Full-stratum quality factor Q building method, system, equipment and medium

Abstract

The invention provides a full-stratum quality factor Q building method, system, equipment and medium. The method comprises the following steps: obtaining a low-frequency initial interval velocity body through Kriging interpolation based on seismic reflection data, logging data and basic information of a structural horizon; dividing a low-frequency initial interval velocity into a plurality of continuous intervals based on interval velocities and quality factors of multi-point micro-logging and VSP logging information, and respectively counting and screening out an optimal relational expression between the quality factor and the interval velocity in each continuous interval; converting the low-frequency initial interval velocity body into a low-frequency initial quality factor according to the optimal relational expression; obtaining a time-frequency spectrum of the seismic data through S transformation according to the post-stack seismic reflection data; extracting a peak frequency corresponding to each time sampling point by using the time-frequency spectrum of the seismic data, and performing inversion to obtain a relative Q value; and after mean removal and normalization processing are carried out on the relative Q value in each structural layer, the relative Q value is fused with the low-frequency initial quality factor to obtain a well-seismic combined full-stratum quality factor Q building result.

Description

Technical Field

[0001] The present invention relates to the field of geophysical exploration, and in particular to a method, system, equipment and medium for constructing a full-formation quality factor Q. Background Art

[0002] The underground medium has viscosity and has a strong absorption and attenuation effect on seismic wave energy, which reduces the resolution of seismic data and the accuracy of seismic data processing and interpretation. The quality factor Q, as a descriptive attribute of the formation absorption and attenuation effect, is of great significance for improving the resolution of original seismic data, high-precision tomography, geophysical attribute inversion, and analyzing the distribution of underground oil and gas reservoirs.

[0003] Wang Yanguang et al.'s patent "A high-precision quality factor extraction method" calculated and derived the relationship between the main frequency, peak frequency and Q value in the time-frequency spectrum of the generalized S transform, and obtained a calculation method for extracting the Q value using the peak frequency in the generalized S spectrum, which can be applied to seismic exploration to improve the resolution of seismic imaging; An Shujie et al.'s patent "A method for building a full-formation quality factor Q body" uses the correspondence between the statistical layer velocity and the quality factor Q of multi-point micro-logging to convert the surface layer velocity body into a surface quality factor Q body, and uses the correspondence between the statistical layer velocity and the quality factor Q of multi-point VSP logging to convert the mid-deep layer velocity body into a mid-deep quality factor Q body, thereby realizing the construction of the full-formation quality factor Q body.

[0004] The above methods only use post-stack seismic reflection data combined with the peak frequency shift method to extract the Q value. For near-surface areas with strong absorption attenuation or deep areas with weak absorption attenuation, the inversion results have great uncertainty; the single use of multi-point micro-logging to achieve near-surface Q-body construction or the single use of VSP logging to achieve mid-deep Q-body construction does not fully utilize the lateral density of reflection seismic data, and the use of a single statistical formula in near-surface or mid-deep areas is difficult to meet the needs of actual complex geophysical exploration. Summary of the invention

[0005] In view of the problems existing in the prior art, the present invention provides a method, system, equipment and medium for constructing a full-formation quality factor Q body, which meets the needs of actual complex geophysical exploration and obtains a full-formation quality factor Q body that is dense in the horizontal direction and fine in the vertical direction.

[0006] The present invention is achieved through the following technical solutions:

[0007] A method for constructing a whole formation quality factor Q comprises the following steps:

[0008] Based on seismic reflection data, well logging data and basic information of structural layers, the low-frequency initial layer velocity volume is obtained by Kriging interpolation;

[0009] Based on the interval velocity and quality factor of multi-point micro-logging and VSP logging data, the low-frequency initial interval velocity is divided into multiple continuous intervals, and the best relationship between the quality factor and interval velocity in each interval is statistically analyzed and screened using the power exponential and quadratic regression equations.

[0010] The low-frequency initial layer velocity volume is converted into a low-frequency initial quality factor according to the optimal relationship;

[0011] Based on the post-stack seismic reflection data, the time-frequency spectrum of the seismic data is obtained by S transformation;

[0012] The peak frequency corresponding to each time sampling point is extracted using the time-frequency spectrum of seismic data;

[0013] The relative Q value is obtained by inversion using the peak frequency shift method;

[0014] The relative Q values ​​were de-averaged and normalized in each structural layer;

[0015] The de-averaged and normalized relative Q value is combined with the low-frequency initial quality factor to obtain the full-formation quality factor Q construction result combined with well and seismic data.

