A microlog constrained near-surface Q inversion method

Abstract

The present application relates to the field of oil and gas exploration seismic data processing, in particular to a kind of micro logging constrained near-surface Q inversion method.The method comprises the following steps: picking up the first arrival time of micro logging data;Extract the first arrival wavelet amplitude spectrum of micro logging data;Calculate the Q value at the micro logging point;Pick up the first arrival time of ground seismic data;Extract the relative equivalent Q of the first arrival of adjacent two ground seismic data;Under the constraint of micro logging Q, the near-surface Q model is obtained by inversion.The method improves the accuracy of near-surface Q modeling, can combine the advantages of micro logging and ground seismic data, and at the same time, with the help of the longitudinal high precision of micro logging data and the lateral high sampling rate of ground seismic data, it is beneficial to carry out near-surface absorption attenuation compensation processing.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of oil and gas exploration seismic data processing, in particular to a micro-logging constrained near-surface Q inversion method. BACKGROUND

[0002] Compensating for the surface absorption effect, improving the high-frequency energy of the reflection signal, and widening the effective frequency bandwidth of the seismic record are of great significance to improving the seismic resolution. Accurate Q modeling is an important prerequisite for compensating for the near-surface absorption and attenuation.

[0003] Using micro-logging data to observe and estimate the near-surface absorption structure and eliminate the influence of near-surface absorption and attenuation on the resolution of seismic data is an important attempt in recent years of high-resolution seismic exploration. The applicant's prior application CN110261904A, a near-surface Q value inversion and classification evaluation method based on generalized S transform, includes the following steps: step 1, sorting adjacent two data; step 2, respectively applying generalized S transform to obtain high-resolution time-frequency spectrum; step 3, extracting the corresponding first signal instantaneous amplitude spectrum; step 4, applying the log spectrum ratio method to invert and fit the Q value between two channels; step 5, repeating steps (1)-(4) to invert the Q value between adjacent channels in all micro-logging data; step 6, generating a Q value curve varying with depth at each well site; step 7, calculating the average attenuation effect factor in combination with each layer thickness and interval velocity; and step 8, performing near-surface classification and evaluation based on the average attenuation effect factor. The near-surface Q value inversion and classification evaluation method based on generalized S transform provides effective technical support and protection for deepening the research on acquisition methods for complex structure and stratum lithology exploration and improving acquisition quality and efficiency.

[0004] Micro-logging data can obtain relatively accurate Q values at micro-logging points, but interpolation is required when applied, and there is a problem of insufficient lateral accuracy. Ground seismic can cover the entire area in the lateral direction with a high lateral sampling rate, and the first arrival wave thereof can also be used to estimate the Q value of the surface, but the accuracy is insufficient. The inventors have found that most near-surface Q modeling methods only use one of micro-logging or ground seismic data, and a few methods for jointly calculating the near-surface Q value using both types of data are to first establish an empirical relationship formula between Q and velocity, generate a relationship volume, and then simply convert the velocity to Q. Such methods are based on empirical formulas and have low accuracy and reliability, and cannot meet the requirements of high-resolution seismic exploration. SUMMARY

[0005] The main purpose of the present application is to provide a micro-logging constrained near-surface Q inversion method, which can improve the accuracy of near-surface Q modeling, has high reliability, and can meet the requirements of high-resolution seismic exploration.

[0006] To achieve the above-mentioned purpose, the present application adopts the following technical solutions:

[0007] The application provides a micro-logging constrained near-surface Q inversion method, which comprises the following steps: picking up the micro-logging data first arrival time; extracting the micro-logging data first arrival wavelet amplitude spectrum; calculating the Q value at the micro-logging point; picking up the ground seismic data first arrival time; extracting the relative equivalent Q of the adjacent two channels of the ground seismic data first arrival; and inversing the near-surface Q model under the micro-logging Q constraint.

