Method for determining brittleness parameters of deep reservoirs and related equipment

Abstract

The present application provides a method, device, electronic device, and storage medium for determining brittle parameters of a deep reservoir. The method comprises: obtaining seismic data of a target reservoir and the P-wave velocity, S-wave velocity, density, and elastic impedance of well logging; determining the on-hole brittle parameter information of the well logging based on the P-wave velocity, S-wave velocity, and density; determining the elastic impedance body of the target reservoir based on the seismic data and the elastic impedance; determining an angle-dependent weighting coefficient matrix of a target elastic impedance equation based on the elastic impedance and the on-hole brittle parameters; and determining the brittle parameters of the target reservoir based on the angle-dependent weighting coefficient matrix and the elastic impedance body.

Description

Technical Field

[0001] The present application relates to the field of oil and gas geophysical exploration, and in particular to a method for determining brittleness parameters of deep reservoirs and related equipment. Background Art

[0002] With the continuous expansion of oil and gas exploration, global oil and gas exploration has expanded to the field of unconventional oil and gas. Tight oil and gas, such as tight sandstone oil and gas, shale oil and gas, etc., as important global reserve resources, their exploration and development technology has become a hot topic and difficulty in current research. Tight oil and gas refers to unconventional oil and gas existing in tight reservoirs, which has strong low permeability characteristics, high brittleness, and easy fracturing. Both seismic rock physics experiments and exploration practices have shown that Young's modulus and Poisson's ratio can better characterize the brittleness of rocks. Tight sandstone and shale reservoirs with high brittleness have higher Young's modulus and lower Poisson's ratio characteristics. Compared with shale oil and gas reservoirs abroad, my country's marine shale oil and gas are buried deeper, and deep reservoir seismic data usually have low illumination, low signal-to-noise ratio, insufficient resolution, and especially lack of large-angle incidence information, which makes the brittleness parameters obtained by existing methods not very accurate. Summary of the Invention

[0003] In response to the above problems, the present application provides a method for determining the brittleness parameters of a reservoir and related equipment.

[0004] The present application provides a method for determining brittleness parameters of deep reservoirs, comprising:

[0005] Obtaining seismic data and well logging data of the target reservoir including compressional wave velocity, shear wave velocity, density, elastic impedance and;

[0006] Determining the uphole brittleness parameter information of the well logging based on the P-wave velocity, S-wave velocity and density;

[0007] determining an elastic impedance volume of a target reservoir based on the seismic data and the elastic impedance;

[0008] determining an angle-dependent weighting coefficient matrix of a target elastic impedance equation based on the elastic impedance and the uphole brittleness parameter;

[0009] The brittleness parameter of the target reservoir is determined based on the angle-dependent weighting coefficient matrix and the elastic impedance body.

[0010] In some embodiments, the uphole brittleness parameter information includes: uphole Young's modulus and uphole Poisson's ratio; and determining the uphole brittleness parameter information of the well logging based on the longitudinal wave velocity, shear wave velocity, and density includes:

[0011] The uphole Young's modulus of the well logging is calculated using a first calculation formula based on the longitudinal wave velocity, the shear wave velocity and the density, wherein the first calculation formula is:

[0012]

[0013] The uphole Poisson's ratio of the well logging is calculated using a second calculation formula based on the longitudinal wave velocity and the shear wave velocity, wherein the second calculation formula is:

[0014]

[0015] Among them, v p is the longitudinal wave velocity, v s is the shear wave velocity and ρ is the density.

[0016] In some embodiments, the elastic impedance includes: first angle elastic impedance data on the well logging and second angle elastic impedance data on the well logging, and determining an angle-dependent weighting coefficient matrix of a target elastic impedance equation based on the elastic impedance and the well brittleness parameter includes:

[0017] Inputting the first angle elastic impedance data on the well logging and the uphole brittleness parameter into a third calculation model to determine an angle-dependent weighting coefficient corresponding to the first angle elastic impedance data;

[0018] The second angle elastic impedance data on the well logging and the uphole brittleness parameter are input into the third calculation model to determine the angle-dependent weighting coefficient corresponding to the second angle elastic impedance data. The third calculation model is:

[0019]

[0020] is the angle-dependent weighting coefficient, is the elastic impedance data, is the elasticity parameter;

[0021] An angle-dependent weighting coefficient matrix is ​​determined based on the angle-dependent weighting coefficient corresponding to the first elastic angle impedance data and the angle-dependent weighting coefficient corresponding to the second elastic angle impedance data.

[0022] In some embodiments, determining the brittleness parameter of the target reservoir based on the angle-dependent weighting coefficient matrix and the elastic impedance body includes:

[0023] The angle-dependent weighted coefficient matrix and the elastic impedance body are input into a target elastic impedance equation, and the brittleness parameter of the target reservoir is calculated using a least squares algorithm or a conjugate gradient algorithm.

[0024] In some embodiments, the method further comprises:

[0025] Constructing binomial reflection characteristic equation of deep brittleness parameter;

[0026] Determine the two-term elastic impedance equation based on the brittle parameter based on the binomial reflection characteristic equation;

[0027] Normalizing the two-term elastic impedance equation so that the dimension of the two-term elastic impedance equation is the same as the dimension of the impedance data;

[0028] Perform linearization processing on the normalized two-term elastic impedance equation to obtain a linearized elastic impedance equation;

[0029] The target elastic impedance equation is determined based on the linearized elastic impedance equation.

[0030] In some embodiments, the target elastic impedance equation is:

[0031]

[0032] in, is the angle-dependent weighting coefficient matrix, is the elastic impedance data, is the elasticity parameter.

[0033] In some embodiments, the method further comprises:

[0034] Obtaining rock modulus, reservoir rock parameter information and well logging data of a target area, wherein the target reservoir is within the target area;

[0035] Establishing a rock modulus calculation model for the target reservoir based on the rock modulus, the reservoir rock parameter information, and the well logging data;

[0036] reconstructing a longitudinal wave curve based on the rock modulus calculation model;

[0037] Iteratively inverting and correcting the model parameters of the rock modulus calculation model based on the measured curve in the logging data and the longitudinal wave curve to obtain a target rock modulus calculation model;

[0038] The shear wave velocity of the target reservoir is obtained based on the target rock modulus calculation model.

[0039] The embodiment of the present application provides a device for determining brittleness parameters of a reservoir, comprising:

[0040] The first acquisition module is used to obtain seismic data of the target reservoir and the P-wave velocity, S-wave velocity, density, and elastic impedance of the well logging;

[0041] A first determining module is configured to determine the on-hole brittleness parameter information of the well logging based on the P-wave velocity, S-wave velocity and density;

[0042] a second determining module, configured to determine an elastic impedance volume of a target reservoir based on the seismic data and the elastic impedance;

[0043] a third determining module, configured to determine an angle-dependent weighting coefficient matrix of a target elastic impedance equation based on the elastic impedance and the uphole brittleness parameter;

[0044] A fourth determination module is configured to determine a brittle parameter of the target reservoir based on the angle-dependent weighted coefficient matrix and the elastic impedance body.

