of the present invention, the step of correcting the target rock physics model according to the model correction reference parameters to obtain the corrected rock physics model may include: determining the target inversion model correction reference parameter variable corresponding to the model correction reference parameters in the target rock physics model; substituting the values ​​of the model correction reference parameters into the target inversion model correction reference parameter variable corresponding to the model correction reference parameters in the target rock physics model to obtain the corrected rock physics model.

[0055] S140. Based on the modified rock physics model, the target inversion algorithm is used to invert and predict the reservoir physical parameters of the target area data volume of the formation.

[0056] Among them, the formation target area data volume can be a specific data volume for which reservoir physical parameters need to be predicted.

[0057] Correspondingly, after obtaining the modified rock physics model, the numerical values ​​of some model parameters in the modified rock physics model can be obtained by measuring the data volume of the target stratigraphic region. The measured parameter values ​​are then input into the modified rock physics model, so that the reservoir physical parameters of the target stratigraphic region data volume can be predicted by using the target inversion algorithm through the modified rock physics model.

[0058] This invention constructs a target rock physics model based on known well logging correlation parameters, and uses a target inversion algorithm to invert the target inversion model correction reference parameters of the target rock physics model to obtain model correction reference parameters. The target rock physics model is then corrected based on these model correction reference parameters to obtain a corrected rock physics model. Finally, reservoir physical parameters of the target formation data volume are predicted based on the corrected rock physics model. Because the target rock physics model is corrected using model correction reference parameters, which are obtained by inverting reference parameter data from the actual model, the accuracy of the corrected rock physics model is improved. This solves the problem of prediction errors that exist when constructing sandstone rock physics models based on empirical data and predicting reservoir physical parameters, thus improving the accuracy and scientific validity of reservoir physical parameter prediction.

[0059] Figure 2 This is a flowchart of a method for quantitative prediction of reservoir physical parameters provided by an embodiment of the present invention. This embodiment is based on the above embodiment and is further specified. In this embodiment, taking a sandstone petrophysical model considering pore shape as the target petrophysical model as an example, various specific optional implementation methods for constructing the target petrophysical model based on known well logging correlation parameters are given. Correspondingly, such as Figure 2 As shown, the method in this embodiment may include:

[0060] S210. Construct a target rock physics model based on the known well logging correlation parameters.

[0061] Optionally, logging-related parameters may include P-wave velocity, S-wave velocity, density, porosity, water saturation, and clay content.

[0062] In this embodiment of the invention, the relationship between various well logging parameters, such as P-wave velocity, density, porosity, water saturation, and clay content, can be established using a sandstone petrophysical model that considers pore shape. This enables the modeling process of the sandstone petrophysical model based on pore shape. Here, the known P-wave velocity, S-wave velocity, and density are the elastic parameters of the known well logging, while porosity, water saturation, and clay content are the physical property parameters of the known well logging.

[0063] Accordingly, step S210 may include the following operations:

[0064] S211. Calculate the bulk modulus, shear modulus, and density of the equivalent rock matrix for the known well logging.

[0065] In an embodiment of the invention, optionally, the bulk modulus K of the equivalent rock matrix for known well logging can be calculated using the Voigt-Reuss-Hill (VRH) averaging model. mat Shear modulus μ mat and density ρ mat .

[0066] In a specific example, assuming a sandstone petrophysical model that considers pore shape is used as the target petrophysical model, the bulk modulus, shear modulus, and density of the equivalent rock matrix from known well logging can be calculated based on the following formulas:

[0067]

[0068]

[0069] To further eliminate the influence of porosity on clay content, in this embodiment of the invention, optionally, the clay content after eliminating the influence of porosity can be calculated based on the porosity and the known clay content from well logging. That is, the clay content after eliminating the influence of porosity can be calculated based on the following formula:

[0070]

[0071] Among them, K V It is the upper bound of the Voigt of the rock matrix bulk modulus. It is the clay content after eliminating the influence of porosity, K shale It is the bulk modulus of the clay mineral component, K sand It is the bulk modulus of the sandy mineral component, K R It is the Reuss lower bound of the bulk modulus of the rock matrix (the Reuss lower bound refers to the lower bound strength in the Reuss model), K mat It is the bulk modulus of the equivalent rock matrix, μ V It is the Voigt upper bound of the rock matrix shear modulus (the Voigt upper bound can be the upper bound strength in the Voigt-Reuss-Hill model), μ shale It is the shear modulus of the clay mineral component, μ sand It is the shear modulus of the sandy mineral component, μ R It is the Reuss lower bound of the rock matrix shear modulus, μ mat It is the shear modulus of the equivalent rock matrix, ρmat It is the density of the equivalent rock matrix, ρ shale It is the density of the clay mineral component, ρ sand It is the density of the sandy mineral component, V shale Given the known clay content from well logging, φ represents porosity. V shale Both φ and φ are known quantities, and can be obtained from the well logging curves of known well logs.

[0072] S212. Calculate the equivalent bulk modulus and density of the pore fluid in the known well logging.

[0073] Assuming the rock pores contain a mixture of natural gas and water, the equivalent bulk modulus and density of the pore fluid can be calculated using Wood's formula (used to calculate the mixed bulk modulus of pore fluids) (Wood, 1955).

[0074] In a specific example, continuing with the example above, the equivalent bulk modulus and density of the pore fluid in a well log can be calculated based on the following formula:

[0075] K fl =(1-S w ) / K gas +S w / K w (9)

[0076] ρ fl =(1-S w )ρ gas +S w ρ w (10)

[0077] Among them, K fl S represents the equivalent bulk modulus of the mixed pore fluid, i.e., the equivalent bulk modulus of the pore fluid known from well logging; w K represents the water saturation level, which is a known quantity. gas It is the bulk modulus of natural gas, K. w It is the bulk modulus of water, ρ fl ρ is the equivalent density of the mixed pore fluid, i.e., the density of the pore fluid in the known well log; gas It is the density of natural gas, ρ w It is the density of water.

[0078] S213. Calculate the bulk modulus of the fluid-saturated rock in the target rock physical model based on the bulk modulus of the equivalent rock matrix, the porosity, the equivalent bulk modulus of the pore fluid, and the first geometric coefficient considering the change in pore aspect ratio.

[0079] Among them, the first geometric coefficient that considers the change in pore aspect ratio is a geometric coefficient that takes into account the change in pore aspect ratio.

[0080] S214. Calculate the shear modulus of the fluid-saturated rock in the target rock physical model based on the shear modulus of the equivalent rock matrix from the known well logging, the porosity, and the second geometric coefficient considering the change in pore aspect ratio.

[0081] Among them, the second geometric coefficient that considers the change in pore aspect ratio is another geometric coefficient that considers the change in pore aspect ratio.

[0082] In a specific example, continuing with the above example, we can refer to the generalized expression of the self-consistent rock physics model, introducing a first geometric coefficient considering the variation in pore aspect ratio and a second geometric coefficient considering the variation in pore aspect ratio. Referring to the generalized expression of the self-consistent SCA (Self-Consistent Approximation) rock physics model, we can use the following formula to calculate the bulk modulus and shear modulus of the fluid-saturated rock in the target rock physics model:

[0083]

[0084] in, P is the bulk modulus of fluid-saturated rock in the target rock physics model. * The first geometric coefficient to consider is the variation in the aspect ratio of the pores. Q is the shear modulus of fluid-saturated rock in the target rock physics model. * It is the second geometric coefficient that takes into account the variation of the pore aspect ratio.

[0085] Furthermore, the geometric coefficients considering both the first and second pore aspect ratio variations can be calculated based on the following formulas:

[0086]

[0087] in:

[0088]

[0089] F6=1+A[1+fR(f+θ)]+B(1-θ)(3-4R) (20)

[0090]

[0091] F9=A[(R-1)f-Rθ]+Bθ(3-4R) (23)

[0092] This includes:

[0093]

[0094] In the above formula, it is assumed that the shear modulus of the pore fluid is 0, i.e., μ fl =0,ν mat α is the Poisson's ratio of the equivalent rock matrix, and α is the aspect ratio of the equivalent pore size.

