Seismic data high-resolution processing method and device and electronic equipment

Abstract

The invention relates to the technical field of seismic data processing, and discloses a seismic data high-resolution processing method and device and electronic equipment. The method comprises the following steps: extracting a time-varying wavelet; removing wavelets, and calculating odd and even reflection coefficients; constructing high-frequency components of the seismic data by using a deconvolution method; and a broadband reflection coefficient body is constructed, and based on the extracted odd part and even part of the seismic reflection coefficient and the inverted seismic data high-frequency component, the obtained sliding time window weight factor and the seismic data high-frequency component are combined to construct a high-resolution seismic data body. According to the technical scheme, based on the sliding time window weight factor, the resolution of the seismic data can be well improved; and the resolution ratio improvement degree is controlled according to geological requirements. According to the technical scheme, the signal-to-noise ratio of the seismic data after the resolution is properly improved is high, the signal fidelity is high, geological tasks can be well completed, and the well seismic calibration effect of thin interbed reservoir prediction is good.

Description

Technical Field

[0001] The present invention relates to the technical field of seismic data processing, and in particular to a method, device and electronic equipment for high-resolution processing of seismic data. Background Art

[0002] As China's oil and gas exploration and development business expands to thin reservoirs and shale oil and gas layers, higher technical requirements have been put forward for improving the resolution of seismic data, and a series of processing methods for improving resolution have been formed. There are many methods to improve the resolution of seismic data, which can be mainly divided into three categories: the first is frequency enhancement technology, that is, broadening the seismic spectrum within the effective frequency band to improve the resolution of seismic data; the second is Q compensation technology, which improves the resolution of seismic data by establishing a formation absorption attenuation model and obtaining the Q factor for inverse Q filtering; the third is frequency extension technology, which can use the original seismic and geological data information to obtain information outside the seismic frequency band to achieve the purpose of improving the resolution of seismic data.

[0003] The seismic signal is the convolution of the wavelet and the reflection coefficient. Theoretically, if there is an accurate wavelet, the corresponding reflection coefficient can be obtained by using the denoised seismic data. In mathematical model, it can be expressed as:

[0004] S(t)=W(t)*R(t)+N

[0005] Where S(t) is the seismic signal, W(t) is the seismic wavelet, R(t) is the reflection coefficient, and N is the noise signal.

[0006] When predicting thin reservoirs, the main focus is on vertical (temporal) resolution, that is, the ability to resolve the minimum formation thickness. There are three commonly used criteria:

[0007] 1. Rayleigh criterion: defined by the principle of optical imaging, when the wavelength difference of the reflected wave from the top and bottom interfaces is ≥ half a wavelength, the two sub-waves are considered to be distinguishable; the thickness of the thin layer defined by this criterion is λ / 4, i.e., λ / 4 is the limiting vertical resolution;

[0008] 2. Ricker criterion: Assuming that the time difference between two Rickers arriving at a certain formation is not less than the width of the two inflection points of the sub-wave main lobe, they can be distinguished, otherwise, they cannot be distinguished; the width between the negative extreme points of two Ricker sub-waves is about λ / 4.6;

[0009] 3.Widess criterion: In the case of zero noise, λ / 8 is the limiting resolution, and values ​​smaller than this value are indistinguishable.

[0010] All three criteria assume that the Ricker wavelet is zero phase. Actual seismic records are mixed phase wavelets due to formation filtering, which is why the resolution of seismic signals cannot reach the theoretical resolution. Therefore, for the current exploration and development of thin interbedded sandstone and mudstone reservoirs and unconventional shale reservoirs, the resolution of conventional seismic data cannot meet the requirements of high-resolution reservoir prediction and description, and seismic data needs to be processed to improve resolution.

