Frequency-division seismic gas-bearing reservoir identification method based on matching pursuit

Abstract

The invention discloses a frequency-division seismic gas-bearing reservoir identification method based on matching pursuit. The problem that an air layer and an aquifer are difficult to distinguish in the prior art is solved. The method comprises the following steps: S1, collecting basic data; s2, analyzing seismic amplitude reflection characteristics of the two types of well reservoir sections in the state that the two types of well reservoir sections contain different fluids; s3, spectrum analysis is carried out on the reservoir sections of the pure gas well and the water outlet well, and the difference of the reservoir sections of the two types of wells on the seismic spectrum is analyzed; s4, performing frequency division processing on the amplitude-preserved seismic data by adopting a matching pursuit method, and separating out a plurality of single-frequency bodies; s5, analyzing the difference of the two types of reservoir sections, and obtaining that the low-frequency end single-frequency body seismic section can distinguish the two types of reservoirs; and S6, counting the coincidence rate of the attribute graph and the fluid test condition of the drilled reservoir in the research area, and identifying the pure gas area distribution range of the reservoir section. According to the method, the amplitude difference of the two types of well reservoir sections can be more clearly reflected on a single-frequency (section) seismic section of a specific frequency or a specific frequency band, and the identification precision of the gas reservoir is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil exploration and development, and particularly relates to a method for identifying gas-bearing reservoirs by frequency-divided seismic based on matching pursuit. Background Art

[0002] At present, the seismic identification methods for gas-bearing reservoirs at home and abroad usually use seismic multi-attribute analysis to qualitatively depict the favorable areas of gas-bearing reservoirs, and use seismic inversion methods to quantitatively predict the development parameters of reservoirs.

[0003] A large amount of information such as the physical property changes of reservoirs and the composition of reservoir saturated fluids is contained in seismic data. Seismic attribute analysis takes seismic attribute data as the carrier, extracts the useful information hidden in the seismic data, and plays an important role in reservoir prediction, oil and gas bearing identification, description of reservoir characteristic parameters, reservoir dynamic monitoring, etc. Some conventional seismic attributes include seismic amplitude attributes, waveform attributes, frequency attributes, phase attributes, attenuation characteristics, structural attributes, etc. When identifying gas-bearing reservoirs, seismic attributes related to frequency are mostly used, and time-frequency analysis means is one of the important means for hydrocarbon detection using seismic data. The high-precision time-frequency spectrum decomposition method is the key to increasing the reliability of detecting gas-bearing reservoirs. Time-frequency analysis technology is a method of analyzing signals using the joint function of time and frequency. The commonly used time-frequency analysis methods in seismology mainly include Fourier transform, wavelet transform, Hilbert-Huang transform, S transform, generalized S transform, etc., and the matching pursuit algorithm has higher vertical resolution.

[0004] Seismic inversion refers to the process of imaging and predicting the spatial structure and physical properties of underground rock formations using seismic observation data with certain prior information (such as known geological laws and drilling and logging data) as constraints. The purpose is to reverse the spatial distribution characteristics of rock geophysical parameters such as wave impedance, velocity, and density of underground media according to seismic data, and obtain reservoir physical property parameters such as porosity to carry out reservoir prediction. According to the different inversion geological results, seismic inversion can be divided into structural inversion and wave impedance inversion; according to the different inversion data, seismic inversion can be divided into post-stack inversion and pre-stack inversion. The pre-stack AVO inversion is mostly used in the identification of seismic gas-bearing reservoirs. It is a pre-stack seismic inversion method that mainly uses the characteristics of the pre-stack common reflection point offset gather and the angle partial stack gather changing with the offset or incident angle to carry out formation elastic parameter estimation.

[0005] The accuracy of predicting the gas-bearing distribution of reservoirs by conventional seismic attributes is relatively low, and it is also difficult to distinguish gas layers from water layers and solve the problem of gas-water distribution. The pre-stack AVO inversion has relatively high requirements for the signal-to-noise ratio of pre-stack seismic gathers, and the implementation difficulty is relatively large. The amplitude difference between pure gas layers and gas-water layers cannot be distinguished on conventional full-frequency seismic profiles. Summary of the Invention

[0006] The present invention aims at the problem in the prior art that it is difficult to distinguish gas layers from aquifers in the background art, and provides a method for identifying gas-bearing reservoirs by frequency-divided seismic based on matching pursuit. This method for identifying gas-bearing reservoirs by frequency-divided seismic based on matching pursuit has higher resolution and can more clearly reflect the amplitude difference between pure gas layers and gas-aquifer layers on the single-frequency (section) seismic profiles at specific frequencies or specific frequency segments, improving the identification accuracy of gas layers.

