Fluid detection method and device based on pre-stack formation quality factor and medium

Abstract

The invention relates to the technical field of oil-gas exploration geophysical comprehensive prediction, and discloses a fluid detection method and device based on a pre-stack stratum quality factor and a medium. According to the method, dynamic correction stretching compensation is carried out on a pre-stack gather, and the event stability of the pre-stack gather is guaranteed; geologic structure guiding filtering is carried out on the pre-stack gather, and the signal-to-noise ratio of the pre-stack gather is guaranteed; seismic data time-frequency analysis of wavelet transformation is carried out on the pre-stack seismic data, and seismic data frequency domain data are obtained; defining the number of floors and calculating the travel time of each floor; based on the frequency domain data, a simulated annealing method is adopted to solve each stratum quality factor; and performing data standardization processing to obtain the fluid distribution of the target interval. According to the technical scheme, on the basis of accurate estimation of the pre-stack gather stratum quality factors, low-saturation oil and gas reservoir fluid detection can be achieved, the fluid detection precision is improved, efficient exploration and development of oil and gas can be assisted, and good application and popularization value is achieved.

Description

Technical Field

[0001] This invention relates to the field of integrated geophysical prediction technology for oil and gas exploration, specifically to a fluid detection method, device, and medium based on pre-stack formation quality factors, belonging to reservoir geophysical fluid detection technology. Background Technology

[0002] Currently, fluid detection primarily relies on amplitude "bright spot" attributes. For low-permeability oil and gas reservoirs, seismic amplitude or impedance information is insufficient to identify gas-water layers. However, variations in low- and high-yield gas layers, or changes in physical properties, increase the difficulty of hydrocarbon detection using conventional "bright spot" or AVO techniques. This is mainly due to variations in formation lithology, physical properties, and fluid type; simply using amplitude anomalies and AVO characteristics for fluid prediction introduces certain errors. Among failed cases, AVO was most prevalent in Class III gas reservoirs. Due to the propagation characteristics of seismic waves in oil and gas-bearing formations, extremely low oil and gas saturation, especially gas saturation, can cause a significant decrease in seismic velocity. Once gas saturation exceeds 10%, neither P-wave nor S-wave velocities show significant changes. These low-gas-saturation gas layers exhibit similar velocity and amplitude AVO characteristics to high-yield gas layers, introducing considerable uncertainty into oil and gas detection using amplitude anomalies and AVO techniques.

[0003] Compared to seismic wave velocity and amplitude, seismic energy attenuation within formations is more than ten times more sensitive to hydrocarbon-bearing strata and their degree of hydrocarbon content (Clark, 1998). A 2005 study by Nur et al. showed a correlation between seismic attenuation and formation gas saturation; seismic energy attenuation can be used to predict formation hydrocarbon saturation. Many factors cause seismic wave energy attenuation, broadly categorized into two types: one related to seismic wave propagation characteristics, such as spherical diffusion impedance filtering; the other reflecting the intrinsic properties of the medium, namely, seismic energy attenuation within the formation. This attenuation is related to friction between fluids and the framework, and between mineral frameworks, caused by seismic wave stress. The degree of attenuation is evaluated using the formation intrinsic quality factor, i.e., the Q factor. Formation intrinsic quality reflects information such as lithology, fluid type, fluid saturation, pressure, and permeability. Therefore, studying seismic wave energy attenuation can effectively characterize underground reservoir features and simultaneously detect hydrocarbons.

[0004] In view of this, this invention addresses the uncertainty in seismic amplitude and post-stack attenuated fluid detection by proposing a fluid detection scheme based on formation factors from pre-stack gathers, thus solving the problem of fluid detection in low-saturation oil and gas reservoirs. Summary of the Invention

[0005] This invention provides a fluid detection method, apparatus, and medium based on pre-stack formation quality factors to solve the aforementioned technical problems of the prior art.

[0006] According to a first aspect of the present invention, a fluid detection method based on pre-stack formation quality factors is provided, comprising:

[0007] Based on the input pre-stack seismic data, dynamic correction and stretching compensation are performed on the pre-stack gather to ensure the in-phase axis stability of the pre-stack gather.

[0008] Based on the input pre-stack seismic data, geological structure-guided filtering is performed on the pre-stack gather to ensure the signal-to-noise ratio of the pre-stack gather;

[0009] Wavelet transform seismic data time-frequency analysis is performed on the pre-stack seismic data to obtain frequency domain data of the seismic data;

[0010] Define the number of stratigraphic layers and calculate the travel time for each layer;

[0011] Based on frequency domain data, the simulated annealing method is used to obtain the quality factor of each formation.