[0045] An embodiment of the present application provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, any one of the above-mentioned methods for determining the brittleness parameters of a deep reservoir is executed.

[0046] An embodiment of the present application provides a storage medium, which stores a computer program that can be executed by one or more processors and can be used to implement any of the above-mentioned methods for determining the brittleness parameters of deep reservoirs.

[0047] The present application provides a method, device, electronic device and storage medium for determining the brittleness parameters of a deep reservoir. The method obtains the P-wave velocity, S-wave velocity, density, elastic impedance and seismic data of well logging in a target reservoir, determines the uphole brittleness parameter information of the well logging based on the P-wave velocity, S-wave velocity and density, determines the elastic impedance body of the target reservoir based on the seismic data and the elastic impedance, determines the angle-dependent weighting coefficient matrix of the target elastic impedance equation based on the elastic impedance and the uphole brittleness parameters, and determines the brittleness parameters of the target reservoir based on the angle-dependent weighting coefficient matrix and the elastic impedance body. This method can predict the brittleness parameters of the target reservoir and improve the prediction accuracy of the brittleness parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Hereinafter, the present application will be described in more detail based on embodiments with reference to the accompanying drawings.

[0049] Figure 1 A schematic diagram of a process flow for determining brittleness parameters of a deep reservoir provided in an embodiment of the present application;

[0050] Figure 2 A schematic diagram of the implementation flow of another method for determining brittleness parameters of deep reservoirs provided in an embodiment of the present application;

[0051] Figure 3 A schematic diagram of a process flow for implementing another method for determining brittleness parameters of a deep reservoir provided in an embodiment of the present application;

[0052] Figure 4The figure shows a comparison between the shear wave estimation result of a well in the work area according to the embodiment of the present application and the measured curve;

[0053] Figure 5 A schematic structural diagram of a device for determining brittleness parameters of a deep reservoir provided in an embodiment of the present application;

[0054] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0055] In the drawings, like components are given like reference numerals, and the drawings are not drawn to scale. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0057] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0058] If similar descriptions of "first\second\third" appear in the application documents, the following explanation will be added. In the following description, the terms "first\second\third" are only used to distinguish similar objects and do not represent a specific order for the objects. It can be understood that "first\second\third" can be interchanged with a specific order or sequence where permitted, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0059] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0060] To address the problems existing in the related art, embodiments of the present application provide a method for determining a reservoir brittleness parameter, which is applicable to electronic devices such as computers and mobile terminals. The functions implemented by the method for determining a reservoir brittleness parameter provided in embodiments of the present application can be implemented by a processor of the electronic device invoking program code, wherein the program code can be stored in a computer storage medium.

[0061] Example 1

[0062] The present invention provides a method for determining the brittleness parameters of a reservoir. Figure 1 A schematic diagram of a method for determining brittleness parameters of a reservoir provided in an embodiment of the present application is shown in FIG. Figure 1 Shown, including:

[0063] Step S101: Obtain seismic data of the target reservoir and logging data of longitudinal wave velocity, shear wave velocity, density, and elastic impedance.

[0064] In the embodiments of the present application, seismic data and well logging data on the target reservoir, including compressional wave velocity, shear wave velocity, density, and elastic impedance, can be obtained through input from an input device, such as a keyboard, mouse, voice input device, or measurement device; through an external storage device, such as a USB flash drive or mechanical hard drive; through a network, such as the Internet or a local area network; or by reading local data. In the embodiments of the present application, the target reservoir is a reservoir in the area to be studied.

[0065] Step 102: determining the uphole brittleness parameter information of the well logging based on the P-wave velocity, S-wave velocity and density.

[0066] In an embodiment of the present application, the uphole brittle parameter information includes: uphole Young's modulus and uphole Poisson's ratio. The uphole Young's modulus of the well logging can be calculated based on the longitudinal wave velocity, shear wave velocity and density, and the uphole Poisson's ratio of the well logging can be obtained based on the longitudinal wave velocity and shear wave velocity.

[0067] In the embodiment of the present application, the Young's modulus of the well logging is calculated using a first calculation formula based on the longitudinal wave velocity, shear wave velocity and density.

[0068] The first calculation formula is:

[0069] In the embodiment of the present application, the uphole Poisson's ratio of the well logging is calculated using a second calculation formula based on the longitudinal wave velocity and the shear wave velocity, wherein the second calculation formula is:

[0070]

[0071] Among them, v p is the longitudinal wave velocity, v s is the shear wave velocity and ρ is the density.

[0072] Step S103: determining the elastic impedance volume of the target reservoir based on the seismic data and the elastic impedance.

[0073] In the embodiment of the present application, the elastic group antibody is three-dimensional data.

[0074] Step S104 : determining an angle-dependent weighted coefficient matrix of a target elastic impedance equation based on the elastic impedance and the uphole brittleness parameter.

[0075] In an embodiment of the present application, the elastic impedance includes: first-angle elastic impedance data on the well logging and second-angle elastic impedance data on the well logging; in an embodiment of the present application, determining the angle-dependent weighted coefficient matrix of the target elastic impedance equation based on the elastic impedance body and the well brittleness parameter can be achieved in the following manner: inputting the first-angle elastic impedance data and the elastic parameters corresponding to the first-angle elastic impedance data into a third calculation model to determine the angle-dependent weighted coefficient corresponding to the first-angle elastic impedance data; inputting the second-angle elastic impedance data and the elastic parameters corresponding to the second-angle elastic impedance data into the third calculation model to determine the angle-dependent weighted coefficient corresponding to the second-angle elastic impedance data, and the third calculation model is:

[0076]

[0077] in, is the angle-dependent weighting coefficient, is the elastic impedance data, is the elasticity parameter;

[0078] An angle-dependent weighting coefficient matrix is ​​determined based on the angle-dependent weighting coefficient corresponding to the first elastic angle impedance data and the angle-dependent weighting coefficient corresponding to the second elastic angle impedance data.

[0079] After the angle-dependent weighting coefficient matrix is ​​determined, the target elastic impedance equation may be updated based on the angle-dependent weighting coefficient matrix.

[0080] In the embodiment of the present application, the target elastic impedance equation is:

[0081]

[0082] in, is the angle-dependent weighting coefficient matrix, is the elastic impedance data, is the elasticity parameter.

[0083] Step S105 : determining the brittleness parameter of the target reservoir based on the angle-dependent weighted coefficient matrix and the elastic impedance body.

[0084] After the angle-dependent weighting coefficient matrix is ​​determined, the angle-dependent weighting coefficient matrix and the elastic impedance body may be input into a target elastic impedance equation.

