Method and system for quantitatively evaluating acquisition parameters of vibroseis in desert area

Abstract

The invention provides a method and system for quantitatively evaluating desert area vibroseis acquisition parameters, and the method comprises the steps: determining an appropriate vibroseis targeted processing flow through the high-density three-dimensional seismic test data of a desert area vibroseis, carrying out the data recombination of original full data through designing a plurality of different degradation acquisition schemes, and carrying out the quantitative evaluation of the desert area vibroseis acquisition parameters. And then data processing is carried out by using the same processing flow and corresponding parameters, analysis in the aspects of signal-to-noise ratio, fracture and reservoir is carried out on the processed post-stack data, and the effects of several different schemes are compared to determine an appropriate acquisition observation system, so that accurate quantitative evaluation of the acquisition parameters of the vibroseis in the desert area is realized. And powerful technical support is provided for further popularization of a controllable seismic source exploration technology in the area in the future.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploration and development, and particularly relates to a method and system for quantitatively evaluating the acquisition parameters of vibrators in desert areas. Background Art

[0002] At present, the data processing of vibrators in the desert area of the Tarim Basin has not yet fully formed a mature processing process and method. This is mainly due to the fact that the Ordovician carbonate reservoirs in this area are buried deeply, and seismic signals are severely affected by complex surface layers and absorption attenuation. As a result, the signal-to-noise ratio of vibrator seismic data is low and the amplitude is weak. Therefore, in actual production, it is often necessary to use an ultra-high-density acquisition observation system for exploration. Even so, problems such as the low-quality first arrival picking of massive data that restricts the data processing effect, the vibrator noise suppression problem, the wavelet processing, and the imaging problem of ultra-deep carbonate rocks are all in the initial research stage.

[0003] Regarding the design of the acquisition parameters of vibrators, in the past, most of them relied on the analysis of old well-shot excitation data to determine the bin size, line length, acquisition width, etc., and then determined the fold according to experience, unable to quantitatively evaluate more accurately. This is likely to cause two results. One is that the design of the observation system is overly strengthened. Although it can meet the geological exploration requirements, it will greatly increase the economic cost. The other is that the parameters of the observation system are not strengthened enough to complete the geological target. Therefore, there is an urgent need for a more scientific and reasonable method to quantitatively evaluate the acquisition observation system of vibrators in desert areas.

[0004] The quantitative evaluation of the acquisition parameters of vibrators is a new topic, which is directly related to the smooth popularization of vibrator exploration technology in desert areas and is of great significance. Summary of the Invention

[0005] To solve the problems that the existing design of the acquisition parameters of vibrators cannot be quantitatively evaluated more accurately, resulting in increased economic costs and the inability to complete geological targets, etc., the present invention provides a method for quantitatively evaluating the acquisition parameters of vibrators in desert areas. By using the high-density three-dimensional seismic test data of vibrators in desert areas to confirm a suitable targeted processing process for vibrators, through designing a variety of different degraded acquisition schemes, recombining the original full data, then using the same processing process and corresponding parameters to process the data and analyzing the post-stack data after processing in terms of signal-to-noise ratio, fractures, and reservoirs, comparing the effects of several different schemes to determine the appropriate acquisition observation parameters, realizing the accurate quantitative evaluation of the acquisition parameters of vibrators in desert areas, and providing strong technical support for the further popularization of vibrator exploration technology in this area in the future. The present invention also relates to a system for quantitatively evaluating the acquisition parameters of vibrators in desert areas.

[0006] The technical solution of the present invention is as follows:

[0007] A method for quantitatively evaluating the acquisition parameters of vibrators in desert areas, characterized by including the following steps:

[0008] In the first step, taking the high-density three-dimensional seismic test data of vibrators in desert areas as the original full data, using the first arrival picking technology based on AI, the amplitude suppression technology based on frequency division by partition, the well control compensation and cascade deconvolution technology for data processing, confirming the targeted full data processing flow of vibrators, and using the TTI anisotropic velocity modeling technology on the basis of the targeted full data of vibrators for prestack depth migration imaging processing, so as to obtain the prestack depth migration seismic profile of full data;

[0009] In the second step, on the basis of the original full data, several different degraded acquisition schemes are automatically designed and generated according to the number of receiving channels, longitudinal and transverse bin sizes, and shot-receiver offsets, so as to recombine the original full data to form data volumes of different degraded acquisition schemes, and then use the same targeted full data processing flow and corresponding parameters of the vibrators for data processing and prestack depth migration imaging processing to obtain the prestack depth migration seismic profiles of different degraded acquisition schemes;

[0010] In the third step, analyze and extract the signal-to-noise ratio of the target layer, fracture coherence attributes, and reservoir amplitude attributes of the prestack depth migration seismic profiles of different degraded acquisition schemes;

[0011] In the fourth step, compare the analysis and extraction results of different degraded acquisition schemes with the effects of the corresponding attributes of the original full data to conduct quantitative evaluation of the signal-to-noise ratio, fracture, and reservoir attribute volumes, so as to determine the acquisition parameters of vibrators in desert areas.