[0012] Data standardization processing is performed to obtain the fluid distribution of the target layer; in the data standardization processing, the calculated formation quality factor is standardized to remove outliers, and data within a reasonable range is standardized.

[0013] Preferably, in the process of performing dynamic correction and stretching compensation on the pre-stack gather,

[0014] Based on the stretching compensation factor for pre-stack trace motion correction compensation, the relationship between the original amplitude spectrum and the stretched amplitude spectrum of the seismic data can be expressed as:

[0015] B s (f,t)=βB(f,t) (2)

[0016] In the formula, B s B(f,t) and B(f,t) are the spectra of the stretched wavelet and the original wavelet, respectively, β is the stretching compensation factor, f is the frequency and t is the time.

[0017] have:

[0018]

[0019] Equation (3) shows that zero-offset seismic data is not stretched.

[0020] Preferably, the wavelet transform seismic data time-frequency analysis performed on the pre-stack seismic data obtains the frequency domain data of the seismic data.

[0021] Filtering is performed on the time-frequency data volume of wavelet transform time-frequency analysis to ensure signal stability.

[0022] Preferably, in defining the number of stratigraphic layers and calculating the travel time of each layer,

[0023] The travel time for each layer is calculated using the ray casting method.

[0024] Preferably, in the step of using simulated annealing to determine the formation quality factor for each formation,

[0025] Assuming the research objective is simplified to a layered model, with the quality factor of each layer being constant, then the quality factor of the research objective is expressed as Q[1:M]≡(Q1,Q2,…,Q M ), where Q M Let m be the quality factor of the m-th stratum (m∈[1:M]), where M is the total number of strata in the geological model;

[0026] The correlation coefficient is used to characterize the difference between the amplitude spectrum after attenuation compensation and the amplitude spectrum of the reference signal. For a series of signals {s1(t), s2(t), ..., s...} N The correlation coefficient of} is defined as:

[0027]

[0028] Among them, s N (t) is the Nth signal;

[0029] In the two-layer medium model, the quality factors of the first and second layers are Q1 and Q2, respectively, and the reflection coefficients of the first and second layers are located at the top and bottom of the second layer; (S1, S2, S3, S4, S 56 (S1, S2, S3, S4, S5) are the corresponding common center point receivers, (x1, x2, x3, x4, x5) are the corresponding shot-receiver distances, the reference signal is the near-offset seismic signal (R1), and the reflected seismic waves (R2, R3, R4, R5) are the target seismic signals.

[0030] Let 2·d(x1,1) and 2·d(x1,2) be the propagation distances of the reflected seismic wave at the first offset in the first and second layers, respectively; and let 2·d(x5,1) and 2·d(x5,2) be the propagation distances of the reflected seismic wave at the fifth offset in the first and second layers, respectively. Assuming the amplitude spectrum B(f,t2,x5) of the seismic wave at the fifth offset, then its attenuation compensation spectrum... Represented as:

[0031]

[0032]

[0033] Where v1 and v2 are the velocities of the first and second layers, respectively, d is the propagation distance, and t2 is the propagation time of the second layer;

[0034] Therefore, the objective function for the optimal solution of the two-layer model is defined as:

[0035]

[0036] Among them, f max and f min These are the maximum and minimum frequencies used when calculating the correlation coefficient, respectively, and h is the number of common center point gathers;

[0037] Using near-offset reflected seismic waves as reference information, the objective function of the optimal solution of the multi-layer model is defined as:

[0038]

[0039]

[0040] Where M is the number of stratigraphic layers in the model, f max and f min These represent the maximum and minimum frequencies used in calculating the correlation coefficient, respectively. B(f,t1,x1) represents the amplitude spectrum of the reflected seismic wave at the offset x1 and the two-way travel time t1. The offset distance is x h For a round trip, the cost is t. m The amplitude spectrum of the reflected seismic wave after attenuation compensation, where H is the common center point gather;

[0041] In the calculation, the reflected seismic wave at the first offset is used as the reference seismic signal, and the attenuation compensation of the amplitude spectrum of the reflected seismic waves at other offsets is expressed as follows:

[0042]

[0043] Among them, Q k For k th Formation quality factor, Δt(x) h (k) represents the h-th offset x h The travel time difference between the reflected seismic wave and the first offset x1, for k th Layers, there are;

[0044] Δt(x h ,k)=t(x h ,k)-t(x1,k)k∈[1:m] (14)

[0045] Where t(x) h (k) represents the offset distance x h The kth th When the reflected wave travels through the strata, t(x1,k) is the distance x1 and the k-th wave. th When reflected waves travel through the strata.