[0008] Further, the Q value at the micro-logging point is calculated by using the frequency spectrum ratio method, and satisfies:

[0009]

[0010] Wherein, Q i,i+1 is the formation Q value between the i-th shot and the i+1-th shot, f is the frequency, Δt i,i+1 is the first arrival time difference of the i-th shot and the i+1-th shot, W i (f) is the first arrival wavelet amplitude spectrum of the i-th shot, and ln represents logarithm.

[0011] Further, the Q value at the micro-logging point can also be calculated by using the following method, which comprises the following steps: calculating the micro-logging data first arrival wavelet amplitude spectrum; calculating the micro-logging data first arrival wavelet energy according to the amplitude spectrum; distinguishing different lithology and physical properties of the formation according to the relationship between the first arrival wavelet energy and the depth; correcting the energy abnormal shot caused by different excitation point coupling degrees in the same lithology and physical property formation by trend fitting; and scanning the optimal layer Q value from the energy matching angle.

[0012] Still further, the single-scale Gabor transform method is used to calculate the micro-logging data first arrival wavelet amplitude spectrum.

[0013] Still further, the micro-logging data first arrival wavelet energy is calculated according to the amplitude spectrum, and the micro-logging data first arrival wavelet energy calculation formula is as follows:

[0014]

[0015] Wherein, P i is the first arrival wavelet energy of the i-th shot in the micro-logging data, f is the frequency, W i (f) is the first arrival wavelet amplitude spectrum of the i-th shot.

[0016] Still further, the corrected energy satisfies:

[0017]

[0018] Wherein, P i+1 is the first arrival wavelet energy of the i+1-th shot, f is the frequency, t i is the first arrival time of the i-th shot, W i (f) is the first arrival wavelet amplitude spectrum of the i-th shot, and Qi,i+1 Q is the formation Q value between the i-th and i+1-th shots.

[0019] Further, the near-surface Q model obtained by the inversion under the micro-logging Q constraint satisfies:

[0020] phi (m, y) = ||y-Fm|| 2 + lambda||m-eta m w || 2 ;

[0021] Wherein, y is the relative equivalent Q of the adjacent two-way first arrival of the ground seismic, m is the formation Q model, F is the ray path of the first arrival of the ground seismic data, lambda is the damping coefficient, m w is the micro-logging Q value, eta is the weighted constraint coefficient of the micro-logging Q, and phi is the objective function.

[0022] Compared with the prior art, the present application has the following advantages:

[0023] The method improves the near-surface Q modeling precision, can combine the advantages of micro-logging and ground seismic data, and is beneficial to the near-surface absorption and attenuation compensation processing by means of the longitudinal high precision of the micro-logging data and the lateral high sampling rate of the ground seismic data. DETAILED DESCRIPTION

[0024] The drawings accompanying the specification of the present application form a part of the present application and serve to provide further understanding of the present application, and the illustrative embodiments thereof, together with the description of the present application, serve to explain the present application, and do not constitute improper limitations on the present application.

[0025] Figure 1 The figure is a flowchart of the near-surface Q inversion method under the micro-logging constraint according to the embodiment 1 of the present application;

[0026] Figure 2 The figure is a schematic diagram of a typical micro-logging data and its first arrival picking according to the embodiment 1 of the present application;

[0027] Figure 3 The figure is a schematic diagram of a micro-logging single-channel record and its first arrival picking according to the embodiment 1 of the present application;

[0028] Figure 4 The figure is the first arrival wavelet amplitude spectrum of a micro-logging data according to the embodiment 1 of the present application;

[0029] Figure 5 The figure is the micro-logging Q value extracted by the application frequency spectrum ratio method according to the embodiment 1 of the present application;

[0030] Figure 6 The figure is a schematic diagram of ground seismic data and its first arrival picking according to the embodiment 1 of the present application;

[0031] Figure 7Relative equivalent Q of adjacent two-way first arrival wavelet of the ground seismic data described in the embodiment 1 of the present application;