[0012] Preferably, in the first step, according to the original full data, the first arrival optimization technology is used in combination with the first arrival picking technology based on AI, the amplitude suppression technology based on frequency division by partition, the well control compensation and cascade deconvolution technology, the coherent noise suppression combined with cross-array domain denoising technology, and the velocity analysis and residual static correction technology for analysis, confirm the targeted full data processing flow of vibrators, and use the surface migration technology, high-precision grid tomography technology, and TTI anisotropic velocity modeling technology on the basis of the targeted full data of vibrators for prestack depth migration imaging processing.

[0013] Preferably, in the second step, on the basis of the original full data, several different degraded acquisition schemes are automatically designed and generated according to the number of receiving channels, longitudinal and transverse bin sizes, shot-receiver offsets, receiver / gun line distances, transverse-longitudinal ratios, total coverage times, and shot density.

[0014] Preferably, in the second step, the original full data is recombined to form data bodies of different degraded acquisition schemes, and then the same controllable source targeted full data processing flow and corresponding parameters of the AI-based first arrival picking technology, amplitude suppression technology based on partitioned frequency, well control compensation and cascade deconvolution technology are used for data processing, and pre-stack depth migration imaging processing is carried out to construct the final pre-stack gathers of different degraded acquisition schemes, obtain the pre-stack depth migration seismic profiles of different degraded acquisition schemes, and form the pre-stack depth migration effects of different degraded acquisition schemes.

[0015] Preferably, in the first step, on the basis of the controllable source targeted full data, the TTI anisotropic velocity modeling technology is used for pre-stack depth migration imaging processing, and high-precision migration velocity is obtained through pre-stack depth migration velocity iteration, and then the pre-stack depth migration seismic profile of the full data is obtained to form the pre-stack depth migration effect of the full data, and then signal-to-noise ratio analysis, fracture attribute analysis and reservoir amplitude prediction are carried out to obtain the effects of the corresponding attributes of the original full data.

[0016] Preferably, in the third step, the signal-to-noise ratio of the target layer, fracture coherence attribute and reservoir amplitude attribute of the pre-stack depth migration seismic profiles of different degraded acquisition schemes are analyzed and extracted; and the pre-stack depth migration seismic profile of the full data obtained in the first step is subjected to signal-to-noise ratio analysis, fracture attribute analysis and reservoir amplitude prediction to obtain the effects of the corresponding attributes of the original full data.

[0017] A system for quantitatively evaluating the acquisition parameters of a controllable source in a desert area, characterized by comprising: a first module, a second module, a third module and a fourth module connected in sequence;

[0018] The first module uses the high-density three-dimensional seismic test data of the controllable source in the desert area as the original full data, and uses the AI-based first arrival picking technology, amplitude suppression technology based on partitioned frequency, well control compensation and cascade deconvolution technology for data processing, confirms the controllable source targeted full data processing flow, and uses the TTI anisotropic velocity modeling technology for pre-stack depth migration imaging processing on the basis of the controllable source targeted full data, and then obtains the pre-stack depth migration seismic profile of the full data;

[0019] The second module, on the basis of the original full data, automatically designs and generates a number of different degraded acquisition schemes according to the number of receiving channels, vertical and horizontal bin sizes, and source-receiver offsets, to recombine the original full data to form data bodies of different degraded acquisition schemes, and then uses the same controllable source targeted full data processing flow and corresponding parameters for data processing and pre-stack depth migration imaging processing to obtain the pre-stack depth migration seismic profiles of different degraded acquisition schemes;

[0020] The third module analyzes and extracts the signal-to-noise ratio of the target layer, fracture coherence attributes, and reservoir amplitude attributes of the prestack depth migration seismic profiles of different degraded acquisition schemes.

[0021] The fourth module compares the analysis and extraction results of different degraded acquisition schemes with the original full data for corresponding attributes to quantitatively evaluate the signal-to-noise ratio, fractures, and reservoir attribute volumes, thereby determining the vibrator acquisition parameters in the desert area.

[0022] Preferably, the first module analyzes based on the original full data using first-arrival optimization technology combined with AI-based first-arrival picking technology, amplitude suppression technology based on partitioned frequency, well control compensation and cascaded deconvolution technology, coherent noise suppression combined with cross-array domain denoising technology, and velocity analysis and residual static correction technology, confirms the vibrator-targeted full data processing flow, and performs prestack depth migration imaging processing using surface migration technology, high-precision grid tomography technology, and TTI anisotropic velocity modeling technology on the basis of the vibrator-targeted full data.

[0023] Preferably, the second module automatically designs and generates a number of different degraded acquisition schemes based on the original full data according to the number of receiving channels, longitudinal and transverse bin sizes, shot-receiver offset, receiver / gun line distance, aspect ratio, total coverage times, and shot density.

