Earthquake characterization method and device for predicting liquidity of underground shale oil and application
By employing seismic characterization methods and utilizing the fluidity index at 70% of the low-frequency peak frequency, combined with post-stack seismic data and generalized S-transform, fluid fluidity attributes are extracted, solving the problem of large-scale prediction of shale oil reservoir fluidity and achieving accurate identification of oil and gas sweet spots.
Patent Information
- Application Number
- CN202410578053.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-05-10
- Publication Date
- 2025-11-11
AI Technical Summary
Existing technologies struggle to predict the liquidity of shale oil reservoirs over large spatial areas, and traditional methods are only suitable for small-scale evaluations.
Using seismic characterization methods, the fluidity at 70% of the low-frequency peak frequency is used as a measure of the overall fluidity of shale oil reservoirs. Generalized S-transform is performed on post-stack seismic data to extract fluid fluidity properties. Combined with the fitted sub-spectrum, the fluidity of underground shale oil is predicted.
It enables accurate prediction of shale oil reservoir fluidity over a wide area, identification of oil and gas sweet spots, and improves the spatial prediction capability of shale oil exploration.
Smart Images
Figure CN120928436A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of oil and gas geophysics, specifically to a seismic characterization method, apparatus, and application for predicting the flowability of underground shale oil. Background Technology
[0002] With the increasing demand for oil and gas energy and the continuous improvement of oil and gas exploration and development, unconventional shale oil resources have attracted more and more attention from various countries. Compared with conventional oil reservoirs, unconventional shale oil reservoirs are often characterized by low porosity and low permeability. Finding "sweet spots" with relatively high porosity and permeability and relatively good oil and gas flowability in unconventional reservoirs with low porosity and low permeability is one of the key issues for realizing the commercial development of unconventional reservoirs.
[0003] Fluidity is a parameter characterizing the ease with which oil and gas move through underground porous media. Many factors influence oil and gas mobility, including crude oil viscosity, rock porosity, rock permeability, fluid pressure, the oil, water, and gas content of the fluid, as well as ambient temperature and formation pressure. Generally, the lower the oil and gas viscosity, the higher the rock porosity, the higher the rock permeability, the higher the reservoir pressure, and the higher the gas content in the reservoir, the better the fluid mobility of the oil and gas reservoir. The fluid mobility in the reservoir is a comprehensive reflection of the above factors.
[0004] Currently, the evaluation of fluid flowability in shale oil reservoirs in the oil and gas industry mainly relies on laboratory measurements based on geological, rock characteristics, and fluid physical properties. This traditional evaluation method is only suitable for assessing the fluidity of reservoirs within a small spatial area near the core sampling location. How to predict shale oil fluidity over a large spatial area using seismic methods is a problem of great interest to the geophysical community. Summary of the Invention
[0005] This invention provides a seismic characterization method, apparatus, and application for predicting the flowability of underground shale oil, thereby solving the aforementioned technical problem that existing technologies cannot predict the flowability of shale oil over a large spatial area.
[0006] According to a first aspect of the present invention, a seismic characterization method for predicting the fluidity of underground shale oil is provided, wherein the fluidity at the frequency corresponding to 70% of the low-frequency peak frequency is used as a measure of the overall fluidity of the shale oil reservoir.
[0007] According to a second aspect of the present invention, a seismic characterization method for predicting the fluidity of underground shale oil is provided, comprising:
[0008] S1. Perform a generalized S-transform based on post-stack seismic data to obtain time-frequency domain results;
[0009] S2. Fitting the sub-spectrum;
[0010] S3. Based on the time-frequency domain results and the fitted sub-spectrum, extract the fluid flow properties as a measure of the overall flowability of the shale oil reservoir.
[0011] Preferably, in step S1, a generalized S-transform is performed on each trace of the post-stack seismic data to obtain the time-frequency domain results of the corresponding trace.
[0012] Preferably, in step S1, the generalized S-transform of formula (6) is used to decompose the seismic trace into time and frequency domains to obtain high-precision time and frequency domain results of the seismic trace.