[0016] Furthermore, the regression equation in power exponential form is: Q = a × V b ,

[0017] The quadratic regression equation is: Q = c × V 2 +d×V+e;

[0018] Where Q is the quality factor, V is the layer velocity, b is the power index, and V b is the power term (the layer velocity V to the power of b), a is the coefficient of the power term, V 2 is a quadratic term (the square of the layer velocity V), c is the coefficient of the quadratic term, d is the coefficient of the linear term, and e is a constant term;

[0019] The process of counting and selecting the best relationship between quality factor and interval velocity in each continuous interval is as follows:

[0020] In each interval, the power exponential and quadratic regression equations were used to regress the quality factor using the layer velocity, and the determination coefficient R^ 2 As a statistic to measure the goodness of fit, R^ 2 The regression equation closest to 1 is taken as the best relationship between quality factor and layer velocity.

[0021] Furthermore, the process of converting the low-frequency initial layer velocity volume into the low-frequency initial quality factor according to the optimal relationship is:

[0022] The formula in power exponential form is:

[0023] The formula in quadratic form is:

[0024] Where V initial is the low-frequency initial layer velocity body, Q initial is the low-frequency initial quality factor, a is the power term coefficient in the optimal relationship, b is the power exponent in the optimal relationship, is the power term (V initial b), c is the coefficient of the quadratic term in the optimal relationship, is the quadratic term (V initial 2nd power), d is the coefficient of the first-order term in the optimal relationship, and e is the constant term in the optimal relationship.

[0025] Furthermore, the process of obtaining the time-frequency spectrum of seismic data by S transformation is as follows:

[0026]

[0027] Where s(τ,f) is the time-frequency spectrum of seismic data, μ(t) is the input post-stack seismic data, τ is the time in the time-frequency domain, i is the imaginary unit, f is the frequency, t is the time, dt is the differential of t, and w(t,f) is the Gaussian window function. Its specific expression is:

[0028]

[0029] In the formula, is the scale factor that controls the width of the Gaussian window, where m>0, n<0, 0.4 <m+nf<1。

[0030] Furthermore, the process of extracting the peak frequency corresponding to each time sampling point using the time-frequency spectrum of the seismic data is as follows:

[0031]

[0032]

[0033] In the formula, f c is the centroid frequency corresponding to each time sampling point of the seismic data, f p is the peak frequency.

[0034] Furthermore, the process of obtaining the relative Q value by inversion using the peak frequency shift method is as follows:

[0035]

[0036] In the formula, Q peak Relative Q value, f p represents the peak frequency of the source wavelet after propagation for time t, fm Represents the peak frequency of the source wavelet.

[0037] Furthermore, the process of performing de-averaging and normalization processing on the relative Q value in each structural layer is as follows:

[0038] Q p =Q peak -mean(Q peak )

[0039]

[0040] In the formula, mean(.) means taking the mean of (.), max(.) means taking the maximum value of (.), min(.) means taking the minimum value of (.), Q p is the relative Q value of the mean value, Q pD is the relative Q value after removing the mean and normalization;

[0041] The process of combining the de-averaged normalized relative Q value with the low-frequency initial quality factor to obtain the full-formation quality factor Q body construction result of well-seismic combination is as follows:

[0042] Q est =Q inital +δ×Q pD

[0043] In the formula, Q est represents the quality factor of the entire formation, Q inital represents the low-frequency initial quality factor, and δ represents the standard deviation of the quality factor in the multi-point micro-logging and VSP logging data.