[0085] In the embodiment of the present application, the target elastic impedance equation is:

[0086]

[0087] in, is the angle-dependent weighting coefficient matrix, is the elastic impedance data, is the elasticity parameter.

[0088] In the embodiment of the present application, the angle-dependent weighted coefficient matrix and the elastic impedance body are input into the target elastic impedance equation, and the brittleness parameter of the target reservoir is calculated using the least squares algorithm or the conjugate gradient algorithm.

[0089] The present application provides a method for determining the brittleness parameters of a deep reservoir. The method obtains the P-wave velocity, S-wave velocity, density, elastic impedance and seismic data of well logging in a target reservoir, determines the uphole brittleness parameter information of the well logging based on the P-wave velocity, S-wave velocity and density, determines the elastic impedance body of the target reservoir based on the seismic data and the elastic impedance, determines the angle-dependent weighting coefficient matrix of the target elastic impedance equation based on the elastic impedance and the uphole brittleness parameters, and determines the brittleness parameters of the target reservoir based on the angle-dependent weighting coefficient matrix and the elastic impedance body. This method can predict the brittleness parameters of the target reservoir and improve the prediction accuracy of the brittleness parameters.

[0090] Example 2

[0091] Based on the above embodiments, the present invention provides a method for determining the brittleness parameters of deep reservoirs. Figure 2 A schematic diagram of the implementation flow of another method for determining the brittleness parameters of a deep reservoir provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, the method includes:

[0092] Step S201: constructing a binomial reflection characteristic equation of deep brittleness parameters.

[0093] Currently, prestack brittleness parameter prediction is generally based on conventional three-parameter inversion. Conventional three-parameter inversion has two problems: first, the indirect calculation of brittleness parameters leads to large cumulative errors, and second, the density term obtained by inversion is not accurate enough. In the embodiments of this application, considering the lack of large-angle reflection response in deep seismic, the contribution of brittleness parameters to the reflection coefficient is explored, and a two-term AV0 reflection characteristic equation for deep brittleness parameters is derived, laying the foundation for direct square derivation of brittleness parameters.

[0094] Actual rock is a two-phase medium consisting of a solid skeleton and pore fluid. Based on Gassmann's poroelasticity theory, Russell et al. proposed using the Gassmann fluid term for fluid identification. Its expression is as follows:

[0095]

[0096] Among them, γ dry 2 is the square of the ratio of the longitudinal and transverse wave velocities of dry rock. Then, a three-term AVO approximation equation, namely the Russell approximation, is derived based on the Gassmann fluid term (f), shear modulus (u), and density (ρ):

[0097]

[0098] Among them, γ sat 2 is the square of the ratio of the longitudinal and transverse wave velocities of saturated rock. Later, scholars derived the two-term AVO approximate equation based on the Gassmann fluid term (f) and the shear modulus (u) based on this formula:

[0099]

[0100] Among them, the fluid term (f), shear modulus (u) and density (ρ), γ dry 2 is the square of the ratio of the longitudinal and transverse wave velocities of dry rock

[0101] In the embodiment of the present application, the two-term AV0 reflection characteristic equation of the deep brittleness parameter is derived based on the relationship between the elastic parameters.

[0102] In an isotropic medium, the relationship between Young's modulus E, Poisson's ratio σ and shear modulus u is: make Then we have:

[0103] Dividing both sides by the shear modulus yields: Also because:

[0104]

[0105] Where σ1 and σ2 represent the Poisson's ratios of the upper and lower layers, respectively.

[0106] In isotropic media, Poisson's ratio is related to the ratio of longitudinal and transverse wave velocities γ sat The relationship is: So the calculation is:

[0107] on the other hand, The same ones are:

[0108]

[0109] ρ、V p 、V s The density, longitudinal wave velocity, and shear wave velocity of the saturated rock respectively; Δρ / ρ, ΔVp / V p , ΔV s / V s are the density reflection coefficient, the longitudinal wave velocity reflection coefficient, and the shear wave velocity reflection coefficient respectively; is the square of the ratio of longitudinal and transverse wave velocities of dry rock, is the square of the ratio of longitudinal and transverse wave velocities of saturated rock,

[0110] Due to Gardner's formula, It is not applicable to any work area, so it is further generalized to a more general format:

[0111]

[0112] Among them, a and r1 are the fitting coefficients of the density and longitudinal wave velocity data of the actual working area. From the above formula, we can get:

[0113]

[0114] Zhang F et al. used the following assumptions when deriving the ray elastic impedance:

[0115]

[0116] Where r2 is the fitting coefficient between the shear wave velocity reflection coefficient and the density reflection coefficient.

[0117] So we can get the relationship between Gassmann fluid term and density:

[0118]

[0119] The shear modulus Likewise,

[0120]

[0121] and then,

[0122] Therefore, the relationship between the Gassmann fluid term and the shear modulus is:

[0123]

[0124] So by Further available

[0125]

[0126] That is, the binomial reflection characteristic equation of deep brittleness parameters is constructed in the above way.

[0127] This is the binomial reflection characteristic equation.

[0128] Step S202 : determining a two-term elastic impedance equation based on brittle parameters based on the binomial reflection characteristic equation.

[0129] In the embodiment of the present application, based on the derivation of the two-term AVO reflection characteristic equation of the deep brittleness parameter, a two-term elastic impedance equation of the brittleness parameter is constructed.

[0130] Connolly proposed the concept of elastic impedance and derived the elastic impedance equation based on the longitudinal and transverse wave velocities and density based on the Aki-Richards approximation. Among them, the elastic impedance equation based on the longitudinal and transverse wave velocities and density is:

[0131]

[0132] Among them, R pp is the reflection coefficient, θ is the incident angle, and EI is the elastic impedance.

[0133] The relationship between the pre-stack reflection coefficient and the elastic impedance is established through the above equation. This application uses Connolly's idea of ​​deriving the elastic impedance equation to derive a two-term elastic impedance equation based on Young's modulus and Poisson's ratio:

[0134] EI(θ)=E a(θ) σ b(θ) ;

[0135] Among them, the expressions of the exponential terms a(θ) and b(θ) are:

[0136]

[0137] Step S203 : performing normalization processing on the two-term elastic impedance equation so that the dimension of the two-term elastic impedance equation is the same as the dimension of the impedance data.

[0138] In the embodiment of the present application, the Whitcomb elastic impedance normalization method is used to normalize the elastic impedance equation so that its dimension is consistent with the longitudinal wave impedance:

[0139]

[0140] Where E0 and σ0 are the average values ​​of Young's modulus and Poisson's ratio, respectively; A0 is the elastic impedance normalization parameter, which is expressed as follows:

[0141]

[0142] Step S204 : performing linearization processing based on the normalized two-term elastic impedance equation to obtain a linearized elastic impedance equation.