[0046] Preferably, the fluid detection method based on pre-stack formation quality factors includes:

[0047] Input the pre-stack seismic data, including the pre-stack gather data and root mean square velocity of the reservoir to be predicted, and accurately calibrate the stratigraphic position by comprehensively utilizing geological exploration information to determine the range of the target layer in the study.

[0048] According to a second aspect of the present invention, a fluid detection device based on pre-stack formation quality factors is provided, comprising:

[0049] The pre-stack seismic data dynamic correction and stretching compensation module is used to perform dynamic correction and stretching compensation on the pre-stack gather based on the input pre-stack seismic data to ensure the in-phase axis stability of the pre-stack gather.

[0050] The pre-stack seismic data geological structure-guided filtering module is used to perform geological structure-guided filtering on the pre-stack gather based on the input pre-stack seismic data to ensure the signal-to-noise ratio of the pre-stack gather.

[0051] The wavelet transform time-frequency analysis module is used to perform wavelet transform seismic data time-frequency analysis on the pre-stack seismic data to obtain the frequency domain data of the seismic data.

[0052] The travel time calculation module is used to define the number of stratigraphic layers and calculate the travel time for each layer.

[0053] The formation quality factor calculation module is used to determine the quality factor of each formation based on frequency domain data using simulated annealing; and

[0054] The data standardization module is used for data standardization processing to obtain the fluid distribution of the target layer. In the data standardization processing, the calculated formation quality factor is standardized to remove outliers, and data within a reasonable range is standardized.

[0055] Preferably, the fluid detection device based on pre-stack formation quality factors includes:

[0056] The input module is used to input the pre-stack seismic data, including pre-stack gather data and root mean square velocity of the reservoir to be predicted, and to accurately calibrate the stratigraphic horizon by comprehensively utilizing geological exploration information to determine the scope of the target layer under study; and / or

[0057] The planar map extraction and optimization module is used to extract the mean and / or maximum and / or minimum values ​​within a time window to obtain the distribution of stratum quality factors in different time windows.

[0058] According to a third aspect of the present invention, an electronic device is provided, comprising:

[0059] Memory; and

[0060] processor;

[0061] The memory is used to store one or more computer instructions; the one or more computer instructions are executed by the processor to implement the method described in any of the above.

[0062] According to a fourth aspect of the present invention, a readable storage medium is provided, wherein computer instructions are stored thereon; wherein, when executed by a processor, the computer instructions implement the method described in any of the preceding claims.

[0063] The technical solution of this invention extends the calculation of formation quality factors from post-stack seismic data to pre-stack gathers by applying pre-stack seismic gathers. The optimized formation quality factors exhibit strong noise resistance and improved fluid response capabilities. Simulated annealing is used to enhance computational efficiency and result stability. Based on accurate estimation of formation quality factors using pre-stack gathers, this invention enables fluid detection in low-saturation oil and gas reservoirs, improving the accuracy of fluid detection and facilitating efficient oil and gas exploration and development. It has significant potential for widespread application. Attached Figure Description

[0064] Figure 1 This is a flowchart illustrating a fluid detection method based on pre-stack formation quality factors in one embodiment.

[0065] Figure 2 This is a logic block diagram of a fluid detection method based on pre-stack formation quality factors in one embodiment;

[0066] Figure 3 This is a calculation result of a forward model in one embodiment, which is a cross-plot of formation factors with different water saturation and formation P-wave and S-wave velocity ratios;

[0067] Figure 4 One embodiment is a cross-plot of longitudinal wave and VpVs properties;

[0068] Figure 5 This is a schematic diagram of a two-layer medium model containing a quality factor Q in one embodiment;

[0069] Figure 6 This is an example of a seismic profile (top) and a stratigraphic quality factor attribute profile (bottom) of a gas field in Ordos.

[0070] Figure 7 This is an example of amplitude fluid detection results from a gas field in Ordos.

[0071] Figure 8 This is a fluid detection map of formation quality factor attributes in a gas field in Ordos, as shown in one embodiment.

[0072] Figure 9This is a schematic diagram of a fluid detection device based on pre-stack formation quality factors in one embodiment. Detailed Implementation

[0073] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0074] To enable those skilled in the art to better understand the present invention, the technical solution of one embodiment will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are merely some, not all, embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort should fall within the scope of protection of the present invention.

[0075] It should be noted that the terms "first," "second," etc., used in this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be used interchangeably where appropriate for the embodiments of the invention described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0076] It should be understood that when an element (such as a layer, film, region, or substrate) is described as being "on" another element, the element may be directly on the other element, or there may be an intermediate element present. Moreover, in this invention, when an element is described as being "connected" to another element, the element may be "directly connected" to the other element, or "connected" to the other element via a third element.