[0095] S215. Calculate the density of the fluid-saturated rock in the target rock physical model based on the density of the equivalent rock matrix, the porosity, and the density of the pore fluid.

[0096] In a specific example, continuing with the example above, the density of fluid-saturated rock in the target rock physical model can be calculated based on the following formula:

[0097] ρ=(1-φ)ρ mat +φρ fl (29)

[0098] Where ρ is the density of the formation rock known from the well logging, that is, the density of the fluid-saturated rock in the target rock physical model. mat It is the density of the equivalent rock matrix, φ is the porosity, and ρ is the density of the equivalent rock matrix. fl The density of the pore fluid in the well log is known.

[0099] S216. Calculate the longitudinal wave velocity of the fluid-saturated rock in the target rock physical model based on the bulk modulus, shear modulus, and density of the fluid-saturated rock in the target rock physical model.

[0100] In a specific example, continuing with the example above, the P-wave velocity of fluid-saturated rock in the target rock physical model can be calculated based on the following formula:

[0101]

[0102] in, It is the bulk modulus of fluid-saturated rock in the target rock physical model. ρ is the shear modulus of fluid-saturated rock in the target rock physics model, and ρ is the density of the formation rock from the known well logging.

[0103] S217. Calculate the transverse wave velocity of the fluid-saturated rock in the target rock physical model based on the shear modulus and density of the fluid-saturated rock in the target rock physical model.

[0104] In a specific example, continuing with the example above, the transverse wave velocity of fluid-saturated rock in the target rock physical model can be calculated based on the following formula:

[0105]

[0106] Among them, V S It is the shear wave velocity of fluid-saturated rock in the target rock physics model. ρ is the shear modulus of fluid-saturated rock in the target rock physics model, and ρ is the density of the formation rock from the known well logging.

[0107] Thus, the target rock physics model is completed by combining the Voigt-Reuss-Hill average model and the SCA model. The role of the target rock physics model is to establish the mapping relationship between elastic parameters (i.e., P-wave velocity, S-wave velocity, and density) and physical property parameters (i.e., porosity, clay content, and water saturation).

[0108] S220. The target inversion algorithm is used to invert the target inversion model correction reference parameters of the target rock physics model to obtain the model correction reference parameters.

[0109] S230. Correct the target rock physics model according to the model correction reference parameters to obtain the corrected rock physics model.

[0110] S240. Based on the modified rock physics model, invert and predict the reservoir physical parameters of the target area data volume of the strata.

[0111] The above technical solution uses known logging elastic parameters and physical property parameters to generate a sandstone rock physical model that considers pore shape as the target rock physical model. In the process of modeling the sandstone rock physical model that considers pore shape, the influence of porosity on clay content is eliminated, resulting in a more accurate target rock physical model. This further improves the accuracy of the corrected target rock physical model, thereby improving the accuracy and scientific nature of reservoir physical property parameter prediction.

[0112] Figure 3 This is a flowchart of a method for quantitative prediction of reservoir physical parameters provided by an embodiment of the present invention. This embodiment is based on the above embodiment and is further specified. In this embodiment, various specific optional implementation methods are given for obtaining the model correction reference parameters by inverting the target inversion model correction reference parameters of the target rock physics model using a target inversion algorithm. Accordingly, such as Figure 3 As shown, the method in this embodiment may include:

[0113] S310. Construct a target rock physics model based on the known logging correlation parameters.

[0114] S320. The target inversion algorithm is used to invert the target inversion model correction reference parameters of the target rock physics model to obtain the model correction reference parameters.

[0115] Optionally, the target inversion algorithm includes an adaptive step-size stochastic inversion algorithm; the model correction reference parameters include the elastic modulus of sandy minerals, the elastic modulus of muddy minerals, and pore shape parameters.

[0116] Among them, the adaptive step-size stochastic inversion algorithm is a composite optimization algorithm that combines an adaptive parameter adjustment mechanism with the idea of ​​stochastic inversion. It is mainly used to solve the parameter inversion problem of complex nonlinear systems.

[0117] The elastic modulus of sandy minerals can include the bulk modulus K of the sandy mineral components. sand Shear modulus μ of sandy mineral components sand The elastic modulus of argillaceous minerals can include the bulk modulus K of the argillaceous mineral components. shale Shear modulus μ of clay mineral components shale The pore shape parameter can be the equivalent pore aspect ratio α.

[0118] In this embodiment of the invention, an adaptive step-size stochastic inversion algorithm with varying sampling space can be applied to invert the elastic modulus of sandy minerals (including the bulk modulus and shear modulus of sandy mineral components) and the elastic modulus of argillaceous minerals (including the bulk modulus and shear modulus of argillaceous mineral components), as well as pore shape parameters (equivalent pore aspect ratio), so as to correct the target rock physical model based on the inverted parameters.

[0119] Accordingly, step S320 may include the following operations:

[0120] S321. Determine the current mean and current variance of the current probability density distribution of the corrected reference parameters of the target inversion model.

[0121] In this embodiment of the invention, for each target inversion model correction reference parameter that needs to be inverted, the corresponding model correction reference parameters can be obtained by sequentially employing an adaptive step-size stochastic inversion algorithm. When inverting each target inversion model correction reference parameter, the current mean and current variance of the current probability density distribution of that target inversion model correction reference parameter can be determined first. When starting the inversion using the adaptive step-size stochastic inversion algorithm, the prior probability density distribution of the target inversion model correction reference parameter can be given as the current probability density distribution, and the mean and variance of the prior probability density distribution can be given as the current mean and current variance of the current probability density distribution, respectively.

[0122] For example, for the bulk modulus of sandy mineral components, the prior probability density distribution of the bulk modulus of sandy mineral components can be determined as the current probability density distribution, and the mean and variance of the prior probability density distribution of the bulk modulus of sandy mineral components can be given as the current mean and current variance of its current probability density distribution, respectively.

[0123] For example, for the shear modulus of sandy mineral components, the prior probability density distribution of the shear modulus of sandy mineral components can be determined as the current probability density distribution, and the mean and variance of the prior probability density distribution of the shear modulus of sandy mineral components can be given as the current mean and current variance of its current probability density distribution, respectively.

[0124] For example, for the bulk modulus of clay mineral components, the prior probability density distribution of the bulk modulus of clay mineral components can be determined as the current probability density distribution, and the mean and variance of the prior probability density distribution of the bulk modulus of clay mineral components can be given as the current mean and current variance of its current probability density distribution.

[0125] For example, for the shear modulus of clay mineral components, the prior probability density distribution of the shear modulus of the clay mineral components can be determined as the current probability density distribution, and the mean and variance of the prior probability density distribution of the shear modulus of the clay mineral components can be given as the current mean and current variance of the current probability density distribution.

[0126] For example, for an equivalent porosity, the prior probability density distribution of the equivalent porosity can be determined as the current probability density distribution, and the mean and variance of the prior probability density distribution of the equivalent porosity can be given as the current mean and current variance of its current probability density distribution.

[0127] Optionally, the reference parameters for each target inversion model can be inverted sequentially using an adaptive step-size stochastic inversion algorithm, or they can be inverted simultaneously using an adaptive step-size stochastic inversion algorithm. This embodiment of the invention does not impose any restrictions on this.

[0128] S322. Randomly sample a first set number of initial sampling points for the reference parameters of the target inversion model according to the current probability density distribution.

[0129] The first set quantity can be configured as needed. For example, the first set quantity can be configured as N. The first initial sampling point can be a sampling point obtained by randomly sampling the numerical values ​​of the reference parameters of the target inversion model during the first sampling stage.

