Heterogeneity sandstone reservoir physical property parameter prediction method and device

Abstract

The invention discloses a heterogeneous sandstone reservoir physical property parameter prediction method and device. The method comprises the steps that on the basis of seismic record data of a to-be-inverted heterogeneous sandstone reservoir, physical property parameters of the reservoir are obtained through a model used for representing the relation between seismic records and the physical property parameters of the reservoir, and the model is established through the following steps that a VRH average and fluid uniform mixing formula, a Pride model and a Gassmann equation are used for representing the relation between the seismic records and the physical property parameters of the reservoir; establishing a rock physical model of the heterogeneous sandstone reservoir; performing linearization processing on the rock physical model by using Taylor expansion to obtain a rock physical model linearization formula, and calculating a corresponding Jacobian matrix; and substituting the obtained Jacobian matrix into a rock physical model linearization formula, and establishing the model in combination with a seismic reflection coefficient equation. According to the method, direct inversion of the heterogeneous sandstone reservoir physical property parameters can be carried out, the inversion precision is high, and the reliability is high.

Description

Technical Field

[0001] The invention relates to the technical field of oil and gas exploration, and in particular to a method and device for predicting physical property parameters of heterogeneous sandstone reservoirs. Background Art

[0002] Reservoir physical parameters mainly refer to the three parameters of porosity, shale content and water saturation, which quantitatively characterize the reservoir performance of the reservoir and are an important basis for reservoir evaluation and fluid identification. Seismic data contains rich information on seismic elastic parameters and physical parameters. Conventional reservoir physical parameter prediction is mainly divided into two steps: the first step is to obtain seismic elastic parameters from seismic data using conventional seismic inversion methods, and the second step is to predict reservoir physical parameters from elastic parameters using rock physics inversion. However, the traditional physical parameter inversion method will have uncertainty transmission and error accumulation, so it is necessary to carry out direct inversion of reservoir physical parameters.

[0003] To perform direct inversion of physical parameters, it is necessary to establish a mathematical relationship between physical parameters and seismic records, which mainly includes two parts: the first is the relationship between seismic records and elastic parameters, which can be established through the Zeoppritz equation and the seismic convolution model; the second is the relationship between elastic parameters and physical parameters. In actual production, this relationship is relatively complex and is usually obtained using empirical relationships or rock physics models. However, empirical relationships are usually only applicable to a specific area and lack physical support, while seismic rock physics modeling is too complex to extract the corresponding mathematical relationship. To this end, Lang et al. (2018) proposed a rock physics model linearization method. Based on the Gassmann equation and the critical porosity model, the rock physics model was Taylor expanded to obtain the first-order approximate expression of the rock physics inversion problem. Summary of the invention

[0004] The inventors found that for highly heterogeneous tight sandstone reservoirs, under the combined effects of consolidation diagenesis, pressure and temperature environment, there are certain differences in the composition, particle size, cementation method and pore structure of rock minerals between different strata, while the critical porosity model is relatively simple and difficult to effectively characterize the rock physical characteristics of heterogeneous sandstone.

[0005] In order to at least partially solve the technical problems existing in the prior art, the inventors have made the present invention, and through specific implementation methods, provide a method and device for predicting the physical property parameters of heterogeneous sandstone reservoirs, which can directly invert the physical property parameters of heterogeneous sandstone reservoirs, and the inversion has high accuracy and strong reliability.

[0006] In a first aspect, an embodiment of the present invention provides a method for predicting physical property parameters of a heterogeneous sandstone reservoir, comprising:

[0007] Based on the seismic record data of the heterogeneous sandstone reservoir to be inverted, the physical property parameters of the reservoir are obtained by a model used to characterize the relationship between the seismic record and the reservoir physical property parameters, wherein the model is established by the following steps:

[0008] The rock physics model of heterogeneous sandstone reservoir is established by using VRH average, fluid uniform mixing formula, Pride model and Gassmann equation;

[0009] The rock physics model is linearized by using Taylor expansion to obtain a linearization formula of the rock physics model and calculate a corresponding Jacobian matrix;

[0010] The obtained Jacobian matrix is ​​substituted into the linearization formula of the rock physics model, and combined with the seismic reflection coefficient equation, a model for characterizing the relationship between seismic records and reservoir physical property parameters is established.