[0044] The full formation quality factor Q construction system combined with well-seismic analysis includes:

[0045] The first processing module is used to obtain a low-frequency initial layer velocity volume through Kriging interpolation based on seismic reflection data, well logging data and basic information of structural layers;

[0046] The second processing module is used to divide the low-frequency initial layer velocity into multiple continuous intervals based on the layer velocity and quality factor of multi-point micro-logging and VSP logging data, and use the power exponential formula and the quadratic equation to respectively count and screen out the best relationship between the layer velocity and the quality factor in each continuous interval;

[0047] A conversion module, for converting a low-frequency initial layer velocity body into a low-frequency initial quality factor according to an optimal relationship;

[0048] The third processing module is used to obtain the time-frequency spectrum of seismic data through S transformation based on the post-stack seismic reflection data;

[0049] A fourth processing module is used to extract the peak frequency corresponding to each time sampling point by using the time-frequency spectrum of the seismic data;

[0050] A fifth processing module is used for obtaining a relative Q value by inverting using a peak frequency shift method;

[0051] The sixth processing module is used to perform de-averaging and normalization processing on the relative Q value in each structural layer;

[0052] The output module is used to merge the de-averaged normalized relative Q value with the low-frequency initial quality factor to obtain the well-seismic combined full-formation quality factor Q body construction result.

[0053] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, steps of a method for constructing a quality factor Q of a whole formation are implemented.

[0054] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for building a full-formation quality factor Q.

[0055] Compared with the prior art, the present invention has the following beneficial technical effects:

[0056] The present invention provides a full-formation quality factor Q body construction method, system, equipment and medium, comprising the following steps: based on seismic reflection data, well logging data and basic information of structural layers, a low-frequency initial layer velocity body is obtained by Kriging interpolation; based on the layer velocity and quality factor of multi-point micro-well logging and VSP well logging data, the low-frequency initial layer velocity is divided into multiple continuous intervals, and the best relationship between the quality factor and the layer velocity in each continuous interval is respectively counted and screened by using power exponent and a quadratic regression equation; according to the best relationship, the low-frequency initial layer velocity body is converted into a low-frequency initial quality factor; according to post-stack seismic reflection data, a time-frequency spectrum of seismic data is obtained by S transformation; the peak frequency corresponding to each time sampling point is extracted by using the time-frequency spectrum of seismic data; relative Q value is obtained by inversion using the peak frequency shift method; relative Q value is respectively de-meaned and normalized in each structural layer; the de-meaned normalized relative Q value is merged with the low-frequency initial quality factor to obtain the full-formation quality factor Q body construction result combined with well seismic; the present invention uses Kriging to construct a full-formation quality factor Q body. Based on the establishment of low-frequency initial layer velocity body by Li Jin interpolation, the natural advantages of multi-point micro-logging and VSP logging in formation structure investigation are comprehensively utilized. The low-frequency initial layer velocity body is converted into a low-frequency initial quality factor Q body through the statistical relationship between layer velocity and quality factor. The multi-source information fusion strategy is used to realize the organic fusion of the low-frequency initial quality factor Q value of the whole formation and the relative Q value obtained by the peak frequency shift method inversion, and a fine full-formation attenuation model is constructed, laying the foundation for further attenuation compensation processing; the present application uses the strategy of combining well and seismic to realize the organic fusion of the low-frequency initial quality factor Q body and the relative Q value inverted by the peak frequency shift method, and constructs a fine full-formation attenuation model. Compared with the traditional frequency domain Q value estimation method, the well-seismic combination method can obtain a stable and reliable near-surface low-frequency quality factor Q value; compared with the use of multi-point micro-logging to establish the surface Q body and multi-point VSP to establish the mid-deep Q body, the well-seismic combination method plays the role of low-frequency absolute Q constraining the mid-high frequency relative Q, reflecting the relationship between the peak frequency of the wavelet amplitude spectrum and the change over time. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a flowchart of a method for constructing a whole formation quality factor Q according to the present invention;

[0058] Figure 2 It is a flowchart of a method for constructing a quality factor Q of a whole formation in an embodiment of the present invention;

[0059] Figure 3 To obtain the low-frequency initial velocity volume by interpolation between wells;

[0060] Figure 4 To convert the low-frequency initial velocity body into a low-frequency initial quality factor body using the relation;

[0061] Figure 5To calculate the time-frequency spectrum of post-stack seismic reflection data using generalized S transform;

[0062] Figure 6 To extract the peak frequency corresponding to each time sampling point of the time spectrum;

[0063] Figure 7 To obtain the relative Q value by using post-stack seismic reflection data combined with the peak frequency shift method;

[0064] Figure 8 The Q body is the quality factor of the entire formation obtained by fusing the relative Q value obtained by inversion with the low-frequency initial quality factor. DETAILED DESCRIPTION

[0065] The present invention is further described in detail below in conjunction with specific embodiments, which are intended to explain the present invention rather than to limit it.