[0130] Accordingly, in the current iteration, the reference parameters of the current target inversion model can be randomly sampled according to the current probability density distribution, and a first set number of initial sampling points can be obtained.

[0131] S323. Set a first objective function, and use the first objective function as a constraint to update the current probability density distribution of the corrected reference parameters of the objective inversion model.

[0132] The first objective function can be an objective function used to constrain the current probability density distribution of the corrected reference parameters of the updated objective inversion model.

[0133] S324. According to the updated target inversion model, correct the current probability density distribution of the reference parameters, collect a second set number of first target sampling points from the first set number of first initial sampling points, and statistically analyze the distribution pattern of the second set number of first target sampling points to obtain the updated mean and updated variance.

[0134] The second set quantity can be configured as needed. For example, the second set quantity can be configured as aqa·N, where aqa represents the reduction factor. The first target sampling point can be a sampling point obtained by resampling the first initial sampling point in the second sampling stage. The updated mean can be the mean of the target inversion model corrected reference parameters in the distribution pattern of the second set quantity of the first target sampling points. The updated variance can be the variance of the target inversion model corrected reference parameters in the distribution pattern of the second set quantity of the first target sampling points. The first and second set quantities can be integers.

[0135] Accordingly, after completing the first sampling step, a first objective function can be set as a constraint, thereby updating the current probability density distribution of the target inversion model's corrected reference parameters based on the constraints of the first objective function. After the current probability density distribution of the target inversion model's corrected reference parameters is updated, sampling can continue from the first set number of first initial sampling points obtained in the first sampling, according to the updated current probability density distribution of the target inversion model's corrected reference parameters, to collect a second set number of first target sampling points. Since the current probability density distribution of the target inversion model's corrected reference parameters is updated with the constraints of the first objective function, the first target sampling points collected based on the constraints of the first objective function can be the sampling point data closest to the actual situation, for example, they can be the second set number of first target sampling points with the smallest first objective function. Accordingly, after collecting the first target sampling points, the distribution pattern of the second set number of first target sampling points can be statistically analyzed, thereby obtaining the updated mean and updated variance of the target inversion model's corrected reference parameters based on the data collected in the second round.

[0136] In an optional embodiment of the present invention, the step of updating the current probability density distribution of the target inversion model correction reference parameters using the first objective function as a constraint, and collecting a second set number of first target sampling points from the first set number of first initial sampling points according to the updated current probability density distribution of the target inversion model correction reference parameters, may include: rearranging the first initial sampling points sequentially according to the magnitude of the first objective function to update the current probability density distribution of the target inversion model correction reference parameters; and collecting the second set number of first target sampling points from the rearranged first set number of first initial sampling points during the current iteration.

[0137] Specifically, when updating the current probability density distribution of the corrected reference parameters of the target inversion model using the first objective function as a constraint, and then re-collecting a second set number of first target sampling points from the first set number of first initial sampling points according to the updated probability density distribution of the corrected reference parameters of the target inversion model, the first initial sampling points can be rearranged sequentially according to the magnitude of the first objective function. The order of the first initial sampling points according to the magnitude of the first objective function will differ depending on how closely the trend of the first objective function approximates the actual situation. Optionally, if a smaller value of the first objective function is closer to the actual situation, the first initial sampling points can be rearranged sequentially in ascending order of the first objective function; if a larger value of the first objective function is closer to the actual situation, the first initial sampling points can be rearranged sequentially in descending order of the first objective function. After the first initial sampling points are arranged, a second set number of first target sampling points can be collected again from the re-sorted first set number of first initial sampling points in the current iteration.

[0138] S325. Replace the current mean with the updated mean, replace the current variance with the updated variance, and update the current probability density distribution based on the updated current mean, the updated current variance, and the amplification factor.

[0139] Accordingly, after collecting a second set number of first target sampling points and re-statistically updating the mean and variance based on these second set number of first target sampling points, the current mean can be replaced with the updated mean, and the current variance can be replaced with the updated variance. This allows for a second update of the current probability density distribution based on the updated current mean, updated current variance, and amplification factor. The purpose of updating the current probability density distribution again using the amplification factor is to avoid the sample space of the selected second set number of first target sampling points being too small, and to amplify the variance σ. 2 This approach allows the resampling process in each iteration to include more cases.

[0140] S326. Determine if the current iteration count has reached the first maximum iteration count. If yes, execute S327; otherwise, return to execute S322.

[0141] The first maximum number of iterations can be the maximum number of iterations set when iterating the reference parameters of the target inversion model of the target rock physics model using an adaptive step-size random inversion algorithm.

[0142] S327. The mean value of the target inversion model correction reference parameters generated in the last iteration process is used as the model correction reference parameter corresponding to the target inversion model correction reference parameter.

[0143] Once the target inversion model correction reference parameters are iteratively inverted using the adaptive step-size stochastic inversion algorithm, the mean value of the target inversion model correction reference parameters generated in the last iteration can be used as the final value of the target inversion model correction reference parameters calculated in the current iteration, thus obtaining the model correction reference parameters corresponding to the target inversion model correction reference parameters.

[0144] Figure 4 This is a flowchart illustrating the iterative process of an adaptive step-size stochastic inversion algorithm provided in an embodiment of the present invention. In a specific example, such as... Figure 4 As shown, when using the adaptive step-size stochastic inversion algorithm to iteratively retrieve the model correction reference parameters corresponding to the target inversion model correction reference parameters, in the first iteration, the mean μ and variance σ of the prior probability density distribution of the currently retrieved target inversion model correction reference parameters can be given first. 2 And according to the probability density distribution X~N(μ,σ) 2 Randomly sample N sampling points as the first initial sampling points.

[0145] Furthermore, an objective function is set for the inversion process of correcting the reference parameters of the target inversion model, serving as a constraint to update the current probability density distribution of the corrected reference parameters. Since the well logging data contains known P-wave velocity, S-wave velocity, porosity, clay content, and water saturation, the objective function set for the inversion process of correcting the reference parameters of the target inversion model can be: E = [V P,well -V P (K sand ,K shale ,μ sand ,μ shale ,α)] 2 +[V S,well -V S (K sand ,K shale ,μ sand ,μ shale ,α)] 2 Among them, VP,well Given the known P-wave velocity in well logging, V S,well Given the shear wave velocity in the well logging, V P (K sand ,K shale ,μ sand ,μ shale α) represents the elastic modulus of sandy minerals, elastic modulus of argillaceous minerals, and equivalent porosity as variables, given the porosity, clay content, and water saturation of the well logs. Using formula... Calculated longitudinal wave velocity, V S (K sand ,K shale ,μ sand ,μ shale α) represents the elastic modulus of sandy minerals, elastic modulus of argillaceous minerals, and equivalent porosity as variables, given the porosity, clay content, and water saturation of the well logs. Using formula... Calculated transverse wave velocity.

[0146] Specifically, the first initial sampling points can be rearranged sequentially according to the magnitude of the first objective function (either from smallest to largest or from largest to smallest) to update the current probability density distribution of the corrected reference parameters of the objective inversion model. In the current iteration, a second set number of first objective sampling points are collected from the reordered first set number of first initial sampling points. Assuming the first set number is N, the second set number can be aqa·N.

[0147] In other words, during each iteration, we can select aqa·N sampling points that best approximate the actual situation (i.e., aqa·N sampling points that minimize or maximize the objective function). Under the Gaussian distribution assumption, we can statistically analyze the distribution pattern of the selected aqa·N sampling points to obtain a new mean μ and variance σ. 2 To replace the original mean and variance.

[0148] Furthermore, let the distribution X ~ N(μ, apa·σ) be used. 2 The algorithm resamples N initial sampling points and starts the next iteration to achieve convergence.