[0011] In a second aspect, an embodiment of the present invention provides a device for predicting physical property parameters of a heterogeneous sandstone reservoir, comprising: a model building module and a reservoir physical property parameter prediction module;

[0012] The model building module is used to establish a rock physics model of a heterogeneous sandstone reservoir by using VRH average, fluid uniform mixing formula, Pride model and Gassmann equation; linearize the rock physics model by using Taylor expansion to obtain a rock physics model linearization formula, and calculate the corresponding Jacobian matrix; substitute the obtained Jacobian matrix into the rock physics model linearization formula, and establish a model for characterizing the relationship between seismic records and reservoir physical property parameters in combination with the seismic reflection coefficient equation;

[0013] The reservoir physical property parameter prediction module is used to obtain the physical property parameters of the reservoir based on the seismic record data of the heterogeneous sandstone reservoir to be inverted through the model pre-established by the model establishment module.

[0014] In a third aspect, an embodiment of the present invention provides a computer storage medium, wherein the computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, the above-mentioned method for predicting physical property parameters of heterogeneous sandstone reservoirs is implemented.

[0015] In a fourth aspect, an embodiment of the present disclosure provides a server, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for predicting physical property parameters of heterogeneous sandstone reservoirs when executing the program.

[0016] The beneficial effects of the above technical solution provided by the embodiment of the present invention include at least:

[0017] (1) The method for predicting the physical property parameters of heterogeneous sandstone reservoirs provided in the embodiment of the present invention performs a first-order linear approximation of the rock physics model based on the Gassmann equation and the Pride model, and constructs a mathematical relationship between the physical property parameters and the seismic records in combination with the seismic reflection coefficient equation, i.e., a model, thereby achieving direct prediction of the physical property parameters of heterogeneous sandstone reservoirs, reducing the cumulative error of parameter prediction, and achieving high prediction accuracy and strong reliability.

[0018] (2) The method for predicting physical property parameters of heterogeneous sandstone reservoirs provided in an embodiment of the present invention uses Bayesian linearization inversion to obtain the posterior probability distribution of physical property parameters, introduces a high-resolution prior constrained physical property parameter model, and establishes a target functional for physical property parameter inversion. According to the posterior probability distribution, a random sampling algorithm is used to extract physical property parameter samples, and the extracted samples are optimized through the target functional to finally obtain the physical property parameter inversion results, thereby effectively improving the resolution of physical property parameter prediction.

[0019] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0020] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0022] Figure 1 This is a flow chart of establishing a model for characterizing the relationship between seismic records and reservoir physical property parameters in the first embodiment of the present invention;

[0023] Figure 2 for Figure 1 Specific implementation flow chart of step S11;

[0024] Figure 3 This is a flow chart of a method for predicting physical property parameters of heterogeneous sandstone reservoirs in Embodiment 2 of the present invention;

[0025] Figure 4 It is the relationship diagram between elastic parameters and porosity of actual rock physics model and linearized rock physics model;

[0026] Figure 5 It is the relationship diagram between elastic parameters and shale content of actual rock physics model and linearized rock physics model;

[0027] Figure 6 It is the relationship diagram between elastic parameters and water saturation of actual rock physics model and linearized rock physics model;

[0028] Figure 7 The actual logging data of the well;

[0029] Figure 8 Elastic parameters calculated for heterogeneous rock physics models and critical porosity models as well as actual well logging elastic parameters;

[0030] Fig. 9 elastic parameters calculated for the actual rock physics model and the linearized rock physics model and the actual well logging elastic parameters;

[0031] Fig.10 Inversion of linearized rock physics model and actual logging physical property parameters;

[0032] Fig.11 To compare the linearized rock physics model inversion with the indirect physical property parameter prediction;

[0033] Fig.12 Comparison of predicted results of physical property parameters after optimization for Metropolis;

[0034] Fig.13 is the linearized inversion profile of physical property parameters;

[0035] Fig.14 Prediction profile of physical property parameters after optimization for Metropolis;

[0036] Fig.15 Schematic diagram of the structure of a device for predicting physical property parameters of heterogeneous sandstone reservoirs in an embodiment of the present invention. DETAILED DESCRIPTION

[0037] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0038] It should be understood that the terms described in the present invention are only for describing special embodiments and are not intended to limit the present invention. In addition, for the numerical range in the present invention, it should be understood that each intermediate value between the upper and lower limits of the scope is also specifically disclosed. Each smaller range between the intermediate value in any stated value or stated range and any other stated value or intermediate value in the described range is also included in the present invention. The upper and lower limits of these smaller ranges can be independently included or excluded in the scope.

[0039] Unless otherwise indicated, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which the invention belongs. Although the present invention describes only preferred methods and materials, any methods and materials similar or equivalent to those described herein may also be used in the implementation or testing of the present invention. All documents mentioned in this specification are incorporated by reference to disclose and describe the methods and / or materials related to the documents. In the event of a conflict with any incorporated document, the content of this specification shall prevail.