[0011] Among the above, the three types of processing methods for improving resolution have their own assumptions and scope of application. The frequency-enhancing technology represented by adaptive decomposition to improve resolution assumes that data at different depths have different wavelets, and at a single depth, the wavelet phase spectrum remains unchanged and the amplitude spectrum decays; its advantage is good amplitude preservation, suitable for seismic data with bell-shaped spectrum, stable and reliable calculation results, and can be used for attribute analysis and reservoir prediction; its disadvantage is that the resolution improvement is limited. The precise compensation technology represented by inverse Q filtering assumes that there is a quality factor that is independent of frequency, and the mathematical attenuation model is consistent with the earth absorption mechanism; its advantage is that both amplitude and phase can be accurately restored; its disadvantage is that the attenuation mechanism in different regions is quite different, and it is difficult to accurately match the mathematical model. The frequency extension technology represented by super-resolution processing technology assumes that the reflection coefficient is sparse, the stratum is horizontally continuous, and the seismic wavelet is known; its advantage is that it can process seismic data with higher resolution; its disadvantage is that parameter optimization and result evaluation are difficult, and the result data is not suitable for attribute analysis.

[0012] In order to solve the problem that the stability and reliability of seismic data processed by spectrum extension technology are greatly affected by parameters and parameter optimization and evaluation are difficult, a high-resolution processing method for seismic data based on sliding time window weight factor is proposed. Summary of the invention

[0013] The present application provides a method, device and electronic equipment for high-resolution processing of seismic data to solve the above-mentioned technical problems in the prior art.

[0014] According to one aspect of the present application, an embodiment provides a method for high-resolution processing of seismic data, comprising:

[0015] Extracting time-varying wavelets;

[0016] Remove the wavelets and calculate the odd and even reflection coefficients;

[0017] Use deconvolution method to construct high frequency components of seismic data;

[0018] A broadband reflection coefficient volume is constructed, wherein a high-resolution seismic data volume is constructed by combining the acquired sliding time window weight factor with the high-frequency component of the seismic data based on the extracted odd and even parts of the seismic reflection coefficient and the inverted high-frequency component of the seismic data.

[0019] In one embodiment, the high-resolution processing method for seismic data includes: obtaining the sliding time window weight factor based on post-stack processed seismic data and well data; wherein, obtaining the sliding time window weight factor based on post-stack processed seismic data and well data includes:

[0020] The formation reflection coefficient is obtained based on the sonic logging data and density logging data. The reflection coefficient is calculated using the reflection coefficient formula.

[0021]

[0022] In the formula, R (t) is the reflection coefficient calculated from the logging data, ρ is the density, V is the velocity, Δt is the acoustic time difference, t is the time position of the current formation during the two-way travel, and t+Δt is the time position of the adjacent formation during the two-way travel at the next moment;

[0023] Construct the sliding window weight factor λ.

[0024] In one embodiment, the formation reflection coefficient is obtained based on the sonic logging data and the density logging data, and a theoretical calculation formula is provided:

[0025]

[0026] Where V i is the speed of sound waves; Δt i It is the sound wave time difference, unit is us / m;

[0027] And convert the time-depth of equation (1) to obtain the round-trip travel time t, we have:

[0028]

[0029] Where i is the sampling point number, N is a natural number, Δh is the depth sampling thickness, V i is the speed of sound waves.

[0030] In one embodiment, constructing the sliding window weight factor λ includes:

[0031] In the well-constrained area, use formula (3) to check whether the weight factor in each time window is reasonable;

[0032] In the area without well constraints, the sliding time window weight factor is solved by the iterative method, and the rationality of the solution is judged by the result of training the seismic data with the well data;

[0033] The sliding window weight factor changes with the time window, and time-varying linear weighting is performed when the time window is selected.

[0034] In one embodiment, in the well-constrained area, formula (3) is used to check whether the weight factor in each time window is reasonable. The broadband reflection coefficient finally outputted is consistent with the reflection coefficient calculated by the logging data, indicating that the determined weight factor is reasonable.