[0007] The problem of the present invention can be solved by the following technical solutions: This method for identifying gas-bearing reservoirs by frequency-divided seismic based on matching pursuit includes the following steps: S1. Collect basic data of the study area; S2. Based on the basic data collected from the study area, pull out the seismic profiles of pure gas wells and water-producing wells, and analyze the seismic amplitude reflection characteristics of the reservoir sections of these two types of wells under different fluid states; S3. Based on the amplitude-preserved seismic data, conduct spectral analysis on the reservoir sections of pure gas wells and water-producing wells, and analyze the differences in the seismic spectra of the reservoir sections of these two types of wells; S4. Based on the differences in the seismic spectra of the reservoir sections of these two types of wells, use the matching pursuit method to perform frequency division processing on the amplitude-preserved seismic data and separate several single-frequency bodies; separate all single-frequency bodies; S5. For the separated single-frequency bodies, pull out the well-connected seismic profiles of each single-frequency body, analyze the differences between the reservoir sections of two types of wells, namely pure gas high-yield wells and gas-water co-producing wells, and then it can be concluded that the low-frequency single-frequency body seismic profiles can distinguish these two types of reservoirs; S6. Along the position of the seismic profile corresponding to the reservoir, extract the maximum amplitude attribute map of the low-frequency single-frequency body section, count the coincidence rate between the attribute map and the reservoir fluid test conditions of the drilled wells in the study area, and identify the distribution range of the pure gas area in the reservoir section.

[0008] Further, the basic data of the study area in step S1 includes seismic data and logging data.

[0009] Further, the seismic data includes: amplitude-preserved seismic data, seismic interpretation horizons; The logging data includes: conventional logging curves, drilling and logging data, well deviation data, layer data, logging interpretation results, single-well test production capacity data, and regional geological overview.

[0010] Further, the method of pulling out the seismic profiles of pure gas wells and water-producing wells in step S2 and analyzing the seismic amplitude reflection characteristics of the reservoir sections of these two types of wells under different fluid states includes: Perform well-seismic calibration based on the amplitude-preserved seismic data, conventional logging curves, well deviation data, layer data, and seismic interpretation horizons; Combined with the single-well test production data, pull the conventional seismic profiles of pure gas wells and water-producing wells after well-seismic calibration, and analyze the seismic amplitude reflection characteristics of the reservoir section under different fluid states; Since the amplitude values of the reservoir sections of the two types of wells on the conventional seismic profiles are quite similar, it is impossible to distinguish the gas-bearing differences between the reservoir of pure gas wells and water-producing wells.

[0011] Furthermore, the method for performing spectral analysis on the reservoir sections of pure gas wells and water-producing wells in step S3 to analyze the differences in the seismic spectra of the reservoir sections of the two types of wells includes: According to the amplitude-preserved seismic data, conventional logging curves, well deviation data, stratification data, and seismic interpretation horizons, find the corresponding positions of the gas-producing reservoirs of pure gas high-yield wells and gas-water co-producing wells on the conventional seismic profiles respectively. Use the seismic signal spectral analysis theory in signal analysis to perform spectral analysis on the corresponding reservoir sections of pure gas high-yield wells and gas-water co-producing wells respectively. Determine the effective frequency band range and dominant frequency of the seismic data of the target layer according to the fact that most of the energy of the signal is concentrated in a relatively narrow frequency band and the part with the highest frequency in the signal; Based on the determined dominant frequency and effective frequency band range of the seismic data of the target layer, compare the resonance phenomenon in the low-frequency band and the signal attenuation characteristics in the high-frequency band in the spectrogram of the same type of pure gas high-yield wells; compare the characteristics of weak signals in the low-frequency band and strong signals in the high-frequency band in the spectrogram of the same type of gas-water co-producing wells; based on the spectral characteristics of the gas-producing reservoir sections of these two types of wells, comprehensively compare the significant differences in the low and high frequency bands of the spectra of these two types of wells.