[0024] Preferably, the second module recombines the data of the original full data to form a data volume of different degraded acquisition schemes, then uses the same vibrator-targeted full data processing flow and corresponding parameters of AI-based first-arrival picking technology, amplitude suppression technology based on partitioned frequency, and well control compensation and cascaded deconvolution technology for data processing, and performs prestack depth migration imaging processing to construct the final pre-offset gather of different degraded acquisition schemes, obtains the prestack depth migration seismic profiles of different degraded acquisition schemes, and forms the prestack depth migration effects of different degraded acquisition schemes.

[0025] The beneficial effects of the present invention are:

[0026] The method for quantitatively evaluating the acquisition parameters of vibrators in desert areas provided by the present invention is an acquisition parameter evaluation method based on geological target imaging in the acquisition and processing methods of oil and gas seismic exploration. Taking the high-density three-dimensional seismic test data of vibrators in desert areas as the original full data, data processing and analysis are carried out using the first arrival picking technology based on AI, the amplitude suppression technology based on zoning and frequency division, the well control compensation and cascade deconvolution technology. Through the fine processing of the original seismic data, the targeted full data processing flow of vibrators is confirmed, and a high-quality full data pre-stack depth migration seismic profile is processed; then a variety of different degraded acquisition schemes are designed, the original full data is recombined according to the degraded schemes, and then the same processing flow and corresponding parameters are used to process the degraded data to obtain the pre-stack depth migration seismic profiles of different degraded acquisition schemes; analyze and extract the signal-to-noise ratio, fractures, and reservoir properties of the main target layers of the profiles of different degraded acquisition schemes, analyze the signal-to-noise ratio, fractures, and reservoirs of the post-stack data after processing, compare the effects of several different schemes, analyze and determine to what extent the observation system can still be equivalent to the effect of the original full data, so as to determine the appropriate acquisition parameters, realize the accurate quantitative evaluation of the acquisition parameters of vibrators in desert areas, and provide strong technical support for the further popularization of vibrator exploration technology in this area in the future. The present invention discloses a processing flow suitable for the exploration of deep carbonate rocks by vibrators in desert areas, which has good applicability and popularization on the basis of existing data, the research ideas and technical methods are highly feasible, and provides good technical support for the application and popularization of vibrators in deep carbonate rock fault-controlled reservoirs in desert areas.

[0027] Preferably, the first arrival optimization technique is combined with the AI-based first arrival picking technique, the amplitude suppression technique based on partition and frequency division, the well control compensation and cascade deconvolution technique, the coherent noise suppression combined with cross-line domain body denoising technique, and the velocity analysis and residual static correction technique for analysis according to the original full data, to confirm the targeted full data processing flow of the vibrator, and the prestack depth migration imaging processing is carried out by using the surface migration technique, the high-precision grid tomography technique, and the TTI anisotropic velocity modeling technique on the basis of the targeted full data of the vibrator. In the actual data processing, the first arrival picking accuracy problem of the vibrator is solved by adopting the first arrival optimization technique + the AI-based first arrival picking technique; the "black triangle" strong energy noise is preferably removed by the amplitude suppression technique based on partition and frequency division; the shallow multiple refractions are preferably suppressed by using the coherent noise suppression combined with the cross-line domain body denoising technique, and the noise suppression problem of the vibrator data is solved; the problem of the vibrator wavelet consistency is solved by using the well control compensation and cascade deconvolution. The imaging accuracy of faults and beads in the target horizon is improved by using the "true" surface migration technique, the high-precision grid tomography technique, and the TTI anisotropic velocity modeling technique, thereby forming a vibrator data processing flow suitable for the desert area, obtaining a post-stack seismic data volume with high fidelity and high signal-to-noise ratio, and a high-precision prestack depth migration velocity field.

[0028] The present invention also relates to a system for quantitatively evaluating the acquisition parameters of vibrators in the desert area. This system corresponds to the above method for quantitatively evaluating the acquisition parameters of vibrators in the desert area, and can be understood as a system for implementing the method for quantitatively evaluating the acquisition parameters of vibrators in the desert area. This system works in cooperation with four modules executed in sequence, uses the high-density three-dimensional seismic test data of vibrators in the desert area, studies a suitable targeted processing flow of vibrators, recombines the data of the original full data by designing a variety of different degraded acquisition schemes, then processes the data by using the same processing flow and corresponding parameters, analyzes the signal-to-noise ratio, faults, and reservoirs of the post-stack data after processing, compares the effects of several different schemes, thereby determining a suitable acquisition observation system, realizing the accurate quantitative evaluation of the acquisition parameters of vibrators in the desert area, solving the problems that the existing acquisition parameter design of vibrators cannot be quantitatively evaluated accurately enough, resulting in increased economic costs and inability to complete geological targets, etc., being suitable for the processing flow of vibrator data in the desert area, determining the suitable acquisition parameters of vibrators in the Shunbei Desert area of the Tarim Basin, and providing strong technical support for the further promotion of vibrator exploration technology in this area in the future. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flowchart of the method for quantitatively evaluating the acquisition parameters of vibrators in the desert area according to the present invention.