[0035] Among them, the weight factors of the odd reflection coefficients are α, the weight factors of the even reflection coefficients are β, and α+β=1, then:

[0036] R (t) =αR (t)odd +βR (t)even (4)

[0037] R (t) =(1-β)R (t)odd +βR (t)even (5)

[0038]

[0039] In the formula, the calculated β is the initial weight factor of the reflection coefficient, R (t) is the reflection coefficient calculated from logging data, R (t)odd is the odd part of the reflection coefficient, R (t)even is the even part of the reflection coefficient.

[0040] In one embodiment, the sliding time window weight factor varies with the time window, and in the time-varying linear weighting when the time window is selected, at t i At the moment, a sliding window is constructed using a time-varying linear weighted algorithm, and the sliding window weight factor λ is calculated; further, suppose that at t i The weight factor of the reflection coefficient at time is β i , at t i+1 The weight factor of the reflection coefficient at time is β i+1 , then at t i ~t i+1 Sliding window weight factor λ between time periods i There is the following time-varying linear weighted relationship:

[0041]

[0042]

[0043] Where, t i With t i+1 is the time position at a certain moment during round-trip travel, in ms; W and δ are the sliding window length and step size, in ms; β i With β i+1 is the weight factor of the even reflection coefficient at the corresponding time position; i∈(1,2,3......N), where N is a natural number.

[0044] According to one aspect of the present application, an embodiment provides a high-resolution seismic data processing device, comprising:

[0045] An extraction module, used for extracting time-varying wavelets;

[0046] A processing module, used to remove sub-waves and calculate odd and even reflection coefficients;

[0047] A first construction module is used to construct high frequency components of seismic data using a deconvolution method; and

[0048] The second construction module is used to construct a broadband reflection coefficient volume, wherein a high-resolution seismic data volume is constructed by combining the obtained sliding time window weight factor with the high-frequency component of the seismic data based on the extracted odd and even parts of the seismic reflection coefficient and the inverted high-frequency component of the seismic data.

[0049] In one embodiment, the high-resolution processing device for seismic data includes an obtaining module; the obtaining module is used to obtain the sliding time window weight factor according to post-stack processed seismic data and well data; wherein, obtaining the sliding time window weight factor according to post-stack processed seismic data and well data includes:

[0050] The formation reflection coefficient is obtained based on the sonic logging data and density logging data. The reflection coefficient is calculated using the reflection coefficient formula.

[0051]

[0052] In the formula, R (t) is the reflection coefficient calculated from the logging data, ρ is the density, V is the velocity, Δt is the acoustic time difference, t is the time position of the current formation during the two-way travel, and t+Δt is the time position of the adjacent formation during the two-way travel at the next moment;

[0053] Construct the sliding window weight factor λ.

[0054] According to one aspect of the present application, an embodiment provides an electronic device, including:

[0055] Memory; and

[0056] processor;

[0057] Wherein, the memory is used to store one or more computer instructions; the one or more computer instructions are executed by the processor to implement any of the methods described above.

[0058] According to one aspect of the present application, an embodiment provides a readable storage medium having computer instructions stored thereon; wherein, when the computer instructions are executed by a processor, any of the methods described above is implemented.

[0059] The technical solution of the above embodiment of the present application improves the resolution of seismic data based on the sliding time window weight factor; it can not only well improve the resolution of seismic data; but also control the degree of resolution improvement according to geological requirements. The technical solution of the above embodiment of the present application, after moderately improving the resolution, the seismic data has a high signal-to-noise ratio and high signal fidelity, which can well complete geological tasks and has a good effect on well-seismic calibration for thin interbedded reservoir prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 is a flow chart of a method for high-resolution processing of seismic data in an embodiment;

[0061] Figure 2 is a schematic diagram of decomposition of seismic reflection coefficients in an embodiment;

[0062] Figure 3 is a schematic diagram of calculating the weight factor of the non-well constraint area in an embodiment;