[0077] Example 1

[0078] Please refer to Figure 1 , Figure 2 , Figure 5 This embodiment provides a fluid detection method based on pre-stack formation quality factors, including the following steps:

[0079] S1. Based on the input pre-stack seismic data, perform dynamic correction and stretching compensation on the pre-stack gather to ensure the in-phase axis stability of the pre-stack gather;

[0080] S2. Based on the input pre-stack seismic data, perform geological structure-guided filtering on the pre-stack gather to ensure the signal-to-noise ratio of the pre-stack gather;

[0081] S3. Perform wavelet transform seismic data time-frequency analysis on the pre-stack seismic data to obtain seismic data frequency domain data;

[0082] S4. Define the number of stratigraphic layers and calculate the travel time of each layer, i.e., calculate the travel time of the seismic reflection waves of each layer;

[0083] S5. Based on frequency domain data (at different offsets), the simulated annealing method is used to obtain the quality factor of each formation;

[0084] S6. Data standardization processing to obtain the fluid distribution of the target layer; in the data standardization processing, the calculated formation quality factor is standardized to remove outliers, and the data within a reasonable range is standardized.

[0085] In one embodiment, step S1 flattens the pre-stack gather to ensure the stability of the in-phase axis of the pre-stack gather; wherein, dynamic correction compensation is performed on the pre-stack gather based on the stretching compensation factor. The main purpose of dynamic correction is to simulate the zero-shot-receiver-offset seismic trace with the non-zero-shot-receiver-offset seismic trace, which is one of the most critical steps in seismic data processing. However, dynamic correction will cause the frequency components of the non-zero-shot-receiver-offset seismic signal to shift towards lower frequencies. Therefore, if researchers ignore the changes in the seismic signal caused by the dynamic correction stretching, the predicted quality factor is usually smaller than the actual quality factor. Studies have shown that surface signal distortion increases with increasing offset and decreases with increasing root mean square velocity.

[0086] In the same common center point gather, the travel time of a seismic wave with an offset of x is expressed as (Dix, 1955):

[0087]

[0088] Where t0 is the two-way travel time of the zero-offset seismic wave, v(t0) is the NMO velocity at t0, and x is the offset. Since the NMO velocity typically increases with increasing burial depth, the dynamic correction of the seismic wave decreases with time, causing time-domain wavelet stretching. To avoid the impact of NMO stretching on quality factor prediction, this invention introduces a stretching compensation factor (Dunkin and Levin, 1973) to compensate for wavelet distortion caused by dynamic correction. The relationship between the original amplitude spectrum and the stretched amplitude spectrum of the seismic data can be expressed as (Dunkin and Levin, 1973):

[0089] B s (f,t)=βB(f,t) (2)

[0090] In the formula, B sB(f,t) and B(f,t) are the spectra of the stretched wavelet and the original wavelet, respectively, β is the stretching compensation factor, f is the frequency and t is the time.

[0091] have:

[0092]

[0093] Equation (3) shows that zero-offset seismic data is not stretched. The dynamic correction increases with the offset and decreases with the decrease of the two-way travel time.

[0094] In one embodiment, in step S3, the time-frequency data volume of wavelet transform time-frequency analysis is filtered to ensure signal stability.

[0095] In one embodiment, in step S4, the ray casting method is applied to calculate the travel time of each layer.

[0096] In one embodiment, step S5 predicts the formation quality factor Q based on the pre-stack gather using the simulated annealing method. The calculation of the subsurface formation quality factor Q is considered a global optimization, and the quality factor of each layer is simultaneously calculated using the simulated annealing method. Assuming the research objective is simplified to a layered model, and the quality factor of each layer is constant, the quality factor of the research objective is expressed as Q[1:M]≡(Q1,Q2,…,Q…). M ), where Q M Let m be the quality factor of the m-th stratum (m∈[1:M]), where M is the total number of strata in the geological model;

[0097] If the calculated quality factor can accurately characterize the viscoelastic attenuation of seismic waves, then the amplitude spectrum after attenuation compensation can have the same expression as the amplitude spectrum of the reference seismic wave; therefore, this invention uses the correlation coefficient to characterize the difference between the amplitude spectrum after attenuation compensation and the amplitude spectrum of the reference signal, for a series of signals {s1(t),s2(t),…,s…} N The correlation coefficient of} is defined as:

[0098]

[0099] Among them, s N (t) is the Nth signal;

[0100] Figure 5A two-layer medium model is shown, with quality factors Q1 and Q2 for the first and second layers, respectively. The reflection coefficients of the first and second layers are located at the top and bottom of the second layer. In the figure, the five stars (S1, S2, S3, S4, S5) represent the seismic source, the five inverted triangles (S1, S2, S3, S4, S5) represent the corresponding common center point receivers, and (x1, x2, x3, x4, x5) represent the corresponding shot-receiver distances. The reference signal is the near-offset seismic signal (R1), and the reflected seismic waves (R2, R3, R4, R5) represent the target seismic signals.