[0149] The adaptive step-size stochastic inversion algorithm requires pre-defined parameters including a first set number N, a first maximum number of iterations maxnum, a reduction factor aqa (which must be an integer), and a magnification factor apa. The purpose of the reduction factor aqa is to select aqa·N first target sampling points from the N initial sampling points that are closest to the actual situation for statistical analysis. Generally, the reduction factor aqa is set to be less than 1 and greater than 0. The magnification factor (apa) is used to avoid the sample space of the selected aqa·N first target sampling points being too small by amplifying the variance σ. 2 This approach allows the resampling process in each iteration to include more cases.

[0150] When using the adaptive step-size stochastic inversion algorithm with varying sampling space to correct the reference parameters of the target inversion model for the elastic modulus and equivalent porosity of rock matrix minerals, the initial mean and variance of the elastic modulus of the matrix minerals can be found in the Rock Physics Handbook (Mavko et al., 2009). Specifically, the initial mean μ of the bulk modulus of sandy minerals is 37 GPa, and the variance σ... 2 The initial mean μ of the shear modulus of sandy minerals is 44 GPa, and the variance σ is 9. 2 The initial mean μ of the bulk modulus of the argillaceous minerals is 21 GPa, and the variance σ is 9. 2 The initial mean μ of the shear modulus of the clay minerals is 21 GPa, and the variance σ is 9. 2 The value is 9; the initial mean μ of the equivalent porosity aspect ratio α is 0.1, and the variance σ is 9. 2 The value is 0.09. Optionally, the first set quantity for each iteration can be set to N = 20, the first maximum number of iterations maxnum = 30, the shrinkage factor aqa = 0.3, and the magnification factor apa = 1.2.

[0151] Figure 5 This is a schematic diagram of the data distribution of known well logging data provided in an embodiment of the present invention. In a specific example, using... Figure 5 Taking known well logging data as an example, the elastic moduli of sandy minerals and argillaceous minerals calculated by applying the adaptive step-size stochastic inversion algorithm with varying sampling space are K, respectively. sand =38.89 GPa, K shale =13.55GPa, μ sand =28.17 GPa and μ shale =3.54GPa, with an equivalent porosity aspect ratio of α =0.08. Substituting the elastic moduli of the sandy and muddy minerals obtained from the inversion into the target rock physics model above, such as into the target rock physics model shown in formulas (1) to (31), the modified rock physics model can be obtained.

[0152] Figure 6 This is a comparison chart of P-wave velocity, S-wave velocity, and density calculated based on a modified rock physics model, provided by an embodiment of the present invention, with actual P-wave velocity, S-wave velocity, and density obtained from well logging. Figure 6 As shown, the red curve represents the P-wave velocity, S-wave velocity, and density calculated by the modified rock physics model, while the blue curve represents the actual logging parameters. This demonstrates that the P-wave velocity, S-wave velocity, and density calculated by the modified rock physics model have a high degree of fit with the actual logging P-wave velocity, S-wave velocity, and density.

[0153] S330. Correct the target rock physics model according to the model correction reference parameters to obtain the corrected rock physics model.

[0154] S340. Based on the modified rock physics model, invert and predict the reservoir physical parameters of the target area data volume of the strata.

[0155] The above technical solution uses known well logging elastic and physical property parameters, combined with an adaptive step-size stochastic inversion algorithm with varying sampling space, to estimate the elastic modulus of the matrix minerals in the target rock physics model, thereby correcting the rock physics model. This can make rock physics modeling more accurate and reduce rock physics modeling errors.

[0156] Figure 7 This is a flowchart of a method for quantitative prediction of reservoir physical parameters provided by an embodiment of the present invention. This embodiment is based on the above embodiment and is further specified. In this embodiment, various specific optional implementation methods are given for predicting reservoir physical parameters of a target area data volume of a formation based on the modified rock physics model. Correspondingly, as... Figure 7 As shown, the method in this embodiment may include:

[0157] S410. Construct a target rock physics model based on the known logging correlation parameters.

[0158] S420. The target inversion algorithm is used to invert the target inversion model correction reference parameters of the target rock physics model to obtain the model correction reference parameters.

[0159] S430. Correct the target rock physics model according to the model correction reference parameters to obtain the corrected rock physics model.

[0160] S440. Based on the modified rock physics model, establish the mapping relationship between the known corrected elastic parameters of well logging and the reservoir physical property parameters.

[0161] The modified rock physics model can characterize the mapping relationship between elastic parameters and reservoir physical property parameters. Therefore, the mapping relationship between known well logging modified elastic parameters and reservoir physical property parameters can be established based on the modified rock physics model.

[0162] S450. Based on the known mapping relationship between the corrected elastic parameters of well logging and the reservoir physical property parameters, and combined with the elastic parameters of the formation target area data volume, establish a second objective function.

[0163] The elastic parameters of the stratigraphic target area data volume are obtained through pre-stack seismic inversion. These elastic parameters may include the P-wave velocity, S-wave velocity, and density of the stratigraphic target area data volume. The second objective function can be an objective function used to invert and calculate the reservoir properties of the stratigraphic target area data volume.

[0164] Correspondingly, a second objective function can be established by combining the known porosity, clay content, and water saturation from well logging with a modified rock physics model, based on the mapping relationship between the modified elastic parameters and reservoir physical property parameters from the known well logging, and combining the elastic parameters of the formation target area data volume.

[0165] In a specific example, taking the sandstone rock physics model considering pore shape in the above example as the target rock physics model, the target rock physics model can be modeled using the above equations (1) to (31). Accordingly, based on the target rock physics model of equations (1) to (31), the following rock physics model can be obtained:

[0166] [V P,rpm V S,rpm ,ρ rpm ] = f rpm (φ,V shale ,S w (33)

[0167] In the above formula, the mapping relationship f rpm That is, the elastic modulus K of sandy minerals obtained by inversion using known well logging data. sand and μ sand Elastic modulus K of clay minerals shale and μ shale And the rock physics model corrected for the equivalent porosity α, [V P,rpm V S,rpm ,ρ rpm [The values ​​represent the elastic parameters P-wave velocity, S-wave velocity, and density calculated using a modified rock physics model.]

[0168] Furthermore, based on the above equation (33), a second objective function is established for the quantitative prediction of reservoir physical parameters:

[0169] F = [V] P,inv -V P,rpm ] 2 +[V S,inv -V S,rpm ]2 +[ρ inv -ρ rpm ] 2 (34)

[0170] Figure 8 This is a schematic diagram illustrating the effect of pre-stack inversion profile of elastic parameters for a stratigraphic target area data volume provided in an embodiment of the present invention. Figure 8 (a) is the pre-stack inversion profile of P-wave velocity. Figure 8 (b) is the pre-stack inversion profile of shear wave velocity. Figure 8 (c) is the density pre-stack inversion profile.

[0171] Among them, V P,inv It is the P-wave velocity of the stratigraphic target area data volume obtained from pre-stack seismic inversion, such as Figure 8 As shown in (a), V S,inv It is the shear wave velocity of the stratigraphic target area data volume obtained from pre-stack seismic inversion, such as Figure 8 As shown in (b), ρ inv It is the density of the stratigraphic target area data volume obtained from pre-stack seismic inversion, such as Figure 8 As shown in (c).

[0172] S460. The reservoir physical parameters of the target area data volume are inverted and predicted using the target inversion algorithm through the second objective function.

[0173] In this embodiment of the invention, after inverting the pre-stack seismic data of the target stratigraphic region to obtain the P-wave velocity, S-wave velocity, and density, a modified rock physics model is used to determine the mapping relationship between the known well-logged modified elastic parameters and reservoir physical parameters. Then, based on the mapping relationship between the known well-logged modified elastic parameters and reservoir physical parameters included in the modified rock physics model, a second objective function is established. Finally, the target inversion algorithm is used to invert and predict the reservoir physical parameters of the target stratigraphic region data volume.