[0143] Elastic impedance inversion can be viewed as multiple implementations of post-stack seismic inversion applied to partially stacked seismic data at different angles. In the above formula, elastic impedance has a nonlinear relationship with Young's modulus and Poisson's ratio. In order to extract brittle parameters from elastic impedance data, it is linearized:

[0144]

[0145] Step S205 : determining the target elastic impedance equation based on the linearized elastic impedance equation.

[0146] In the embodiment of the present application, the target elastic impedance equation is:

[0147]

[0148] in, is the angle-dependent weighting coefficient matrix, is the elastic impedance data, is the elasticity parameter.

[0149] In the embodiment of the present application, the target elastic impedance equation can be expressed as Ax=b, where A is a coefficient matrix, x is the elastic parameter to be determined, and b is the elastic impedance data.

[0150] Step S206: Obtain seismic data of the target reservoir and logging P-wave velocity, S-wave velocity, density, and elastic impedance.

[0151] In the embodiments of the present application, seismic data and well logging data on the target reservoir, including compressional wave velocity, shear wave velocity, density, and elastic impedance, can be obtained through input from an input device, such as a keyboard, mouse, voice input device, or measurement device; through an external storage device, such as a USB flash drive or mechanical hard drive; through a network, such as the Internet or a local area network; or by reading local data. In the embodiments of the present application, the target reservoir is a reservoir in the area to be studied.

[0152] Step 207: Determine the uphole brittleness parameter information of the well logging based on the P-wave velocity, S-wave velocity and density.

[0153] In an embodiment of the present application, the uphole brittle parameter information includes: uphole Young's modulus and uphole Poisson's ratio. The uphole Young's modulus of the well logging can be calculated based on the longitudinal wave velocity, shear wave velocity and density, and the uphole Poisson's ratio of the well logging can be obtained based on the longitudinal wave velocity and shear wave velocity.

[0154] In the embodiment of the present application, the Young's modulus of the well logging is calculated using a first calculation formula based on the longitudinal wave velocity, shear wave velocity and density.

[0155] The first calculation formula is:

[0156] In the embodiment of the present application, the uphole Poisson's ratio of the well logging is calculated using a second calculation formula based on the longitudinal wave velocity and the shear wave velocity, wherein the second calculation formula is:

[0157]

[0158] Among them, v p is the longitudinal wave velocity, v s is the shear wave velocity and ρ is the density.

[0159] Step S208: determining the elastic impedance volume of the target reservoir based on the seismic data and the elastic impedance.

[0160] In the embodiment of the present application, the elastic group antibody is three-dimensional data.

[0161] Step S209 : determining an angle-dependent weighted coefficient matrix of a target elastic impedance equation based on the elastic impedance and the uphole brittleness parameter.

[0162] In an embodiment of the present application, the first-angle elastic impedance data on the well logging and the second-angle elastic impedance data on the well logging; in an embodiment of the present application, determining the angle-dependent weighted coefficient matrix of the target elastic impedance equation based on the elastic impedance body and the uphole brittle parameter can be achieved in the following manner: inputting the first-angle elastic impedance data and the elastic parameters corresponding to the first-angle elastic impedance data into a third calculation model to determine the angle-dependent weighted coefficient corresponding to the first-angle elastic impedance data; inputting the second-angle elastic impedance data and the elastic parameters corresponding to the second-angle elastic impedance data into the third calculation model to determine the angle-dependent weighted coefficient corresponding to the second-angle elastic impedance data, and the third calculation model is:

[0163]

[0164] in, is the angle-dependent weighting coefficient, is the elastic impedance data, is the elasticity parameter;

[0165] An angle-dependent weighting coefficient matrix is ​​determined based on the angle-dependent weighting coefficient corresponding to the first elastic angle impedance data and the angle-dependent weighting coefficient corresponding to the second elastic angle impedance data.

[0166] After the angle-dependent weighting coefficient matrix is ​​determined, the target elastic impedance equation may be updated based on the angle-dependent weighting coefficient matrix.

[0167] In the embodiment of the present application, the target elastic impedance equation is:

[0168]

[0169] in, is the angle-dependent weighting coefficient matrix, is the elastic impedance data, is the elasticity parameter.

[0170] Step S210 : determining the brittleness parameter of the target reservoir based on the angle-dependent weighted coefficient matrix and the elastic impedance body.

[0171] After the angle-dependent weighting coefficient matrix is ​​determined, the angle-dependent weighting coefficient matrix and the elastic impedance body may be input into a target elastic impedance equation.

[0172] In the embodiment of the present application, the target elastic impedance equation is:

[0173]

[0174] in, is the angle-dependent weighting coefficient matrix, is the elastic impedance data, is the elasticity parameter.

[0175] In the embodiment of the present application, the angle-dependent weighted coefficient matrix and the elastic impedance body are input into the target elastic impedance equation, and the brittleness parameter of the target reservoir is calculated using the least squares algorithm or the conjugate gradient algorithm.

[0176] The method for determining the brittleness parameters of deep reservoirs provided in the embodiments of the present application derives a two-term AVO reflection characteristic equation for the deep brittleness parameters and constructs a two-term elastic impedance equation for the brittleness parameters. Based on this equation, the quantitative characterization relationship between angle-dependent elastic impedance data and brittleness parameters is utilized. In combination with the well logging brittleness parameter data calculated in the actual work area and the wellside channel elastic impedance inversion results, a linear regression optimization algorithm is used to obtain angle-dependent weighting coefficients that meet the target work area, thereby achieving direct pre-stack seismic inversion prediction of deep brittleness parameters. This brittleness parameter prediction method based on deep seismic reflection improves the accuracy of inversion results while taking into account the low illumination, low signal-to-noise ratio, insufficient resolution, and lack of high-angle incidence information of deep reservoir seismic data.

[0177] Example 3

[0178] Based on the above embodiments, the present application further provides a method for determining the brittleness parameters of deep reservoirs. Figure 3 Another method for determining the brittleness parameters of a reservoir provided in the embodiment of the present application is as follows: Figure 3 As shown, the method includes:

[0179] Step S301: Obtain rock modulus, reservoir rock parameter information and logging data of a target area, wherein the target reservoir is within the target area.

[0180] In an embodiment of the present application, the rock modulus, reservoir rock parameter information and logging data of the target area can be input through an input device, which can be a keyboard, a mouse, a voice input device, a measuring device, etc.; can also be input through an external storage device, which can be a USB flash drive, a mechanical hard disk, etc.; can also be received through a network, such as the Internet, a local area network; can also be obtained by reading local data, etc.

[0181] In the embodiment of the present application, reservoir rock parameter information can be obtained through experiments, and the reservoir rock parameter information may include porosity, aspect ratio, etc. The well logging data may include P-wave and S-wave velocities, density, etc. The rock modulus may include shear modulus, bulk modulus, etc.

[0182] Step S302: establishing a rock modulus calculation model for the target reservoir based on the rock modulus, the reservoir rock parameter information, and the well logging data.

