Organic matter content prediction method, device, computer equipment and readable medium

Abstract

This specification relates to the field of geological exploration technology, and in particular to an organic matter content prediction method, device, computer equipment, and readable medium. The organic matter content prediction method includes determining a sample anisotropy parameter vector and a sample organic matter content vector corresponding to the anisotropy parameter vector based on received well logging data; expanding the sample anisotropy parameter vector using seismic data and well logging data to obtain a profile anisotropy parameter matrix; and predicting a profile organic matter content matrix corresponding to the profile anisotropy parameter matrix based on the relationship between the sample anisotropy parameter vector and the sample organic matter content vector corresponding to the anisotropy parameter vector. Utilizing the embodiments of this specification, it is possible to determine the corresponding organic matter content data based on the anisotropy parameters, making the process of determining the organic matter content data simpler and the results more accurate.

Description

Technical Field

[0001] This specification relates to the field of geological exploration technology, and in particular to a method, device, computer equipment and readable medium for predicting organic matter content. Background Art

[0002] Shale oil and gas reservoirs are typically anisotropic due to the influence of organic matter content, clay, and fracture orientation. Currently, organic matter content is predicted by fitting the relationship between organic matter content and density, combined with seismic amplitude-with-offset (AVO) inversion. However, in the presence of anisotropic shale, inversion using isotropic seismic AVO yields inaccurate results. Furthermore, density-based seismic inversion often yields multiple solutions in the presence of anisotropy and lacks stability when performed at large angles.

[0003] How to improve the accuracy of organic matter content prediction under anisotropic conditions is an urgent problem that needs to be solved in existing technologies. Summary of the Invention

[0004] To solve the problems in the prior art, the embodiments of this specification provide a method, apparatus, computer device and readable medium for predicting organic matter content, which realizes determining the corresponding organic matter content data based on anisotropic parameters, making the process of determining the organic matter content data simpler and the results more accurate.

[0005] In order to solve the above technical problems, the specific technical solutions of this specification are as follows:

[0006] On the one hand, the embodiments of this specification provide a method for predicting organic matter content, comprising:

[0007] determining, based on the received well logging data, a sample anisotropy parameter vector and a sample organic matter content vector corresponding to the anisotropy parameter vector;

[0008] Expanding the sample anisotropy parameter vector using seismic data and the well logging data to obtain a profile anisotropy parameter matrix; and

[0009] Based on the relationship between the sample anisotropy parameter vector and the sample organic matter content vector corresponding to the anisotropy parameter vector, a profile organic matter content matrix corresponding to the profile anisotropy parameter matrix is ​​predicted.

[0010] Furthermore, the method of expanding the sample anisotropy parameter vector using the seismic data and the well logging data to obtain a profile anisotropy parameter matrix further includes:

[0011] Determining an anisotropy parameter formula based on the seismic data and the well logging data; and

[0012] The anisotropy parameter formula is used to process the well logging data and the sub-seismic data corresponding to each point in the profile to obtain the profile anisotropy parameter corresponding to each point in the profile, so as to determine the profile anisotropy parameter matrix.

[0013] Furthermore, the profile anisotropy parameter formula further includes,

[0014]

[0015] Wherein, the σ represents the profile anisotropy parameter, the B represents the first parameter matrix, the ρ represents the density, and the Characterizes the shear wave velocity.

[0016] Furthermore, the determination of the profile anisotropy parameter formula further includes:

[0017] Determining a three-parameter initial parameter matrix based on the well logging data; and

[0018] The three-parameter initial parameter matrix and the linear forward operator obtained from the three-term approximate formula of the anisotropic medium reflection coefficient are processed using an iterative model to obtain the profile anisotropic parameter formula, and the three-term approximate formula of the anisotropic medium reflection coefficient is determined based on the seismic data.

[0019] Furthermore, the iterative model further includes,

[0020] m i =m0+η[G T G + μQ(m)] -1 G T Δd

[0021] Wherein, m0 represents the three-parameter initial parameter matrix, η represents the iteration step size, G represents the linear forward operator, μ represents the regularization weight, Q(m) represents the diagonally dominant matrix, and Δd represents the residual term.

[0022] Furthermore, the three-term approximate formula of the anisotropic medium reflection coefficient further includes:

[0023]

[0024] Wherein, the θ represents the target parameter, the target parameter is determined by the seismic data, the B represents the first parameter matrix, the A represents the second parameter matrix, the C represents the third parameter matrix, and the Characterizes the shear wave velocity, and the Characterizes the longitudinal wave velocity.

