Seismic data-based permeability prediction method and device, storage medium and equipment

Abstract

The invention belongs to the technical field of geophysical oil-gas exploration, and provides a permeability prediction method and device based on seismic data, a storage medium and equipment. According to the method, a pseudo lithofacies characterization curve and a permeability related parameter curve are constructed based on a rock physical model and actual data characteristics, and the reservoir permeability based on seismic data is obtained by adopting a seismic data inversion algorithm. According to the method, the pseudo lithofacies curve is innovatively provided, and the curve is applied to lithofacies characterization and classification for the first time; a theoretical interpretable permeability related parameter curve is obtained by innovatively adopting a target correlation analysis algorithm, and a better permeability earthquake prediction result is obtained on the basis of the theoretical interpretable permeability related parameter curve; the purposes of supporting reservoir permeability characteristic description and efficient exploration and development of oil and gas fields are achieved.

Description

Technical Field

[0001] The present invention belongs to the technical field of geophysical oil and gas exploration, and particularly relates to a method and device for predicting permeability based on seismic data, a storage medium, and a device. Background Art

[0002] The pore and permeability characteristics of oil and gas reservoirs have always been important factors related to reservoir productivity and oil and gas field exploration and development. At present, there are many ways to predict porosity based on seismic data, but the prediction of permeability based on seismic data is relatively rare. Among them, using neural networks or machine learning methods to predict permeability is one of the feasible solutions, but this solution has no theoretical support and generally adopts a black box mode. The prediction results have strong contingency due to parameter effects and weak interpretability. Some scholars have adopted a permeability fitting method controlled by pore structure for seismic prediction of permeability and achieved good results. Based on this, this patent proposes a new technology for predicting permeability based on seismic data.

[0003] When using seismic data to predict permeability, a key point is how to establish a connection between the permeability parameter and seismic data or, more narrowly, the seismic reflection coefficient. Generally speaking, there are specific relationships between seismic reflection coefficients and various elastic parameters, and elastic parameters can be relatively maturely obtained using seismic data. Therefore, it becomes particularly important to build a bridge between elastic parameters and permeability. Rock physics modeling is the solution to this important problem. Some scholars have adopted a rock physics model based on sun, derived the shear compliance factor, and constructed the relationship between permeability, elastic parameters, and porosity under the constraint of this factor; some have made improvements on this basis, derived the shear Lee factor based on the Lee model, and combined the pore aspect ratio as a constraint, using a two-factor (shear Lee factor and pore aspect ratio) constraint to predict permeability using seismic elastic parameters and porosity. Almost all attempts are based on existing rock physics models for formula sorting and re-derivation, without considering the complexity of the relationship between permeability and seismic data in the actual prediction process; moreover, the parameters obtained through derivation (such as the shear Lee factor and pore aspect ratio) are generally only used as the basis for lithofacies classification. After lithofacies division, the results of seismic porosity prediction and elastic parameter prediction will be used again to obtain an estimate of permeability in a neural network manner. This method (process) instead, to a certain extent, weakens the feasibility and advantages of the method of deriving formulas from theory. In other words, the parameters obtained using theoretical formulas are put back into the "black box mode" box.

[0004] Therefore, there is an urgent need for a method for predicting permeability based on seismic data. Summary of the Invention

[0005] The object of the present invention is to construct a reservoir permeability prediction method based on seismic data. This method first combines theoretical derivation with actual data to obtain the classification parameters of pseudo-lithofacies, and then through the target correlation analysis algorithm, inherits the classification results of pseudo-lithofacies, fully retains the theoretical basis, obtains the explicit calculation formula of permeability with elastic parameters and porosity, and further realizes the estimation of permeability using seismic data.

[0006] In a first aspect, the present invention proposes a permeability prediction method based on seismic data, including: constructing a permeability correlation curve by using a correlation curve analysis algorithm with the pseudo-lithofacies curve as a constraint;

[0007] The permeability correlation curve is denoted as PERM, and its expression is:

[0008] PERM = UF * phi * A + B * Ip + C * Is

[0009] In the formula, A, B, and C are parameters to be fitted; phi is porosity, * is the multiplication symbol, Ip is the longitudinal wave impedance, Is is the shear wave impedance, and UF is the pseudo-lithofacies curve.

[0010] According to the present invention, in practical applications, it is obtained by combining with actual data. The acquisition process is as follows: first, set B and C to zero, and obtain parameter A using the target parameter correlation as a criterion. Then, after A is determined, set C to zero and determine B. Finally, determine parameter C.