[0066] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0067] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0068] The present invention provides a method for constructing a whole formation quality factor Q. Figure 1 As shown, the following steps are included:

[0069] S1: Based on the basic information of seismic reflection data, well logging data and structural layers, the low-frequency initial layer velocity volume is obtained by Kriging interpolation;

[0070] S2: Based on the layer velocity and quality factor of multi-point micro-logging and VSP logging data, the low-frequency initial layer velocity is divided into multiple continuous intervals, and the power exponent and quadratic regression equation are used to statistically analyze and screen the best relationship between the layer velocity and the quality factor in each continuous interval;

[0071] S3: Convert the low-frequency initial layer velocity volume into a low-frequency initial quality factor according to the optimal relationship;

[0072] S4: Based on the post-stack seismic reflection data, the time-frequency spectrum of the seismic data is obtained by S transformation;

[0073] S5: Extract the peak frequency corresponding to each time sampling point using the time-frequency spectrum of the seismic data;

[0074] S6: Relative Q value is obtained by inversion using the peak frequency shift method;

[0075] S7: De-average and normalize the relative Q values ​​in each structural layer;

[0076] S8: The de-averaged normalized relative Q value is combined with the low-frequency initial quality factor to obtain the full-formation quality factor Q construction result combined with well-seismic.

[0077] It should be noted that the order of the steps in the present application is not unique, and those skilled in the art can adjust the order according to actual production needs.

[0078] Preferably, the regression equation in power exponential form is: Q = a × V b ,

[0079] The quadratic regression equation is: Q = c × V 2 +d×V+e;

[0080] Where Q is the quality factor, V is the layer velocity, b is the power index, and V b is the power term (the layer velocity V to the power of b), a is the coefficient of the power term, V 2 is a quadratic term (the square of the layer velocity), c is the coefficient of the quadratic term, d is the coefficient of the linear term, and e is a constant term.

[0081] The process of counting and selecting the best relationship between the inner velocity and the quality factor in each continuous interval is as follows:

[0082] In each interval, the power exponential and quadratic regression equations were used to regress the quality factor using the layer velocity, and the determination coefficient R^ 2 As a statistic to measure goodness of fit. In each interval, select R^ 2 The regression equation closest to 1 is the best relationship between quality factor and layer velocity. It should be noted that R^2 The value range of 2 is [0, 1]. The closer the value of R^

[0083] Preferably, the process of converting the low-frequency initial layer velocity body into the low-frequency initial quality factor according to the optimal relationship is as follows:

[0084] The formula in power-exponential form is:

[0085] The formula in the form of a quadratic equation of one variable is:

[0086] In the formula, V initial is the low-frequency initial velocity body, Q initial is the low-frequency initial quality factor, b is the power exponent, is the power-exponential term (the b-th power of V initial ), a is the coefficient of the power-exponential term, is the quadratic term (the 2nd power of V initial ), c is the coefficient of the quadratic term, d is the coefficient of the linear term, and e is the constant term.

[0087] Preferably, the process of obtaining the time-frequency spectrum of seismic data by S transform is as follows:

[0088]

[0089] In the formula, s(τ, f) is the time-frequency spectrum of seismic data, μ(t) is the input post-stack seismic data, τ is the time in the time-frequency domain, i is the imaginary unit, f is the frequency, t is the time, dt is the differential of t, and w(t, f) is the Gaussian window function, and its specific expression is:

[0090]

[0091] In the formula, is the scale factor that controls the width of the Gaussian window, where m > 0, n < 0, 0.4 < m + nf < 1, μ(t) is the input post-stack seismic reflection data, and s(τ, f) is the time-frequency spectrum of seismic data.

[0092] Preferably, the process of extracting the peak frequency corresponding to each time sampling point using the time-frequency spectrum of seismic data is as follows:

[0093]

[0094]

[0095] In the formula, s(τ, f) is the time-frequency spectrum of seismic data, f c is the centroid frequency corresponding to each time sampling point of the seismic data, f pis the peak frequency, f is the frequency, and df is the differential of f.

[0096] Preferably, the process of obtaining the relative Q value by inversion using the peak frequency shift method is:

[0097]

[0098] In the formula, Q peak Relative Q value, f p represents the peak frequency of the signal after propagation for time t, f m Represents the peak frequency of the source wavelet.