[0040] Embodiment 1

[0041] Embodiment 1 of the present invention provides a method for establishing a model for characterizing the relationship between seismic records and reservoir physical property parameters, and the process thereof is as follows: Figure 1 As shown, the following steps are included:

[0042] Step S11: using VRH average, fluid uniform mixing formula, Pride model and Gassmann equation, a rock physics model of the heterogeneous sandstone reservoir is established.

[0043] Under the combined effects of consolidation, pressure and temperature, the composition, particle size, cementation mode and pore structure of the rock minerals in heterogeneous sandstone reservoirs vary among different strata. Therefore, the Pride model is used to replace the traditional critical porosity model, see Figure 2 The specific modeling process is as follows:

[0044] Step S111: using VRH averaging to obtain the rock matrix elastic modulus and density of the heterogeneous sandstone reservoir.

[0045] The components of the heterogeneous tight sandstone rock matrix are mainly rigid particles and clay. The rigid particles are mainly quartz and feldspar. Using VRH average, the rock matrix bulk modulus K of the heterogeneous sandstone reservoir is obtained respectively. m , shear modulus G m and density ρ m :

[0046]

[0047]

[0048] ρ m =(1-V c )ρ q +V c ρ c (3)

[0049] In formulas (1)-(3), V c is the clay content; Kc and G c are the bulk modulus and shear modulus of clay; K q and G q are the bulk modulus and shear modulus of rigid particles respectively; ρ q and ρ c They are rigid particle density and clay density respectively.

[0050] Step S112: Based on the rock matrix elastic modulus, the dry rock skeleton elastic modulus is calculated using the Pride model.

[0051] Calculation of the bulk modulus K of dry rock skeleton using the Pride model d and shear modulus G d :

[0052]

[0053]

[0054] In equations (4) and (5), φ is the porosity and c is the consolidation coefficient. The Pride model effectively characterizes the differences in rock physical characteristics between different layers by introducing the concept of consolidation parameters.

[0055] Step S113: Obtain the elastic modulus and density of the mixed fluid through the fluid uniform mixing formula.

[0056] The bulk modulus K of the mixed fluid is obtained by the fluid uniform mixing formula f With density ρ f :

[0057]

[0058] ρ f =(1-S w )ρ hc +S w ρ w (7)

[0059] In formulas (6) and (7), K hc and ρ hc are the bulk modulus and density of oil and gas respectively; K w is the bulk modulus of water; S w is water saturation; ρ w is the density of water.

[0060] Step S114: Based on the rock matrix elastic modulus, the dry rock skeleton elastic modulus and the mixed fluid elastic modulus, the saturated rock equivalent elastic modulus is calculated using the Gassmann equation.

[0061] Calculation of equivalent bulk modulus K of saturated rock using Gassmann equation sat and the equivalent shear modulus G sat :

[0062]

[0063] G sat =G d (9).

[0064] Step S115: Determine the saturated rock density based on the rock matrix density and the mixed fluid density, and determine the longitudinal wave velocity and the transverse wave velocity of the saturated rock based on the saturated rock density and the equivalent elastic modulus.

[0065] The saturated rock density is determined based on the rock matrix density and the mixed fluid density. The longitudinal wave velocity and the shear wave velocity of the saturated rock are determined based on the saturated rock density and the equivalent elastic modulus as follows:

[0066] ρ=ρ m (1-φ)+ρ f φ (10)

[0067]

[0068]

[0069] In formulas (10)-(12), ρ is the saturated rock density, V P and V S are the longitudinal wave velocity and the shear wave velocity of saturated rock, respectively.

[0070] Step S116: Obtain a rock physics model.

[0071] Step S12: linearize the rock physics model using Taylor expansion to obtain a linearization formula of the rock physics model and calculate the corresponding Jacobian matrix.

[0072] The rock physics modeling process is relatively complicated. Taylor expansion is used to linearize the rock physics model. The relationship between elastic parameters and physical parameters can be characterized by the following formula:

[0073]

[0074] To perform direct inversion of physical parameters, it is necessary to construct a forward relationship between physical parameters and seismic records, which is mainly divided into two parts. The first part is the relationship between physical parameters and elastic parameters, which is mainly obtained by rock physics linearization. Formula (13) is changed into a linearized formula, which can be written as:

[0075]

[0076] In equations (13) and (14), η = [φ, V c ,S w ], characterizing physical property parameters; q = [lnK sat ,lnG sat ,lnρ], represents the natural logarithm of elastic parameters; f represents the rock physics model; is the Jacobian matrix at the independent variable η0 (i.e., the known physical property parameters obtained through well logging data, etc.).