[0183] Step S303: reconstructing a longitudinal wave curve based on the rock modulus calculation model.

[0184] Step S304 , performing iterative inversion to correct the model parameters of the rock modulus calculation model based on the measured curves in the logging data and the longitudinal wave curve, to obtain a target rock modulus calculation model.

[0185] In the embodiment of the present application, when performing inverse iterative optimization, the objective function of the optimization adopts a nonlinear optimization algorithm. For example, an optimized and improved simulated annealing algorithm is adopted. Since different model parameters have different effects on the objective function, they should have their own perturbation methods. Ingber gives the mechanism for generating new model parameters mik+1 by the kth iterative perturbation of model parameters mi as follows:

[0186]

[0187] in, u i ∈U[0,1], Inbger believes that if the model parameters follow the cooling mechanism t i (k)=t0 exp(-c i k1 / N )(c i is an adjustable parameter, N is the number of model parameters) and annealing is performed, and the global optimal solution will be obtained eventually, but it is still necessary to accept the control temperature T in the criterion. Here, the control temperature cooling criterion is T k =aT k-1 , where a is a given parameter, less than but close to 1.

[0188] According to the influence of the model parameters on the objective function, the temperature is appropriately raised during the annealing process. The operation is as follows: S i is m i Sensitivity factor relative to the objective function E(m): For the model parameter m i The control temperature t of the kth iteration ik The following changes will be made:

[0189]

[0190] Among them S max =max(S i ), so when S i When S is small, max / S i >1, that is, Reheating can make the optimization process jump out of the search for insensitive model parameters and turn to searching for more sensitive model parameters.

[0191] The SA (simulated annealing) algorithm is a randomized optimization method. Many parameters affect the solution search process. If the parameters are not set properly, the final solution may not be the global optimal solution. Therefore, without interfering with the annealing process, a screening memory operation is added to the SA. This retains the "current optimal solution" during the optimization process and continuously updates it, making the SA algorithm more intelligent.

[0192] Step S305: obtaining the shear wave velocity of the target reservoir based on the target rock modulus calculation model.

[0193] The shear wave velocity of the target reservoir can be obtained by inversion using the target rock modulus.

[0194] Step S306: Obtain seismic data of the target reservoir and logging P-wave velocity, S-wave velocity, density, and elastic impedance.

[0195] Step S307: determining the uphole brittleness parameter information of the well logging based on the P-wave velocity, S-wave velocity and density.

[0196] Step S308: determining the elastic impedance volume of the target reservoir based on the seismic data and the elastic impedance.

[0197] Step S309 : determining an angle-dependent weighted coefficient matrix of a target elastic impedance equation based on the elastic impedance and the uphole brittleness parameter.

[0198] Step S310: determining the brittleness parameter of the target reservoir based on the angle-dependent weighted coefficient matrix and the elastic impedance body.

[0199] The method for determining the brittleness parameters of a reservoir provided in the embodiment of the present application carries out seismic shear wave velocity estimation based on rock physics, and uses the estimated shear wave velocity to calculate the uphole brittleness parameters. At the same time, based on the poroelasticity theory, a two-term AVO reflection characteristic equation for the deep brittleness parameters is derived, and a two-term elastic impedance equation for the brittleness parameters is constructed. On this basis, the quantitative characterization relationship between the angle-dependent elastic impedance data and the brittleness parameters is utilized, and the well logging brittleness parameter data calculated in the actual work area and the well bypass elastic impedance inversion results are combined. A linear regression optimization algorithm is used to obtain the angle-dependent weighting coefficient that meets the target work area, thereby realizing the direct pre-stack seismic inversion prediction of the deep brittleness parameters. The brittleness parameter prediction method based on deep seismic reflection makes the inversion results more accurate, taking into account the low illumination, low signal-to-noise ratio, insufficient resolution, and lack of large-angle incidence information of deep reservoir seismic data.

[0200] Example 4

[0201] Based on the above embodiments, the present invention further provides a method for determining the brittleness parameters of a deep reservoir. Taking actual data from a certain work area as an example, the present invention further provides a method for determining the brittleness parameters of a reservoir. The method includes:

[0202] Step S1, estimating seismic shear wave velocity of deep oil and gas reservoirs.

[0203] Combining empirical rock physics relationships, experimental data, and actual logging data, a model for calculating the rock modulus of the target reservoir was established. The calculated modulus was used to reconstruct the P-wave curve, and an iterative inversion correction of the rock modulus with the measured curve was established. Ultimately, key elastic parameters such as S-wave velocity were estimated, laying a data foundation for prestack seismic inversion of elastic characteristic parameters. Objective function optimization can be performed using a nonlinear optimization algorithm; in this case, an optimized and improved simulated annealing algorithm was employed.

[0204] Since different model parameters have different effects on the objective function, they should have their own perturbation methods. Ingber gives the model parameter m i The kth iteration perturbation generates new model parameters m i k+1 The mechanism is:

[0205]

[0206] in, u i ∈U[0,1], Inbger believes that if the model parameters follow the cooling mechanism t i (k) = t0exp(-c i k 1 / N )(c i is an adjustable parameter, N is the number of model parameters) and annealing is performed, and the global optimal solution will be obtained eventually, but it is still necessary to accept the control temperature T in the criterion. Here, the control temperature cooling criterion is T k =aT k-1 , where a is a given parameter, less than but close to 1.

[0207] According to the influence of the model parameters on the objective function, the temperature is appropriately raised during the annealing process. The operation is as follows: S i is m i Sensitivity factor relative to the objective function E(m): For the model parameter m i The control temperature t of the kth iteration ik The following changes will be made:

[0208]

[0209] Among them S max =max(S i ), so when S i When S is small, max / S i >1, that is, Reheating can make the optimization process jump out of the search for insensitive model parameters and turn to searching for more sensitive model parameters.

[0210] The SA (simulated annealing) algorithm is a randomized optimization method. Many parameters affect the solution search process. If the parameters are not set properly, the final solution may not be the global optimal solution. Therefore, without interfering with the annealing process, a screening memory operation is added to the SA. This retains the "current optimal solution" during the optimization process and continuously updates it, making the SA algorithm more intelligent.

[0211] Figure 4 FIG. 1 is a comparison diagram of the shear wave estimation result of a well in the work area according to the embodiment of the present application and the measured curve, as shown in FIG. Figure 4 As shown in the figure, VP is the measured curve and VS is the estimated curve. It can be seen that the measured curve and the estimated curve basically coincide with each other, indicating that the prediction result is highly accurate.

[0212] Step 2: Calculation of well logging brittleness parameters.

[0213] The wellbore brittle parameter curve is calculated based on the estimated shear wave curve, including Young's modulus E and Poisson's ratio σ. The corresponding formula is: where v p is the longitudinal wave velocity, v s is the shear wave velocity and ρ is the density.

[0214] Step 3: Derivation of the two-term AVO reflection characteristic equation based on deep brittleness parameters.