[0101] Let 2·d(x1,1) and 2·d(x1,2) be the propagation distances of the reflected seismic wave at the first offset in the first and second layers, respectively; and let 2·d(x5,1) and 2·d(x5,2) be the propagation distances of the reflected seismic wave at the fifth offset in the first and second layers, respectively. Assuming the amplitude spectrum B(f,t2,x5) of the seismic wave at the fifth offset, then its attenuation compensation spectrum... Represented as:

[0102]

[0103]

[0104] Where v1 and v2 are the velocities of the first and second layers, respectively, d is the propagation distance, and t2 is the propagation time of the second layer;

[0105] Therefore, the objective function for the optimal solution of the two-layer model is defined as:

[0106]

[0107] Among them, f max and f min These are the maximum and minimum frequencies used when calculating the correlation coefficient, respectively, and h is the number of common center point gathers;

[0108] Using near-offset reflected seismic waves as reference information, the objective function of the optimal solution of the multi-layer model is defined as:

[0109]

[0110] Where M is the number of stratigraphic layers in the model, f max and f min These represent the maximum and minimum frequencies used in calculating the correlation coefficient, respectively. B(f,t1,x1) represents the amplitude spectrum of the reflected seismic wave at the offset x1 and the two-way travel time t1. The offset distance is x h For a round trip, the cost is t. m The amplitude spectrum of the reflected seismic wave after attenuation compensation, where H is the common center point gather;

[0111] In the calculation, the reflected seismic wave at the first offset is used as the reference seismic signal, and the attenuation compensation of the amplitude spectrum of the reflected seismic waves at other offsets is expressed as follows:

[0112]

[0113] Among them, Q k For k th Formation quality factor, Δt(x) h (k) represents the h-th offset x h The travel time difference between the reflected seismic wave and the first offset x1, for k th Layers, there are;

[0114] Δt(x h ,k)=t(x h ,k)-t(x1,k)k∈[1:m] (14)

[0115] Where t(x) h (k) represents the offset distance x h The kth th When the reflected wave travels through the strata, t(x1,k) is the distance x1 and the k-th wave. th When reflected waves travel through the strata.

[0116] In one embodiment, in step S6, the formation quality factor is standardized by combining production data to finally obtain the fluid distribution of the target formation.

[0117] In one embodiment, the fluid detection method based on pre-stack formation quality factors includes the following steps:

[0118] S01. Input the pre-stack seismic data, including the pre-stack gather data and root mean square velocity of the reservoir to be predicted, and accurately calibrate the stratigraphic position by comprehensively utilizing geological exploration information to determine the range of the target layer under study.

[0119] In summary, for ease of understanding, the fluid detection method based on pre-stack formation quality factors, starting from data input, includes the following steps:

[0120] S01. Input the pre-stack gather data and root mean square velocity of the reservoir to be predicted, and accurately calibrate the stratigraphic position by comprehensively utilizing geological information, well logging information and synthetic seismic records, and determine the range of the target layer for study.

[0121] S1. Dynamic correction and stretching compensation are performed on the common center point gather in the time domain to eliminate the problem of uneven seismic reflection phase axis in pre-stack gathers and ensure the quality of pre-stack gathers.

[0122] S2. Time-domain construction of guided filtering for common center point gathers eliminates the low signal-to-noise ratio problem and ensures the quality of pre-stack gathers;

[0123] S3. Perform wavelet transform seismic data time-frequency analysis on pre-stack seismic data, and filter the time-frequency data volume to ensure signal stability;

[0124] S4. Define the number of stratigraphic layers and calculate the travel time of seismic waves reflected from each layer. In this study, the ray method is used to calculate the travel time of each layer.

[0125] S5. Apply simulated annealing method to obtain the formation quality factor Q for each formation. Combine with production data, standardize the formation quality factor to obtain the fluid distribution of the target formation.

[0126] The fluid detection method based on pre-stack formation quality factors described above can be used to predict the gas content of three-dimensional reservoirs.

[0127] This invention discloses a fluid detection method based on pre-stack formation quality factors. It employs pre-stack seismic gathers, using near-offset reflected waves as reference information, to derive a multi-layer model formation quality factor algorithm suitable for stratigraphic sedimentary variation characteristics. The method extends formation quality factor calculation from post-stack seismic data to pre-stack gathers, and the optimized formation quality factors exhibit strong noise resistance, improving fluid response capabilities. Simulated annealing is applied to enhance computational efficiency and result stability. This method, based on accurate estimation of formation quality factors from pre-stack gathers, enables fluid detection in low-saturation oil and gas reservoirs and improves their prediction accuracy, serving the identification of exploration potential areas and the optimization of development schemes for tight oil and gas reservoirs.