[0077] To linearize the rock physics model, it is necessary to calculate the corresponding Jacobian matrix J ac (φ,V c ,S w ):

[0078]

[0079] Through the corresponding derivation, the Jacobian matrix expression of the linearized heterogeneous sandstone model can be obtained:

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087]

[0088]

[0089] in,

[0090]

[0091]

[0092]

[0093]

[0094] Step S13: Substitute the obtained Jacobian matrix into the linearization formula of the rock physics model, and combine it with the seismic reflection coefficient equation to establish a model for characterizing the relationship between seismic records and reservoir physical property parameters.

[0095] According to Gray's approximation formula, the seismic reflection coefficient R(θ) can be written as:

[0096]

[0097] Where θ is the incident angle, γ = V P / V S , t is the round-trip travel time of seismic waves propagating underground.

[0098] According to formula (14) and formula (15), formula (29) can be rewritten as:

[0099]

[0100]

[0101]

[0102]

[0103] In the formula, c ij (i=1,2,3; j=1,2,3) are elements in the Jacobian matrix.

[0104] Therefore, the earthquake record d can be written as:

[0105] d=WADη+e=Gη+e (34)

[0106] In formula (34), d is the seismic record data; W is the wavelet matrix; D is the difference matrix; G = WAD, representing the forward matrix; e is the seismic noise, with a mean of 0 and a variance of Σ e ; A is the parameter matrix, including the above α φ , α c and α s .

[0107] The above formula (34) is a model established to characterize the relationship between seismic records and reservoir physical parameters.

[0108] Embodiment 2

[0109] The second embodiment of the present invention provides a method for predicting physical property parameters of heterogeneous sandstone reservoirs. Based on the seismic record data of the heterogeneous sandstone reservoir to be inverted, the physical property parameters of the reservoir are obtained by using a model for characterizing the relationship between seismic records and reservoir physical property parameters established according to the method in the first embodiment. Figure 3As shown, the process includes the following steps:

[0110] Step S31: Based on the seismic record data and the initial physical property parameter model of the heterogeneous sandstone reservoir to be inverted, the posterior probability distribution of the physical property parameter solution is obtained by using the Bayesian linear inversion method through a model used to characterize the relationship between seismic records and reservoir physical property parameters.

[0111] There are many ways to solve the linearized seismic inversion problem, such as the least squares algorithm or the Bayesian method. Here, the Bayesian linearized inversion method is used to obtain the posterior probability distribution of the inversion result:

[0112] μ ηd =μ η +Σ η G T (G∑ η G T +Σ e ) -1 (d-Gμ η ) (35)

[0113] Σ ηd =Σ η -Σ η G T (GΣ η G T +∑ e ) -1 GΣ η (36)

[0114] In formulas (35) and (36), μ ηd With Σ ηd They represent the posterior mean and posterior variance of η respectively; μ η With Σ η They represent the prior mean and prior variance of η respectively.

[0115] Step S32: Using the obtained prior constrained physical property parameter model, establish a target functional for physical property parameter inversion.

[0116] The priori constrained physical property parameter model can be a high-resolution rock physical model obtained through geostatistical simulation based on well logging data in the reservoir. The introduction of the priori constrained physical property parameter model improves the vertical resolution of the inversion.

[0117] The target functional J(η) for inversion of physical property parameters is established as follows:

[0118]

[0119] In formula (37), η * is the sample drawn; κ is the weight factor; It is a priori constrained physical property parameter model.

[0120] Step S33: According to the posterior probability distribution, physical property parameter samples are extracted by using a random sampling algorithm, the extracted samples are optimized by the target functional, and the obtained optimal samples are used as the prediction results of the physical property parameters of the reservoir to be inverted.

[0121] For the posterior mean μ m|d is the central value, and the post-test variance Σ md Random sampling is performed in the local sampling interval of the variance to obtain the physical property parameter samples.

[0122] Furthermore, the random sampling algorithm may adopt the Meropolis-Hastings sampling algorithm.

[0123] After sampling, the synthetic seismic records of the samples are compared with the actual seismic records, thereby satisfying the constraints of the seismic data while eliminating the smoothing effect brought by the Bayesian linearization inversion.

[0124] The Metropolis acceptance criterion is used to optimize the selected samples through the target functional. The Metropolis acceptance criterion introduces the concept of acceptance probability to improve the ability of the current solution to jump out of the local extreme value. Drawing on the convergence strategy of the simulated annealing algorithm, the acceptance probability formula can be expressed as follows:

[0125]

[0126] In formula (38), k is a constant, T represents the annealing temperature, and η' is the optimal sample at present.