[0174] In an optional embodiment of the present invention, the step of inverting and predicting reservoir physical parameters of a target area data volume using the target inversion algorithm through the second objective function may include: determining the current probability density distribution of the reservoir physical parameters of the target area data volume, and randomly sampling a first predetermined number of second initial sampling points of the reservoir physical parameters according to the current probability density distribution of the reservoir physical parameters; updating the current probability density distribution of the reservoir physical parameters using the second objective function as a constraint condition, and collecting a second predetermined number of second target sampling points from the first predetermined number of second initial sampling points according to the updated current probability density distribution of the reservoir physical parameters, and statistically analyzing the second predetermined number of... The distribution pattern of the second target sampling points is analyzed to obtain the updated mean and updated variance. The current mean of the current probability density distribution is replaced by the updated mean, and the current variance of the current probability density distribution is replaced by the updated variance. The current probability density distribution is then updated based on the updated current mean, the updated current variance, and the amplification factor. The process returns to the previous step, where a first predetermined number of second initial sampling points are randomly sampled according to the current probability density distribution of the reservoir physical parameters until the current iteration number reaches the second maximum iteration number. The mean of the reservoir physical parameters of the formation target area data volume generated in the last iteration is used as the reservoir physical parameters of the formation target area data volume.

[0175] The second initial sampling point can be a sampling point obtained by preliminary sampling of the reservoir physical property parameters of the formation target area data volume. The second target sampling point can be a sampling point obtained by secondary sampling of the reservoir physical property parameters of the formation target area data volume. The second maximum number of iterations can be the maximum number of iterations set when iteratively inverting the reservoir physical property parameters of the formation target area data volume using an adaptive step-size random inversion algorithm.

[0176] In this embodiment of the invention, for each reservoir property parameter of the target stratigraphic region data volume that needs to be inverted, the corresponding model correction reference parameters can also be obtained by sequentially applying the adaptive step-size stochastic inversion algorithm. When inverting each reservoir property parameter of the target stratigraphic region data volume, the current mean and current variance of the current probability density distribution of the reservoir property parameter can be determined first. When starting the inversion using the adaptive step-size stochastic inversion algorithm, the prior probability density distribution of the reservoir property parameter can be given as the current probability density distribution, and the mean and variance of the prior probability density distribution can be given as the current mean and current variance of the current probability density distribution, respectively.

[0177] For example, for the porosity physical property parameters of the data volume of the target area of ​​the formation, the prior probability density distribution of the porosity of the data volume of the target area of ​​the formation can be determined as the current probability density distribution, and the mean and variance of the prior probability density distribution of the porosity of the data volume of the target area of ​​the formation can be given as the current mean and current variance of its current probability density distribution.

[0178] For example, for the physical property parameters of the clay content of the data volume of the target area of ​​the stratum, the prior probability density distribution of the clay content of the data volume of the target area of ​​the stratum can be determined as the current probability density distribution, and the mean and variance of the prior probability density distribution of the clay content of the data volume of the target area of ​​the stratum can be given as the current mean and current variance of its current probability density distribution.

[0179] For example, for the physical property parameters of water saturation of the target area data volume, the prior probability density distribution of water saturation of the target area data volume can be determined as the current probability density distribution, and the mean and variance of the prior probability density distribution of water saturation of the target area data volume can be given as the current mean and current variance of its current probability density distribution.

[0180] Optionally, the physical properties of the target data volume in each stratum can be inverted and calculated sequentially using an adaptive step-size random inversion algorithm, or they can be inverted and calculated simultaneously using an adaptive step-size random inversion algorithm. This embodiment of the invention does not impose any restrictions on this.

[0181] Optionally, the mean and variance of the prior probability density distribution of each physical property parameter of the target stratigraphic region data volume can be configured using empirical values ​​or based on the actual collected partial data. This embodiment of the invention does not impose any restrictions on this.

[0182] In this embodiment of the invention, the process of iteratively retrieving the reference parameters of the target inversion model using the adaptive step-size stochastic inversion algorithm and correcting the reference parameters is exactly the same as the process of iteratively retrieving the physical property parameters of the stratigraphic target area data volume using the adaptive step-size stochastic inversion algorithm, the only difference being the type of parameters being inverted.

[0183] Figure 9 This is a flowchart illustrating the iterative process of another adaptive step-size stochastic inversion algorithm provided in an embodiment of the present invention. In a specific example, such as... Figure 9 As shown, and also refer to Figure 4In the second iteration of the adaptive step-size stochastic inversion algorithm to invert the physical property parameters of the formation target area data volume, in the current iteration, a first predetermined number of initial sampling points can be randomly sampled according to the current probability density distribution of the reservoir physical property parameters of the current formation target area data volume. After completing the first sampling step, the current probability density distribution of the reservoir physical property parameters of the formation target area data volume can be updated based on the second objective function as a constraint. After the current probability density distribution of the reservoir physical property parameters of the formation target area data volume is updated, sampling can continue from the first predetermined number of second initial sampling points obtained in the first sampling according to the updated current probability density distribution of the reservoir physical property parameters of the formation target area data volume, so as to collect a second predetermined number of second target sampling points. Since the current probability density distribution of the reservoir physical property parameters of the formation target area data volume is updated by the constraint of the second objective function, the second target sampling points collected based on the constraint of the second objective function can be the sampling point data closest to the actual situation, for example, it can be the second predetermined number of second target sampling points with the minimum second objective function. Correspondingly, after acquiring the second target sampling points, the distribution pattern of the second set number of second target sampling points can be statistically analyzed, thereby obtaining the updated mean and updated variance of the reservoir physical property parameters of the formation target area data volume based on the data acquired in the second round.

[0184] Specifically, when updating the current probability density distribution of reservoir physical parameters of the formation target area data volume using the second objective function as a constraint, and then collecting a second set number of second target sampling points from the first set number of second initial sampling points according to the updated probability density distribution of reservoir physical parameters of the formation target area data volume, the second initial sampling points can be rearranged sequentially according to the magnitude of the second objective function. The order of the second initial sampling points according to the magnitude of the second objective function will differ depending on how closely the trend of the second objective function approximates the actual situation. Optionally, if a smaller value of the second objective function is closer to the actual situation, the second initial sampling points can be rearranged sequentially in ascending order of the second objective function; if a larger value of the second objective function is closer to the actual situation, the second initial sampling points can be rearranged sequentially in descending order of the second objective function. After the second initial sampling points are arranged, a second set number of second target sampling points can be collected again from the rearranged first set number of second initial sampling points during the current iteration.

[0185] Accordingly, after collecting a second set number of second target sampling points and re-statistically updating the mean and variance based on these second set number of second target sampling points, the current mean can be replaced with the updated mean, and the current variance can be replaced with the updated variance. This allows for a second update of the current probability density distribution based on the updated current mean, updated current variance, and amplification factor. The purpose of updating the current probability density distribution again using the amplification factor is to avoid the sample space of the selected second set number of second target sampling points being too small, and to amplify the variance σ. 2 This approach allows the resampling process in each iteration to include more cases.

[0186] Once the reservoir property parameters of the target area data volume are iteratively inverted using the adaptive step-size stochastic inversion algorithm, the mean value of the reservoir property parameters of the target area data volume generated in the last iteration can be used as the final value of the reservoir property parameters of the target area data volume calculated in the current iteration, thereby realizing the prediction of the reservoir property parameters of the target area data volume.

[0187] Figure 10 This is a schematic diagram illustrating the comparison between reservoir physical parameters obtained from formation target area data volume inverted based on a variable sampling space random inversion algorithm, and reservoir physical parameters obtained from actual well logging within the formation target area data volume, according to an embodiment of the present invention. Figure 10 As shown, the red curve represents the reservoir physical property parameters predicted and inverted by the modified rock physics model, while the blue curve represents the corresponding actual logging parameters. Figure 11 This is a schematic diagram illustrating the predicted profile effect of reservoir physical property parameters of a stratigraphic target area data volume provided in an embodiment of the present invention. Figure 11 Figure (a) shows the predicted porosity of the target stratigraphic region data volume. Figure 11 Figure (b) shows the predicted clay content of the target stratigraphic region data volume. Figure 11 Figure (c) shows the predicted water saturation of the target stratigraphic region data volume.