[0025] Furthermore, the linear forward operator further includes,

[0026] G=K·D·S

[0027] Wherein, G represents the linear forward operator, K represents a coefficient matrix, the coefficient matrix is ​​determined based on the three-term approximation formula of the anisotropic medium reflection coefficient, D represents a difference matrix, and S represents a wavelet convolution matrix.

[0028] On the other hand, the embodiment of this specification also provides an organic matter content prediction device, comprising:

[0029] a first determining unit, configured to determine, based on the received well logging data, a sample anisotropy parameter vector and a sample organic matter content vector corresponding to the anisotropy parameter vector;

[0030] an expansion unit, configured to expand the sample anisotropy parameter vector using seismic data and the well logging data to obtain a profile anisotropy parameter matrix; and

[0031] The second determining unit is configured to predict a profile organic matter content matrix corresponding to the profile anisotropy parameter matrix based on a relationship between the sample anisotropy parameter vector and the sample organic matter content vector corresponding to the anisotropy parameter vector.

[0032] On the other hand, an embodiment of this specification further provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above method when executing the computer program.

[0033] On the other hand, an embodiment of this specification further provides a computer-readable storage medium having computer instructions stored thereon, which implement the above method when executed by a processor.

[0034] Using the embodiments of this specification, the sample anisotropy parameter and the organic matter content corresponding to the sample anisotropy parameter are determined based on well logging data, thereby obtaining a sample anisotropy parameter vector and a sample organic matter content vector. The sample anisotropy parameter vector is then expanded using the well logging data to obtain a profile anisotropy parameter matrix representing the entire profile. Furthermore, the relationship between the sample anisotropy parameter vector and the sample organic matter content vector corresponding to the anisotropy parameter vector is determined, and based on this relationship, a profile organic matter content matrix corresponding to the profile anisotropy parameter matrix is ​​predicted. This achieves the prediction of unique organic matter content data corresponding to each coordinate position based on the anisotropy parameter. This improves the accuracy and stability of organic matter content predictions. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of this specification. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0036] Figure 1 The figure shows a schematic diagram of an implementation system of an organic matter content prediction method according to an embodiment of this specification;

[0037] Figure 2 Shown is a flow chart of a method for predicting organic matter content according to an embodiment of this specification;

[0038] Figure 3 The figure shows a flow chart of a method for determining a profile anisotropy parameter matrix according to an embodiment of the present specification;

[0039] Figure 4 Shown is a schematic diagram of a method for predicting organic matter content according to another embodiment of this specification;

[0040] Figure 5A Shown is a schematic diagram of well logging data according to an embodiment of this specification;

[0041] Figure 5B The figure shows a schematic diagram of the relationship between the anisotropy parameter of a sample and the organic matter content of the sample according to an embodiment of this specification;

[0042] Figure 5C Shown is a schematic diagram of a comparison of the first parameter matrix inversion result and the shear wave shear modulus term inversion result and the well bypass according to an embodiment of this specification;

[0043] Figure 5D Shown is a schematic diagram comparing the profile anisotropy parameter matrix and the well bypass according to one embodiment of this specification;

[0044] Figure 5E Shown is a schematic diagram comparing the cross-section organic matter content matrix and the well bypass in Example 1 of this specification;

[0045] Figure 6 The figure shows a schematic structural diagram of an organic matter content prediction device according to an embodiment of the present specification;

[0046] Figure 7 This is a structural diagram of a computer device according to an embodiment of this specification.

[0047] [Description of Reference Numerals]

[0048] 101. Collection terminal;

[0049] 102. Server;

[0050] 103. User terminal;

[0051] 401, well logging data;

[0052] 4011. Organic matter content of multiple samples;

[0053] 402. Earthquake data;

[0054] 410. Anisotropic parameter calculation model;

[0055] 421, multiple sample anisotropy parameters;

[0056] 430. Anisotropy parameter formula;

[0057] 441, profile anisotropy parameters;

[0058] 450, parameter-organic matter content model;

[0059] 460, profile organic matter content matrix;

[0060] 501. Seismic profile corresponding to the first parameter matrix;

[0061] 502. Seismic profile of inversion results of shear wave shear modulus term;

[0062] 503. A comparison diagram of the data corresponding to the first parameter matrix and the well bypass data;

[0063] 504. Comparison chart of data corresponding to the inversion result of shear wave shear modulus term and well bypass data;

[0064] 511. Seismic profile corresponding to the profile anisotropy parameter matrix;

[0065] 512. Comparison of profile anisotropy parameter vector and well bypass sample anisotropy parameter vector;

[0066] 521. Seismic profile corresponding to the profile organic matter content matrix;

[0067] 522. Comparison of organic matter content vectors of profile and well bypass samples;

[0068] 610. First determining unit;

[0069] 620, extension unit;

[0070] 630, second determining unit;

[0071] 702. Computer equipment;

[0072] 704. Processing equipment;

[0073] 706. Storage resources;

[0074] 708, driving mechanism;

[0075] 710, input / output module;

[0076] 712. Input devices;

[0077] 714. Output device;

[0078] 716. Presentation equipment;

[0079] 718. Graphical User Interface;

[0080] 720, network interface;

[0081] 722, communication link;

[0082] 724. Communication bus. DETAILED DESCRIPTION

[0083] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the embodiments described are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of this specification.