[0127] The method for predicting physical property parameters of heterogeneous sandstone reservoirs provided in an embodiment of the present invention performs first-order linear approximation of the rock physics model based on the Gassmann equation and the Pride model, and constructs a mathematical relationship between the physical property parameters and the seismic records in combination with the seismic reflection coefficient equation, i.e., a model, thereby achieving direct prediction of the physical property parameters of heterogeneous sandstone reservoirs, reducing the cumulative error of parameter prediction, and achieving high prediction accuracy.

[0128] The method for predicting physical property parameters of heterogeneous sandstone reservoirs provided in an embodiment of the present invention uses Bayesian linearization inversion to obtain the posterior probability distribution of physical property parameters, introduces a high-resolution prior constrained physical property parameter model, establishes a target functional for physical property parameter inversion, and extracts physical property parameter samples based on the posterior probability distribution using a random sampling algorithm. The extracted samples are optimized through the target functional to finally obtain the physical property parameter inversion results, thereby effectively improving the resolution of physical property parameter prediction.

[0129] Actual project cases

[0130] In order to test the accuracy of the linearized rock physics model, the actual rock physics model and the linearized rock physics model were used to carry out sandstone rock physics forward simulation analysis. Figure 4 The changing relationships among bulk modulus, shear modulus, density and porosity are shown respectively. In the figure, the mud content varies from 0 to 50% at intervals of 10%, the water saturation is a constant of 40%, and the porosity varies uniformly from 4% to 15%. Figure 5 The relationship between bulk modulus, shear modulus, density and shale content is shown. In the figure, the porosity changes from 5% to 20% at 5% intervals, the water saturation is a constant of 40%, and the shale content C changes uniformly from 0 to 50%. Figure 6 The relationship between the bulk modulus, shear modulus, density and water saturation is shown respectively. In the figure, the porosity changes from 5% to 20% at intervals of 5%, the shale content is a constant of 10%, and the water saturation changes uniformly from 0 to 100%. It can be seen that the linearized rock physics model is in good agreement with the actual rock physics model. There is a certain error when the porosity (or shale content and water saturation) is low and high. This is because the Taylor expansion is performed at the mean value of the porosity (or shale content and water saturation). The density of the linearized rock physics model is basically the same as that of the actual rock physics model, because the density calculation formula itself is linear.

[0131] The rock physics linearization model is tested using actual logging data. Figure 7 The elastic parameters calculated by the heterogeneous rock physics model and the critical porosity model are shown in Figure 8 As shown in the figure (dashed line: actual logging data; black solid line: linearized rock physics model inversion; gray solid line: indirect prediction). It can be found that the elastic parameters predicted by the heterogeneous rock physics model are more consistent with the actual well curve, indicating that the heterogeneous rock physics model is more suitable for heterogeneous reservoirs. The elastic parameter prediction results of the actual rock physics model and the linearized prediction results are shown in Fig. 9 As shown in the figure (dashed line: actual rock physics model; gray solid line: elastic parameters calculated by linearized rock physics model; black solid line: actual logging elastic parameters). It can be found that the elastic modulus predicted by the two methods is consistent with the elastic modulus of the real well data, indicating that both methods can effectively establish the relationship between porosity, shale content, water saturation and bulk modulus, shear modulus, and density. Afterwards, the logging curve is inverted and tested. Fig.10 is the linearized inversion result (black solid line: actual logging data; dashed line: mean of linearized inversion; gray solid line: upper and lower limits of posterior distribution), Fig.11Comparison of the results of linearized inversion and indirect inversion of physical parameters (dashed line: actual logging data; black solid line: linearized rock physics model inversion; gray solid line: indirect prediction). It can be found that the prediction of physical parameters based on linearized direct inversion is more accurate and reduces the generation of cumulative errors. Fig.12 The results after Metropolis optimization using the linearized results (dashed line: actual logging data; black solid line: predicted results after Metropolis optimization; gray solid line: predicted results after linearized inversion). It can be found that both the linearized inversion results and the predicted results after Metropolis optimization can achieve good results, but the results after Metropolis optimization are more consistent with the actual logging curve and have higher resolution. Finally, the linearized inversion test is performed on the seismic data. The Bayesian linearized inversion method is used to invert the physical parameters (see Fig.13 ), Fig.13 The porosity, shale content and gas saturation obtained by linear inversion are shown respectively. Fig.14 The physical property parameter prediction results after Metropolis optimization are shown respectively. It can be seen from the inversion results that the physical property parameters of the main target layer obtained by the two inversion results are in good agreement with the physical property parameters on Well A, and the spatial distribution of the physical property parameters of the main target layer can be obtained, especially the physical property parameter prediction results after Metropolis optimization are better than the inversion results of Bayesian linearization.