[0215] Current prestack brittleness parameter prediction is typically based on conventional three-parameter inversion, which presents two problems: first, the indirect calculation of brittleness parameters can lead to large cumulative errors, and second, the inverted density term is inaccurate. Taking into account the lack of large-angle reflection response in deep seismic data, this paper explores the contribution of brittleness parameters to the reflection coefficient and derives a two-term AV0 reflection characteristic equation for deep brittleness parameters, laying the foundation for direct brittleness parameter prediction.

[0216] Actual rock is a two-phase medium consisting of a solid skeleton and pore fluid. Based on the poroelasticity theory proposed by Gassmann, Russell et al. proposed using the Gassmann fluid term for fluid identification. Its expression is as follows:

[0217]

[0218] where γ dry 2 is the square of the ratio of the longitudinal and transverse wave velocities of dry rock. Then, a three-term AVO approximation equation, namely the Russell approximation, is derived based on the Gassmann fluid term (f), shear modulus (u), and density (ρ):

[0219]

[0220] γ sat 2 is the square of the ratio of the longitudinal and transverse wave velocities of saturated rock. Later, scholars derived the two-term AVO approximate equation based on the Gassmann fluid term (f) and the shear modulus (u) based on this formula:

[0221]

[0222] The present invention derives a two-term AV0 reflection characteristic equation of deep brittle parameters based on the relationship between elastic parameters.

[0223] In an isotropic medium, the relationship between Young's modulus E, Poisson's ratio σ and shear modulus u is: make Then we have:

[0224] Dividing both sides by the shear modulus yields: Also because:

[0225]

[0226] Where σ1 and σ2 represent the Poisson's ratios of the upper and lower layers, respectively.

[0227] In isotropic media, Poisson's ratio is related to the ratio of longitudinal and transverse wave velocities γ sat The relationship is: So the calculation is:

[0228] on the other hand, The same ones are:

[0229]

[0230] ρ、V p 、V s The density, longitudinal wave velocity, and shear wave velocity of the saturated rock respectively; Δρ / ρ, ΔV p / V p , ΔV s / V s are the density reflection coefficient, the longitudinal wave velocity reflection coefficient, and the shear wave velocity reflection coefficient respectively; is the square of the ratio of longitudinal and transverse wave velocities of dry rock, is the square of the ratio of longitudinal and transverse wave velocities of saturated rock,

[0231] Due to Gardner's formula, It is not applicable to any work area, so it is further generalized to a more general format:

[0232]

[0233] Among them, a and r1 are the fitting coefficients of the density and longitudinal wave velocity data of the actual working area. From the above formula, we can get:

[0234]

[0235] Zhang F et al. used the following assumptions when deriving the ray elastic impedance:

[0236]

[0237] Where r2 is the fitting coefficient between the shear wave velocity reflection coefficient and the density reflection coefficient.

[0238] So we can get the relationship between Gassmann fluid term and density:

[0239]

[0240] The shear modulus Likewise,

[0241]

[0242] and then,

[0243] Therefore, the relationship between the Gassmann fluid term and the shear modulus is:

[0244]

[0245] So by Further available

[0246]

[0247] The above equation is the derived two-term AV0 reflection characteristic equation of the deep brittleness parameter.

[0248] To verify the accuracy of the two-term approximate AVO equation, a three-layer sandstone model was designed to perform accuracy analysis on the approximate equation, the Zoeppritz exact equation, and the Russell approximate equation. The model parameters are shown below.

[0249]

[0250] The middle layer of the model is low-impedance gas-bearing sandstone, and the overlying and underlying strata are high-impedance water-bearing sandstone. Therefore, the top interface of the gas-bearing sandstone is a negative wave impedance interface, and the bottom interface is a positive wave impedance interface. The reflection coefficient of the top interface of the gas-bearing sandstone in the table is calculated using the Zoeppritz equation, the Russell approximate formula, and the new approximate formula derived by the present invention, and the difference in reflection coefficient between the Russell approximate equation and the new approximate equation relative to the precise Zoeppritz equation is calculated. When the incident angle is not greater than 20°, the accuracy of the newly derived approximate equation is comparable to that of the Russell approximation, and the reflection coefficient curves calculated by the two almost overlap. However, as the incident angle increases, the errors between the two approximations and the Zoeppritz equation gradually increase. Therefore, when the incident angle is not greater than 20°, the newly derived approximate equation has a good approximation to the Zoeppritz equation, and its accuracy error is within an acceptable range, meeting the small-angle approximation accuracy requirements.

[0251] Step 4: Construction and inversion of the brittle parameter two-term elastic impedance equation.

[0252] Based on the derived two-term AVO reflection characteristic equation for deep brittle parameters, a two-term elastic impedance equation for brittle parameters was constructed. By utilizing the quantitative characterization relationship between angle-dependent elastic impedance data and brittle parameters, combined with the logging data in the actual work area and the elastic impedance inversion results of the well bypass, a linear regression optimization algorithm was used to obtain the angle-dependent weighted coefficient that meets the target work area, thereby realizing the direct pre-stack seismic inversion prediction of deep brittle parameters.

[0253] Connolly proposed the concept of elastic impedance and derived the elastic impedance equation based on longitudinal and transverse wave velocities and density based on the Aki-Richards approximation.

[0254]

[0255] Among them, R pp is the reflection coefficient, θ is the angle of incidence, and EI is the elastic impedance. The above formula establishes the relationship between the pre-stack reflection coefficient and the elastic impedance. Based on Connolly's idea of ​​deriving the elastic impedance equation, the present invention derives a two-term elastic impedance equation based on Young's modulus and Poisson's ratio:

[0256] EI(θ)=E a(θ) σ b(θ) ;

[0257] The expressions of the exponential terms a(θ) and b(θ) are:

[0258]

[0259] The elastic impedance normalization method of Whitcombe (2002) is used to normalize the elastic impedance equation so that its dimension is consistent with the longitudinal wave impedance:

[0260]

[0261] Where E0 and σ0 are the average values ​​of Young's modulus and Poisson's ratio, respectively; A0 is the elastic impedance normalization parameter, which is expressed as follows:

[0262]

[0263] Elastic impedance inversion can be viewed as multiple implementations of post-stack seismic inversion applied to partially stacked seismic data at different angles. In the above formula, elastic impedance has a nonlinear relationship with Young's modulus and Poisson's ratio. In order to extract brittle parameters from elastic impedance data, it is linearized:

[0264]

[0265] The two deep angles θ1 and θ2 can be expressed in the following matrix form and solved:

[0266]

[0267] This formula can be expressed as Ax = b, where A is the coefficient matrix, x is the elastic parameter to be determined, and b is the elastic impedance data. To maintain the stability of the solution, the elastic impedance obtained from the inversion of the near-well seismic trace and the well logging data can be used to calculate the weight coefficients a(θ) and b(θ):

[0268]

[0269] Substitute the wellside channel data of elastic impedance at two angles into the above formula respectively and calculate the coefficient matrix in combination with the logging curve. Substitute the coefficient matrix A into the previous equation and use the least squares algorithm or conjugate gradient algorithm to directly extract Young's modulus and Poisson's ratio, and then carry out the brittle parameter prediction of deep complex reservoirs.