[0030] Figure 2This is the preferred processing flow chart for the controlled-source high-density three-dimensional seismic test data in the desert area of the present invention.

[0031] Figure 3 It is a comparison chart of the signal-to-noise ratio of the target layer in the full data and profiles of different degradation schemes.

[0032] Figure 4 It is a comparison chart of the fracture coherence attributes of the full data and different degradation schemes.

[0033] Figure 5 It is a comparison chart of the reservoir amplitude attributes of the full data and different degradation schemes. Specific implementation manner

[0034] To understand the content of the invention more clearly, it will be described in detail in combination with the drawings and embodiments.

[0035] The present invention relates to a method for quantitatively evaluating the acquisition parameters of controlled sources in the desert area. Using the controlled-source high-density three-dimensional test data in the desert area, through fine processing of the original seismic data, post-stack depth migration seismic data that can meet geological requirements is obtained, and the parameters of the processing flow suitable for the controlled-source data in the desert area are summarized; on this basis, a series of acquisition observation system degradation schemes are designed, the original full data is recombined according to the degradation scheme, and then the degraded original data is processed using the same processing flow; by analyzing the data quality of several different degradation schemes, relevant attributes such as signal-to-noise ratio, fracture, and reservoir are extracted, and the post-stack data after processing is analyzed in terms of signal-to-noise ratio, fracture, and reservoir, and the effects of several different schemes are compared to determine to what extent the observation system degrades and still can be equivalent to the effect of the original full data, so as to determine reasonable acquisition parameters and provide a strong basis for future exploration and development based on controlled-source seismic acquisition in the desert area. As Figure 1 shown in the flow chart, it includes:

[0036] The first step is to use the controlled-source high-density three-dimensional seismic test data in the desert area as the original full data, and perform data processing using techniques such as AI-based first arrival picking technology, amplitude suppression technology based on zoning and frequency division, well control compensation and cascade deconvolution technology, etc., to confirm the targeted full data processing flow for controlled sources, and perform pre-stack depth migration imaging processing using the TTI anisotropic velocity modeling technology on the basis of the targeted full data for controlled sources, and then obtain the pre-stack depth migration seismic profile of the full data. This step is essentially to analyze the original high-density controlled-source high-density three-dimensional test data, determine appropriate processing flows and methods, and process high-quality pre-stack depth migration seismic profiles of the full data.

[0037] As Figure 2The processing flow of the vibroseis high-density three-dimensional seismic test data in the desert area (hereinafter referred to as the vibroseis data in the desert area) can process the data through demultiplexing and preprocessing, micro-logging constrained tomographic static correction, KL transform to suppress surface waves, near-shot strong energy noise suppression, coherent noise suppression, cross-line array conical filtering suppression, frequency-divided abnormal amplitude suppression, spherical spreading amplitude compensation, surface-consistent amplitude compensation, surface-consistent deconvolution, first velocity analysis and residual static correction, predictive deconvolution, frequency-divided abnormal amplitude suppression, surface-consistent amplitude compensation, second velocity analysis and residual static correction, third velocity analysis and residual static correction, multiple suppression in CMP gather, amplitude processing based on fold number, etc., to confirm the targeted full-data processing flow of vibroseis. And on the basis of the targeted full-data of vibroseis, prestack depth migration imaging processing is carried out by using surface migration technology, high-precision grid tomography technology, TTI anisotropic velocity modeling, etc. It can be divided into two processing flows. One is anisotropic prestack time migration velocity analysis, prestack time migration volume migration, multiple suppression in CRP gather, resolution improvement in CRP gather, stacking in CRP gather, post-stack noise suppression, post-stack resolution improvement and filtering gain. The other is surface velocity inversion filling, igneous rock velocity geological modeling, grid tomography velocity inversion, anisotropic parameter modeling, anisotropic prestack depth migration, multiple suppression in CIP gather, resolution improvement in CIP gather, stacking in CIP gather, post-stack noise suppression, post-stack resolution improvement and filtering gain. High-precision migration velocity is obtained through prestack depth migration velocity iteration, and then a prestack depth migration seismic profile of full data is obtained, forming the prestack depth migration effect of full data. Signal-to-noise ratio analysis, fracture attribute analysis and reservoir amplitude prediction can be carried out again to obtain the effects of the corresponding attributes of the original full data.