[0063] Figure 4 is a schematic diagram of time-varying linear weighting of weight factors in an embodiment;

[0064] Figure 5 is a comparison diagram of the actual data and inversion results of the A2 well in the CX area in an embodiment;

[0065] Figure 6 is a comparative cross-section diagram of the DX region before and after inversion in an embodiment;

[0066] Figure 7 is a comparative cross-section diagram of shale before and after inversion in the ZZ area in an embodiment;

[0067] Figure 8 It is a comparative cross-section diagram before and after the colored inversion of the earthquake in the ZZ area in an embodiment;

[0068] Fig. 9 The diagram is a schematic diagram of the structure of a high-resolution seismic data processing device in an embodiment. DETAILED DESCRIPTION

[0069] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0070] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only embodiments of a part of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present application.

[0071] It should be noted that the terms "first", "second" etc. in the present application are used to distinguish similar objects, and need not be used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged in appropriate circumstances, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, the process, method, system, product or equipment comprising a series of steps or units need not be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0072] 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. Moreover, in this application, 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 through a third element.

[0073] Embodiment 1

[0074] Please refer to Figure 1 , Figure 4 An embodiment provides a method for high-resolution processing of seismic data, comprising the following steps:

[0075] S1. Extract time-varying wavelets;

[0076] S2. Remove the wavelet and calculate the odd and even reflection coefficients;

[0077] S3. Use deconvolution method to construct high frequency components of seismic data;

[0078] S4. Construct a broadband reflection coefficient volume, wherein a high-resolution seismic data volume is constructed by combining the obtained sliding window weight factor with the high-frequency component of the seismic data based on the extracted odd and even parts of the seismic reflection coefficient and the inverted high-frequency component of the seismic data.

[0079] In one embodiment, the high-resolution processing method for seismic data includes: S5. Obtaining the sliding time window weight factor based on post-stack processed seismic data and well data; wherein step S5 includes the following sub-steps:

[0080] S51. Obtain formation reflection coefficient based on acoustic logging data and density logging data, wherein the reflection coefficient is calculated using the reflection coefficient formula.

[0081]

[0082] In the formula, R (t) is the reflection coefficient calculated from the logging data, ρ is the density, V is the velocity, Δt is the acoustic time difference, t is the time position of the current formation during the two-way travel, and t+Δt is the time position of the adjacent formation during the two-way travel at the next moment;

[0083] S52. Construct a sliding window weight factor λ; Further, step S52 includes the following sub-steps:

[0084] S521. In the well-constrained area, use formula (3) to check whether the weight factor in each time window is reasonable;

[0085] S522. In the area without well constraints, the sliding time window weight factor is solved by the iterative method, and the rationality of the solution is judged by the result of the training of well data on seismic data;

[0086] S523. The sliding time window weight factor λ varies with the time window, and time-varying linear weighting is performed when the time window is selected.

[0087] In one embodiment, in step S51, there is a theoretical calculation formula:

[0088]

[0089] Where V i is the speed of sound waves; Δt i It is the sound wave time difference, unit is us / m;

[0090] And convert the time-depth of equation (1) to obtain the round-trip travel time t, we have:

[0091]

[0092] Where i is the sampling point number, N is a natural number, Δh is the depth sampling thickness, V i is the speed of sound waves.

[0093] In one embodiment, in step S521, the broadband reflection coefficient finally outputted is consistent with the reflection coefficient calculated by the logging data, indicating that the determined weight factor is reasonable; wherein, assuming that the weight factors of the odd reflection coefficient and the even reflection coefficient are α and β respectively, and α+β=1, then:

[0094] R (t) =αR (t)odd +βR (t)even(4)

[0095] R (t) =(1-β)R (t)odd +βR (t)even (5)

[0096]

[0097] In the formula, the calculated β is the initial weight factor of the reflection coefficient, R (t) is the reflection coefficient calculated from logging data, R (t)odd is the odd part of the reflection coefficient, R (t)even is the even part of the reflection coefficient.