[0084] It should be noted that the terms "first," "second," and the like in the description and claims of this specification and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate so that the embodiments of this specification described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, apparatus, product, or device comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or devices.

[0085] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0086] Figure 1The system diagram for implementing an organic matter content prediction method according to an embodiment of this specification is shown. The system may include: a data acquisition terminal 101, a server 102, and a user terminal 103. The data acquisition terminal 101, server 102, and user terminal 103 communicate with each other via a network. The network may include a local area network (LAN), a wide area network (WAN), the Internet, or a combination thereof, and is connected to a website, user devices (e.g., computing devices), and back-end systems. The data acquisition terminal 101 includes sensors that collect well logging data and transmit the collected data to the server 102. After receiving the well logging data, server 102 determines the sample anisotropy parameter vector and the sample organic matter content vector corresponding to the anisotropy parameter vector; uses the seismic data and well logging data to expand the sample anisotropy parameter vector to obtain a profile anisotropy parameter matrix; and based on the relationship between the sample anisotropy parameter vector and the sample organic matter content vector corresponding to the anisotropy parameter vector, predicts a profile organic matter content matrix corresponding to the profile anisotropy parameter matrix, and sends the profile organic matter content matrix to user terminal 103. It should be noted that a profile organic matter content map can also be constructed based on the profile organic matter content matrix, and the profile organic matter content map can be sent to user terminal 103.

[0087] Alternatively, the server 102 may be a node of a cloud computing system (not shown), or each server 102 may be a separate cloud computing system including multiple computers interconnected by a network and operating as a distributed processing system.

[0088] In an optional embodiment, the user terminal 103 may include electronic devices such as, but not limited to, smartphones, acquisition devices, desktop computers, tablet computers, laptop computers, smart speakers, digital assistants, augmented reality (AR) / virtual reality (VR) devices, smart wearable devices, and the like. Optionally, the operating system running on the electronic device may include, but is not limited to, Android, iOS, Linux, Windows, and the like.

[0089] In addition, it should be noted that Figure 1 What is shown is only an application environment provided in this specification. In actual application, multiple user terminals 103 may be included, and this specification does not impose any limitation.

[0090] like Figure 2The flowchart of a method for predicting organic matter content in an embodiment of this specification is shown. The organic matter content prediction process is described in this figure, but it may include more or fewer operating steps based on conventional or non-creative work. The order of steps listed in the embodiment is only one way of executing the steps among many orders, and does not represent the only order of execution. When the actual system or device product is executed, it can be executed in sequence or in parallel according to the method shown in the embodiment or the accompanying drawings. Specifically, Figure 2 As shown, the method may include:

[0091] S210, determining a sample anisotropy parameter vector and a sample organic matter content vector corresponding to the anisotropy parameter vector based on the received logging data;

[0092] S220, using seismic data and well logging data, expanding the sample anisotropy parameter vector to obtain a profile anisotropy parameter matrix;

[0093] S230, predicting a profile organic matter content matrix corresponding to the profile anisotropy parameter matrix based on a relationship between the sample anisotropy parameter vector and the sample organic matter content vector corresponding to the anisotropy parameter vector;

[0094] Using the embodiments of this specification, the sample anisotropy parameter and the organic matter content corresponding to the sample anisotropy parameter are determined based on well logging data, thereby obtaining a sample anisotropy parameter vector and a sample organic matter content vector. The sample anisotropy parameter vector is then expanded using the well logging data to obtain a profile anisotropy parameter matrix representing the entire profile. Furthermore, the relationship between the sample anisotropy parameter vector and the sample organic matter content vector corresponding to the anisotropy parameter vector is determined, and based on this relationship, a profile organic matter content matrix corresponding to the profile anisotropy parameter matrix is ​​predicted. This achieves the prediction of unique organic matter content data corresponding to each coordinate position based on the anisotropy parameter. This improves the accuracy and stability of organic matter content predictions.