[0270] The embodiment of the present application provides a method for determining the brittleness parameters of deep reservoirs, which includes four parts: estimation of seismic shear wave velocity of deep oil and gas reservoirs, calculation of well logging brittle parameters, derivation of a two-term AVO reflection characteristic equation based on deep brittle parameters, and construction and inversion of a two-term elastic impedance equation for brittle parameters. The present invention first carries out seismic shear wave velocity estimation based on rock physics, and calculates the wellbore brittle parameters using the estimated shear wave velocity. At the same time, based on the poroelasticity theory, it derives a two-term AVO reflection characteristic equation for deep brittle parameters and constructs a two-term elastic impedance equation for brittle parameters. On this basis, it utilizes the quantitative characterization relationship between angle-dependent elastic impedance data and brittle parameters, combines the well logging brittle parameter data calculated in the actual work area and the wellside elastic impedance inversion results, and uses a linear regression optimization algorithm to obtain the angle-dependent weighted coefficient that meets the target work area, thereby realizing direct pre-stack seismic inversion prediction of deep brittle parameters. The brittleness parameter prediction method based on deep seismic reflection makes the inversion results more accurate, taking into account the low illumination, low signal-to-noise ratio, insufficient resolution and lack of large-angle incidence information of deep reservoir seismic data.

[0271] Example 5

[0272] Based on the foregoing embodiments, an embodiment of the present application provides a device for determining the brittleness parameters of deep reservoirs. The modules included in the device, and the units included in each module, can be implemented by a processor in a computer device; of course, they can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP), or a field programmable gate array (FPGA), etc.

[0273] The embodiment of the present application provides a device for determining brittleness parameters of deep reservoirs. Figure 5 A schematic diagram of a device for determining brittleness parameters of a deep reservoir provided in an embodiment of the present application is shown in FIG. Figure 5 As shown, the apparatus 500 for determining the brittleness parameters of a deep reservoir includes:

[0274] The first acquisition module 501 is used to acquire seismic data of the target reservoir and the P-wave velocity, S-wave velocity, density, and elastic impedance of the well logging;

[0275] A first determining module 502 is configured to determine the wellbore brittleness parameter information of the well logging based on the P-wave velocity, S-wave velocity and density;

[0276] A second determination module 503 is configured to determine an elastic impedance volume of a target reservoir based on the seismic data and the elastic impedance;

[0277] A third determination module 504 is configured to determine an angle-dependent weighted coefficient matrix of a target elastic impedance equation based on the elastic impedance and the uphole brittleness parameter;

[0278] The fourth determination module 505 is configured to determine the brittleness parameter of the target reservoir based on the angle-dependent weighted coefficient matrix and the elastic impedance body.

[0279] In some embodiments, the uphole brittleness parameter information includes: uphole Young's modulus and uphole Poisson's ratio, and the first determination module 502 includes:

[0280] A first determining unit is configured to calculate the uphole Young's modulus of the well logging using a first calculation formula based on the longitudinal wave velocity, the shear wave velocity, and the density, wherein the first calculation formula is:

[0281]

[0282] The second determining unit is configured to calculate the uphole Poisson's ratio of the well logging using a second calculation formula based on the P-wave velocity and the S-wave velocity, wherein the second calculation formula is:

[0283]

[0284] Among them, v p is the longitudinal wave velocity, v s is the shear wave velocity, ρ is the density, E is the Young's modulus, and σ is the Poisson's ratio.

[0285] In some embodiments, the elastic impedance body includes: first angle elastic impedance data on the well logging and second angle elastic impedance data on the well logging, and the third determination module 504 includes:

[0286] a fourth determining unit, configured to input the first angle elastic impedance data and the elastic parameter corresponding to the first angle elastic impedance data into a third calculation model to determine an angle-dependent weighted coefficient corresponding to the first angle elastic impedance data;

[0287] a fifth determining unit, configured to input the second-angle elastic impedance data and the elastic parameter corresponding to the second-angle elastic impedance data into the third calculation model to determine an angle-dependent weighted coefficient corresponding to the second-angle elastic impedance data, wherein the third calculation model is:

[0288]

[0289] in, is the angle-dependent weighting coefficient, is the elastic impedance data, is the elasticity parameter;

[0290] A sixth determining unit is configured to determine an angle-dependent weighting coefficient matrix based on the angle-dependent weighting coefficient corresponding to the first elastic angle impedance data and the angle-dependent weighting coefficient corresponding to the second elastic angle impedance data.

[0291] In some embodiments, the fourth determining module 505 includes:

[0292] The seventh determining unit is configured to input the angle-dependent weighted coefficient matrix and the elastic impedance body into a target elastic impedance equation, and calculate the brittleness parameter of the target reservoir using a least squares algorithm or a conjugate gradient algorithm.

[0293] In some embodiments, the apparatus 500 for determining brittleness parameters of a deep reservoir further includes:

[0294] A building block for constructing binomial reflection characteristic equations for deep brittleness parameters;

[0295] a fourth determining module, configured to determine a binomial elastic impedance equation based on a brittle parameter based on the binomial reflection characteristic equation;

[0296] a sixth determining module, configured to perform normalization processing on the two-term elastic impedance equation so that the dimension of the two-term elastic impedance equation is the same as the dimension of the impedance data;

[0297] a seventh determination module, configured to perform linearization processing based on the normalized two-term elastic impedance equation to obtain a linearized elastic impedance equation;

[0298] An eighth determination module is configured to determine the target elastic impedance equation based on the linearized elastic impedance equation.

[0299] In some embodiments, the target elastic impedance equation is:

[0300]

[0301] in, is the angle-dependent weighting coefficient matrix, is the elastic impedance data, is the elasticity parameter.

[0302] In some embodiments, the apparatus 500 for determining brittleness parameters of a deep reservoir further includes:

[0303] A second acquisition module is used to acquire rock modulus, reservoir rock parameter information and logging data of a target area, wherein the target reservoir is within the target area;

[0304] An establishment module, configured to establish a rock modulus calculation model of the target reservoir based on the rock modulus, the reservoir rock parameter information and the well logging data;

[0305] A reconstruction module, configured to reconstruct a longitudinal wave curve based on the rock modulus calculation model;

[0306] a ninth determination module, configured to perform iterative inversion to modify the model parameters of the rock modulus calculation model based on the measured curve in the well logging data and the longitudinal wave curve, so as to obtain a target rock modulus calculation model;

[0307] The tenth determination module is used to obtain the shear wave velocity of the target reservoir based on the target rock modulus calculation model.