[0011] As a specific implementation manner of the present invention, the expression of the pseudo-lithofacies curve UF is:

[0012]

[0013] In the formula, ρ ms is the corrected matrix mineral density; is the connected porosity, V SS is the shear wave velocity of the corrected rock matrix, U ma is the shear modulus of the rock matrix with non-connected porosity.

[0014] As a specific implementation manner of the present invention, the expression of the corrected matrix mineral density ρ ms is:

[0015]

[0016] In the formula, ρ m is the density of the initial matrix mineral, is the non-connected porosity, ρ fl is the density of the fluid in the non-connected pores.

[0017] As a specific implementation manner of the present invention, the non-connected porosity The acquisition method includes the following steps:

[0018] (1) Select the search range of the disconnected porosity;

[0019] (2) Using the matching of the measured P-wave velocity and the theoretically calculated velocity in the target area as a constraint condition, optimize by using the simulated annealing algorithm to obtain the disconnected porosity.

[0020] As a specific implementation manner of the present invention, the method includes the following steps:

[0021] S1: Construct a pseudo-lithofacies curve based on well logging data;

[0022] S2: Construct a permeability-related curve controlled by the pseudo-lithofacies curve;

[0023] S3: Calculate the permeability of the target area based on seismic data.

[0024] As a specific implementation manner of the present invention, in the step S1, it includes the following steps:

[0025] S11: Calculate the effective compression modulus C s and the shear modulus U s ;

[0026] The rock physics model is constructed as follows:

[0027]

[0028]

[0029] In the formula, V PS , V SS are the longitudinal and transverse wave velocities of the modified rock matrix; V Pm , V Sm are the initial longitudinal and transverse wave velocities of the rock matrix, which can be calculated from the moduli of the matrix constituent minerals; V Pf is the longitudinal wave velocity of the fluid in the disconnected pores. It is generally considered that the disconnected pores contain immobile formation water; is the disconnected porosity, is the total porosity, is the connected porosity; ρ m is the density of the initial matrix minerals; ρ fl is the density of the fluid in the disconnected pores;

[0030] Calculate the longitudinal wave velocity of the rock matrix containing disconnected pores using formula (1), and estimate its transverse wave velocity using the Raymer extended formula (2);

[0031] By modifying the velocity value of the rock matrix, the effective compression modulus C of the modified rock matrix is obtained through Equations (3) and (4). s and the shear modulus U s :

[0032] U s = ρ ms v 2 ss (3)

[0033]

[0034] where: ρ ms is the density of the modified matrix mineral;

[0035]

[0036] S12: The bulk modulus K of the rock matrix considering the disconnected porosity ma = 1 / C s ; the shear modulus of the rock matrix with disconnected porosity is U ma = U s , the density of the rock matrix is ρ ma = ρ ms , the bulk modulus K dry and the shear modulus U dry ;

[0037] The calculation process of the bulk modulus and shear modulus of the rock skeleton is as follows:

[0038]

[0039]

[0040] where K dry is the bulk modulus of the rock skeleton; U dry is the shear modulus of the rock skeleton; Z is the densification coefficient.

[0041] S13: According to the effective compression modulus C of the modified rock matrix obtained in step S11 s , the shear modulus U of the rock skeleton obtained in step S12 dry and the rock physics model are combined to obtain the pseudo-lithofacies curve.

[0042] Through the derivation of Equations (2), (3) and (7), the following is obtained:

[0043]

[0044] Taking the parameters related to the densification coefficient on the left side as the pseudo-lithofacies curve, also known as the disconnected factor, and rewriting it as UF (unconnected factor), the expression is

[0045]

[0046] The disconnected factor reflects the pore disconnectedness and the relationship between porosity and permeability. Therefore, its value can distinguish lithofacies with high and low permeability. So, the disconnected factor curve can also be called the pseudo-lithofacies curve.

[0047] It should be noted that all the conditions required to obtain the pseudo-lithofacies curve can be obtained from well logging data; the only thing that needs to be calculated is the disconnected porosity. This parameter can be obtained through the following process:

[0048] (1) Select the search range of the disconnected porosity;

[0049] (2) Using the matching between the measured P-wave velocity and the theoretically calculated velocity in the study area as a constraint condition, use the simulated annealing algorithm for optimization to obtain the disconnected porosity.

[0050] Obtain the disconnected porosity After that, substitute it into formula (9) to calculate the pseudo-lithofacies curve.

[0051] As a specific implementation manner of the present invention, in the step S3, based on seismic data, use the well-controlled Kriging interpolation algorithm to obtain the pseudo-lithofacies curve of the target area.

[0052] Furthermore, the elastic parameter prediction and porosity prediction results obtained by using the prestack inversion technology can be used to characterize the permeability of the whole area.