[0132] Based on the inventive concept of the present invention, an embodiment of the present invention further provides a device for predicting physical property parameters of heterogeneous sandstone reservoirs. The structure of the device is as follows: Fig.15 As shown, including:

[0133] The model building module 151 is used to establish a rock physics model of a heterogeneous sandstone reservoir by using VRH average, fluid uniform mixing formula, Pride model and Gassmann equation; linearize the rock physics model by using Taylor expansion to obtain a linearization formula of the rock physics model, and calculate the corresponding Jacobian matrix; substitute the obtained Jacobian matrix into the linearization formula of the rock physics model, and establish a model for characterizing the relationship between seismic records and reservoir physical parameters in combination with the seismic reflection coefficient equation;

[0134] The reservoir physical property parameter prediction module 152 is used to obtain the physical property parameters of the reservoir based on the seismic record data of the heterogeneous sandstone reservoir to be inverted through the model pre-established by the model establishment module.

[0135] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0136] Based on the inventive concept of the present invention, an embodiment of the present invention further provides a computer storage medium, in which computer executable instructions are stored. When the computer executable instructions are executed by a processor, the above-mentioned method for predicting physical property parameters of heterogeneous sandstone reservoirs is implemented.

[0137] Based on the inventive concept of the present invention, an embodiment of the present invention also provides a server, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for predicting physical property parameters of heterogeneous sandstone reservoirs when executing the program.

[0138] Unless otherwise specifically stated, terms such as processing, computing, calculating, determining, displaying, etc. may refer to the actions and / or processes of one or more processing or computing systems, or similar devices, which operate and convert data represented as physical (e.g., electronic) quantities within registers or memories of a processing system into other data similarly represented as physical quantities within memories, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals may be represented using any of a variety of different techniques and methods. For example, data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, light fields or particles, or any combination thereof.

[0139] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the scope of protection of the present disclosure. The attached method claims present the elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy described.

[0140] In the above detailed description, various features are grouped together in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that the embodiments of the claimed subject matter require more features than those set forth in each claim. On the contrary, as reflected in the appended claims, the invention is in a state of having less than all the features of the disclosed individual embodiments. Therefore, the appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0141] Those skilled in the art will also appreciate that the various illustrative logic blocks, modules, circuits, and algorithmic steps described in conjunction with the embodiments herein can all be implemented as electronic hardware, computer software, or a combination thereof. In order to clearly illustrate the interchangeability between hardware and software, various illustrative components, blocks, modules, circuits, and steps are generally described above around their functions. Whether such functions are implemented as hardware or software depends on specific applications and the design constraints imposed on the entire system. A skilled person can implement the described functions in an alternative manner for each specific application, but such implementation decisions should not be interpreted as departing from the scope of protection of the present disclosure.

[0142] The steps of the method or algorithm described in conjunction with the embodiments herein may be directly embodied as hardware, a software module executed by a processor, or a combination thereof. The software module may be located in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a mobile disk, a CD-ROM, or any other form of storage medium well known in the art. An exemplary storage medium is connected to the processor so that the processor can read information from the storage medium and can write information to the storage medium. Of course, the storage medium may also be an integral part of the processor. The processor and the storage medium may be located in an ASIC. The ASIC may be located in a user terminal. Of course, the processor and the storage medium may also be present in a user terminal as discrete components.

[0143] For software implementation, the techniques described in this application can be implemented with modules (e.g., procedures, functions, etc.) that perform the functions described in this application. These software codes can be stored in a memory unit and executed by a processor. The memory unit can be implemented within the processor or outside the processor. In the latter case, it is coupled to the processor in a communication manner via various means, which are well known in the art.

[0144] The above description includes examples of one or more embodiments. Of course, it is impossible to describe all possible combinations of components or methods for the purpose of describing the above embodiments, but it should be recognized by those skilled in the art that the various embodiments may be further combined and arranged. Therefore, the embodiments described herein are intended to cover all such changes, modifications and variations that fall within the scope of protection of the appended claims. In addition, with respect to the term "comprising" used in the specification or claims, the word is covered in a manner similar to the term "including", just as "including," is explained as a transitional word in the claims. In addition, any term "or" used in the specification of the claims is intended to mean "non-exclusive or".