[0128] Example 2

[0129] Please refer to Figure 9 One embodiment provides a fluid detection device based on pre-stack formation quality factors, which adopts the following structure:

[0130] 1. The pre-stack seismic data dynamic correction and stretching compensation module 10 is used to perform dynamic correction and stretching compensation on the pre-stack gather based on the input pre-stack seismic data to ensure the in-phase axis stability of the pre-stack gather.

[0131] 2. The pre-stack seismic data geological structure-guided filtering module 20 has a structural-guided filtering function for pre-stack gathers. It is used to perform geological structure-guided filtering on the pre-stack gathers based on the input pre-stack seismic data to ensure the signal-to-noise ratio of the pre-stack gathers and improve the quality of seismic data.

[0132] 3. The wavelet transform time-frequency analysis module 30 is used to perform wavelet transform seismic data time-frequency analysis on the pre-stack seismic data to obtain seismic data frequency domain data;

[0133] 4. The travel time calculation module 40 is used to define the number of stratigraphic layers and calculate the travel time for each layer;

[0134] 5. The formation quality factor calculation module 50 is used to obtain each formation quality factor based on frequency domain data (at different offsets) using the simulated annealing method;

[0135] 6. The data standardization module 60 is used for data standardization processing to obtain the fluid distribution of the target layer; in the data standardization processing, the calculated formation quality factor is standardized to remove outliers, and the data within a reasonable range is standardized to ensure the rationality and comparability of the data.

[0136] In one embodiment, the fluid detection device based on pre-stack formation quality factors includes: an input module 010; the input module 010 is used to input the pre-stack seismic data, wherein the pre-stack gather data and root mean square velocity of the reservoir to be predicted are input, and the stratigraphic position is accurately calibrated by comprehensively utilizing geological exploration information to determine the range of the target layer under study.

[0137] In one embodiment, the fluid detection device based on pre-stack formation quality factors includes: a planar map extraction and optimization module 70; the planar map extraction and optimization module 70 is used to extract the mean and / or maximum and / or minimum values ​​within a time window as needed, and obtain the distribution of formation quality factors in different time windows.

[0138] It should be noted that the apparatus of the present invention is used to implement the methods in the above embodiments, and each module in the apparatus corresponds to each step in the method.

[0139] Example 3

[0140] The following is in conjunction with the appendix Figure 1-8 The present invention provides a fluid detection method based on pre-stack formation quality factors through case studies.

[0141] This invention presents a fluid detection method based on pre-stack formation quality factors. Addressing the issues of seismic AVO attributes being affected by formation lithology variations and poor post-stack Q-attenuation prediction, this invention proposes for the first time a fluid detection technology based on the formation quality factor Q of pre-stack gathers and develops a corresponding device. In this technology, dynamic correction compensation is first used to flatten and restore the amplitude of the CMP gather. Then, simulated annealing technology is used to calculate the Q-attenuation parameter of the pre-stack gather, and this parameter is then used for fluid detection. Comparing the results of fluid detection based on pre-stack Q-factor attributes and amplitude attributes, pre-stack Q-attenuation improves detection accuracy by up to 20%, demonstrating that this method has significant application potential and can effectively support efficient exploration and development of oil and gas fields.

[0142] As can be seen from the test results of the forward model of bilayer media with different saturation levels, Figure 3 In the middle, the formation quality factor parameters can distinguish between high-yield gas layers and low-yield gas layers; Figure 4This is a cross-plot of P-wave velocity and formation P-wave / S-wave velocity ratio for different water saturation levels, i.e., a cross-plot of P-wave and P-wave / S-wave velocity ratio attributes. On this cross-plot, the difference in P-wave velocity between high-gas-producing layers and low-gas-producing layers is very small, making it difficult to distinguish them from the P-wave velocity alone; this shows that the formation quality factor can well predict low-saturation oil and gas reservoirs.