[0038] There are problems in the vibroseis seismic data in the desert area, such as poor first arrival quality, severe various interferences, poor signal-to-noise ratio, wavelet consistency, weak energy of the target layer, unclear imaging of beaded fractures, etc. In the actual data processing, the first arrival picking accuracy problem of vibroseis is solved by adopting the first arrival optimization technology + AI-based first arrival picking technology; the harmonic interference is well suppressed by using the vibroseis force signal; the "black triangle" strong energy noise is well removed by the amplitude suppression technology based on zoning and frequency division; the shallow multiple refractions are well suppressed by using the coherent noise suppression combined with the body denoising technology in the cross-line array domain, and the noise suppression problem of vibroseis data is solved; the wavelet consistency problem of vibroseis is solved by using well control compensation and cascaded deconvolution; the imaging accuracy of fractures and beads in the target layer is improved by using "true" surface migration, high-precision grid tomography, and TTI (Tilted Transversely Isotropic) anisotropic velocity modeling. In this way, a vibroseis data processing flow suitable for the desert area is formed, and a post-stack seismic data volume with high fidelity and high signal-to-noise ratio, as well as a high-precision prestack depth migration velocity field, are obtained.

[0039] In the second step, based on the original full data, several different degraded acquisition schemes are automatically designed and generated according to the number of receiving channels, vertical and horizontal bins, and source-receiver offset, so as to recombine the original full data to form data volumes of different degraded acquisition schemes. Then, the same targeted full data processing flow and corresponding parameters of the vibrator are used for data processing, and pre-stack depth migration imaging processing is carried out to obtain pre-stack depth migration seismic profiles of different degraded acquisition schemes. This step is essentially to design a variety of different degraded acquisition schemes (abbreviated as degraded schemes), recombine the original full data according to the degraded schemes, use the same processing flow and processing parameters to process the degraded data, and obtain pre-stack depth migration profiles of different degraded schemes.

[0040] Based on the acquisition geometry of the original full data, a series of degraded schemes are designed by reducing the receiving array, expanding the bin, etc. For example, the designed acquisition geometry degraded scheme shown in Table 1. Based on the original full data, several different degraded acquisition schemes are automatically designed and generated according to data volumes such as the number of receiving channels, vertical / horizontal bin, maximum source-receiver offset, receiver / source line spacing, aspect ratio, total coverage times, and shot density. The first column is the name of the data volume, the original scheme in the second column is the acquisition scheme of the original full data, and the 3rd - 7th columns are 5 different degraded schemes designed, and the original seismic data is extracted according to the degraded schemes. According to the processing flow, method, and pre-stack depth migration velocity volume obtained from the original full data processing, pre-stack depth migration imaging processing is carried out on the seismic data of each extracted scheme. That is to say, the same targeted full data processing flow and corresponding parameters of the vibrator based on AI-based first arrival picking technology, amplitude suppression technology based on partition and frequency division, well control compensation, and cascade deconvolution technology are used for data processing, and pre-stack depth migration imaging processing is carried out to construct the final pre-offset gathers of different degraded acquisition schemes, obtain pre-stack depth migration seismic profiles of different degraded acquisition schemes, and form the pre-stack depth migration effects of different degraded acquisition schemes.

[0041] Table 1

[0042]

[0043] In the third step, the signal-to-noise ratio of the target layer, fracture coherence attribute, and reservoir amplitude attribute of the pre-stack depth migration seismic profiles of different degraded acquisition schemes are analyzed and extracted. This step is essentially the extraction of geological attributes of the degraded schemes, and the signal-to-noise ratio of the target layer, fracture coherence attribute, and reservoir amplitude attribute are extracted from the final seismic data volumes processed by several degraded schemes.

[0044] Specifically, the calculations involved in the signal-to-noise ratio, fracture coherence attribute, and reservoir amplitude attribute are as follows:

[0045] Signal-to-noise ratio: That is, S / N, which is the ratio of the effective signal to the noise. The higher the signal-to-noise ratio, the stronger the effective reflection signal of the data.

[0046] Fault coherence attribute: The fault coherence attribute essentially explains the problem based on the continuity or discontinuity of adjacent traces. Discontinuity means low coherence.

[0047] Suppose the cross-correlation function of the longitudinal survey line m delayed at time t for two adjacent seismic traces x(n) and y(n) is:

[0048]

[0049] Among them, the magnitude of m is related to the dip angle of the formation, and k is the length of the time window. The selection of the size of the time window k must be appropriate. If the value of k is too large, the difference between the coherence values will decrease, which is very unfavorable for the identification of small structures. At the same time, the calculation amount also increases; if the value of k is too small, the influence of interference will be relatively large. Generally speaking, we take the value of k as T (T is the apparent period).

[0050] Reservoir amplitude attribute: Extract the root-mean-square amplitude attribute along the layer according to the target horizon.

[0051] Root-mean-square amplitude:

[0052] Among them, Ai represents the absolute amplitude value of each point in the seismic data volume, represents the average amplitude value of each point in this data volume, and n represents the number of analyzed data segments.

[0053] In the fourth step, compare the analysis and extraction results of different degraded acquisition schemes with the effects of the corresponding attributes of the original full data to conduct quantitative evaluations of the signal-to-noise ratio, faults, and reservoir attribute volumes, so as to determine the controllable source acquisition parameters in the desert area. This step is essentially a quantitative analysis of the evaluation results, analyzing and determining to what extent the observation system degrades and still can be equivalent to the effect of the original full data, so as to determine the appropriate controllable source acquisition parameters in the desert area.