[0013]
[0014] Where S(τ,f) is the time spectrum of the signal, h(t) is the seismic signal to be analyzed, f is the frequency, t is the time, τ is the time point of analysis, i is the imaginary unit, and the parameters λ and p together adjust the rate at which the length of the time window changes with the frequency.
[0015] Among them, based on the different signals, taking the energy concentration of the main seismic phase axis in the reservoir section and the ability to be clearly distinguished in the time and frequency domain as the standard, different combinations of λ and p values are selected to obtain high-precision seismic trace time-frequency decomposition results.
[0016] Preferably, in step S2, the sub-wavelengths at different time points are obtained by numerical fitting method on the time-frequency decomposition results of the seismic trace.
[0017] Preferably, the sub-spectrum is fitted using a multi-parameter formula (7).
[0018]
[0019] Where k and N are arbitrarily set constants, A is an undetermined parameter, and a n The polynomial coefficients with respect to frequency f are to be determined.
[0020] Specifically, using the seismic trace time spectrum obtained in step S1, the least squares spectral simulation method is employed to fit the wavelet spectrum amplitude, i.e., given k and N; the coefficients A and a are calculated. n and A and a n Substituting into formula (7) yields a smooth fitting curve |W(f)| of the earthquake record amplitude spectrum, which is the fitted wavelet amplitude spectrum.
[0021] Preferably, in step S3, fluid flow properties are extracted at the 70% peak frequency of the fitted sub-spectrum, wherein the flow property value at the 70% peak frequency of the sub-spectrum is extracted using formula (4).
[0022]
[0023] Where F is the fluid flow rate of the reservoir, R is the reflection coefficient of the fast P-wave, ω is the angular frequency of the seismic signal, and C is a function of the angular frequency ω.
[0024] Preferably, the seismic characterization method for predicting the fluidity of underground shale oil further includes:
[0025] S01. Input the post-stack seismic data.
[0026] According to a third aspect of the present invention, a flow property obtained based on the seismic characterization method for predicting the flowability of underground shale oil as described in any one of the preceding claims is provided to predict underground oil and gas sweet spots, wherein a large flow property value indicates an oil and gas sweet spot.
[0027] According to a fourth aspect of the present invention, a seismic characterization device for predicting the fluidity of underground shale oil is provided, comprising:
[0028] The transformation module is used to perform a generalized S-transform based on post-stack seismic data to obtain time-frequency domain results.
[0029] The fitting module is used to fit the sub-wavelength spectrum; and
[0030] The extraction module is used to extract fluid flow properties as a measure of the overall flowability of shale oil reservoirs based on the time-frequency domain results and the fitted sub-spectrum.
[0031] According to a fifth aspect of the present invention, an electronic device is provided, comprising:
[0032] Memory; and
[0033] processor;
[0034] 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.
[0035] According to a sixth 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.
[0036] The technical solution of this invention, based on fluid flow properties obtained from a seismic characterization method for predicting the fluidity of underground shale oil, can accurately predict shale oil sweet spots. In this invention, low-frequency seismic information is primarily used to characterize reservoir fluidity; this attribute value can effectively predict the location of actual underground shale oil sweet spots. Furthermore, this technical solution can predict shale oil fluidity over a large spatial area. Attached Figure Description
[0037] Figure 1This is a flowchart of a seismic characterization method for predicting the fluidity of underground shale oil in one embodiment;
[0038] Figure 2 This is a flowchart illustrating a seismic characterization method for predicting underground oil and gas sweet spots based on the prediction of underground shale oil flowability in one embodiment.
[0039] Figure 3 This is a schematic diagram of the structure of a seismic characterization device for predicting the flowability of underground shale oil in one embodiment;
[0040] Figure 4 This is a schematic diagram of a seismic profile of an actual shale oil reservoir in one embodiment.