[0099] Preferably, the process of performing de-averaging and normalization processing on the relative Q value in each structural layer is:

[0100] Q p =Q peak -mean(Q peak )

[0101]

[0102] In the formula, mean(.) means taking the mean of (.), max(.) means taking the maximum value of (.), min(.) means taking the minimum value of (.), Q p is the relative Q value after removing the mean, Q pD is the relative Q value after removing the mean and normalization;

[0103] The process of combining the de-averaged normalized relative Q value with the low-frequency initial quality factor to obtain the full-formation quality factor Q body construction result of well-seismic combination is as follows:

[0104] Q est =Q inital +Q pD ×δ

[0105] In the formula, Q est represents the quality factor of the entire formation, Q inital represents the low-frequency initial quality factor, and δ represents the standard deviation of the quality factor in multi-point micro-logging and VSP logging.

[0106] Embodiment 1:

[0107] like Figure 2 As shown, the following steps are included:

[0108] S1. Collect basic data such as seismic data, well logging data, and stratigraphic data to obtain the well time-depth relationship and seismic processing velocity, and obtain the low-frequency initial velocity volume V through Kriging interpolation. initial ,like Figure 3 shown.

[0109] S2. Collect the interval velocity V and quality factor Q of multi-point micro-logging and VSP logging, and divide the interval velocity V into multiple continuous intervals within the maximum-minimum velocity range of the low-frequency initial velocity body. Use the formula in the form of power exponent and the formula in the form of quadratic equation to statistically analyze and screen the relationship between the quality factor Q and the interval velocity V in each interval, where:

[0110] The formula for power exponential form is: Q = a × V b , the statistical results are:

[0111] V = 2500-3500m / s,

[0112] Q=0.00006V 1.8524

[0113] V = 3500-4200m / s,

[0114] Q=0.0001V 1.777

[0115] V=4200-4500m / s,

[0116] Q=0.0004V 1.6085

[0117] V=4500-5500m / s.

[0118] Q=0.00007V 1.829 ;

[0119] The formula in the form of a quadratic equation is: Q = c × V 2 +d×V+e, the statistical result is:

[0120] V<650m / s,

[0121] Q=(-5E-05)V 2 +0.0635V-12.822

[0122] 650m / s <V<2500m / s,

[0123] Q=(-6E-06)V 2 +0.0345V-9.0675.

[0124] S3, combined with the relationship between the quality factor Q and the layer velocity V obtained in S2, according to the velocity size of each time sampling point in the low-frequency initial velocity body Vinitial obtained in S1, select the statistical relationship corresponding to the velocity, and convert the low-frequency initial velocity body Vinitial into a low-frequency initial quality factor body Qinitial, such as Figure 4 As shown,

[0125] The formula in exponential form is:

[0126] The formula in the form of a quadratic equation of one variable is:

[0127] S4. Input the post-stack seismic reflection data μ(t), and calculate the time-frequency spectrum s(τ, f) of the seismic data using the S transform, as Figure 5 shown; where the expression of the forward S transform is

[0128]

[0129] In the formula, s(τ, f) is the time-frequency spectrum of the seismic data, μ(t) is the input post-stack seismic data, τ is the time in the time-frequency domain, i is the imaginary unit, f is the frequency, t is the time, dt is the differential of t, and w(t, f) is the Gaussian window function, and its specific expression is

[0130]

[0131] In the formula, f is the frequency, t is the time, is the scale factor that controls the width of the Gaussian window, where m > 0, n < 0, 0.4 < m + nf < 1, which can increase the focusing degree of the time-frequency analysis of the seismic reflection signal.

[0132] S5. Calculate the centroid frequency f c corresponding to each time sampling point of the seismic data using the time-frequency spectrum s(τ, f), and then extract the peak frequency f p , which can improve the stability of the algorithm, as Figure 6 shown;

[0133]

[0134]

[0135] S6. Finally, inversely calculate the relative quality factor Q by combining the peak frequency shift method, as peak shown, Figure 7 where

[0136]

[0137] In the formula, f p represents the peak frequency of the signal after propagation for time t, and f m represents the peak frequency of the source wavelet.