[0032] Figure 8 Near-surface Q model and its local enlarged view obtained by inverting the actual data of a certain work area described in the embodiment 1 of the present application;

[0033] Figure 9 Seismic profile comparison chart before and after the absorption and attenuation compensation by applying the near-surface Q model described in the embodiment 1 of the present application;

[0034] Figure 10 Seismic profile spectrum comparison chart before and after the compensation described in the embodiment 1 of the present application. DETAILED DESCRIPTION

[0035] It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the present application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0036] It should be noted that the terms used herein are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present application. As used herein, the singular form is intended to include the plural form unless the context clearly indicates otherwise, and it should also be understood that when the terms "comprise" and / or "include" are used in the specification, there is a presence of the features, steps, operations and / or combinations thereof.

[0037] In order to enable those skilled in the art to more clearly understand the technical solutions of the present application, the technical solutions of the present application will be described in detail below with specific embodiments.

[0038] Embodiment 1

[0039] As shown in the formula (1), the microlog-constrained near-surface Q inversion method includes the following steps: Figure 1

[0040] Step 1: picking up the first arrival time of the microlog data;

[0041] Selecting the long-short window energy ratio method to automatically pick up the first arrival of the microlog data.

[0042] Step 2: extracting the first arrival wavelet amplitude spectrum of the microlog data;

[0043] On the basis of completing step 1, the first arrival wavelet amplitude spectrum of adjacent two-way microlog data is extracted.

[0044] Step 3: applying the frequency spectrum ratio method to calculate the Q value at the microlog point;

[0045] ​On the basis of completing step 1 and step 2, according to the first arrival time and the first arrival wavelet amplitude spectrum of the adjacent two traces of the microlog data, the microlog Q value is solved by using the frequency spectrum ratio method. Specifically, the microlog Q value satisfies the following formula (1):

[0046]

[0047] Wherein, Q i,i+1 is the formation Q value between the i-th shot and the i+1-th shot, f is the frequency, Δt i,i+1 is the first arrival time difference of the i-th shot and the i+1-th shot, W i (f) is the first arrival wavelet amplitude spectrum of the i-th shot, and ln represents taking logarithm.

[0048] Step 4: picking the first arrival time of the surface seismic data;

[0049] On the basis of completing step 3, the first arrival time of the surface seismic data is further picked, preferably, the long-short window energy ratio method is used to automatically pick the first arrival of the surface seismic data.

[0050] Step 5: extracting the relative equivalent Q of the first arrivals of the adjacent two traces of the surface seismic data by using the frequency spectrum ratio method;

[0051] On the basis of completing step 4, the relative equivalent Q of the first arrivals of the adjacent two traces of the surface seismic data is further extracted, preferably, the frequency spectrum ratio method is used to extract the relative equivalent Q of the surface seismic data.

[0052] Step 6: obtaining the near-surface Q model by inversion under the constraint of the microlog Q.

[0053] On the basis of completing step 4, under the constraint of the microlog Q, the near-surface Q model is obtained by inversion according to the first arrival time and the relative equivalent Q of the surface seismic data, satisfying the following formula (2):

[0054] φ(m,y)=||y-Fm|| 2 +λ||m-ηm w || 2 ;

[0055] Wherein, y is the relative equivalent Q of the first arrivals of the adjacent two traces of the surface seismic data, m is the formation Q model, F is the ray path of the first arrival of the surface seismic data, λ is the damping coefficient, m w is the microlog Q value, η is the weighted constraint coefficient of the microlog Q, and φ is the objective function.

[0056] Thus, the near-surface Q model can be obtained.