[0012] Furthermore, the differences between the reservoir sections of the two types of wells, pure gas wells and water-producing wells, in the seismic spectrum in the low-frequency band and high-frequency band are mainly manifested as follows: the low-frequency energy value of the reservoir section of pure gas high-yield wells is significantly higher than that of gas-water co-producing wells, and the high-frequency energy value is significantly lower than that of gas-water co-producing wells.

[0013] Furthermore, the method for using the matching pursuit method to perform frequency division processing on the amplitude-preserved seismic data and separate several single-frequency bodies in step S4 includes: According to the dominant frequency and effective frequency band range of the seismic data and the amplitude-preserved seismic data, use the matching pursuit algorithm in time-frequency analysis methods, construct a complete time-frequency atom library using common wavelets, and use projection pursuit to decompose the amplitude-preserved seismic data into the weighted sum of many atomic signals; find the solution closest to the original amplitude-preserved seismic data based on the weighted sum, so as to decompose and reconstruct the amplitude-preserved seismic data, perform frequency division processing according to a fixed frequency division principle, and separate the original amplitude-preserved seismic data into several single-frequency bodies according to the dominant frequency and effective frequency band range. This single-frequency body can be understood as an optimal solution of the original amplitude-preserved seismic data at a fixed frequency value and only contains the seismic signal characteristics of the original amplitude-preserved seismic data at this single frequency value.

[0014] Furthermore, the frequency division principle is: Centered on the dominant frequency of the seismic data of the target layer and within the range of the effective frequency band, one single-frequency body is separated at fixed frequency intervals.

[0015] Furthermore, for the separated single-frequency bodies in step S5, the well-connected profiles of each single-frequency body are pulled, and the differences between the two types of reservoir sections of pure gas wells and water-producing wells are analyzed. The method includes: According to the well-seismic calibration results in step S2 and the amplitude-preserved seismic data, the single-frequency body seismic profile and the full-frequency band seismic profile of the main frequency band are pulled. By comparing and analyzing, under the conditions of different single-frequency body seismic profiles, the differences in the amplitude energy responses of the reservoir sections of these two types of wells, namely pure gas high-yield wells and gas-water co-production wells, at the corresponding reservoir section positions of the same gas-producing reservoir are analyzed. Furthermore, it is determined that the single-frequency body seismic profile at the low-frequency end can distinguish these two types of reservoirs.

[0016] Furthermore, the method for extracting the maximum amplitude attribute map of the low-frequency end single-frequency body and statistically analyzing the coincidence rate between the attribute map and the reservoir fluid test conditions of the drilled wells in the study area in step S6 includes: According to the amplitude-preserved seismic data, seismic interpretation horizons, and using the matching pursuit method to perform frequency division processing on the separated single-frequency bodies, along the reservoir position, the maximum positive amplitude within the time window is analyzed, and the maximum amplitude attribute plan of the low-frequency end single-frequency body is extracted; the higher the energy value on the amplitude attribute plan, the greater the probability of gas-bearing distribution at that position, and the lower the energy value, the smaller the probability of gas-bearing distribution at that position, so as to identify the gas-bearing distribution range of the target reservoir. Using the single-well test production capacity data, statistically analyze the coincidence rate between the maximum amplitude attribute map and the reservoir fluid test conditions of the drilled wells in the study area. The higher the energy value on the amplitude attribute plan, the higher the gas production of the drilled reservoir fluid test at that position, and the lower the energy value, the lower the gas production of the drilled reservoir fluid test at that position, so as to verify the distribution range of the pure gas area in the reservoir section.

[0017] (1) This method uses the matching pursuit algorithm in time-frequency analysis to perform frequency division on seismic data. Under the condition of ensuring the frequency division accuracy, the matching pursuit algorithm has higher resolution than conventional methods such as Fourier transform, wavelet transform, and S transform.

[0018] (2) This method avoids the interference of seismic data in non-favorable frequency bands on the gas-bearing identification process. By using the matching pursuit algorithm to perform frequency division processing on amplitude-preserved seismic data, the extracted single-frequency body seismic data can more clearly reflect the amplitude differences between pure gas layers and aquifers on the single-frequency (section) seismic profiles at specific frequencies or specific frequency sections, improving the prediction accuracy of gas layers.