[0095] According to one embodiment of the present disclosure, well logging data is collected and processed by downhole acquisition equipment and may include, for example, density, shear wave velocity, compressional wave velocity, and sample organic matter content. A sample anisotropy parameter vector includes multiple sample anisotropy parameters, each corresponding to a coordinate position downhole. A sample organic matter content vector includes multiple sample organic matter contents, each corresponding to a coordinate position downhole.

[0096] After receiving the well logging data, the anisotropy parameter calculation model is used to process the well logging data and determine the sample anisotropy parameter to obtain a sample anisotropy parameter vector. It should be noted that the sample anisotropy parameter can be, for example, the equivalent anisotropy parameter σ defined by Tsvankin and Thomsen (1994). The anisotropy parameter calculation model can be any model that can determine the equivalent anisotropy parameter based on the well logging data.

[0097] Based on the coordinate position, the determined sample anisotropy parameters are used to construct a sample anisotropy parameter vector corresponding to this well channel, and the sample organic matter content vector corresponding to this sample anisotropy parameter vector is determined. For example, if the sample anisotropy parameter vector is (a1, b1, c1, d1) and the sample organic matter content vector is (a2, b2, c2, d2), the sample anisotropy parameter a1 and the sample organic matter content a2 have the same coordinate position; the sample anisotropy parameter b1 and the sample organic matter content b2 have the same coordinate position; the sample anisotropy parameter c1 and the sample organic matter content c2 have the same coordinate position; and the sample anisotropy parameter d1 and the sample organic matter content d2 have the same coordinate position.

[0098] Seismic data is data that can be used to characterize earthquakes, such as discrete amplitude values. Well-seismic calibration is performed on the seismic data, converting the depth domain data into the time domain and the offset domain into the angle domain. The seismic data is stacked at 10, 20, 30, and 35 degrees to reduce the impact of noise, and mixed-phase wavelets are estimated for each angle profile.

[0099] Based on seismic data, well logging data, and sample anisotropy parameters, a correspondence between anisotropy parameter values ​​and the seismic and well logging data is determined. Furthermore, based on this correspondence, the seismic and well logging data are processed to determine predicted anisotropy parameter vectors for all traces within the profile, excluding the trace corresponding to the sample anisotropy parameter vector. Based on the coordinate position of each trace in the profile, a profile anisotropy parameter matrix is ​​constructed, derived from the sample anisotropy parameter vector and multiple predicted anisotropy parameter vectors. The profile anisotropy parameter matrix includes multiple profile anisotropy parameters, each corresponding to a coordinate position in the profile. It should be noted that the multiple profile anisotropy parameters corresponding to a trace in the profile anisotropy parameter matrix may not be sample anisotropy parameters, but may be predicted anisotropy parameter vectors determined by this correspondence. That is, based on the corresponding relationship, the predicted anisotropy parameters corresponding to each coordinate position in the section are determined, and the predicted anisotropy parameters are used as the section anisotropy parameters to construct the section anisotropy parameter matrix.

[0100] A mathematical modeling method is used to process the sample anisotropy parameter and sample organic matter content corresponding to each position coordinate, construct a mapping relationship between the sample anisotropy parameter and the sample organic matter content, and use this mapping relationship as the relationship between the sample anisotropy parameter vector and the sample organic matter content vector. The mathematical modeling method can be, for example, a linear regression method. The mapping relationship can be, for example, a linear regression function.

[0101] After determining the relationship between the sample anisotropy parameter vector and the sample organic matter content vector, this relationship is used to process each profile anisotropy parameter in the profile anisotropy parameter matrix and predict the profile organic matter content corresponding to each profile anisotropy parameter. Based on the first position of each profile anisotropy parameter in the profile anisotropy parameter matrix, the second position of the corresponding profile organic matter content is determined. Based on this second position, a profile organic matter content matrix is ​​constructed from the multiple profile organic matter contents. The first position and the second position are identical.

[0102] Figure 3 The figure shows a flow chart of a method for determining a profile anisotropy parameter matrix according to an embodiment of the present specification. The process of determining the profile anisotropy parameter matrix is ​​described in this figure, but conventional or non-creative work may include more or fewer operation steps. Figure 3 As shown, the method may include:

[0103] S321, determining anisotropy parameter formula based on seismic data and well logging data;

[0104] S322, using anisotropy parameter formula, processing the well logging data and the sub-seismic data corresponding to each point in the profile to obtain the profile anisotropy parameter corresponding to each point in the profile to determine the profile anisotropy parameter matrix.

[0105] According to another embodiment of the present specification, the anisotropy parameter formula characterizes the correspondence between seismic data and well logging data and the anisotropy parameter.