[0098] In one embodiment, in step S523, if Figure 4 The sliding window weight factor changes with the time window. When the time window is selected, the time-varying linear weighting is performed. i At the moment, a sliding window is constructed using a time-varying linear weighted algorithm, and the sliding window weight factor λ is calculated; further, suppose that at t i The weight factor of the reflection coefficient at time is β i , at t i+1 The weight factor of the reflection coefficient at time is β i+1 , then at t i ~t i+1 Sliding window weight factor λ between time periods i There is the following time-varying linear weighted relationship:

[0099]

[0100]

[0101] Where, t i With t i+1 is the time position at a certain moment during round-trip travel, in ms; W and δ are the sliding window length and step size, in ms; β i With β i+1 is the weight factor of the even reflection coefficient at the corresponding time position; i∈(1,2,3......N), where N is a natural number.

[0102] The high-resolution processing method of seismic data in the present application combines post-stack processed seismic data with well data, and obtains sliding time window weight factors according to geological target requirements, so as to achieve the purpose of combining high-resolution seismic data that can complete geological tasks and have high fidelity.

[0103] Embodiment 2

[0104] Please refer to Figure 2-Figure 4An embodiment provides a method for high-resolution processing of seismic data. The following will explain the technical solution of the present application with the ideas of the inventor, which will help those skilled in the art to be familiar with the ideas of the present application. Among them, some technical points are explained in detail, so that those skilled in the art can fully understand the technical solution of the present application.

[0105] The high-resolution processing method of seismic data includes the following steps:

[0106] S1. Time-varying wavelet extraction W(t);

[0107] S2. Remove the sub-wave W(t) from the original seismic data S(t) and calculate the odd (R (t)odd ), even (R (t)even )Reflection coefficient( Figure 2 );

[0108] S3. Use deconvolution method to construct earthquake high frequency components;

[0109] S4. Calculate the sliding time window weight factor based on the post-stack processed seismic data and well data;

[0110] S5. The odd part R(t) of the seismic reflection coefficient extracted according to step S2 odd and even part R(t) even The high-frequency components of the seismic data inverted in step S3 are combined with the sliding time window weight factors in step S4 to construct a high-resolution seismic data volume.

[0111] In one embodiment, step S4 includes the following sub-steps:

[0112] S41. Calculate the formation reflection coefficient based on the acoustic logging data and the density logging data;

[0113] Specifically, according to the theoretical calculation formula:

[0114]

[0115] Where V i is the speed of sound waves; Δt i It is the sound wave time difference, unit is us / m;

[0116] Then, convert the time to depth and find the round trip time t, then we have:

[0117]

[0118] Where i is the sampling point number, N is a natural number, Δh is the depth sampling thickness, V i is the speed of sound waves;

[0119] Then use the reflection coefficient formula to calculate the reflection coefficient,

[0120]

[0121] In the formula, R (t) is the reflection coefficient calculated from the logging data, ρ is the density, V is the velocity, Δt is the acoustic time difference, t is the time position of the current formation during the two-way travel, and t+Δt is the time position of the adjacent formation during the two-way travel at the next moment;

[0122] S42. Construct a sliding window weight factor λ.

[0123] In one embodiment, step S42 includes the following sub-steps:

[0124] S421. In the well-constrained area, determine whether the weight factor in each time window is reasonable, and use formula (3) to test. The broadband reflection coefficient outputted in the end is consistent with the reflection coefficient calculated by the logging data, indicating that the determined weight factor is reasonable, as shown in formulas (4), (5), and (6).

[0125] R (t) =αR (t)odd +βR (t)even (4)

[0126] R (t) =(1-β)R (t)odd +βR (t)even (5)

[0127]

[0128] In the formula, the calculated β is the initial weight factor of the reflection coefficient, R (t) is the reflection coefficient calculated from logging data; R (t)odd is the odd part of the reflection coefficient ( Figure 2 );R (t)even is the even part of the reflection coefficient ( Figure 2 );

[0129] S422. In the area without well constraints, the sliding window weight factor is calculated using the Figure 3 The iterative method is given to solve the problem. The rationality of the solution is judged by the result of training the seismic data with the well data.