[0054] Analyze and extract the signal-to-noise ratio, fault coherence attribute, and reservoir amplitude attribute of the target horizon of the pre-stack depth migration seismic profiles of different degraded acquisition schemes; and compare them with the effects of the corresponding attributes of the original full data obtained from the signal-to-noise ratio analysis, fault attribute analysis, and reservoir amplitude prediction of the pre-stack depth migration seismic profile of the full data in the first step respectively. Conduct quantitative evaluations of the results of several degraded schemes in terms of the signal-to-noise ratio, and conduct comparisons of different attributes for the effective seismic reflections and bead reflections in the target horizon ( Figures 3 to 5 ), determine to what extent the observation system degrades and still can be equivalent to the effect of the original full data, and determine the appropriate acquisition shot density. Such as Figure 3The figure shows the comparison of the signal-to-noise ratio of the target layer in the profiles of full data and five different degradation schemes. Figure 4 The figure shows the comparison of the fracture coherence attributes of full data and five different degradation schemes. Figure 5 The figure shows the comparison of the reservoir amplitude attributes of full data and five different degradation schemes.

[0055] From Figure 3 It can be seen that when the vibroseis shot gather density is above 10 million traces (degradation scheme 1, degradation scheme 2), the signal-to-noise ratio of the data is not much different from that of the full data; after the vibroseis shot gather density is lower than 6.2 million traces (degradation scheme 4, degradation scheme 5), the signal-to-noise ratio of the data drops rapidly.

[0056] From Figure 4 It can be seen that there is little difference in the fracture coherence attributes of degradation schemes 1, 2, and 3, and the background noise of degradation schemes 4 and 5 is relatively heavy, which affects the identification of small fractures.

[0057] From Figure 5 It can be seen that the root mean square amplitude of the reservoir of degradation schemes 1 and 2 is basically the same as that of the full data, and the bead imaging and accuracy are basically the same. Near the main fracture of degradation schemes 3, 4, and 5, affected by the decrease in the signal-to-noise ratio of the data, the bead imaging accuracy is significantly reduced, and false beads appear.

[0058] Based on the comprehensive comparison, it is considered that the vibroseis shot gather density in the study area should at least reach the observation system of degradation scheme 2, that is, the shot gather density reaches more than 10 million traces.

[0059] The present invention also relates to a system for quantitatively evaluating the acquisition parameters of vibroseis in desert areas. This system corresponds to the above method for quantitatively evaluating the acquisition parameters of vibroseis in desert areas, and can be understood as a system for implementing the method for quantitatively evaluating the acquisition parameters of vibroseis in desert areas. The system includes a first module, a second module, a third module, and a fourth module connected in sequence;

[0060] The first module uses the high-density 3D seismic test data of vibroseis in desert areas as the original full data, and performs data processing using the first arrival picking technology based on AI, the amplitude suppression technology based on partitioned frequency, the well control compensation and cascade deconvolution technology, determines the targeted full data processing flow for vibroseis, and performs pre-stack depth migration imaging processing using the TTI anisotropic velocity modeling technology on the basis of the targeted full data for vibroseis, and then obtains the pre-stack depth migration seismic profile of the full data;

[0061] The second module, based on the original full data, automatically designs and generates several different degraded acquisition schemes according to the number of receiving channels, vertical and horizontal bin sizes, and source-receiver offset, so as to recombine the original full data to form data volumes of different degraded acquisition schemes. Then, the same targeted full data processing flow and corresponding parameters of the vibrator are used for data processing, and prestack depth migration imaging processing is carried out to obtain prestack depth migration seismic profiles of different degraded acquisition schemes;

[0062] The third module analyzes and extracts the signal-to-noise ratio of the target layer, fracture coherence attribute, and reservoir amplitude attribute of the prestack depth migration seismic profiles of different degraded acquisition schemes;

[0063] The fourth module compares the analysis and extraction results of different degraded acquisition schemes with the original full data for corresponding attribute comparison to conduct quantitative evaluation of the signal-to-noise ratio, fracture, and reservoir attribute volumes, so as to determine the vibrator acquisition parameters in the desert area.

[0064] Preferably, the first module analyzes according to the original full data by using the first arrival optimization technology combined with the AI-based first arrival picking technology, amplitude suppression technology based on partitioned frequency, well control compensation and cascade deconvolution technology, coherent noise suppression combined with cross-array domain volume denoising technology, velocity analysis and residual static correction technology, confirms the targeted full data processing flow of the vibrator, and performs prestack depth migration imaging processing by using surface migration technology, high-precision grid tomography technology, and TTI anisotropic velocity modeling technology on the basis of the targeted full data of the vibrator.