[0019] Single-frequency (band) seismic profiles at specific frequencies or specific frequency bands can more clearly reflect the amplitude differences between pure gas layers and aquifers. Therefore, fine and accurate frequency division processing is an important means for identifying gas-bearing reservoirs. So, to solve the problem of difficult discrimination between gas and water layers, spectral analysis of amplitude-preserved seismic data is carried out. On the basis of accurate frequency division, reasonably selecting the frequencies (bands) of low and high frequencies for frequency (band) gas-bearing property research is the key to improving the prediction accuracy of gas reservoirs. Description of the Drawings

[0020] Appendix Figure 1 is the flow chart of the method of the present invention; Appendix Figure 2 is the conventional cross-well seismic profile of a pure gas high-yield well and a gas-water co-production well pulled in the embodiment of the present invention; Appendix Figure 3 is the spectrogram of spectral analysis and comparison of spectral differences in the embodiment of the present invention; (wherein: a is the spectrum of the reservoir section of a pure gas high-yield well; b is the spectrum of the reservoir section of a gas-water co-production well) Appendix Figure 4 is the comparison diagram between the single-frequency body cross-well profile and the full-frequency band cross-well profile pulled in the embodiment of the present invention; Appendix Figure 5 is the prediction plan view of the gas-bearing distribution range of the main frequency maximum amplitude attribute in the embodiment of the present invention (wherein: b is the partial enlarged view of a); Detailed Embodiment

[0021] To make the objectives, technical solutions and advantages of the present invention clearer, the embodiments of the present invention will be further described in detail below in conjunction with the drawings.

[0022] As Figure 1 shown, a method for identifying gas-bearing reservoirs by frequency division seismic based on matching pursuit includes the following steps: S1. Collect basic data of the study area; including post-stack amplitude-preserved seismic data, conventional logging curves, drilling and logging data, well deviation data, stratification data, logging interpretation results, seismic interpretation horizons, single-well test production data and regional geological overview; S2. Based on the basic data collected in the study area, pull the seismic profiles of pure gas wells and water-producing wells, and analyze the seismic amplitude reflection characteristics of the reservoir sections of the two types of wells under different fluid states; According to the post-stack amplitude-preserved seismic data, conventional logging curves, well deviation data, stratification data and seismic interpretation horizons, well-seismic calibration is carried out; combining with the single-well test production data, pull the conventional seismic profiles of pure gas high-yield wells and gas-water co-production wells after well-seismic calibration, and analyze the seismic amplitude reflection characteristics of the reservoir sections under different fluid states; since the amplitude values of the reservoir sections of the two types of wells on the conventional seismic profile are quite similar, the gas-bearing differences between the two types of reservoirs cannot be distinguished.

[0023] S3. Based on the amplitude-preserved seismic data, perform spectral analysis on the reservoir sections of pure gas wells and water-producing wells, and analyze the differences in seismic spectra of the reservoir sections of these two types of wells. The specific methods include: According to the amplitude-preserved seismic data, conventional logging curves, well deviation data, stratification data, and seismic interpretation horizons, respectively find the corresponding positions of the gas-producing reservoirs of high-yield pure gas wells and gas-water co-producing wells on the conventional seismic profiles. Use the seismic signal spectral analysis theory in signal analysis to perform spectral analysis on the corresponding reservoir sections of the gas-producing reservoirs of high-yield pure gas wells and gas-water co-producing wells. Determine the effective frequency band range and dominant frequency of the seismic data of the target layer according to the fact that most of the energy of the signal is concentrated in a relatively narrow frequency band and the part with the highest frequency in the signal. Based on the determined dominant frequency and effective frequency band range of the seismic data of the target layer, compare the resonance phenomenon in the low-frequency band and the signal attenuation characteristics in the high-frequency band in the spectrograms of similar high-yield pure gas wells; compare the characteristics of weak signals in the low-frequency band and strong signals in the high-frequency band in the spectrograms of similar gas-water co-producing wells; based on the spectral characteristics of the gas-producing reservoir sections of these two types of wells, comprehensively compare the significant differences in the low- and high-frequency bands of the spectra of these two types of wells.

[0024] The differences in the low-frequency and high-frequency bands of the seismic spectra of the reservoir sections of these two types of wells, namely pure gas wells and water-producing wells, are mainly manifested as follows: the low-frequency energy value of the reservoir section of high-yield pure gas wells is significantly higher than that of gas-water co-producing wells, while the high-frequency energy value is significantly lower than that of gas-water co-producing wells.