Claims

1. A method for predicting physical property parameters of heterogeneous sandstone reservoirs, characterized in that: include: Based on the seismic record data of the heterogeneous sandstone reservoir to be inverted, the physical property parameters of the reservoir are obtained by a model used to characterize the relationship between the seismic record and the reservoir physical property parameters, wherein the model is established by the following steps: The rock physics model of heterogeneous sandstone reservoir is established by using VRH average, fluid uniform mixing formula, Pride model and Gassmann equation; The rock physics model is linearized by using Taylor expansion to obtain a linearization formula of the rock physics model and calculate a corresponding Jacobian matrix; The obtained Jacobian matrix is ​​substituted into the linearization formula of the rock physics model, and combined with the seismic reflection coefficient equation, a model for characterizing the relationship between seismic records and reservoir physical property parameters is established.

2. The method according to claim 1, characterized in that The rock physics model of the heterogeneous sandstone reservoir is established by using the VRH average, fluid uniform mixing formula, Pride model and Gassmann equation, including: VRH average is used to obtain the rock matrix elastic modulus and density of heterogeneous sandstone reservoirs; Based on the elastic modulus of rock matrix, the elastic modulus of dry rock skeleton is calculated using the Pride model; The elastic modulus and density of the mixed fluid are obtained through the fluid uniform mixing formula; Based on the elastic modulus of rock matrix, dry rock skeleton and mixed fluid, the equivalent elastic modulus of saturated rock is calculated using the Gassmann equation. The saturated rock density is determined based on the rock matrix density and the mixed fluid density. The longitudinal wave velocity and the transverse wave velocity of the saturated rock are determined based on the saturated rock density and the equivalent elastic modulus to obtain the rock physics model.

3. The method according to claim 2, characterized in that The method of obtaining the rock matrix elastic modulus and density of the heterogeneous sandstone reservoir by using VRH average includes: Using VRH averaging, the rock matrix bulk modulus K of the heterogeneous sandstone reservoir is obtained respectively. m , shear modulus G m and density ρ m : r m =(1-V c )r q +V c r c (3) In formulas (1)-(3), V c is the clay content; K c and G c are the bulk modulus and shear modulus of clay; K q and G q are the bulk modulus and shear modulus of rigid particles respectively; ρ q and ρ c They are rigid particle density and clay density respectively.

4. The method according to claim 3, characterized in that The method of calculating the elastic modulus of dry rock skeleton by using the Pride model includes: Calculation of the bulk modulus K of dry rock skeleton using the Pride model d and shear modulus G d : In equations (4) and (5), φ is the porosity and c is the consolidation coefficient.

5. The method according to claim 4, characterized in that The elastic modulus and density of the mixed fluid are obtained by the fluid uniform mixing formula, including: The bulk modulus K of the mixed fluid is obtained by the fluid uniform mixing formula f With density ρ f : r f =(1-S w )r hc +S w r w (7) In formulas (6) and (7), K hc and ρ hc are the bulk modulus and density of oil and gas respectively; K w is the bulk modulus of water; S w is water saturation; ρ w is the density of water.

6. The method according to claim 5, characterized in that The method of calculating the equivalent elastic modulus of saturated rock using the Gassmann equation based on the rock matrix elastic modulus, the dry rock skeleton elastic modulus and the mixed fluid elastic modulus includes: Based on the elastic modulus of rock matrix, dry rock skeleton and mixed fluid, the equivalent bulk modulus K of saturated rock is calculated using the Gassmann equation. sat and the equivalent shear modulus G sat : G sat =G d (9).

7. The method according to claim 6, characterized in that The method of determining the saturated rock density based on the rock matrix density and the mixed fluid density, and determining the longitudinal wave velocity and the transverse wave velocity of the saturated rock based on the saturated rock density and the equivalent elastic modulus, comprises: The saturated rock density is determined based on the rock matrix density and the mixed fluid density. The longitudinal wave velocity and the shear wave velocity of the saturated rock are determined based on the saturated rock density and the equivalent elastic modulus as follows: p=p m (1-φ)+ρ f f (10) In formulas (10)-(12), ρ is the saturated rock density, V P and V S are the longitudinal wave velocity and the shear wave velocity of saturated rock, respectively.

8. The method according to claim 7, characterized in that The rock physics model is linearized by using Taylor expansion to obtain a linearization formula of the rock physics model, including: The rock physics model is linearized using Taylor expansion, and the linearization formula of the rock physics model is obtained as follows: In formula (13), η=[φ,V c ,S w ], characterizing physical property parameters; q = [lnK sat ,lnG sat ,lnρ], represents the natural logarithm of elastic parameters; f represents the rock physics model; is the Jacobian matrix at the independent variable η0.