[0143] To further verify the effectiveness of this invention, actual data testing was conducted in a gas field in Ordos. The He 1 section of the Xiashihezi Formation in this gas field is the main gas-producing layer. Due to its proximity to the basin edge, the gas reservoir has a low filling rate and low gas saturation. Figure 6 On a seismic profile of a series of wells, it can be seen that there is basically no difference in the amplitude of drilling at different production rates. However, the formation quality factor attributes respond to the heterogeneity of the channel sand bodies and show anomalies in the upper and lower gas layers. Figure 7 This is a fluid detection diagram of amplitude attributes in section 1 of a gas field in Ordos. The amplitude fluid detection results show that the amplitude strength does not match the well productivity well. Figure 8 This is a pre-stack Q-attenuation attribute fluid detection map of the He 1 section in a gas field in Ordos, i.e., a pre-stack formation quality factor attribute fluid detection plan view. It shows a high degree of agreement with productive wells (the agreement between Q-factor attributes and well productivity is significantly improved). For example, well A in the He 1 section produced 18748 m³ of gas. 3 / d, C-section 2 and He-section 1 produce 12,916 m³ of gas per day. 3 / d, F-well section 2 and box section 1 gas production 20261m³ 3 / d, on the amplitude detection plane map, the predicted results do not match the actual results, while the formation quality factor plane map predicts the results accurately, which again shows that the method and device of the present invention can predict low-saturation oil and gas reservoirs very well.

[0144] Example 4

[0145] Based on the same inventive concept, one embodiment of the present invention provides an electronic device, including: a memory and a processor; wherein the memory is used to store one or more computer instructions; the one or more computer instructions are executed by the processor using any of the methods described in the above embodiments.

[0146] Example 5

[0147] Based on the same inventive concept, one embodiment of the present invention provides a readable storage medium storing computer instructions; wherein, when the computer instructions are executed by a processor, they implement the method of any one of the above embodiments.

[0148] One or more of the aforementioned computer instructions can form a program.

[0149] The aforementioned program can run on a processor or be stored in memory (or computer-readable medium). Computer-readable medium includes both permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable medium does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0150] These computer programs may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes can be implemented using different modules, and different steps can be implemented using different modules.

[0151] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A fluid detection method based on pre-stack formation quality factors, characterized in that, include: Based on the input pre-stack seismic data, dynamic correction and stretching compensation are performed on the pre-stack gather to ensure the in-phase axis stability of the pre-stack gather. Based on the input pre-stack seismic data, geological structure-guided filtering is performed on the pre-stack gather to ensure the signal-to-noise ratio of the pre-stack gather; Wavelet transform seismic data time-frequency analysis is performed on the pre-stack seismic data to obtain frequency domain data of the seismic data; Define the number of stratigraphic layers and calculate the travel time for each layer; Based on frequency domain data, the simulated annealing method is used to obtain the quality factor of each formation. Data standardization processing is performed to obtain the fluid distribution of the target layer; in the data standardization processing, the calculated formation quality factor is standardized to remove outliers, and data within a reasonable range is standardized.

2. The fluid detection method based on pre-stack formation quality factors according to claim 1, characterized in that, In the process of dynamic correction and stretching compensation of the pre-stack gather. Based on the stretching compensation factor for pre-stack trace motion correction compensation, the relationship between the original amplitude spectrum and the stretched amplitude spectrum of the seismic data can be expressed as: B s (f,t)=βB(f,t) (2) In the formula, B s B(f,t) and B(f,t) are the spectra of the stretched wavelet and the original wavelet, respectively, β is the stretching compensation factor, f is the frequency and t is the time. have: Equation (3) shows that zero-offset seismic data is not stretched.

3. The fluid detection method based on pre-stack formation quality factors according to claim 1, characterized in that, The wavelet transform-based time-frequency analysis of the pre-stack seismic data is performed to obtain the frequency domain data of the seismic data. Filtering is performed on the time-frequency data volume of wavelet transform time-frequency analysis to ensure signal stability.

4. The fluid detection method based on pre-stack formation quality factors according to claim 1, characterized in that, In defining the number of stratigraphic layers and calculating the travel time of each layer... The travel time for each layer is calculated using the ray casting method.