[0025] S4. Based on the differences in the seismic spectra of the reservoir sections of these two types of wells, use the matching pursuit method to perform frequency division processing on the amplitude-preserved seismic data and separate several single-frequency bodies. The specific methods include: According to the dominant frequency and effective frequency band range of the seismic data and the amplitude-preserved seismic data, use the matching pursuit algorithm in time-frequency analysis methods to construct a complete time-frequency atom library using common wavelets, and use projection pursuit to decompose the amplitude-preserved seismic data into the weighted sum of many atomic signals; find the solution closest to the original amplitude-preserved seismic data based on the weighted sum, so as to decompose and reconstruct the amplitude-preserved seismic data, perform frequency division processing according to fixed frequency division principles, and separate the original amplitude-preserved seismic data into n single-frequency bodies according to the dominant frequency and effective frequency band range.

[0026] This single-frequency body can be understood as an optimal solution of the original amplitude-preserved seismic data at a fixed frequency value, and only contains the seismic signal characteristics of the original amplitude-preserved seismic data at this single frequency value.

[0027] The frequency division principle is: centered on the dominant frequency of the seismic data of the target layer and within the effective frequency band range, separate 1 single-frequency body every fixed frequency interval.

[0028] S5. For the isolated single-frequency bodies, pull the well-connected seismic profiles of each single-frequency body, analyze the differences between the two types of reservoir sections of high-yield pure gas wells and gas-water co-production wells, and then conclude that the low-frequency single-frequency body seismic profiles can distinguish the two types of reservoirs. The method includes: According to the well-seismic calibration results in step S2 and the amplitude-preserved seismic data, pull the single-frequency body seismic profiles and full-frequency seismic profiles in the main frequency band; Compare and analyze the differences in the amplitude energy responses of the reservoir sections of these two types of wells, namely high-yield pure gas wells and gas-water co-production wells, at the corresponding reservoir section positions under different single-frequency body seismic profile conditions; Furthermore, determine that the low-frequency single-frequency body seismic profiles can distinguish these two types of reservoirs.

[0029] S6. Along the seismic profile positions corresponding to the reservoir, extract the maximum amplitude attribute map of the low-frequency single-frequency body, count the coincidence rate between the attribute map and the reservoir fluid test conditions of the drilled wells in the study area, and identify the distribution range of the pure gas area in the reservoir section. The specific method includes: According to the amplitude-preserved seismic data, seismic interpretation horizons, and use the matching pursuit method to frequency-divide and process the isolated single-frequency bodies. Along the reservoir position, analyze the maximum positive amplitude within the time window, and extract the maximum amplitude attribute plan of the low-frequency single-frequency body. The higher the energy value on the amplitude attribute plan, the greater the probability of gas-bearing distribution at that position, and the lower the energy value, the smaller the probability of gas-bearing distribution at that position, so as to identify the gas-bearing distribution range of the target reservoir; Use the single-well test production capacity data to statistically analyze the coincidence rate between the maximum amplitude attribute map and the reservoir fluid test conditions of the drilled wells in the study area. The higher the energy value on the amplitude attribute plan, the higher the gas production of the drilled well reservoir fluid test at that position, and the lower the energy value, the lower the gas production of the drilled well reservoir fluid test at that position, so as to verify the distribution range of the pure gas area in the reservoir section. Embodiment

[0030] As Figure 1 shown, for the second member of the Maokou Formation in the Hechuan-Tongnan area, use a frequency-divided seismic gas-bearing reservoir identification method based on matching pursuit of the present invention, which specifically includes the following steps: S1. Collect the basic data of the study area; including post-stack amplitude-preserved seismic data, conventional logging curves, drilling and logging data, well deviation data, stratification data, logging interpretation results, seismic interpretation horizons, single-well test production capacity data, and regional geological overview.