[0138] S7. After performing de-mean normalization processing on Q peak separately within each structural layer, Q pD

[0139] Qp =Q peak -mean(Q peak )

[0140]

[0141] In the formula, mean(.) means taking the mean of (.), max(.) means taking the maximum value of (.), min(.) means taking the minimum value of (.), Q p is the relative Q value after removing the mean, Q pD is the relative Q value after removing the mean and normalization.

[0142] S8, Q pD With Q inital The quality factor Q of the whole formation combined with well and seismic data is obtained by fusion est ,like Figure 8 As shown,

[0143] Q est =Q inital +δ×Q pD

[0144] In the formula, Q est represents the quality factor of the entire formation, Q inital represents the low-frequency initial quality factor, and δ represents the standard deviation of the quality factor in the multi-point micro-logging and VSP logging data.

[0145] The present invention provides a system for building a full-formation quality factor Q by combining well and seismic data, comprising:

[0146] The first processing module is used to obtain a low-frequency initial layer velocity volume through Kriging interpolation based on seismic reflection data, well logging data and basic information of structural layers;

[0147] The second processing module is used to divide the low-frequency initial layer velocity into multiple continuous intervals based on the layer velocity and quality factor of multi-point micro-logging and VSP logging data, and use the power exponent and the quadratic regression equation to respectively count and screen out the best relationship between the layer velocity and the quality factor in each continuous interval;

[0148] A conversion module, for converting a low-frequency initial layer velocity body into a low-frequency initial quality factor according to an optimal relationship;

[0149] The third processing module is used to obtain the time-frequency spectrum of seismic data through S transformation based on the post-stack seismic reflection data;

[0150] A fourth processing module is used to extract the peak frequency corresponding to each time sampling point by using the time-frequency spectrum of the seismic data;

[0151] A fifth processing module is used for obtaining a relative Q value by inverting using a peak frequency shift method;

[0152] The sixth processing module is used to perform de-averaging and normalization processing on the relative Q value in each structural layer;

[0153] The output module is used to merge the de-averaged normalized relative Q value with the low-frequency initial quality factor to obtain the well-seismic combined full-formation quality factor Q body construction result.

[0154] In another embodiment of the present invention, a computer device is provided, the computer device comprising a processor and a memory, the memory being used to store a computer program, the computer program comprising program instructions, and the processor being used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc., which are the computing core and control core of the terminal, and are suitable for implementing one or more instructions, and are specifically suitable for loading and executing one or more instructions in a computer storage medium to implement a corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a method for building a full-formation quality factor Q.

[0155] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It can be understood that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for building a full-formation quality factor Q in the above embodiment.

[0156] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0157] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks 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.

[0158] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate 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 A function specified in one or more boxes.

[0159] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0160] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for constructing a whole formation quality factor Q, characterized in that: The following steps are involved: Based on seismic reflection data, well logging data and basic information of structural layers, the low-frequency initial layer velocity volume is obtained by Kriging interpolation; Based on the interval velocity and quality factor of multi-point micro-logging and VSP logging data, the low-frequency initial interval velocity is divided into multiple continuous intervals, and the best relationship between the quality factor and interval velocity in each interval is statistically analyzed and screened using the power exponential and quadratic regression equations. The low-frequency initial layer velocity volume is converted into a low-frequency initial quality factor according to the optimal relationship; Based on the post-stack seismic reflection data, the time-frequency spectrum of the seismic data is obtained by S transformation; The peak frequency corresponding to each time sampling point is extracted using the time-frequency spectrum of seismic data; The relative Q value is obtained by inversion using the peak frequency shift method; The relative Q values ​​were de-averaged and normalized in each structural layer; The de-averaged and normalized relative Q value is combined with the low-frequency initial quality factor to obtain the full-formation quality factor Q construction result combined with well and seismic data.

2. A method for constructing a whole formation quality factor Q according to claim 1, characterized in that: The regression equation in power exponential form is: Q = a × V b , The quadratic regression equation is: Q = c × V 2 +d×V+e; Where Q is the quality factor, V is the layer velocity, b is the power index, and V b is the power term (the layer velocity V to the power of b), a is the coefficient of the power term, V 2 is a quadratic term (the square of the layer velocity V), c is the coefficient of the quadratic term, d is the coefficient of the linear term, and e is a constant term; The process of counting and selecting the best relationship between quality factor and interval velocity in each continuous interval is as follows: In each interval, the power exponential and quadratic regression equations were used to regress the quality factor using the layer velocity, and the determination coefficient R^ 2 As a statistic to measure the goodness of fit, R^ 2 The regression equation closest to 1 is taken as the best relationship between quality factor and layer velocity.