[0057] The inversion method described in embodiment 1 will be described in detail below in combination with typical data. As shown in the following table, wherein, Figures 2-10 Figure 2 ​It is a typical micro logging data and its first arrival picking, and the dots in the figure are the picking results; Figure 3 It is a micro logging single channel record and its first arrival picking schematic diagram, and the long-short window energy ratio method can automatically and accurately pick the first arrival; Figure 4 It is the first arrival wavelet amplitude spectrum of a micro logging data; Figure 5 It is the micro logging Q value of a micro logging data by using the spectrum ratio method, and according to the micro logging first arrival time and amplitude spectrum of adjacent two channels, the spectrum ratio method can be used to obtain the formation Q value between adjacent two shots, so that the formation Q value at each depth of the micro logging point can be obtained; Figure 6 It is a ground seismic data and its first arrival picking schematic diagram, and the long-short window energy ratio method can automatically and accurately pick the ground seismic first arrival; Figure 7 It is the relative equivalent Q of the first arrival wavelet of adjacent two channels of the ground seismic data; Figure 8 It is a near-surface Q model and its local enlarged view obtained by inverting actual data of a work area, and the solid line in the figure is the low velocity layer interface and the high velocity layer top interface of the micro logging field interpretation result, which is in good agreement with the Q inversion result; Figure 9 It is a seismic profile comparison diagram before and after using the near-surface Q model for absorption and attenuation compensation, and the resolution is obviously improved after compensation; Figure 10 It is a seismic profile spectrum comparison diagram before and after compensation, the blue line is the spectrum before compensation, and the red line is the spectrum after compensation, and the frequency band is widened and the main frequency is improved after compensation.

[0058] The above results show that the micro logging constrained Q inversion modeling method provided by the application can obtain more accurate and reasonable results, and is more beneficial to near-surface absorption and attenuation compensation.

[0059] Embodiment 2

[0060] The difference between the micro logging constrained near-surface Q inversion method and the method of embodiment 1 is the calculation method of the Q value at the micro logging point, which comprises the following steps:

[0061] Step 1: calculating the first arrival wavelet amplitude spectrum of the micro logging data;

[0062] Among them, as a more preferred embodiment of the application, the single-scale Gabor transform is selected to calculate the first arrival wavelet amplitude spectrum. The process of calculating the first arrival wavelet amplitude spectrum by using the single-scale Gabor transform can be specifically referred to the following formula (1):

[0063]

[0064] Wherein, g a (t) is a Gaussian function, called a window function, a is the width of the window, and the parameter b is used to shift the window. A sliding window g a(t-b), and then Fourier transform to obtain the monoscale Gabor transform result G(a, b, f) of the signal d(t), and the first arrival wavelet amplitude spectrum W of the microlog data can be extracted from G(a, b, f) i (f), and i represents a channel number or a shot number.

[0065] Step 2: Calculating the first arrival wavelet energy of the microlog data according to the amplitude spectrum;

[0066] On the basis of completing step 1, the energy is calculated according to the first arrival wavelet amplitude spectrum, and the energy satisfies formula (2):

[0067]

[0068] Wherein, P i is the first arrival wavelet energy of the i-th shot in the microlog data, f is the frequency, and W i (f) is the first arrival wavelet amplitude spectrum of the i-th shot.

[0069] Step 3: Distinguishing different lithology and physical properties of the stratum according to the relationship between the first arrival wavelet energy and the depth;

[0070] On the basis of completing step 2, the lithology of the stratum at the shot point is distinguished according to the relationship between the first arrival wavelet energy and the depth. According to the collection system of the microlog, the first arrival wavelet energy should monotonically decrease with the depth, and the actual data has a large jump in the first arrival wavelet energy with the depth. It is judged that the different excitation lithology causes, and thus different excitation lithology can be distinguished. If there is no large jump, it is judged that the same lithology causes, and if there is a small jump, it is judged that the different coupling degrees of the excitation point cause.

[0071] Step 4: Correcting the energy abnormal shot caused by different coupling degrees of the excitation point in the same lithology and physical property stratum by trend fitting.