[0130] S423. The sliding window weight factor changes with the time window. When constructing the sliding window, the time window length Δt is determined according to the stratum thickness required for geological resolution. In the actual application process, in order to make the calculation results transition smoothly, time-varying linear weighting is required when selecting the time window (see Appendix Figure 4 ); Specifically, the selected time window length W and sliding time step δ, at t iAt the moment, a sliding window is constructed using a time-varying linear weighted algorithm, and the sliding window weight factor λ is calculated; further, suppose that at t i The weight factor of the reflection coefficient at time is β i , at t i+1 The weight factor of the reflection coefficient at time is β i+1 , then at t i ~t i+1 Sliding window weight factor λ between time periods i There is the following time-varying linear weighted relationship:

[0131]

[0132]

[0133] Where, t i With t i+1 is the time position at a certain moment during round-trip travel, in ms; W and δ are the sliding window length and step size, in ms; β i With β i+1 is the weight factor of the even reflection coefficient at the corresponding time position; i∈(1,2,3......N), where N is a natural number.

[0134] In general, the high-resolution processing method of seismic data based on sliding time window weight factor proposed in the present invention includes five steps: the first step is to extract time-varying wavelets; the second step is to remove wavelets and calculate odd and even reflection coefficients; the third step is to construct seismic high-frequency components by deconvolution method; the fourth step is to obtain sliding time window weight factors; the fifth step is to construct broadband reflection coefficient volume. Compared with the prior art, the technical solution provided by the present application can not only well improve the resolution of seismic data, but also control the degree of resolution improvement according to geological requirements. The seismic data with moderately improved resolution has high signal-to-noise ratio and high signal fidelity, which can well complete geological tasks and has good well-seismic calibration effect in thin interbedded reservoir prediction.

[0135] Embodiment 3

[0136] Please refer to Figure 5-Figure 8 An embodiment provides a high-resolution processing method for seismic data based on a sliding time window weight factor, which is explained below through a case study to facilitate further understanding of its technical solution.

[0137] Attached Figure 5 This is a comparison chart of the actual data of Well A2 in the 3CX area and the inversion results. It can be seen from the figure that the seismic reflection coefficient calculated using the actual data of Well A2 has a good relative relationship with the reflection coefficient of the wellside seismic trace extracted using the present invention, and the seismic reflection coefficient profile obtained by inversion has the characteristics of high resolution, high signal-to-noise ratio, high fidelity, etc.

[0138] Attached Figure 6 This is a comparison of the original seismic profile (above) and the inverted seismic reflection coefficient profile (below) in the DX area. On the original seismic profile, the seismic reflection event axis indicated by the arrow is continuous, while on the seismic reflection coefficient inversion profile, it can be clearly seen that this stratum pinches out in the middle of the profile. Therefore, the sedimentary characteristics of the stratum can be better portrayed.

[0139] Attached Figure 7 The comparison profile of shale in ZZ area before and after inversion. From the figure, it can be seen that the shale on the original seismic profile is a set of strong reflection features (upper), and the high-resolution seismic data processed by the present invention can decompose the original set of strong reflections into upper and lower sets of strong reflections (lower).

[0140] Attached Figure 8 This is a comparison of seismic profiles before and after seismic colored inversion using high-resolution seismic data processed by the present invention in the ZZ region. Drilling data show that the shale in the ZZ region is a set of gas-bearing shale layers, with the top shale being superior to the bottom shale, which cannot be distinguished by the original seismic data. Through the inversion profile of the high-resolution processed data of the present invention, the two layers of shale can be clearly distinguished. This has a good guiding role in drilling trajectory design and tracking.