[0188] The accuracy of the rock physics model is crucial for reservoir property prediction driven by the rock physics model. To improve the accuracy of the rock physics model, this invention, based on a sandstone rock physics model that considers pore shape, combines actual well logging data with an adaptive step-size stochastic inversion algorithm that utilizes spatially varying sampling to invert the elastic modulus and pore shape parameters (i.e., equivalent pore aspect ratio) of sandstone and mudstone matrix minerals. The resulting elastic modulus and pore aspect ratio of the sandstone and mudstone matrix minerals more closely approximate the true reservoir characteristics of the target stratigraphic unit in the studied area. The inverted elastic modulus and equivalent pore aspect ratio of the sandstone and mudstone matrix minerals are then input into the constructed rock physics model to obtain a more accurate corrected rock physics model. To further analyze the spatial distribution characteristics of reservoir properties, pre-stack inversion is performed on seismic data at different incident angles to obtain elastic parameters such as P-wave velocity, S-wave velocity, and density. Given the elastic parameters, a modified rock physics model is used to establish the objective function for inversion. An adaptive step-size stochastic inversion algorithm with varying sampling space is then used to predict reservoir physical parameters. The predicted reservoir physical parameters are more consistent with rock physics theory and are more accurate.

[0189] Specific application scenarios

[0190] This invention combines rock physics theory with an adaptive step-size stochastic inversion algorithm based on varying sampling space, proposing a method for predicting reservoir physical parameters based on this algorithm. First, a sandstone rock physics model considering pore shape is established, suitable for well logging data. Then, an adaptive step-size stochastic inversion algorithm based on varying sampling space is established. This algorithm is used to calculate the elastic modulus and equivalent porosity of sandy and argillaceous minerals, thus correcting the rock physics model. Finally, using the elastic parameters obtained from pre-stack seismic inversion, reservoir physical parameters are predicted using the adaptive step-size stochastic inversion algorithm based on varying sampling space. The inversion process considers the correction of the rock physics model to construct the inversion objective function. Specific implementation details are as follows:

[0191] Step 1: Read the input data, including the known well logging density, clay content, porosity, water saturation, and P-wave velocity;

[0192] Step 2: Establish a sandstone rock physical model considering pore shape based on the above equations (1)-(31);

[0193] Step 3: Provide the initial mean μ and variance σ of the elastic modulus of sandy and argillaceous minerals. 2 Calculate the prior probability density distribution function X ~ N(μ,σ) 2 );

[0194] Step 4: Establish an adaptive step-size random inversion algorithm for sampling space variation and set the number of sampling points N=20, the maximum number of iterations maxnum=30, the shrinkage coefficient aqa=0.3, the magnification coefficient apa=1.2 and the first objective function shown in equation (32);

[0195] Step 5: Update the mean μ and variance σ of the elastic modulus and equivalent porosity of sandy and argillaceous minerals according to the first objective function. 2 And calculate the probability density distribution function X ~ N(μ, apa·σ). 2 );

[0196] Step 6: Determine if the maximum number of iterations (maxnum) has been reached. If not, repeat step 5. If the maximum number of iterations has been reached, output the final elastic modulus and equivalent porosity ratio (K) of the sandy and argillaceous minerals. sand K shale μ sand μ shale And α, thus obtaining the modified rock physics model.

[0197] Step 7: Construct the second objective function shown in equation (34) for reservoir property parameter inversion using the modified rock physics model;

[0198] Step 8: Provide the initial mean μ and variance σ of the reservoir physical properties (porosity, clay content, and water saturation) of the target formation data volume. 2 Calculate the prior probability density distribution function X ~ N(μ,σ) 2 );

[0199] Step 9: Establish an adaptive step-size random inversion algorithm for sampling space variation and set the number of sampling points N = 400, the maximum number of iterations maxnum = 5, the shrinkage coefficient aqa = 0.05, the magnification coefficient apa = 1.2, and the second objective function;

[0200] Step 10: Update the mean μ and variance σ of the reservoir physical properties according to the second objective function. 2 And calculate the probability density distribution function X ~ N(μ, apa·σ). 2 );

[0201] Step 11: Determine if the maximum number of iterations maxnum has been reached. If not, repeat step 10. If the maximum number of iterations has been reached, output the porosity, clay content, and water saturation of the formation target area data volume as the predicted reservoir physical property parameters of the formation target area data volume.

[0202] Optionally, well logging data from within the work area can be used to verify the solution provided in this embodiment of the invention. Known well logging data include... Figure 5 As shown. The known logging data includes reservoir physical properties (i.e., porosity, clay content, and water saturation) and elastic parameters (i.e., P-wave velocity, S-wave velocity, and density). Using... Figure 4 The adaptive step-size stochastic inversion algorithm shown is similar to Figure 5 By combining actual well logging data, the elastic modulus of sandy minerals, the elastic modulus of argillaceous minerals, and the equivalent porosity aspect ratio were inverted to revise the rock physics model summarized in equation (33). The inverted bulk modulus of sandy minerals was 38.89 GPa, and the shear modulus of sandy minerals was 28.17 GPa; the bulk modulus of argillaceous minerals was 13.55 GPa, and the shear modulus of argillaceous minerals was 3.54 GPa; the equivalent porosity aspect ratio was 0.08. Substituting the inverted elastic modulus of sandy minerals, the elastic modulus of argillaceous minerals, and the equivalent porosity aspect ratio into the rock physics model summarized in equation (33) yields the revised rock physics model. Figure 6 For use Figure 5 The porosity, clay content, and water saturation of the actual logging data shown are input with Equation (32) as the objective function, using... Figure 4 The adaptive step-size stochastic inversion algorithm with varying sampling space corrects the P-wave velocity, S-wave velocity, and density output by the rock physics model. Figure 5 A comparison chart of P-wave velocity, S-wave velocity, and density from actual well logging data, from Figure 6 As can be seen from the data, the elastic parameters calculated by the corrected rock physics model are basically consistent with the actual well logging data, which shows the feasibility of the rock physics model technology for correcting the elastic modulus of matrix minerals and pore shape parameters in the embodiments of the present invention. Figure 10 Assumption Figure 5 The elastic parameters longitudinal wave velocity, transverse wave velocity, and density are known quantities. The physical properties porosity, clay content, and water saturation are assumed to be unknown quantities. Using equation (33) as the objective function, the following equation is used... Figure 4 The adaptive step-size stochastic inversion algorithm with varying sampling space is used for reservoir property parameter prediction driven by rock physics models. Figure 5 A comparison chart of porosity, water saturation, and clay content from actual well logging data, from... Figure 10 As can be seen from the data, the inverted physical property parameters are basically consistent with the actual well logging data, which shows the feasibility of the reservoir physical property parameter prediction technology driven by the rock physics model in this embodiment of the invention. Figure 8 The elastic parameters, including P-wave velocity, S-wave velocity, and density, are obtained through pre-stack inversion of seismic data. Reservoir physical parameters are further predicted from these elastic parameters, using Equation (33) as the objective function. The prediction results of the physical parameter profile are as follows: Figure 11 As shown. From Figure 11 As can be seen, the predicted reservoir physical parameters are in good agreement with the actual logging data, which verifies the effectiveness of the technical solution in the embodiments of the present invention.