[0041] Figure 5 This is a schematic diagram of the seismic trace (a) and its generalized S-transform result (b) in one embodiment;
[0042] Figure 6 This is a schematic diagram of the flow property profile of well 1 in one embodiment;
[0043] Figure 7 This is a schematic diagram of the flow property profile of well 2 in one embodiment. Detailed Implementation
[0044] 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.
[0045] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are merely some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.
[0046] 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.
[0047] 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.
[0048] Example 1
[0049] Addressing the issues mentioned in the background technology of unconventional shale oil exploration, this invention proposes a seismic characterization method for predicting the liquidity of underground shale oil reservoirs, specifically for predicting sweet spots. In this seismic characterization method, the liquidity at the frequency corresponding to 70% of the low-frequency peak frequency is used as the overall liquidity of the shale oil reservoir, serving as a measure of its overall liquidity.
[0050] Please refer to Figure 1 , 2 This embodiment provides a seismic characterization method for predicting the fluidity of underground shale oil, including the following steps:
[0051] S01. Input the post-stack seismic data.
[0052] S1. Perform a generalized S-transform based on post-stack seismic data to obtain time-frequency domain results.
[0053] S2. Fitting the sub-spectrum.
[0054] S3. Based on the time-frequency domain results and the fitted sub-spectrum, extract the fluid flow properties as the overall flow properties of the shale oil reservoir, that is, as a measure of the overall flowability of the shale oil reservoir.
[0055] In one embodiment, since fluid flow is a function of frequency, and considering the non-stationary nature of seismic data, time-frequency decomposition of the seismic traces is performed first. Preferably, in step S1, a generalized S-transform is performed on each trace based on the post-stack seismic data to obtain the time-frequency domain results of the corresponding traces. Further, the generalized S-transform of formula (6) is used to decompose the time-frequency of the seismic traces to obtain high-precision time-frequency domain results of the seismic traces.
[0056]
[0057] Where S(τ,f) is the time spectrum of the signal, h(t) is the seismic signal to be analyzed, f is the frequency, t is the time, τ is the time point of analysis, i is the imaginary unit, and the parameters λ and p together adjust the rate at which the length of the time window changes with the frequency.
[0058] In practical applications, different combinations of λ and p values can be selected based on the different signals, taking the concentration of energy in the main seismic phase axes of the reservoir section and the ability to be clearly distinguished in the time and frequency domain as the standard, to obtain high-precision seismic trace time-frequency decomposition results.
[0059] In one embodiment, the seismic trace spectrum exhibits significant jumps, which can lead to instability in the extraction of fluid flow properties. Therefore, simulating the wavelet amplitude spectrum can eliminate the influence of the formation reflection coefficient. Preferably, in step S2, the wavelet spectrum at different time points is obtained using a numerical fitting method on the time-frequency decomposition results of the seismic trace.
[0060] In one embodiment, the amplitude spectrum of the wavelet is typically smooth and exhibits a certain regularity. Based on this characteristic, to better represent the wavelet spectrum, it is preferable to use a multi-parameter formula (7) to fit the wavelet spectrum.
[0061]
[0062] Where k and N are arbitrarily set constants, A is an undetermined parameter, and a n The polynomial coefficients with respect to frequency f are to be determined.
[0063] Specifically, using the seismic trace time spectrum obtained in step S1, the least squares spectral simulation method is employed to fit the wavelet spectrum amplitude, i.e., given k and N; the coefficients A and a are calculated. n and A and a n Substituting into formula (7) yields a smooth fitting curve |W(f)| of the earthquake record amplitude spectrum, which is the fitted wavelet amplitude spectrum.
[0064] In step S3 of one embodiment, fluid flow properties are extracted at the 70% peak frequency of the fitted sub-spectrum. The flow property value at the 70% peak frequency of the sub-spectrum is extracted using formula (4).
[0065]
[0066] Where F is the fluid flow rate of the reservoir, R is the reflection coefficient of the fast P-wave, ω is the angular frequency of the seismic signal, and C is a function of the angular frequency ω.