3. A method for constructing a whole formation quality factor Q according to claim 1, characterized in that: The process of converting the low-frequency initial layer velocity volume into the low-frequency initial quality factor according to the optimal relationship is: The formula in power exponential form is: The formula in quadratic form is: Where V initial is the low-frequency initial layer velocity body, Q initial is the low-frequency initial quality factor, a is the power term coefficient in the optimal relationship, b is the power exponent in the optimal relationship, is the power term (V initial b), c is the coefficient of the quadratic term in the optimal relationship, is the quadratic term (V initial 2nd power), d is the coefficient of the first-order term in the optimal relationship, and e is the constant term in the optimal relationship.

4. A method for constructing a whole formation quality factor Q according to claim 1, characterized in that: The process of obtaining the time-frequency spectrum of seismic data by S transformation is as follows: Where s(τ,f) is the time-frequency spectrum of seismic data, μ(t) is the input post-stack seismic data, τ is the time in the time-frequency domain, i is the imaginary unit, f is the frequency, t is the time, dt is the differential of t, and w(t,f) is the Gaussian window function. Its specific expression is: In the formula, is the scale factor that controls the width of the Gaussian window, where m>0, n<0, 0.4 <m+nf<1。 5. A method for constructing a whole formation quality factor Q according to claim 1, characterized in that: The process of extracting the peak frequency corresponding to each time sampling point using the time-frequency spectrum of seismic data is as follows: In the formula, f c is the centroid frequency corresponding to each time sampling point of the seismic data, f p is the peak frequency.

6. A method for constructing a whole formation quality factor Q according to claim 1, characterized in that: The process of obtaining the relative Q value by inversion using the peak frequency shift method is as follows: In the formula, Q peak Relative Q value, f p represents the peak frequency of the source wavelet after propagation for time t, f m Represents the peak frequency of the source wavelet.

7. A method for constructing a quality factor Q of a whole formation according to claim 1, characterized in that: The process of removing the mean and normalizing the relative Q value in each structural layer is as follows: Q p =Q peak -mean(Q peak ) In the formula, mean(.) means taking the mean of (.), max(.) means taking the maximum value of (.), min(.) means taking the minimum value of (.), Q p is the relative Q value of the mean value, Q pD is the relative Q value after removing the mean and normalization; The process of combining the de-averaged normalized relative Q value with the low-frequency initial quality factor to obtain the full-formation quality factor Q body construction result of well-seismic combination is as follows: Q est =Q nital +δ×Q pD In the formula, Q est represents the quality factor of the entire formation, Q inital represents the low-frequency initial quality factor, and δ represents the standard deviation of the quality factor in the multi-point micro-logging and VSP logging data.

8. The full formation quality factor Q construction system combined with well-seismic data is characterized by: A method for constructing a full-formation quality factor Q based on any one of claims 1 to 7, comprising: The first processing module is used to obtain a low-frequency initial layer velocity volume through Kriging interpolation based on seismic reflection data, well logging data and basic information of structural layers; The second processing module is used to divide the low-frequency initial layer velocity into multiple continuous intervals based on the layer velocity and quality factor of multi-point micro-logging and VSP logging data, and use the power exponential formula and the quadratic equation to respectively count and screen out the best relationship between the layer velocity and the quality factor in each continuous interval; A conversion module, for converting a low-frequency initial layer velocity body into a low-frequency initial quality factor according to an optimal relationship; The third processing module is used to obtain the time-frequency spectrum of seismic data through S transformation based on the post-stack seismic reflection data; A fourth processing module is used to extract the peak frequency corresponding to each time sampling point by using the time-frequency spectrum of the seismic data; A fifth processing module is used for obtaining a relative Q value by inverting using a peak frequency shift method; The sixth processing module is used to perform de-averaging and normalization processing on the relative Q value in each structural layer; The output module is used to merge the de-averaged normalized relative Q value with the low-frequency initial quality factor to obtain the well-seismic combined full-formation quality factor Q body construction result.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of a method for constructing a full-formation quality factor Q are implemented as described in any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of a method for constructing a full-formation quality factor Q are implemented as described in any one of claims 1 to 7.

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