[0031] S2. Based on the post-stack amplitude-preserved seismic data, conventional logging curves, well deviation data, stratification data, and seismic interpretation horizons collected, perform well-seismic calibration; combine the single-well test production data to extract the conventional seismic profiles of high-yield pure gas wells and gas-water co-production wells after well-seismic calibration. According to the logging interpretation results and single-well test production data, organize and summarize the reservoir thickness, porosity, and gas test results, and analyze the seismic amplitude reflection characteristics of the reservoir section under different fluid states; on the conventional seismic profile, the reservoir sections of both high-yield pure gas wells and gas-water co-production wells are of strong amplitude, and the gas-bearing property cannot be distinguished; since the amplitude values of the reservoir sections of the two types of wells on the conventional seismic profile are quite similar and the gas-bearing property differences between the two types of reservoirs cannot be distinguished, proceed to the next step.

[0032] As Figure 2 shown, extract the conventional cross-well seismic profiles of high-yield pure gas wells and gas-water co-production wells. The reservoir sections of both types of wells are of strong amplitude, and the gas-bearing property cannot be distinguished.

[0033] S3. According to the amplitude-preserved seismic data, analyze the spectra of the reservoir sections of high-yield pure gas wells and gas-water co-production wells respectively to determine the main frequency and effective frequency band range of the seismic data of the target layer: In the embodiment, the main frequency of the seismic data of the target layer is 35 Hz, and the effective frequency band range is approximately 10 - 55 Hz; on this basis, analyze the differences between the low-frequency band and high-frequency band of the reservoir sections of these two types of wells, namely high-yield pure gas wells and gas-water co-production wells, in the seismic spectrum. The main manifestation is that the low-frequency energy value (20 Hz) of the reservoir section of high-yield pure gas wells is significantly higher than that of gas-water co-production wells.

[0034] As Figure 3 shown, perform spectral analysis to compare the frequency differences on the spectrograms of high-yield pure gas wells and gas-water co-production wells.

[0035] S4. According to the main frequency and effective frequency band range of the seismic data, use the matching pursuit decomposition algorithm in the time-frequency analysis method to perform frequency division processing on the amplitude-preserved seismic data according to a fixed frequency division principle, and separate 10 single-frequency bodies; (Frequency division principle: centered on the main frequency of the seismic data of the target layer, with the effective frequency band as the range, separate 1 single-frequency body every fixed frequency interval of 5 Hz); the effective frequency band range is 10 - 55 Hz, and a total of 10 single-frequency bodies of 10 Hz, 15 Hz, 20 Hz, 25 Hz, 30 Hz, 35 Hz, 40 Hz, 45 Hz, 50 Hz, and 55 Hz are separated.

[0036] S5. According to the well-seismic calibration results and the amplitude-preserved seismic data, pull out each single-frequency body seismic profile and the full-frequency band seismic profile within the effective frequency band; compare and analyze the differences in the amplitude energy responses of the reservoir sections of two types of wells, namely high-yield pure gas wells and gas-water co-producing wells, under different single-frequency body seismic profile conditions. The main manifestations in the embodiments are as follows: the amplitude energy at the water-producing well - Well TS13 is significantly reduced in the 20 Hz main frequency energy data, indicating that the 20 Hz main frequency energy data can be used to identify the developed area of the gas layer in the second member of the Mao Formation; furthermore, determine to use the maximum amplitude attribute in the low-frequency band seismic to distinguish the two types of reservoirs, namely high-yield pure gas wells and gas-water co-producing wells.

[0037] As Figure 4 shown, pull out the comparison diagram of the single-frequency body well-connected profile and the full-frequency band well-connected profile, and determine to distinguish the two types of reservoirs, namely high-yield pure gas wells and gas-water co-producing wells, with the low-frequency energy attribute.

[0038] S6. Based on the amplitude-preserved seismic data and the seismic interpretation horizons, extract the maximum amplitude attribute of the low-frequency band single-frequency body along the reservoir position to identify the gas-bearing distribution range of the reservoir, and list the maximum amplitude attribute of the 20 Hz main frequency energy data preferably to predict the gas-bearing distribution range; use the single-well test production data to compare and analyze the coincidence rate between the maximum amplitude attribute map and the reservoir fluid test situation. Except for Well TS11 which does not match the actual drilling situation, the others are all consistent with the actual drilling situation; according to the higher coincidence rate, apply this method to identify the gas-bearing distribution range of the pure gas area in the reservoir section, indicating that the low-frequency energy data can be used for gas-bearing prediction.

[0039] As Figure 5 shown, preferably the plane map for predicting the gas-bearing distribution range with the maximum amplitude attribute of the 20 Hz main frequency, and determine the coincidence rate between the attribute map and the reservoir fluid test situation.