[0203] In this invention, before predicting reservoir physical parameters of a target formation data volume, the actual well logging data related to the target formation data volume is combined with an adaptive step-size inversion algorithm. The rock physics model is corrected by inverting the elastic modulus of sandy minerals, the elastic modulus of argillaceous minerals, and the equivalent porosity aspect ratio. Then, the corrected rock physics model is used to construct an inversion objective function, and an adaptive step-size stochastic inversion algorithm with varying sampling space is used to predict reservoir physical parameters. The reservoir physical parameters predicted using this method, based on rock physics theory, are further corrected by combining actual well logging data, thus improving the accuracy of reservoir physical parameters driven by the rock physics model.

[0204] It should be noted that all information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data used for analysis, etc.) involved in this disclosure are information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data comply with the relevant laws, regulations and standards of the relevant regions.

[0205] It should be noted that any arrangement or combination of the technical features in the above embodiments also falls within the protection scope of this invention.

[0206] Figure 12 This is a schematic diagram of a reservoir physical parameter quantitative prediction device provided in an embodiment of the present invention, as shown below. Figure 12 As shown, the device includes: a rock physics model construction module 510, a model correction reference parameter acquisition module 520, a rock physics model correction module 530, and a reservoir physical property parameter prediction module 540, wherein:

[0207] The rock physics model building module 510 is used to build a target rock physics model based on known well logging correlation parameters;

[0208] The model correction reference parameter acquisition module 520 is used to invert the target inversion model correction reference parameters of the target rock physics model using a target inversion algorithm to obtain the model correction reference parameters.

[0209] Rock physics model correction module 530 is used to correct the target rock physics model according to the model correction reference parameters to obtain a corrected rock physics model;

[0210] The reservoir property parameter prediction module 540 is used to predict the reservoir property parameters of the target area data volume of the formation by inverting the target inversion algorithm according to the modified rock physics model.

[0211] This invention constructs a target rock physics model based on known well logging correlation parameters, and uses a target inversion algorithm to invert the target inversion model correction reference parameters of the target rock physics model to obtain model correction reference parameters. The target rock physics model is then corrected based on these model correction reference parameters to obtain a corrected rock physics model. Finally, reservoir physical parameters of the target formation data volume are predicted based on the corrected rock physics model. Because the target rock physics model is corrected using model correction reference parameters, which are obtained by inverting reference parameter data from the actual model, the accuracy of the corrected rock physics model is improved. This solves the problem of prediction errors that exist when constructing sandstone rock physics models based on empirical data and predicting reservoir physical parameters, thus improving the accuracy and scientific validity of reservoir physical parameter prediction.

[0212] Optionally, the logging correlation parameters include P-wave velocity, S-wave velocity, density, porosity, water saturation, and clay content; the target rock physics model includes a sandstone rock physics model considering pore shape; the rock physics model construction module 510 is further used to: calculate the bulk modulus, shear modulus, and density of the equivalent rock matrix of the known logging; calculate the equivalent bulk modulus and density of the pore fluid of the known logging; calculate the bulk modulus of the fluid-saturated rock in the target rock physics model based on the bulk modulus of the equivalent rock matrix, the porosity, the equivalent bulk modulus of the pore fluid, and a first geometric coefficient considering the aspect ratio variation of the pores; and calculate the bulk modulus of the fluid-saturated rock in the target rock physics model based on the equivalent rock matrix of the known logging. The shear modulus of the fluid-saturated rock in the target rock physical model is calculated using the shear modulus of the matrix, the porosity, and a second geometric coefficient considering the aspect ratio variation of the pores. The density of the fluid-saturated rock in the target rock physical model is calculated based on the density of the equivalent rock matrix, the porosity, and the density of the pore fluid. The longitudinal wave velocity of the fluid-saturated rock in the target rock physical model is calculated based on its bulk modulus, shear modulus, and density. The transverse wave velocity of the fluid-saturated rock in the target rock physical model is calculated based on its shear modulus and density.

[0213] Optionally, the target inversion algorithm includes an adaptive step-size stochastic inversion algorithm; the model correction reference parameters include the elastic modulus of sandy minerals, the elastic modulus of argillaceous minerals, and pore shape parameters; the model correction reference parameter acquisition module 520 is further configured to: determine the current mean and current variance of the current probability density distribution of the target inversion model correction reference parameters; randomly sample a first set number of first initial sampling points of the target inversion model correction reference parameters according to the current probability density distribution; set a first objective function, use the first objective function as a constraint to update the current probability density distribution of the target inversion model correction reference parameters, and randomly sample a first set number of first initial sampling points of the target inversion model correction reference parameters according to the updated current probability density distribution of the target inversion model correction reference parameters. A second predetermined number of first target sampling points are collected from the initial sampling points. The distribution pattern of the second predetermined number of first target sampling points is statistically analyzed to obtain the updated mean and updated variance. The current mean is replaced by the updated mean, and the current variance is replaced by the updated variance. The current probability density distribution is updated based on the updated current mean, the updated current variance, and the amplification factor. The process returns to the operation of randomly sampling a first predetermined number of first initial sampling points according to the current probability density distribution to correct the reference parameters of the target inversion model, until the current iteration number reaches the first maximum iteration number. The mean of the target inversion model correction reference parameters generated in the last iteration process is used as the model correction reference parameter corresponding to the target inversion model correction reference parameter.

[0214] Optionally, the model correction reference parameter acquisition module 520 is further configured to: rearrange the first initial sampling points sequentially according to the size order of the first objective function to update the current probability density distribution of the target inversion model correction reference parameters; and during the current iteration, collect the second set number of first target sampling points from the rearranged first set number of first initial sampling points.

[0215] Optionally, the reservoir property parameter prediction module 540 is further configured to: establish a mapping relationship between the known well-logged corrected elastic parameters and reservoir property parameters based on the modified rock physics model; establish a second objective function based on the mapping relationship between the known well-logged corrected elastic parameters and reservoir property parameters, combined with the elastic parameters of the formation target area data volume; wherein the elastic parameters of the formation target area data volume are obtained through pre-stack seismic inversion; and the reservoir property parameters of the formation target area data volume are predicted by inversion using the second objective function and the objective inversion algorithm.

[0216] Optionally, the reservoir physical property parameter prediction module 540 is further configured to: determine the current probability density distribution of the reservoir physical property parameters of the formation target area data volume, and randomly sample a first predetermined number of second initial sampling points of the reservoir physical property parameters according to the current probability density distribution of the reservoir physical property parameters; update the current probability density distribution of the reservoir physical property parameters using the second objective function as a constraint condition, and collect a second predetermined number of second target sampling points from the first predetermined number of second initial sampling points according to the updated current probability density distribution of the reservoir physical property parameters, statistically analyze the distribution pattern of the second predetermined number of second target sampling points, and obtain the updated mean and sum. Update the variance; replace the current mean of the current probability density distribution with the updated mean, replace the current variance of the current probability density distribution with the updated variance, and update the current probability density distribution with the updated current mean, the updated current variance, and the amplification factor; return to perform the operation of randomly sampling a first set number of second initial sampling points on the reservoir physical parameters according to the current probability density distribution of the reservoir physical parameters, until it is determined that the current iteration number has reached the second maximum iteration number; take the mean of the reservoir physical parameters of the formation target area data volume generated in the last iteration process as the reservoir physical parameters of the formation target area data volume.

[0217] The above-described reservoir physical parameter quantitative prediction device can execute the reservoir physical parameter quantitative prediction method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the method. Technical details not described in detail in this embodiment can be found in the reservoir physical parameter quantitative prediction method provided in any embodiment of the present invention.

[0218] Since the reservoir physical parameter quantitative prediction device described above is an apparatus capable of executing the reservoir physical parameter quantitative prediction method in the embodiments of the present invention, those skilled in the art can understand the specific implementation and various variations of the reservoir physical parameter quantitative prediction device in this embodiment based on the reservoir physical parameter quantitative prediction method described in the embodiments of the present invention. Therefore, how the reservoir physical parameter quantitative prediction device implements the reservoir physical parameter quantitative prediction method in the embodiments of the present invention will not be described in detail here. Any apparatus used by those skilled in the art to implement the reservoir physical parameter quantitative prediction method in the embodiments of the present invention falls within the scope of protection of this application.