5. The fluid detection method based on pre-stack formation quality factors according to claim 1, characterized in that, The simulated annealing method is used to determine the formation quality factor for each formation. Assuming the research objective is simplified to a layered model, with the quality factor of each layer being constant, then the quality factor of the research objective is expressed as Q[1:M]≡(Q1,Q2,…,Q M ), where Q M Let m be the quality factor of the m-th stratum (m∈[1:M]), where M is the total number of strata in the geological model; The correlation coefficient is used to characterize the difference between the amplitude spectrum after attenuation compensation and the amplitude spectrum of the reference signal. For a series of signals {s1(t), s2(t), ..., s...} N The correlation coefficient of} is defined as: Among them, s N (t) is the Nth signal; In the two-layer medium model, the quality factors of the first and second layers are Q1 and Q2, respectively, and the reflection coefficients of the first and second layers are located at the top and bottom of the second layer; (S1,S2,S3,S4,S5) are the seismic sources, (S1,S2,S3,S4,S5) are the corresponding common midpoint receivers, (x1,x2,x3,x4,x5) are the corresponding shot-receiver distances, the reference signal is the near-offset seismic signal (R1), and the reflected seismic waves (R2,R3,R4,R5) are the target seismic signals; Let 2·d(x1,1) and 2·d(x1,2) be the propagation distances of the reflected seismic wave at the first offset in the first and second layers, respectively; and let 2·d(x5,1) and 2·d(x5,2) be the propagation distances of the reflected seismic wave at the fifth offset in the first and second layers, respectively. Assuming the amplitude spectrum B(f,t2,x5) of the seismic wave at the fifth offset, then its attenuation compensation spectrum... Represented as: Where v1 and v2 are the velocities of the first and second layers, respectively, d is the propagation distance, and t2 is the propagation time of the second layer; Therefore, the objective function for the optimal solution of the two-layer model is defined as: Among them, f max and f min These are the maximum and minimum frequencies used when calculating the correlation coefficient, respectively, and h is the number of common center point gathers; Using near-offset reflected seismic waves as reference information, the objective function of the optimal solution of the multi-layer model is defined as: Where M is the number of stratigraphic layers in the model, f max and f min These represent the maximum and minimum frequencies used in calculating the correlation coefficient, respectively. B(f,t1,x1) represents the amplitude spectrum of the reflected seismic wave at the offset x1 and the two-way travel time t1. The offset distance is x h For a round trip, the cost is t. m The amplitude spectrum of the reflected seismic wave after attenuation compensation, where H is the common center point gather; In the calculation, the reflected seismic wave at the first offset is used as the reference seismic signal, and the attenuation compensation of the amplitude spectrum of the reflected seismic waves at other offsets is expressed as follows: Among them, Q k For k th Formation quality factor, Δt(x) h (k) represents the h-th offset x h The travel time difference between the reflected seismic wave and the first offset x1, for k th Layers, there are; Δt(x h ,k)=t(x h ,k)-t(x1,k)k∈[1:m] (14) Where t(x) h (k) represents the offset distance x h The kth th When the reflected wave travels through the strata, t(x1,k) is the distance x1 and the k-th wave. th When reflected waves travel through the strata.

6. The fluid detection method based on pre-stack formation quality factors according to any one of claims 1-5, characterized in that, The fluid detection method based on pre-stack formation quality factors includes: Input the pre-stack seismic data, including the pre-stack gather data and root mean square velocity of the reservoir to be predicted, and accurately calibrate the stratigraphic position by comprehensively utilizing geological exploration information to determine the range of the target layer in the study.

7. A fluid detection device based on pre-stack formation quality factors, characterized in that, include: The pre-stack seismic data dynamic correction and stretching compensation module is used to perform dynamic correction and stretching compensation on the pre-stack gather based on the input pre-stack seismic data to ensure the in-phase axis stability of the pre-stack gather. The pre-stack seismic data geological structure-guided filtering module is used to perform geological structure-guided filtering on the pre-stack gather based on the input pre-stack seismic data to ensure the signal-to-noise ratio of the pre-stack gather. The wavelet transform time-frequency analysis module is used to perform wavelet transform seismic data time-frequency analysis on the pre-stack seismic data to obtain the frequency domain data of the seismic data. The travel time calculation module is used to define the number of stratigraphic layers and calculate the travel time for each layer. The formation quality factor calculation module is used to obtain each formation quality factor based on frequency domain data using the simulated annealing method. and The data standardization module is used for data standardization processing to obtain the fluid distribution of the target layer. In the data standardization processing, the calculated formation quality factor is standardized to remove outliers, and data within a reasonable range is standardized.

8. The fluid detection device based on pre-stack formation quality factor according to claim 7, characterized in that, The fluid detection device based on pre-stack formation quality factors includes: The input module is used to input the pre-stack seismic data, including pre-stack gather data and root mean square velocity of the reservoir to be predicted, and to accurately calibrate the stratigraphic horizon by comprehensively utilizing geological exploration information to determine the scope of the target layer under study; and / or The planar map extraction and optimization module is used to extract the mean and / or maximum and / or minimum values ​​within a time window to obtain the distribution of stratum quality factors in different time windows.

9. An electronic device, characterized in that, include: Memory; and processor; The memory is used to store one or more computer instructions; the one or more computer instructions are executed by the processor to implement the method according to any one of claims 1 to 6.

10. A readable storage medium, characterized in that, The readable storage medium stores computer instructions; wherein, when executed by a processor, the computer instructions implement the method described in any one of claims 1 to 6.

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