[0053] In a second aspect, the present invention provides a permeability prediction device based on seismic data, including:

[0054] A data collection unit for collecting well logging data;

[0055] A data processing unit for calculating the pseudo-lithofacies curve from the well logging data;

[0056] A fitting unit for constructing a permeability correlation curve by using the relevant curve analysis algorithm with the pseudo-lithofacies curve as a constraint;

[0057] A prediction unit for predicting the permeability of the target area by using the seismic data of the target area.

[0058] In a third aspect, the present invention provides a computer-readable storage medium, and the computer program stored in the computer-readable storage medium can be executed by one or more processors to implement the method as described in the first aspect.

[0059] Fourthly, the present invention provides an electronic device, including a memory and one or more processors. A computer program is stored on the memory. When the computer program is executed by the one or more processors, the method described in the first aspect is performed.

[0060] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0061] 1. Based on the rock physics model and actual data characteristics, the present invention constructs a pseudo-lithofacies characterization curve and a permeability-related parameter curve, and uses a seismic data inversion algorithm to obtain the reservoir permeability based on seismic data. Compared with the prior art, the present invention has better theoretical advancement and theoretical interpretability. The pseudo-lithofacies curve, i.e., the unconnected factor (UF), is innovatively proposed and applied to lithofacies characterization and classification for the first time. The target-related analysis algorithm is innovatively used to obtain a theoretically interpretable permeability-related parameter curve, and based on this, better seismic prediction results of permeability are obtained, achieving the purpose of supporting the description of reservoir permeability characteristics and the efficient exploration and development of oil and gas fields.

[0062] 2. The prediction method of the present invention uses an explicit expression obtained by the correlation curve analysis algorithm, combines the pseudo-lithofacies curve, porosity, and elastic parameters to construct a permeability expression, which is more in line with the understanding of the pore-permeability relationship in theory and common sense. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 Schematic diagram of rock physics modeling in an embodiment of the present invention;

[0064] Figure 2A Schematic diagram of obtaining permeability-related parameters by target-related analysis in an embodiment of the present invention. It can be seen that parameter A takes -2;

[0065] Figure 2B Schematic diagram of obtaining permeability-related parameters by target-related analysis in an embodiment of the present invention. It can be seen that parameter B takes -1;

[0066] Figure 2C Schematic diagram of obtaining permeability-related parameters by target-related analysis in an embodiment of the present invention. It can be seen that parameter C takes 0.54;

[0067] Figure 3 Schematic diagram of comparison between the conventional method and the permeability characterization curve obtained by the present invention;

[0068] Figure 4 Schematic diagram of the prediction result obtained in an embodiment of the present invention; DETAILED DESCRIPTION OF THE EMBODIMENTS

[0069] The present invention will be further described below in conjunction with specific embodiments, but it does not constitute any limitation to the present invention.

[0070] Embodiment

[0071] This embodiment provides a method for predicting permeability based on seismic data. By combining Figures 1-4 , two typical wells are selected for actual data analysis, and the specific details are as follows:

[0072] S1: Construct a pseudo-lithofacies curve based on well logging data;

[0073] S11: Calculate the effective bulk modulus C s and shear modulus U s of the modified rock matrix through a rock physics model and the disconnected porosity;

[0074] The rock physics model is constructed as follows:

[0075]

[0076]

[0077] In the formula, V PS , V SS are the longitudinal and transverse wave velocities of the modified rock matrix; V Pm , V Sm are the initial longitudinal and transverse wave velocities of the rock matrix, which can be calculated from the moduli of the matrix constituent minerals; V Pf is the longitudinal wave velocity of the fluid in the disconnected pores. It is usually considered that the disconnected pores contain immobile formation water; is the disconnected porosity, is the total porosity, is the connected porosity; ρ m is the density of the initial matrix minerals; ρ fl is the density of the fluid in the disconnected pores;

[0078] Among them, the disconnected porosity is obtained through the following process:

[0079] (1) Select the search range of the disconnected porosity;

[0080] (2) Using the matching of the measured longitudinal wave velocity and the theoretical calculated velocity in the study area as a constraint condition, use the simulated annealing algorithm for optimization to obtain the disconnected porosity.