9. The method according to claim 8, characterized in that The calculation of the corresponding Jacobian matrix includes: Calculate the corresponding Jacobian matrix J ac (φ,V c ,S w )as follows: in, in, 10. The method according to claim 9, characterized in that The model established to characterize the relationship between seismic records and reservoir physical property parameters includes: The model established to characterize the relationship between seismic records and reservoir physical parameters is as follows: d=WADη+e=Gη+e (28) In formula (28), d is the seismic record data; W is the wavelet matrix; D is the difference matrix; G = WAD, representing the forward matrix; e is the seismic noise, with a mean of 0 and a variance of Σ e ; A is the parameter matrix, including α φ , α c and α s : In equations (29)-(31), θ is the incident angle; γ = V P / V S ;c ij are the elements in the Jacobian matrix, i = 1, 2, 3; j = 1, 2, 3.

11. The method according to claim 10, characterized in that The seismic record data of the heterogeneous sandstone reservoir to be inverted is used to obtain the physical property parameters of the reservoir through a model used to characterize the relationship between the seismic record and the reservoir physical property parameters, including: Based on the seismic record data and initial physical property parameter model of the heterogeneous sandstone reservoir to be inverted, the posterior probability distribution of the physical property parameter solution is obtained by using the Bayesian linear inversion method through the model used to characterize the relationship between seismic records and reservoir physical property parameters; Using the obtained priori constrained physical property parameter model, the target functional for physical property parameter inversion is established; According to the posterior probability distribution, physical property parameter samples are extracted by using a random sampling algorithm, the extracted samples are optimized by using the target functional, and the obtained optimal samples are used as the prediction results of the physical property parameters of the reservoir to be inverted.

12. The method according to claim 11, characterized in that The a priori constrained physical property parameter model is obtained through geostatistical simulation based on the well logging data in the reservoir.

13. The method according to claim 11, characterized in that The method of using the Bayesian linearized inversion method to obtain the posterior probability distribution of the physical property parameter solution includes: The posterior probability distribution of the physical property parameter solution is obtained by the following formula: m ηd =μ η +S η G T (G∑ η G T +S e ) -1 (d-Gμ η ) (32) S η|d =S η -S η G T (GΣ η G T +∑ e ) -1 GS η (33) In formulas (32) and (33), μ η|d With Σ η|d They represent the posterior mean and posterior variance of η respectively; μ η With Σ η They represent the prior mean and prior variance of η respectively.

14. The method according to claim 13, characterized in that The target functional for establishing the inversion of physical property parameters includes: The target functional J(η) for inversion of physical property parameters is established as follows: In formula (34), η * is the sample drawn; κ is the weight factor; It is a priori constrained physical property parameter model.

15. The method according to claim 14, characterized in that The step of optimizing the extracted samples by using the target functional comprises: The Metropolis acceptance criterion is used to optimize the extracted samples through the target functional.

16. The method according to claim 15, characterized in that The Metropolis acceptance criterion introduces the following acceptance probability formula: In formula (35), k is a constant, T represents the annealing temperature, and η' is the optimal sample at present.

17. The method according to claim 13, characterized in that The method of extracting a sample of physical property parameters by using a random sampling algorithm according to the posterior probability distribution includes: For the posterior mean μ m|d is the central value, and the post-test variance Σ m|d Random sampling is performed in the local sampling interval of the variance to obtain the physical property parameter samples.

18. A device for predicting physical property parameters of heterogeneous sandstone reservoirs, characterized in that: The device comprises a model building module and a reservoir physical property parameter prediction module; The model building module is used to establish a rock physics model of a heterogeneous sandstone reservoir by using VRH average, fluid uniform mixing formula, Pride model and Gassmann equation; linearize the rock physics model by using Taylor expansion to obtain a linearization formula of the rock physics model, and calculate the corresponding Jacobian matrix; Substituting the obtained Jacobian matrix into the linearization formula of the rock physics model, and combining it with the seismic reflection coefficient equation, a model for characterizing the relationship between seismic records and reservoir physical property parameters is established; The reservoir physical property parameter prediction module is used to obtain the physical property parameters of the reservoir based on the seismic record data of the heterogeneous sandstone reservoir to be inverted through the model pre-established by the model establishment module.

19. A computer storage medium, characterized in that: The computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, the method for predicting physical property parameters of heterogeneous sandstone reservoirs according to any one of claims 1 to 17 is implemented.

20. A server, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for predicting physical property parameters of heterogeneous sandstone reservoirs as claimed in any one of claims 1 to 17 is implemented.

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