[0106] Specifically, based on seismic data and well logging data, the anisotropy parameter formula can be determined as follows: based on the well logging data, multiple sample anisotropy parameters are determined; from the seismic data, sub-sample seismic data corresponding to each sample anisotropy parameter is determined; and the correspondence between the well logging data and the sub-sample seismic data and the sample anisotropy parameter is determined, and the correspondence is used as the anisotropy parameter formula.

[0107] Then, the well logging data and the sub-seismic data corresponding to each point in the profile are substituted into the anisotropy parameter formula to obtain the predicted anisotropy parameter corresponding to each point in the profile. It should be noted that each point has a corresponding position coordinate.

[0108] After the predicted anisotropy parameters are determined, a profile anisotropy parameter matrix consisting of the sample anisotropy parameter vector and the predicted anisotropy parameters is constructed based on the coordinate position of each point in the profile. Each profile anisotropy parameter in the profile anisotropy parameter matrix corresponds to the coordinate position of a point in the profile.

[0109] According to another embodiment of the present specification, a cross-sectional anisotropy parameter formula is shown in the following formula (1).

[0110]

[0111] Where σ represents the anisotropy parameter, B represents the first parameter matrix, ρ represents the density, and Characterizes the shear wave velocity. Specifically, the predicted anisotropy parameter in the profile anisotropy parameter is determined by the above formula (1).

[0112] Specifically, the determination of the profile anisotropy parameter formula can be, for example: based on logging data, determine the three-parameter initial parameter matrix; and use an iterative model to process the three-parameter initial parameter matrix and the linear forward operator obtained from the three-term approximate formula of the anisotropic medium reflection coefficient to obtain the profile anisotropy parameter formula, where the three-term approximate formula of the anisotropic medium reflection coefficient is determined based on seismic data.

[0113] The three-parameter initial parameter matrix can be, for example, [lnA0, lnB0, lnC0] T , wherein B0 is a preset first parameter matrix, A0 is a preset second parameter matrix, and C0 is a preset third parameter matrix. Moreover, the preset first parameter matrix, the preset second parameter matrix, and the preset third parameter matrix are determined by well logging data.

[0114] Using the iterative model, the three-parameter initial parameter matrix and the linear forward operator obtained from the three-term approximate formula of the anisotropic medium reflection coefficient are processed to obtain the first parameter matrix. Specifically, the three-parameter initial parameter matrix and the linear forward operator obtained from the three-term approximate formula of the anisotropic medium reflection coefficient are brought into the iterative model, and an iterative solution is performed to obtain the profile anisotropic parameter formula.

[0115] Figure 4 Shown is a schematic diagram of a method for predicting organic matter content according to another embodiment of this specification.

[0116] According to another embodiment of the present specification, the iterative model may be, for example, as shown in the following formula (2).

[0117] m i =m0+η[G T G + μQ(m)] -1 G T Δd formula (2)

[0118] Among them, m0 represents the three-parameter initial parameter matrix, that is, m0 = [lnA0, lnB0, lnC0] T , η represents the iteration step size, G represents the linear forward operator, μ represents the regularization weight, Q(m) represents the diagonally dominant matrix, and Δd represents the residual term. The regularization weight can be determined based on seismic data, specifically, based on the data after taking the logarithm of each item in the initial model data determined by the seismic data, μ = [μ1, μ2, μ3]. The diagonally dominant matrix is ​​the covariance matrix after the initial model data is transformed into a multidimensional random variable. Specifically, the covariance matrix is ​​inverted to obtain a matrix M, and the matrix M is transformed into a diagonally dominant matrix. The diagonally dominant matrix can be, for example, as shown in the following formula (3).

[0119]

[0120] Among them, M ij are all data in the matrix M, and N represents the number of rows and columns of the diagonally dominant matrix.

[0121] The residual term can be determined by the following formula (4), for example.

[0122] Δd=dG(m prior ) Formula (4)

[0123] Among them, Δd represents the residual term, d represents the target seismic data obtained by superimposing the seismic data corresponding to 10 degrees, 20 degrees, 30 degrees and 35 degrees, G(m prior ) represents the initial linear forward operator corresponding to the initial model data.

[0124] Specifically, the target seismic data is determined as shown in the following formula (5).

[0125]

[0126] Among them, d(θ i ,t j ) is the sub-target seismic data corresponding to each moment.

[0127] Specifically, the three-parameter initial parameter matrix and the linear forward operator obtained from the three-term approximate formula of the anisotropic medium reflection coefficient are brought into the iterative model for iterative solution. The obtained profile anisotropy parameter formula can also include, for example: the three-parameter initial parameter matrix and the linear forward operator obtained from the three-term approximate formula of the anisotropic medium reflection coefficient are brought into the iterative model as shown in formula (2) for iterative solution to obtain a first parameter matrix expression, and the first parameter matrix expression is inverted to obtain the profile anisotropy parameter formula as shown in formula (1).