[0081] The longitudinal wave velocity of the rock matrix containing disconnected pores is calculated by formula (1), and the transverse wave velocity of the rock matrix containing disconnected pores is estimated by applying Raymer's extended formula (2);

[0082] From the velocity values of the modified rock matrix, the effective bulk modulus C s and shear modulus U of the modified rock matrix are obtained through formulas (3) and (4)s :

[0083] U s = ρ ms v 2 ss (3)

[0084]

[0085] Where: ρ ms is the corrected matrix mineral density;

[0086]

[0087] S12: The bulk modulus K of the rock matrix considering the disconnected porosity ma = 1 / C s ; The shear modulus of the rock matrix with disconnected porosity is U ma = U s , the rock matrix density is ρ ma = ρ ms , the bulk modulus K of the rock skeleton dry and the shear modulus U dry ;

[0088] The calculation process of the bulk modulus and shear modulus of the rock skeleton is as follows:

[0089]

[0090]

[0091] Among them, K dry is the bulk modulus of the rock skeleton; U dry is the shear modulus of the rock skeleton; Z is the densification coefficient.

[0092] S13: According to the effective compression modulus C of the corrected rock matrix obtained in step S11 s , the shear modulus U of the rock skeleton dry and the rock physics model are combined to obtain the pseudo-lithofacies curve.

[0093] Through the derivation of formulas (2), (3) and (7), the following is obtained:

[0094]

[0095] Taking the parameters related to the densification coefficient on the left side as the pseudo-lithofacies curve UF, the expression is

[0096]

[0097] The rock physics model of the study area is obtained: including disconnected pores and pseudo-lithofacies curves, as Figure 1As shown, the measured curves of P-wave and S-wave velocities and density match well with the curves of the rock physics model. Figure 1 In the first three columns of Figure 1 , the red and blue curves match well, verifying the effectiveness of rock physics modeling. The sixth and seventh columns respectively represent the obtained unconnected pore and pseudo-lithofacies curves.

[0098] S2: Construct the permeability-related curve controlled by the pseudo-lithofacies curve;

[0099] The permeability-related curve is denoted as PERM, and its expression is:

[0100] PERM = UF * phi * A + B * Ip + C * Is

[0101] In the formula, A, B, and C are fitting parameters to be determined; phi is porosity, * is the multiplication symbol, Ip is P-wave impedance, Is is S-wave impedance, and UF is the pseudo-lithofacies curve.

[0102] Intermediate parameters are obtained based on the target correlation analysis algorithm, as Figures 2A-2C shown. Figure 2A It is a schematic diagram of the permeability-related parameters obtained by target correlation analysis. It can be seen that the parameter A takes -2; Figure 2B It is a schematic diagram of the permeability-related parameters obtained by target correlation analysis. It can be seen that the parameter B takes -1; Figure 2C It is a schematic diagram of the permeability-related parameters obtained by target correlation analysis. It can be seen that the parameter C takes 0.54; Combining them forms an explicit expression of permeability. The comparison of the matching degree between this expression and the conventional fluid factor curve and permeability is as Figure 3 shown. It can be seen that the permeability-related parameter curve obtained by the target correlation analysis algorithm in this embodiment can better characterize the permeability characteristics. Except for some errors near 3600m, the characteristics of the lower half of the reservoir are well reflected; while the characteristic curve obtained by the conventional method is significantly different from the permeability curve and is difficult to be used in practice.

[0103] S3: Calculate the permeability of the target area based on seismic data;

[0104] Based on seismic data, a predicted profile of permeability is obtained. It can be seen that the permeability of 4 well logs in the profile matches well with the seismic predicted permeability, and the profile characteristics well show the change of reservoir permeability and also match well with the geological characteristics, as Figure 4 shown.

[0105] In one embodiment, a permeability prediction device based on seismic data is provided, including:

[0106] A data collection unit for collecting well log data;

[0107] A data processing unit for calculating the pseudo-lithofacies curve from the well log data;

[0108] A fitting unit, which constructs a permeability correlation curve by using a correlation curve analysis algorithm with a pseudo-lithofacies curve as a constraint.

[0109] A prediction unit, which predicts the permeability of a target area by using seismic data of the target area.

[0110] In one embodiment, a computer-readable storage medium is provided. The computer program stored in the computer-readable storage medium can be executed by one or more processors to implement the method described in the above embodiment.

[0111] In one embodiment, an electronic device is provided, including a memory and one or more processors. A computer program is stored on the memory. The memory and the one or more processors are communicatively connected to each other. When the computer program is executed by the one or more processors, the method described in the above embodiment is executed.

[0112] In summary, the permeability prediction method based on seismic data of the present invention constructs a pseudo-lithofacies characterization curve and a permeability correlation parameter curve based on a rock physics model and actual data characteristics, and uses a seismic data inversion algorithm to obtain the reservoir permeability based on seismic data.