[0065] Preferably, the second module automatically designs and generates several different degraded acquisition schemes based on the original full data according to the number of receiving channels, vertical and horizontal bin sizes, source-receiver offset, receiver / source line spacing, aspect ratio, total coverage times, and shot density.

[0066] Preferably, the second module recombines the original full data to form data volumes of different degraded acquisition schemes, and then uses the same targeted full data processing flow and corresponding parameters of the vibrator, including the AI-based first arrival picking technology, amplitude suppression technology based on partitioned frequency, well control compensation and cascade deconvolution technology, for data processing, and conducts prestack depth migration imaging processing to construct the final pre-offset gathers of different degraded acquisition schemes, obtains the prestack depth migration seismic profiles of different degraded acquisition schemes, and forms the prestack depth migration effects of different degraded acquisition schemes.

[0067] The system works in coordination with each other through four modules executed in sequence. By using the data of high-density three-dimensional seismic tests with vibrators in the desert area, it studies a suitable targeted processing flow for vibrators. By designing a variety of different degraded acquisition schemes, it recombines the original full data, then processes the data using the same processing flow and corresponding parameters, analyzes the post-stack data after processing in terms of signal-to-noise ratio, fractures, and reservoirs, compares the effects of several different schemes, thereby determining a suitable acquisition observation system, achieving an accurate quantitative evaluation of the acquisition parameters of vibrators in the desert area, solving the problems such as the inability to accurately quantitatively evaluate the existing acquisition parameter design of vibrators, resulting in increased economic costs and the inability to achieve geological targets, being suitable for the processing flow of vibrator data in the desert area, determining the suitable acquisition parameters of vibrators in the Shunbei large desert area of the Tarim Basin, and providing strong technical support for the further promotion of vibrator exploration technology in this area in the future.

[0068] It should be noted that the above specific embodiments can enable those skilled in the art to understand the present invention more comprehensively, but do not limit the present invention in any way. Therefore, although this specification has described the present invention in detail with reference to the drawings and embodiments, those skilled in the art should understand that the present invention can still be modified or equivalently replaced. In short, all technical solutions and their improvements that do not depart from the spirit and scope of the present invention should be covered by the protection scope of the patent of the present invention.

Claims

1. A method for quantitatively evaluating the acquisition parameters of a vibroseis in a desert area, characterized in that Including the following steps: The first step: Taking the desert area vibroseis high-density three-dimensional seismic test data as the original full data, using the AI-based first arrival picking technology, the amplitude suppression technology based on partitioned frequency, the well control compensation and cascade deconvolution technology for data processing, confirming the vibroseis-targeted full data processing flow, and using the TTI anisotropic velocity modeling technology on the basis of the vibroseis-targeted full data for prestack depth migration imaging processing, so as to obtain the prestack depth migration seismic profile of the full data; The second step: On the basis of the original full data, automatically designing and generating several different degraded acquisition schemes according to the number of receiving channels, the longitudinal and transverse bin sizes, and the shot-receiver offset, so as to recombine the original full data to form data volumes of different degraded acquisition schemes, then using the same vibroseis-targeted full data processing flow and corresponding parameters for data processing, and performing prestack depth migration imaging processing to obtain the prestack depth migration seismic profiles of different degraded acquisition schemes; The third step: Analyzing and extracting the signal-to-noise ratio of the target layer, the fracture coherence attribute, and the reservoir amplitude attribute of the prestack depth migration seismic profiles of different degraded acquisition schemes; The fourth step: Comparing the analysis and extraction results of different degraded acquisition schemes with the effects of the corresponding attributes of the original full data to perform quantitative evaluation of the signal-to-noise ratio, fracture, and reservoir attribute volumes, so as to determine the vibroseis acquisition parameters in the desert area.

2. The method for quantitatively evaluating the acquisition parameters of a vibrator in a desert area according to claim 1, characterized in that In the first step, based on the original full data, the first arrival optimization technology is used in combination with the AI-based first arrival picking technology, the amplitude suppression technology based on partitioned frequency, the well control compensation and cascade deconvolution technology, the coherent noise suppression combined with cross-array domain denoising technology, and the velocity analysis and residual static correction technology for analysis, confirming the vibroseis-targeted full data processing flow, and using the surface migration technology, the high-precision grid tomography technology, and the TTI anisotropic velocity modeling technology on the basis of the vibroseis-targeted full data for prestack depth migration imaging processing.

3. The method for quantitatively evaluating the acquisition parameters of a vibrator in a desert area according to claim 1, wherein In the second step, on the basis of the original full data, several different degraded acquisition schemes are automatically designed and generated according to the number of receiving channels, the longitudinal and transverse bin sizes, the shot-receiver offset, the receiver / gun line distance, the transverse-longitudinal ratio, the total coverage times, and the shot density.