[0128] Specifically, the first parameter matrix expression is shown in the following formula (6).

[0129]

[0130] Among them, B represents the first parameter matrix, ρ represents the density, represents the shear wave velocity, and σ represents the profile anisotropy parameter.

[0131] According to another embodiment of the present specification, a three-term approximate formula for the reflection coefficient of anisotropic media may be as shown in the following formula (7), for example.

[0132]

[0133] Among them, θ represents the target parameter, which is determined by seismic data, B represents the first parameter matrix, A represents the second parameter matrix, and C represents the third parameter matrix. characterizes the shear wave velocity, and Characterizes the longitudinal wave velocity.

[0134] Specifically, the three items included in formula (7) may be, for example, initial model data, and the logarithms of the three items are taken to obtain μ1, μ2, and μ3.

[0135] According to another embodiment of the present specification, the linear forward operator may be expressed as shown in the following formula (8).

[0136] G=K·D·S Formula (8)

[0137] Among them, G represents the linear forward operator, K represents the coefficient matrix, the coefficient matrix is ​​determined based on the three-term approximate formula of the anisotropic medium reflection coefficient, D represents the difference matrix, and S represents the wavelet convolution matrix.

[0138] Specifically, the coefficient matrix is ​​the coefficients of the three terms in formula (7), which is

[0139] The difference matrix is, for example, as shown in the following formula (9).

[0140]

[0141] Where D(θ K ) and D in the above are both difference matrices, D(θ K ,t i ) represents the target coefficient corresponding to each iteration, for example,

[0142] According to another embodiment of the present specification, the anisotropy parameter calculation model may be as shown in the following formula (10), for example.

[0143]

[0144] Among them, σ represents the anisotropy parameter of the sample, Characterizes the shear wave velocity, Characterizing the compressional wave velocity, as well as ε and δ are all logging parameters.

[0145] like Figure 4 As shown, after receiving the well logging data 401 corresponding to a track, the well logging data is processed using the anisotropy parameter calculation model 410 shown in the above formula (10) to obtain multiple sample anisotropy parameters 421. From the well logging data 401, the organic matter content 4011 of the multiple samples is determined.

[0146] Based on the well logging data 401, multiple sample anisotropy parameters 421, and seismic data 402, an anisotropy parameter formula 430 as shown in formula (1) is constructed. The well logging data 401, multiple sample anisotropy parameters 421, and seismic data 402 are input into the anisotropy parameter formula 430 to obtain the profile anisotropy parameter 441 for each point in the profile including the channel. It should be noted that the number of profile anisotropy parameters is determined according to the number of points in the profile.

[0147] Based on mathematical modeling methods, regression analysis is performed on multiple sample organic matter contents 4011 and multiple sample anisotropy parameters to generate parameter-organic matter content model 450. Specifically, the data modeling method may be, for example, a support vector regression algorithm. Parameter-organic matter content model 450 represents a mapping relationship between anisotropy parameters and organic matter content.

[0148] Then, the multiple cross-sectional anisotropy parameters 441 are input into the parameter-organic matter content model 450 to obtain the cross-sectional organic matter content corresponding to each cross-sectional anisotropy parameter. Based on the cross-sectional organic matter content, a cross-sectional organic matter content matrix 460 is determined.

[0149] Figure 5A Shown is a schematic diagram of well logging data according to an embodiment of this specification; Figure 5BThe figure shows a schematic diagram of the relationship between the anisotropy parameter of a sample and the organic matter content of the sample according to an embodiment of this specification; Figure 5C Shown is a schematic diagram of a comparison of the first parameter matrix inversion result and the shear wave shear modulus term inversion result and the well bypass according to an embodiment of this specification; Figure 5D Shown is a schematic diagram comparing the profile anisotropy parameter matrix and the well bypass according to one embodiment of this specification; Figure 5E Shown is a schematic diagram comparing the cross-section organic matter content matrix and the well bypass in Example 1 of this specification.

[0150] According to the embodiment of this specification, an oil well in a certain place is detected and the following is obtained: Figure 5A Well logging data, including P-wave velocity (V P ), shear wave velocity (V S ), density (ρ), and logging parameters (ε and δ). It should be noted that other data can be obtained during the actual detection process. This specification uses the above logging data as an example for illustration and is not limited to this.