[0141] Embodiment 4

[0142] Please refer to Fig. 9 , an embodiment provides a high-resolution seismic data processing device in the following form:

[0143] 1. Extraction module

[0144] The extraction module 10 is used to extract the time-varying wavelet;

[0145] 2. Processing module

[0146] The processing module 20 is used to remove sub-waves and calculate odd and even reflection coefficients;

[0147] 3. The first building block

[0148] The first construction module 30 is used to construct the high frequency components of the seismic data using a deconvolution method; and

[0149] 4. Second building block

[0150] The second construction module 40 is used to construct a broadband reflection coefficient volume, wherein a high-resolution seismic data volume is constructed by combining the obtained sliding window weight factor with the high-frequency component of the seismic data based on the extracted odd and even parts of the seismic reflection coefficient and the inverted high-frequency component of the seismic data;

[0151] 5. Obtaining modules

[0152] The obtaining module 50 is used to obtain the sliding time window weight factor according to the post-stack processed seismic data and well data.

[0153] Wherein, obtaining the sliding time window weight factor according to post-stack processed seismic data and well data comprises the following steps:

[0154] 1. Obtain the formation reflection coefficient based on the sonic logging data and density logging data, where the reflection coefficient is calculated using the reflection coefficient formula.

[0155]

[0156] In the formula, R (t) is the reflection coefficient calculated from the logging data, ρ is the density, V is the velocity, Δt is the acoustic time difference, t is the time position of the current formation during the two-way travel, and t+Δt is the time position of the adjacent formation during the two-way travel at the next moment;

[0157] 2. Construct the sliding window weight factor λ.

[0158] It should be noted that the above-mentioned high-resolution seismic data processing device is used to implement the high-resolution seismic data processing method in the above-mentioned embodiments, and each module in the device corresponds to each step in the method.

[0159] Embodiment 5

[0160] Based on the same inventive concept, an embodiment of the present application provides an electronic device, comprising: 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 as a high-resolution seismic data processing method described in any one of the above embodiments.

[0161] Embodiment 6

[0162] Based on the same inventive concept, an embodiment of the present application provides a readable storage medium, on which computer instructions are stored; wherein, when the computer instructions are executed by a processor, the high-resolution seismic data processing method described in any one of the above embodiments is implemented.

[0163] Among them, the above one or more computer instructions can be formed into a program.

[0164] The above program can be run in the processor, or it can be stored in the memory (or computer-readable medium), which includes permanent and non-permanent, removable and non-removable media. 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 technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined in this article, computer-readable media does not include transitory computer-readable media (transit media), such as modulated data signals and carrier waves.

[0165] These computer programs can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps of the functions specified in one or more blocks can be implemented by different modules corresponding to different steps.

[0166] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for high-resolution processing of seismic data, characterized in that: include: Extracting time-varying wavelets; Remove the wavelets and calculate the odd and even reflection coefficients; Use deconvolution method to construct high frequency components of seismic data; A broadband reflection coefficient volume is constructed, wherein a high-resolution seismic data volume is constructed by combining the acquired sliding time window weight factor with the high-frequency component of the seismic data based on the extracted odd and even parts of the seismic reflection coefficient and the inverted high-frequency component of the seismic data.

2. A method for high-resolution processing of seismic data according to claim 1, characterized in that: The high-resolution processing method for seismic data comprises: obtaining the sliding time window weight factor according to post-stack processed seismic data and well data; wherein, obtaining the sliding time window weight factor according to post-stack processed seismic data and well data comprises: The formation reflection coefficient is obtained based on the sonic logging data and density logging data. The reflection coefficient is calculated using the reflection coefficient formula. In the formula, R (t) is the reflection coefficient calculated from the logging data, ρ is the density, V is the velocity, Δt is the acoustic time difference, t is the time position of the current formation during the two-way travel, and t+Δt is the time position of the adjacent formation during the two-way travel at the next moment; Construct the sliding window weight factor λ.