[0072] On the basis of completing step 3, further trend fitting is performed on the small energy jump points in the same excitation lithology stratum to correct the energy abnormality caused by different coupling degrees of the excitation point. Preferably, the trend fitting correction is performed by using polynomial fitting.

[0073] Step 5: Scanning the best layer Q value from the energy matching angle.

[0074] On the basis of completing step 4, the best layer Q value is further determined by scanning from the energy matching angle. Specifically, the best layer Q value satisfies formula (3):

[0075]

[0076] Wherein, P i+1 is the first arrival wavelet energy of the i+1-th shot, f is the frequency, and t iW is the first arrival time of the i-th shot i (f) is the amplitude spectrum of the i-th shot first arrival wavelet, Q i,i+1 is the Q value of the formation between the i-th shot and the i+1-th shot.

[0077] Thus, the Q value of the actual microlog data can be estimated.

[0078] The other steps are the same as in Example 1.

[0079] The above examples are the preferred embodiments of the present application, but the embodiments of the present application are not limited by the above examples, and any changes, modifications, substitutions, combinations, simplifications made without departing from the spirit and principles of the present application should be equivalent replacement methods, and are included in the protection scope of the present application.

Claims

1. A microslogger constrained near-surface Q inversion method characterized by, It comprises the following steps: Pick up the first arrival time of microlog data; Extract the amplitude spectrum of the first arrival wavelet of microlog data; Calculate the Q value at the microlog point; pick up the first arrival time of surface seismic data; extract the relative equivalent Q of the first arrivals of two adjacent channels of surface seismic data; obtain the near-surface Q model by inversion under the constraint of microlog Q; The near-surface Q model obtained by inversion under the constraint of microlog Q satisfies: φ(m, y) = ||y - Fm|| + λ||m - ηm||. 2 ||y - Fm|| + λ||m - ηm||. w ||y - Fm|| + λ||m - ηm||. 2 ||y - Fm|| where y is the relative equivalent Q of the two adjacent ground seismic first arrivals, m is the stratum Q model, F is the ray path of the ground seismic data first arrival, λ is the damping coefficient, m w is the microlog Q value, η is the weighted constraint coefficient of the microlog Q, and φ is the objective function.

2. The method of claim 1, wherein, The Q value at the microlog point is calculated by using the frequency spectrum ratio method, and satisfies: where Q i,i+1 is the Q value of the formation between the ith and i+1th shots, f is the frequency, Δt i,i+1 is the first arrival time difference between the ith and i+1th shots, W i (f) is the first arrival wavelet amplitude spectrum of the ith shot, and ln indicates taking the logarithm.

3. The method of claim 1, wherein, The Q value calculation method at the microlog point comprises the following steps: calculating the amplitude spectrum of the first arrival wavelet of microlog data; calculating the first arrival wavelet energy of microlog data according to the amplitude spectrum; distinguishing different lithology and physical properties of the formation according to the relationship between the first arrival wavelet energy and the depth; correcting the energy abnormal shot caused by different excitation point coupling degrees in the same lithology and physical property formation by trend fitting; and scanning the optimal layer Q value from the energy matching point of view.

4. The method of claim 3, wherein, The single-scale Gabor transform method is used to calculate the amplitude spectrum of the first arrival wavelet of microlog data.

5. The method of claim 3, wherein, The first arrival wavelet energy of microlog data is calculated according to the amplitude spectrum, and the calculation formula of the first arrival wavelet energy of microlog data is as follows: where P i is the first break wavelet energy of the i-th shot in the microlog data, f is the frequency, W i (f) is the first break wavelet amplitude spectrum of the i-th shot.

6. The method of claim 3, wherein, The corrected energy satisfies: where P i+1 is the first arrival wavelet energy of the i+1th shot, f is the frequency, t i is the first arrival time of the ith shot, W i (f) is the amplitude spectrum of the first arrival wavelet of the ith shot, Q i,i+1 is the formation Q value between the ith shot and the i+1th shot.

Images