[0113] In addition, it should be understood that the method or system disclosed in the embodiments provided in the present application can also be implemented in other ways. The method or system embodiments described above are only illustrative. For example, the flowcharts and block diagrams in the drawings show the possible architectures, functions, and operations of the methods and devices according to multiple embodiments of the present application. In this regard, each block in the flowchart or block diagram may represent a module, a computer program segment, or a part of a computer program. A module, a computer program segment, or a part of a computer program includes one or more computer programs for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the drawings, and may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and a computer program.

[0114] In this application, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the process, method, apparatus or device comprising the element; if terms such as "first", "second", etc. are described only for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features or implicitly specifying the sequence of the indicated technical features. In addition, in the description of this application, unless otherwise specified, the terms "a plurality", "many" mean at least two.

[0115] Finally, it should be noted that in the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "an example" or "some examples" means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0116] Although the embodiments of this application have been shown and described above, it can be understood that the above embodiments are all exemplary, and the content described is only an implementation manner adopted for the convenience of understanding this application and is not used to limit this application. Any person skilled in the art within the technical field to which this application pertains can make any modifications and changes in the form of implementation and details without departing from the spirit and scope disclosed by this application, but the protection scope of this application shall still be subject to the scope defined by the appended claims.

Claims

1. A method for predicting permeability based on seismic data, characterized in that, it includes: Constructing a permeability correlation curve by using a correlation curve analysis algorithm with a pseudo-lithofacies curve as a constraint; The permeability correlation curve is denoted as PERM, and its expression is: PERM = UF * phi * A + B * Ip + C * Is In the formula, A, B, and C are parameters to be fitted; phi is porosity, * is the multiplication symbol, Ip is the longitudinal wave impedance, Is is the shear wave impedance, and UF is the pseudo-lithofacies curve.

2. The method for predicting permeability based on seismic data according to claim 1, characterized in that, the expression of the pseudo-lithofacies curve UF is: where ρ ms is the corrected matrix mineral density; is the connected porosity, V SS is the shear wave velocity of the corrected rock matrix, U ma is the shear modulus of the rock matrix with non-connected porosity.

3. The method for predicting permeability based on seismic data according to claim 2, characterized in that, The corrected matrix mineral density ρ ms has the following expression: where ρ m is the density of the initial matrix mineral, is the disconnected porosity, and ρ fl is the density of the fluid in the disconnected pores.

4. The method for predicting permeability based on seismic data according to claim 3, characterized in that, The disconnected porosity The acquisition method thereof includes the following steps: (1) Selecting the search range of the disconnected porosity; (2) Using the simulated annealing algorithm for optimization with the matching of the measured longitudinal wave velocity and the theoretical calculated velocity in the target area as a constraint condition to obtain the disconnected porosity.

5. The method for predicting permeability based on seismic data according to any one of claims 1-4, characterized in that, the method includes the following steps: S1: Constructing a pseudo-lithofacies curve based on well logging data; S2: Constructing a permeability correlation curve controlled by the pseudo-lithofacies curve; S3: Calculating the permeability of the target area based on seismic data.

6. The method for predicting permeability based on seismic data according to claim 5, characterized in that, in the step S1, it includes the following steps: S11: Calculate the effective compression modulus C of the modified rock matrix and the shear modulus U through the rock physics model and the disconnected porosity s and shear modulus U s ; S12: Bulk modulus K of the rock matrix considering the disconnected porosity ma = 1 / C s ; Shear modulus of the rock matrix with disconnected porosity is U ma = U s , Rock matrix density is ρ ma = ρ ms , Bulk modulus K of the rock skeleton dry and shear modulus U dry ; S13: Obtain the effective compression modulus C of the corrected rock matrix according to step S1 s , the shear modulus U of the rock skeleton dry and combine with the rock physical model to obtain the pseudo-lithofacies curve.

7. The method for predicting permeability based on seismic data according to claim 5, characterized in that, in the step S3, based on seismic data, using the well-controlled Kriging interpolation algorithm to obtain the pseudo-lithofacies curve of the target area.

8. A device for predicting permeability based on seismic data, characterized in that, it includes: A data collection unit for collecting well logging data; A data processing unit for calculating a pseudo-lithofacies curve from the well logging data; A fitting unit for constructing a permeability correlation curve by using a correlation curve analysis algorithm with a pseudo-lithofacies curve as a constraint; A prediction unit for predicting the permeability of the target area by using the seismic data of the target area.

9. A computer-readable storage medium, characterized in that, the computer program stored in the computer-readable storage medium can be executed by one or more processors to implement the method according to any one of claims 1-7.

10. An electronic device, characterized in that, it includes a memory and one or more processors, and a computer program is stored on the memory. When the computer program is executed by the one or more processors, it executes the method according to any one of claims 1-7.

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