4. The method for quantitatively evaluating the acquisition parameters of a vibroseis in a desert area according to claim 3, wherein In the second step, the original full data is recombined to form data volumes of different degraded acquisition schemes, then the same vibroseis-targeted full data processing flow and corresponding parameters of the AI-based first arrival picking technology, the amplitude suppression technology based on partitioned frequency, the well control compensation and cascade deconvolution technology are used for data processing, and prestack depth migration imaging processing is performed to construct the final pre-stack gathers of different degraded acquisition schemes, obtain the prestack depth migration seismic profiles of different degraded acquisition schemes, and form the prestack depth migration effects of different degraded acquisition schemes.

5. The method for quantitatively evaluating the acquisition parameters of a vibrator in a desert area according to any one of claims 1 to 3, characterized in that, In the first step, on the basis of the targeted full data of the vibrator, pre-stack depth migration imaging processing is carried out using the TTI anisotropic velocity modeling technology. High-precision migration velocity is obtained through pre-stack depth migration velocity iteration, and then a pre-stack depth migration seismic profile of the full data is obtained, forming the effect of pre-stack depth migration of the full data. Then, signal-to-noise ratio analysis, fracture attribute analysis and reservoir amplitude prediction are carried out to obtain the effects of the corresponding attributes of the original full data.

6. The method for quantitatively evaluating the acquisition parameters of a vibrator in a desert area according to any one of claims 1 to 3, characterized in that, In the third step, the signal-to-noise ratio of the target layer, fracture coherence attributes and reservoir amplitude attributes of the pre-stack depth migration seismic profiles of different degraded acquisition schemes are analyzed and extracted; and the pre-stack depth migration seismic profile of the full data obtained in the first step is subjected to signal-to-noise ratio analysis, fracture attribute analysis and reservoir amplitude prediction to obtain the effects of the corresponding attributes of the original full data.

7. A system for quantitatively evaluating the acquisition parameters of a vibrator in a desert area, characterized in that, Including: A first module, a second module, a third module and a fourth module connected in sequence; The first module uses the high-density three-dimensional seismic test data of the vibrator in the desert area as the original full data, and uses the first arrival picking technology based on AI, the amplitude suppression technology based on partitioned frequency, the well control compensation and cascade deconvolution technology for data processing, confirms the processing flow of the targeted full data of the vibrator, and uses the TTI anisotropic velocity modeling technology for pre-stack depth migration imaging processing on the basis of the targeted full data of the vibrator, and then obtains a pre-stack depth migration seismic profile of the full data; The second module automatically designs and generates a number of different degraded acquisition schemes based on the original full data according to the number of receiving channels, longitudinal and transverse bin sizes, and shot-receiver offsets, to re-combine the original full data to form data volumes of different degraded acquisition schemes, and then uses the same processing flow and corresponding parameters of the targeted full data of the vibrator for data processing and pre-stack depth migration imaging processing to obtain pre-stack depth migration seismic profiles of different degraded acquisition schemes; The third module analyzes and extracts the signal-to-noise ratio of the target layer, fracture coherence attributes and reservoir amplitude attributes of the pre-stack depth migration seismic profiles of different degraded acquisition schemes; The fourth module compares the analysis and extraction results of different degraded acquisition schemes with the original full data for the corresponding attributes to conduct quantitative evaluation of the signal-to-noise ratio, fracture and reservoir attribute volumes, so as to determine the acquisition parameters of the vibrator in the desert area.

8. The system for quantitatively evaluating the acquisition parameters of a vibrator in a desert area according to claim 7, characterized in that, The first module analyzes according to the original full data by using the first arrival optimization technology combined with the first arrival picking technology based on AI, the amplitude suppression technology based on partitioned frequency, the well control compensation and cascade deconvolution technology, the coherent noise suppression combined with cross-line domain volume denoising technology, and the velocity analysis and residual static correction technology, confirms the processing flow of the targeted full data of the vibrator, and uses the surface migration technology, high-precision grid tomography technology and TTI anisotropic velocity modeling technology for pre-stack depth migration imaging processing on the basis of the targeted full data of the vibrator.

9. The system for quantitatively evaluating the acquisition parameters of a vibrator in a desert area according to claim 7, characterized in that, The second module automatically designs and generates a number of different degraded acquisition schemes based on the original full data according to the number of receiving channels, longitudinal and transverse bin sizes, shot-receiver offsets, receiver / gun line distances, transverse-longitudinal ratios, total coverage times and shot density.

10. The system for quantitatively evaluating the acquisition parameters of a vibrator in a desert area according to any one of claims 7 to 9, characterized in that, The second module recombines the original full data to form data bodies of different degraded acquisition schemes, and then uses the same controllable source targeted full data processing flow and corresponding parameters of the AI-based first arrival picking technology, amplitude suppression technology based on zoning and frequency division, well control compensation and cascade deconvolution technology for data processing, and performs pre-stack depth migration imaging processing to construct the final pre-stack gather of different degraded acquisition schemes, obtains the pre-stack depth migration seismic profiles of different degraded acquisition schemes, and forms the pre-stack depth migration effects of different degraded acquisition schemes.

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