[0151] Using formula (10), we process the logging data to obtain the sample anisotropy parameter σ. The relationship between the sample anisotropy parameter σ and the sample organic matter content is plotted, as shown in Figure 1. Figure 5B As shown, it can be seen that the correlation between the sample anisotropy parameter σ and the sample organic matter content is high. Therefore, the corresponding relationship between the sample anisotropy parameter and the sample organic matter content can be constructed to predict the organic matter content and determine the profile organic matter content matrix.

[0152] like Figure 5C As shown, based on the first parameter matrix determined by the above formula (2) and formula (7), the seismic profile 501 corresponding to the first parameter matrix can be determined. For comparison, an inversion calculation is simultaneously performed based on the shear wave shear modulus term to obtain an inversion result, and based on the inversion result, a seismic profile 502 of the shear wave shear modulus term inversion result is determined. At the same time, a comparison diagram 503 of the data corresponding to the first parameter matrix and the well bypass data and a comparison diagram 504 of the data corresponding to the shear wave shear modulus term inversion result and the well bypass data are determined. From the comparison diagram 503 of the data corresponding to the first parameter matrix and the well bypass data and the comparison diagram 504 of the data corresponding to the shear wave shear modulus term inversion result and the well bypass data, it can be seen that the first parameter matrix determined by the method proposed in this specification is more stable and accurate.

[0153] like Figure 5DAs shown, the profile anisotropy parameter matrix determined by the above formula (1) can determine the seismic profile 511 corresponding to the profile anisotropy parameter matrix. Then, the profile anisotropy parameter vector corresponding to the well bypass in the profile anisotropy parameter matrix is ​​compared with the well bypass sample anisotropy parameter vector to obtain a comparison diagram 512 of the profile anisotropy parameter vector and the well bypass sample anisotropy parameter vector. From the seismic profile 511 corresponding to the profile anisotropy parameter matrix and the comparison diagram 512 of the profile anisotropy parameter vector and the well bypass sample anisotropy parameter vector, it can be seen that the profile anisotropy parameter matrix determined by the method proposed in this specification has high stability and goodness of fit.

[0154] like Figure 5E As shown, the parameter-organic matter content model processes each profile anisotropy parameter in the determined profile anisotropy parameter matrix to obtain a profile organic matter content matrix. Furthermore, based on the profile organic matter content matrix, the seismic profile 521 corresponding to the profile organic matter content matrix is ​​determined. The profile organic matter content vector corresponding to the well bypass in the profile organic matter content matrix is ​​compared with the well bypass sample organic matter content vector to obtain a comparison graph 522 of the profile organic matter content vector and the well bypass sample organic matter content vector. As can be seen from the comparison graph 522 of the profile organic matter content vector and the well bypass sample organic matter content vector, the profile organic matter content matrix determined by the method proposed in this specification has a high degree of stability and goodness of fit.

[0155] Figure 6 The figure shows a schematic diagram of the structure of an organic matter content prediction device according to an embodiment of this specification. Figure 6 Shown, including,

[0156] The first determining unit 610 is configured to determine a sample anisotropy parameter vector and a sample organic matter content vector corresponding to the anisotropy parameter vector based on the received well logging data;

[0157] An expansion unit 620 is used to expand the sample anisotropy parameter vector using seismic data and well logging data to obtain a profile anisotropy parameter matrix; and

[0158] The second determining unit 630 is configured to predict a profile organic matter content matrix corresponding to the profile anisotropy parameter matrix based on a relationship between the sample anisotropy parameter vector and the sample organic matter content vector corresponding to the anisotropy parameter vector.

[0159] Since the principle of solving the problem by the above device is similar to that of the above method, the implementation of the above device can refer to the implementation of the above method, and the repeated parts will not be repeated.

[0160] like Figure 7The diagram shows a schematic diagram of the structure of a computer device according to an embodiment of this specification. The apparatus described in this specification may be a computer device according to this embodiment, executing the method described above. Computer device 702 may include one or more processing devices 704, such as one or more central processing units (CPUs), each of which may implement one or more hardware threads. Computer device 702 may also include any storage resources 706 for storing any type of information, such as code, settings, data, etc. For example, and without limitation, storage resources 706 may include any one or more combinations of the following: any type of RAM, any type of ROM, flash memory devices, hard disks, optical disks, etc. More generally, any storage resource may use any technology to store information. Furthermore, any storage resource may provide volatile or non-volatile retention of information. Furthermore, any storage resource may represent a fixed or removable component of computer device 702. In one embodiment, when processing device 704 executes associated instructions stored in any storage resource or combination of storage resources, computer device 702 may perform any operation of the associated instructions. The computer device 702 also includes one or more drive mechanisms 708 for interacting with any storage resources, such as a hard disk drive mechanism, an optical disk drive mechanism, and the like.