[0219] Figure 13A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0220] like Figure 13 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0221] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0222] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as methods for quantitative prediction of reservoir physical parameters.

[0223] A quantitative prediction method for reservoir physical parameters may include: constructing a target petrophysical model based on well logging correlation parameters of known wells; inverting the target inversion model correction reference parameters of the target petrophysical model using a target inversion algorithm to obtain model correction reference parameters; correcting the target petrophysical model based on the model correction reference parameters to obtain a corrected petrophysical model; and using the target inversion algorithm based on the corrected petrophysical model to invert and predict the reservoir physical parameters of the formation target area data volume.

[0224] In some embodiments, the method for quantitatively predicting reservoir physical parameters may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the method for quantitatively predicting reservoir physical parameters described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the method for quantitatively predicting reservoir physical parameters by any other suitable means (e.g., by means of firmware).

[0225] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0226] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0227] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0228] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0229] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0230] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0231] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0232] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A method for quantitative prediction of reservoir physical parameters, characterized in that, include: Construct a target rock physics model based on known well logging correlation parameters; The target inversion algorithm is used to invert the target inversion model correction reference parameters of the target rock physics model to obtain the model correction reference parameters. The target rock physics model is modified according to the model correction reference parameters to obtain the modified rock physics model; Based on the modified rock physics model, the target inversion algorithm is used to invert and predict the reservoir physical parameters of the target area data volume of the formation.

2. The method according to claim 1, characterized in that, The well logging correlation parameters include P-wave velocity, S-wave velocity, density, porosity, water saturation, and clay content; the target rock physics model includes a sandstone rock physics model considering pore shape; the construction of the target rock physics model based on the known well logging correlation parameters includes: Calculate the bulk modulus, shear modulus, and density of the equivalent rock matrix for the known well logging; Calculate the equivalent bulk modulus and density of the pore fluid from the known well logging data; The bulk modulus of the fluid-saturated rock in the target rock physical model is calculated based on the bulk modulus of the equivalent rock matrix, the porosity, the equivalent bulk modulus of the pore fluid, and the first geometric coefficient considering the change in pore aspect ratio. The shear modulus of the fluid-saturated rock in the target rock physical model is calculated based on the shear modulus of the equivalent rock matrix from the known well logging, the porosity, and the second geometric coefficient considering the change in pore aspect ratio. The density of the fluid-saturated rock in the target rock physical model is calculated based on the density of the equivalent rock matrix, the porosity, and the density of the pore fluid. Based on the bulk modulus, shear modulus, and density of the fluid-saturated rock in the target rock physical model, calculate the longitudinal wave velocity of the fluid-saturated rock in the target rock physical model. Based on the shear modulus and density of the fluid-saturated rock in the target rock physical model, calculate the transverse wave velocity of the fluid-saturated rock in the target rock physical model.

3. The method according to claim 1, characterized in that, The target inversion algorithm includes an adaptive step-size stochastic inversion algorithm; the model correction reference parameters include the elastic modulus of sandy minerals, the elastic modulus of argillaceous minerals, and pore shape parameters. The process of using a target inversion algorithm to invert the target inversion model correction reference parameters of the target rock physics model to obtain the model correction reference parameters includes: Determine the current mean and current variance of the current probability density distribution of the corrected reference parameters for the target inversion model; The reference parameters of the target inversion model are corrected according to the current probability density distribution by randomly sampling a first set number of first initial sampling points; A first objective function is set, and the first objective function is used as a constraint to update the current probability density distribution of the target inversion model correction reference parameters. Then, according to the updated current probability density distribution of the target inversion model correction reference parameters, a second set number of first target sampling points are collected from the first set number of first initial sampling points. The distribution pattern of the second set number of first target sampling points is statistically analyzed to obtain the updated mean and updated variance. The current mean is replaced with the updated mean, the current variance is replaced with the updated variance, and the current probability density distribution is updated based on the updated current mean, the updated current variance, and the amplification factor. Return to the operation of randomly sampling the reference parameters of the target inversion model according to the current probability density distribution to a first set number of first initial sampling points, until it is determined that the current iteration number has reached the first maximum iteration number; The mean value of the target inversion model correction reference parameters generated in the last iteration is used as the model correction reference parameters corresponding to the target inversion model correction reference parameters.

4. The method according to claim 3, characterized in that, The step of updating the current probability density distribution of the target inversion model correction reference parameters using the first objective function as a constraint, and collecting a second set number of first target sampling points from the first set number of first initial sampling points according to the updated current probability density distribution of the target inversion model correction reference parameters, includes: The first initial sampling points are rearranged sequentially according to the magnitude of the first objective function to update the current probability density distribution of the corrected reference parameters of the objective inversion model; In the current iteration, the second set number of first target sampling points are collected from the first set number of first initial sampling points that have been reordered.

5. The method according to claim 1, characterized in that, The process of retrieving and predicting reservoir physical parameters of the target area data volume of the formation based on the modified rock physics model and the target inversion algorithm includes: Based on the modified rock physics model, establish the mapping relationship between the known corrected elastic parameters of well logging and the reservoir physical property parameters; Based on the known mapping relationship between the corrected elastic parameters from well logging and the reservoir physical property parameters, a second objective function is established in conjunction with the elastic parameters of the target formation area data volume; wherein, the elastic parameters of the target formation area data volume are obtained through pre-stack seismic inversion method; The reservoir physical parameters of the target area data volume are inverted and predicted using the target inversion algorithm through the second objective function.

6. The method according to claim 5, characterized in that, The process of retrieving and predicting reservoir physical parameters of the target area data volume using the target inversion algorithm through the second objective function includes: Determine the current probability density distribution of reservoir physical property parameters of the target area data volume, and randomly sample a first set number of second initial sampling points of the reservoir physical property parameters according to the current probability density distribution of the reservoir physical property parameters; The second objective function is used as a constraint to update the current probability density distribution of the reservoir physical parameters. Then, according to the updated current probability density distribution of the reservoir physical parameters, a second set number of second target sampling points are collected from the first set number of second initial sampling points. The distribution pattern of the second set number of second target sampling points is statistically analyzed to obtain the updated mean and updated variance. The current mean of the current probability density distribution is replaced with the updated mean, the current variance of the current probability density distribution is replaced with the updated variance, and the current probability density distribution is updated with the updated current mean, the updated current variance, and the amplification factor. Return to the operation of randomly sampling a first set number of second initial sampling points on the reservoir physical parameters according to the current probability density distribution of the reservoir physical parameters, until it is determined that the current iteration number has reached the second maximum iteration number; The mean value of the reservoir physical property parameters of the target area data volume generated in the last iteration is used as the reservoir physical property parameters of the target area data volume.

7. A device for quantitative prediction of reservoir physical parameters, characterized in that, include: The rock physics model building module is used to build a target rock physics model based on known well logging correlation parameters; The model correction reference parameter acquisition module is used to invert the target inversion model correction reference parameters of the target rock physics model using a target inversion algorithm to obtain the model correction reference parameters. A rock physics model correction module is used to correct the target rock physics model according to the model correction reference parameters to obtain a corrected rock physics model. The reservoir physical property parameter prediction module is used to predict the reservoir physical property parameters of the target area data volume of the formation based on the modified rock physics model and the target inversion algorithm.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that is executed by the at least one processor to enable the at least one processor to perform the quantitative prediction method for reservoir physical parameters according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the quantitative prediction method for reservoir physical parameters according to any one of claims 1-6.

10. A computer program product comprising a computer program / instructions, wherein, When the computer program / instruction is executed by the processor, it implements the method for quantitative prediction of reservoir physical parameters as described in any one of claims 1-6.

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