3. A method for high-resolution processing of seismic data according to claim 2, characterized in that: In the above-mentioned calculation of formation reflection coefficient based on sonic logging data and density logging data, there is a theoretical calculation formula: Where V i is the speed of sound waves; Δt i It is the sound wave time difference, unit is us / m; And convert the time-depth of equation (1) to obtain the round-trip travel time t, we have: Where i is the sampling point number, N is a natural number, Δh is the depth sampling thickness, V i is the speed of sound waves.

4. A method for high-resolution processing of seismic data according to claim 2, characterized in that: The construction of the sliding window weight factor λ includes: In the well-constrained area, use formula (3) to check whether the weight factor in each time window is reasonable; In the area without well constraints, the sliding time window weight factor is solved by the iterative method, and the rationality of the solution is judged by the result of training the seismic data with the well data; The sliding window weight factor changes with the time window, and time-varying linear weighting is performed when the time window is selected.

5. A method for high-resolution processing of seismic data according to claim 4, characterized in that: In the well-constrained area, formula (3) is used to test whether the weight factor in each time window is reasonable. The broadband reflection coefficient finally output is consistent with the reflection coefficient calculated by the logging data, indicating that the determined weight factor is reasonable. Among them, the weight factors of the odd reflection coefficients are α, the weight factors of the even reflection coefficients are β, and α+β=1, then: R (t) =αR (t)odd +βR (t)even (4) R (t) =(1-β)R (t)odd +βR (t)even (5) In the formula, the calculated β is the initial weight factor of the reflection coefficient, R (t) is the reflection coefficient calculated from logging data, R (t)odd is the odd part of the reflection coefficient, R (t)even is the even part of the reflection coefficient.

6. A method for high-resolution processing of seismic data according to claim 4, characterized in that: The sliding time window weight factor changes with the time window, and the time-varying linear weighting is performed when the time window is selected. i At the moment, a sliding window is constructed using a time-varying linear weighted algorithm, and the sliding window weight factor λ is calculated; further, suppose that at t i The weight factor of the reflection coefficient at time is β i , at t i+1 The weight factor of the reflection coefficient at time is β i+1 , then at t i ~t i+1 Sliding window weight factor λ between time periods i There is the following time-varying linear weighted relationship: Where, t i With t i+1 is the time position at a certain moment during round-trip travel, in ms; W and δ are the sliding window length and step size, in ms; β i With β i+1 is the weight factor of the even reflection coefficient at the corresponding time position; i∈(1,2,3......N), where N is a natural number.

7. A high-resolution seismic data processing device, characterized in that: include: An extraction module, used for extracting time-varying wavelets; A processing module, used to remove sub-waves and calculate odd and even reflection coefficients; The first building module is used to construct the high-frequency components of seismic data using a deconvolution method; and The second construction module is used to construct a broadband reflection coefficient volume, wherein a high-resolution seismic data volume is constructed by combining the obtained sliding window weight factor with the high-frequency component of the seismic data based on the extracted odd and even parts of the seismic reflection coefficient and the inverted high-frequency component of the seismic data.

8. The high-resolution seismic data processing device according to claim 1, characterized in that: The high-resolution processing device for seismic data includes an obtaining module; the obtaining module is used to obtain the sliding time window weight factor according to the post-stack processed seismic data and the well data; wherein the obtaining of the sliding time window weight factor according to the post-stack processed seismic data and the well data includes: The formation reflection coefficient is obtained based on the sonic logging data and density logging data. The reflection coefficient is calculated using the reflection coefficient formula. In the formula, R (t) is the reflection coefficient calculated from the logging data, ρ is the density, V is the velocity, Δt is the acoustic time difference, t is the time position of the current formation during the two-way travel, and t+Δt is the time position of the adjacent formation during the two-way travel at the next moment; Construct the sliding window weight factor λ.

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 described in any one of claims 1-6.

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

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