[0161] The computer device 702 may also include an input / output module 710 (I / O) for receiving various inputs (via input devices 712) and for providing various outputs (via output devices 714). A specific output mechanism may include a presentation device 716 and an associated graphical user interface (GUI) 718. In other embodiments, the input / output module 710 (I / O), input devices 712, and output devices 714 may not be included, and the computer device 702 may simply be a computer device in a network. The computer device 702 may also include one or more network interfaces 720 for exchanging data with other devices via one or more communication links 722. One or more communication buses 724 couple the components described above together.

[0162] The communication link 722 may be implemented in any manner, for example, via a local area network, a wide area network (e.g., the Internet), a point-to-point connection, etc., or any combination thereof. The communication link 722 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers, etc., governed by any protocol or combination of protocols.

[0163] The embodiments of this specification also provide a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the above method is implemented.

[0164] The embodiments of this specification also provide a computer program product, which includes a computer program. When the computer program is executed by a processor, the above method is implemented.

[0165] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0166] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0167] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0168] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0169] The above specific embodiments further illustrate the purpose, technical solutions and beneficial effects of this specification. It should be understood that the above are only specific embodiments of this specification and are not intended to limit the scope of protection of this specification. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of this specification should be included in the scope of protection of this specification.

Claims

1. A method for predicting organic matter content, characterized in that: include: determining, based on the received well logging data, a sample anisotropy parameter vector and a sample organic matter content vector corresponding to the anisotropy parameter vector; Expanding the sample anisotropy parameter vector using seismic data and the well logging data to obtain a profile anisotropy parameter matrix; and Based on the relationship between the sample anisotropy parameter vector and the sample organic matter content vector corresponding to the anisotropy parameter vector, a profile organic matter content matrix corresponding to the profile anisotropy parameter matrix is ​​predicted.

2. The method according to claim 1, characterized in that The method of expanding the sample anisotropy parameter vector by using the seismic data and the well logging data to obtain a profile anisotropy parameter matrix includes: Determining an anisotropy parameter formula based on the seismic data and the well logging data; and The anisotropy parameter formula is used to process the well logging data and the sub-seismic data corresponding to each point in the profile to obtain the profile anisotropy parameter corresponding to each point in the profile, so as to determine the profile anisotropy parameter matrix.

3. The method according to claim 2, characterized in that The profile anisotropy parameter formula includes: Wherein, σ represents the profile anisotropy parameter, B represents the first parameter matrix, ρ represents the density, and V s0 Characterizes the shear wave velocity.

4. The method according to claim 3, characterized in that The determination of the profile anisotropy parameter formula includes: Determining a three-parameter initial parameter matrix based on the well logging data; and The three-parameter initial parameter matrix and the linear forward operator obtained from the three-term approximate formula of the anisotropic medium reflection coefficient are processed using an iterative model to obtain the profile anisotropic parameter formula, and the three-term approximate formula of the anisotropic medium reflection coefficient is determined based on the seismic data.

5. The method according to claim 4, characterized in that The iterative model includes: m i =m0+η[G T G+μQ(m)] -1 G T Δd Wherein, m0 represents the three-parameter initial parameter matrix, η represents the iteration step size, G represents the linear forward operator, μ represents the regularization weight, Q(m) represents the diagonally dominant matrix, and Δd represents the residual term.

6. The method according to claim 4, characterized in that The three approximate formulas for the anisotropic medium reflection coefficient include: Wherein, θ represents the target parameter, which is determined by the seismic data, B represents the first parameter matrix, A represents the second parameter matrix, C represents the third parameter matrix, and V s0 characterizes the shear wave velocity, and the V p0 Characterizes the longitudinal wave velocity.

7. The method according to claim 4, characterized in that The linear forward operator includes: G=K·D·S Wherein, G represents the linear forward operator, K represents a coefficient matrix, the coefficient matrix is ​​determined based on the three-term approximation formula of the anisotropic medium reflection coefficient, D represents a difference matrix, and S represents a wavelet convolution matrix.

8. An organic matter content prediction device, characterized in that: include: a first determining unit, configured to determine, based on the received well logging data, a sample anisotropy parameter vector and a sample organic matter content vector corresponding to the anisotropy parameter vector; an expansion unit, configured to expand the sample anisotropy parameter vector using seismic data and the well logging data to obtain a profile anisotropy parameter matrix; as well as The second determining unit is configured to predict a profile organic matter content matrix corresponding to the profile anisotropy parameter matrix based on a relationship between the sample anisotropy parameter vector and the sample organic matter content vector corresponding to the anisotropy parameter vector.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which is used to execute the method according to any one of claims 1 to 7 when executed by a processor.

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