[0040] Those of ordinary skill in the art will realize that the embodiments described herein are to help readers understand the implementation methods of the present invention, and it should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific deformations and combinations that do not depart from the essence of the present invention based on the technical revelations disclosed in the present invention, and these deformations and combinations are still within the protection scope of the present invention.

Claims

1. A method for identifying gas-bearing reservoirs in frequency-divided seismic data based on matching pursuit, characterized in that: It includes the following steps: S1. Collect basic data of the study area; S2. Based on the basic data collected in the study area, pull seismic profiles of pure gas wells and water-producing wells, and analyze the seismic amplitude reflection characteristics of the reservoir sections of these two types of wells under different fluid states; S3. Based on the amplitude-preserved seismic data, perform spectral analysis on the reservoir sections of pure gas wells and water-producing wells to analyze the differences in seismic spectra between the reservoir sections of these two types of wells; S4. Based on the differences in seismic spectra between the reservoir sections of these two types of wells, use the matching pursuit method to perform frequency division processing on the amplitude-preserved seismic data and separate several single-frequency bodies; S5. For the separated single-frequency bodies, pull the well-connected seismic profiles of each single-frequency body, analyze the differences between the reservoir sections of two types of reservoirs, namely pure gas high-yield wells and gas-water co-production wells, and then conclude that the single-frequency body seismic profiles at the low-frequency end can distinguish these two types of reservoirs; S6. Along the position of the seismic profile corresponding to the reservoir, extract the maximum amplitude attribute map of the single-frequency body at the low-frequency end, count the coincidence rate between the attribute map and the reservoir fluid test situation of the drilled wells in the study area, and identify the distribution range of the pure gas area in the reservoir section.

2. The method for identifying gas-bearing reservoirs in frequency-divided seismic data based on matching pursuit according to claim 1, characterized in that: The basic data of the study area in step S1 includes seismic data and logging data.

3. The method for identifying gas-bearing reservoirs in frequency-divided seismic data based on matching pursuit according to claim 2, characterized in that: The seismic data includes: amplitude-preserved seismic data and seismic interpretation horizons; The logging data includes: conventional logging curves, drilling and logging data, well deviation data, layer data, logging interpretation results, single-well test production data, and regional geological overview.

4. The method for identifying gas-bearing reservoirs in frequency-divided seismic data based on matching pursuit according to claim 3, characterized in that: The method for pulling seismic profiles of pure gas wells and water-producing wells in step S2 and analyzing the seismic amplitude reflection characteristics of the reservoir sections of these two types of wells under different fluid states includes: Perform well-seismic calibration based on amplitude-preserved seismic data, conventional logging curves, well deviation data, layer data, and seismic interpretation horizons; Combined with single-well test production data, pull the conventional seismic profiles of pure gas wells and water-producing wells after well-seismic calibration, and analyze the seismic amplitude reflection characteristics of the reservoir sections under different fluid states; Since the amplitude values of the reservoir sections of these two types of wells on the conventional seismic profile are quite similar, it is impossible to distinguish the gas-bearing differences between the reservoirs of pure gas wells and water-producing wells.

5. The method for identifying gas-bearing reservoirs in frequency-divided seismic data based on matching pursuit according to claim 4, characterized in that: The method for performing spectral analysis on the reservoir sections of pure gas wells and water-producing wells in step S3 and analyzing the differences in seismic spectra between the reservoir sections of these two types of wells includes: According to the amplitude-preserved seismic data, conventional logging curves, well deviation data, stratification data, and seismic interpretation horizons, find the corresponding positions of the gas-producing reservoirs of high-yield pure gas wells and gas-water co-production wells on the conventional seismic section respectively, and use the seismic signal spectrum analysis theory in signal analysis to perform spectrum analysis on the corresponding reservoir sections of the gas-producing reservoirs of high-yield pure gas wells and gas-water co-production wells; determine the effective frequency band range and dominant frequency of the seismic data of the target layer according to that most of the energy of the signal is concentrated in a relatively narrow frequency band and the part with the highest frequency appears in the signal. Based on the determined dominant frequency and effective frequency band range of the seismic data of the target layer, compare the resonance phenomenon in the low-frequency band and the signal attenuation characteristics in the high-frequency band in the spectrograms of similar high-yield pure gas wells; compare the characteristics of weak signals in the low-frequency band and strong signals in the high-frequency band in the spectrograms of similar gas-water co-production wells; based on the spectral characteristics of the gas-producing reservoir sections of these two types of wells, comprehensively compare the significant differences between these two types of wells in the low-frequency band and the high-frequency band on the spectrum.