[0308] It should be noted that, in the embodiment of the present application, if the above-mentioned method for determining the development parameters is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM, Read Only Memory), a magnetic disk or an optical disk. In this way, the embodiment of the present application is not limited to any specific combination of hardware and software.

[0309] Accordingly, an embodiment of the present application provides a storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the method for determining the brittleness parameters of the reservoir provided in the above embodiment are implemented.

[0310] Example 6

[0311] An embodiment of the present application provides an electronic device; Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application is shown in FIG. Figure 6 As shown, the electronic device 600 includes: a processor 601, at least one communication bus 602, a user interface 603, at least one external communication interface 604, and memory 605. The communication bus 602 is configured to facilitate communication between these components. The user interface 603 may include a display screen, and the external communication interface 604 may include standard wired and wireless interfaces. The processor 601 is configured to execute a program for determining development parameters stored in the memory to implement the steps of the method for determining brittleness parameters of deep reservoirs provided in the above-described embodiment.

[0312] The description of the above electronic device and storage medium embodiments is similar to the description of the above method embodiments and has similar beneficial effects as the method embodiments. For technical details not disclosed in the computer device and storage medium embodiments of this application, please refer to the description of the method embodiments of this application for understanding.

[0313] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments.

[0314] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0315] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0316] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0317] In addition, all functional units in the embodiments of the present application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the above-mentioned integrated units can be implemented in the form of hardware or in the form of hardware plus software functional units.

[0318] Those skilled in the art will understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above-mentioned method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROMs), magnetic disks, optical disks, and other media that can store program codes.

[0319] Alternatively, if the above-mentioned integrated unit of the present application is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a number of instructions for enabling a controller to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROMs, magnetic disks or optical disks.

[0320] The above is merely an embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. A method for determining brittleness parameters of deep reservoirs, characterized in that: The method comprises: Obtain seismic data and well logging data of target reservoirs including P-wave velocity, S-wave velocity, density, and elastic impedance; Determining the uphole brittleness parameter information of the well logging based on the P-wave velocity, S-wave velocity and density; determining an elastic impedance volume of a target reservoir based on the seismic data and the elastic impedance; determining an angle-dependent weighting coefficient matrix of a target elastic impedance equation based on the elastic impedance and the uphole brittleness parameter; The brittleness parameter of the target reservoir is determined based on the angle-dependent weighting coefficient matrix and the elastic impedance body.

2. The method according to claim 1, characterized in that The uphole brittleness parameter information includes: uphole Young's modulus and uphole Poisson's ratio. The uphole brittleness parameter information of the well logging is determined based on the longitudinal wave velocity, shear wave velocity and density, including: The uphole Young's modulus of the well logging is calculated using a first calculation formula based on the longitudinal wave velocity, the shear wave velocity and the density, wherein the first calculation formula is: ； The uphole Poisson's ratio of the well logging is calculated using a second calculation formula based on the longitudinal wave velocity and the shear wave velocity, wherein the second calculation formula is: ； in, is the longitudinal wave velocity, is the shear wave velocity, is the density, E is the Young's modulus, is Poisson's ratio.

3. The method according to claim 1, characterized in that The elastic impedance includes: first angle elastic impedance data on the well logging and second angle elastic impedance data on the well logging, and the angle-dependent weighted coefficient matrix of the target elastic impedance equation determined based on the elastic impedance and the well brittleness parameter includes: Inputting the first angle elastic impedance data on the well logging and the uphole brittleness parameter into a third calculation model to determine an angle-dependent weighting coefficient corresponding to the first angle elastic impedance data; The second angle elastic impedance data on the well logging and the uphole brittleness parameter are input into the third calculation model to determine the angle-dependent weighting coefficient corresponding to the second angle elastic impedance data. The third calculation model is: ； in, is the angle-dependent weighting coefficient, is the elastic impedance data, is the elasticity parameter; An angle-dependent weighting coefficient matrix is ​​determined based on the angle-dependent weighting coefficient corresponding to the first angle elastic impedance data and the angle-dependent weighting coefficient corresponding to the second angle elastic impedance data.

4. The method according to claim 1, wherein The determining of the brittleness parameter of the target reservoir based on the angle-dependent weighted coefficient matrix and the elastic impedance body includes: The angle-dependent weighted coefficient matrix and the elastic impedance body are input into a target elastic impedance equation, and the brittleness parameter of the target reservoir is calculated using a least squares algorithm or a conjugate gradient algorithm.

5. The method according to claim 1, characterized in that The method further comprises: Constructing binomial reflection characteristic equation of deep brittleness parameter; Determine the two-term elastic impedance equation based on the brittle parameter based on the binomial reflection characteristic equation; Normalizing the two-term elastic impedance equation so that the dimension of the two-term elastic impedance equation is the same as the dimension of the impedance data; Perform linearization processing on the normalized two-term elastic impedance equation to obtain a linearized elastic impedance equation; The target elastic impedance equation is determined based on the linearized elastic impedance equation.

6. The method according to claim 5, characterized in that The target elastic impedance equation is: ; in, is the angle-dependent weighting coefficient matrix, is the elastic impedance data, is the elasticity parameter.

7. The method according to claim 1, characterized in that The method further comprises: Obtaining rock modulus, reservoir rock parameter information and well logging data of a target area, wherein the target reservoir is within the target area; Establishing a rock modulus calculation model for the target reservoir based on the rock modulus, the reservoir rock parameter information, and the well logging data; reconstructing a longitudinal wave curve based on the rock modulus calculation model; Iteratively inverting and correcting the model parameters of the rock modulus calculation model based on the measured curve in the logging data and the longitudinal wave curve to obtain a target rock modulus calculation model; The shear wave velocity of the target reservoir is obtained based on the target rock modulus calculation model.

8. A device for determining brittleness parameters of deep reservoirs, characterized in that: include: The first acquisition module is used to obtain seismic data and well logging data of the target reservoir, including compressional wave velocity, shear wave velocity, density, and elastic impedance; A first determining module is configured to determine the on-hole brittleness parameter information of the well logging based on the P-wave velocity, S-wave velocity and density; a second determining module, configured to determine an elastic impedance volume of a target reservoir based on the seismic data and the elastic impedance; a third determining module, configured to determine an angle-dependent weighting coefficient matrix of a target elastic impedance equation based on the elastic impedance and the uphole brittleness parameter; A fourth determination module is configured to determine a brittle parameter of the target reservoir based on the angle-dependent weighted coefficient matrix and the elastic impedance body.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method for determining the brittleness parameters of the deep reservoir according to any one of claims 1 to 7 is executed.

10. A storage medium, characterized in that: The computer program stored in the storage medium can be executed by one or more processors and can be used to implement the method for determining the brittleness parameters of a deep reservoir as claimed in any one of claims 1 to 7.

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