6. The frequency-divided seismic gas-bearing reservoir identification method based on matching pursuit according to claim 5, characterized in that: The differences between the reservoir sections of these two types of wells, namely pure gas wells and water-producing wells, in the low-frequency band and the high-frequency band on the seismic spectrum are mainly manifested as follows: the low-frequency energy value of the reservoir section of high-yield pure gas wells is significantly higher than that of gas-water co-production wells, and the high-frequency energy value is significantly lower than that of gas-water co-production wells.

7. The frequency-divided seismic gas-bearing reservoir identification method based on matching pursuit according to claim 6, characterized in that: In step S4, the matching pursuit method is adopted to perform frequency division processing on the amplitude-preserved seismic data and separate several single-frequency bodies. The method includes: According to the dominant frequency and effective frequency band range of the seismic data and the amplitude-preserved seismic data, adopt the matching pursuit algorithm in time-frequency analysis methods, construct a complete time-frequency atom library using common wavelets, and use projection pursuit to decompose the amplitude-preserved seismic data into the weighted sum of many atomic signals; find the solution closest to the original amplitude-preserved seismic data based on the weighted sum, so as to decompose and reconstruct the amplitude-preserved seismic data, and perform frequency division processing according to a fixed frequency division principle; separate the original amplitude-preserved seismic data into several single-frequency bodies according to the dominant frequency and effective frequency band range; this single-frequency body can be understood as an optimal solution of the original amplitude-preserved seismic data at a fixed frequency value and only contains the seismic signal characteristics of the original amplitude-preserved seismic data at this single frequency value.

8. The frequency-divided seismic gas-bearing reservoir identification method based on matching pursuit according to claim 7, characterized in that: The frequency division principle is: Centering on the dominant frequency of the seismic data of the target layer and with the effective frequency band as the range, separate 1 single-frequency body at each fixed frequency interval.

9. The frequency-divided seismic gas-bearing reservoir identification method based on matching pursuit according to claim 7, characterized in that: In step S5, for the separated single-frequency bodies, pull the well-connected seismic profiles of each single-frequency body and analyze the differences between the reservoir sections of these two types of wells, namely pure gas wells and water-producing wells. The method includes: According to the well-seismic calibration results in step S2 and the amplitude-preserved seismic data, pull the single-frequency body seismic profile and the full-frequency band seismic profile of the main frequency band. Compare and analyze the difference in the amplitude energy response of the reservoir sections of two types of wells, namely pure gas high-yield wells and gas-water co-production wells, at the corresponding reservoir section positions of the same gas-producing reservoir under different single-frequency body seismic profile conditions; Furthermore, determine that the low-frequency single-frequency body seismic profile can distinguish these two types of reservoirs.

10. The frequency-divided seismic gas-bearing reservoir identification method based on matching pursuit according to claim 9, characterized in that: In the step S6, the method for extracting the maximum amplitude attribute map of the low-frequency single-frequency body and statistically analyzing the coincidence rate between the attribute map and the reservoir fluid test situation of the drilled wells in the study area includes: Based on the amplitude-preserved seismic data, seismic interpretation horizons, and single-frequency bodies separated by frequency-divided processing using the matching pursuit method, analyze the maximum positive amplitude within the time window along the reservoir position, and extract the maximum amplitude attribute plan view of the low-frequency single-frequency body; the higher the energy value on the amplitude attribute plan view, the greater the probability of possible gas-bearing distribution at that position, and the lower the energy value, the smaller the probability of possible gas-bearing distribution at that position, so as to identify the gas-bearing distribution range of the target reservoir; Use the single-well test production capacity data to statistically analyze the coincidence rate between the maximum amplitude attribute map and the reservoir fluid test situation of the drilled wells in the study area. The higher the energy value on the amplitude attribute plan view, the higher the gas production of the drilled well reservoir fluid test at that position, and the lower the energy value, the lower the gas production of the drilled well reservoir fluid test at that position, thereby verifying the distribution range of the pure gas area in the reservoir section.

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