[0067] In one embodiment, since the fluid flowability is better and the flow attribute value is larger at oil and gas sweet spots, areas with high flow attribute values often indicate oil and gas sweet spots. Therefore, this embodiment can predict underground oil and gas sweet spots based on the flow attribute obtained by any of the above-described seismic characterization methods for predicting the flowability of underground shale oil, wherein a large flow attribute value indicates an oil and gas sweet spot.
[0068] This invention proposes a seismic characterization method for predicting the fluidity of underground shale oil. This method mainly uses low-frequency seismic information to characterize reservoir fluidity, and this attribute value can predict the location of actual underground shale oil sweet spots relatively well.
[0069] Example 2
[0070] Seismic waves exhibit significant dispersion in oil and gas reservoirs, and the degree of velocity dispersion is closely related to reservoir permeability and fluid viscosity. Due to this velocity dispersion, the seismic reflection coefficient at the interface between fluid-bearing and fluid-free reservoirs is no longer constant but varies with the seismic wave frequency.
[0071] Batzle (2006) defined reservoir fluidity in laboratory rock physics measurements as follows:
[0072]
[0073] Where F represents the fluid flowability of the reservoir, κ represents the rock permeability, and η represents the fluid viscosity. From the definition of flowability, it can be seen that reservoir flowability is directly proportional to rock permeability and inversely proportional to fluid viscosity. Generally, the greater the rock porosity, the more fractures, the simpler the pore space, the higher the pore pressure, the higher the rock permeability, and the better the reservoir fluid flowability. Higher temperatures, higher gas and water content, and lower fluid viscosity, especially for pore fluids, result in higher flowability. Reservoir fluid flowability is the result of the combined effects of rock and pore fluid.
[0074] Silin et al. (2004), from a hydrological perspective, derived an expression for the reflection coefficient at the interface between a fluid-porous medium and an elastic medium, and obtained a low-frequency asymptotic solution, which is expressed as follows:
[0075]
[0076] Where R is the reflection coefficient of the fast longitudinal wave, i is the imaginary unit, R0 and R1 are the real coefficients of the rock and fluid characteristic functions, and ρ f ω is the density of the fluid, and ω is the angular frequency of the seismic signal. When At that time, the fast P-wave reflection coefficient is the same as the seismic reflection coefficient in general seismic exploration.
[0077] Taking the derivative of both sides of formula (2) with respect to ω, we can obtain
[0078]
[0079] Substituting the definition of fluid flow in the reservoir (1) into the above equation and rearranging it, we get...
[0080]
[0081] Where C is a function of angular frequency ω, and its form is as follows:
[0082]
[0083] Equation (5) is the seismic expression of fluid flowability. It is a function of the frequency gradient of the reflection coefficient. Furthermore, fluid flowability is also a function of frequency, and the flowability varies at different frequencies. Based on numerous test results, the flowability at the frequency corresponding to 70% of the low-frequency peak frequency can often well indicate the location of the sweet spot development in actual oil and gas reservoirs. The flowability at 70% of the low-frequency peak frequency will be used as the overall flowability of the reservoir in the following sections.
[0084] This invention proposes a seismic characterization method for predicting the fluidity of underground shale oil based on formula (4). This method is a seismic characterization technique for the fluid flow properties of shale oil reservoirs and is applied to the prediction of shale oil sweet spots. The specific steps are as follows: Figure 1 , 2 As shown:
[0085] 1. The input post-stack seismic data is subjected to a generalized S-transform on each trace to obtain the time-frequency domain results of the corresponding trace.
[0086] Since fluid flow is a function of frequency, and considering the non-stationary nature of seismic data, time-frequency decomposition of the seismic traces is necessary first. This invention uses the generalized S-transform of the following formula (6) to obtain high-precision time-frequency domain results for the seismic traces.
[0087]
[0088] Where S(τ,f) is the time spectrum of the signal, h(t) is the seismic signal to be analyzed, f is the frequency, t is the time, and τ is the time point of analysis. Parameters λ and p jointly adjust the rate at which the length of the time window changes with frequency. In practical applications, depending on the different signals, the standard is that the main seismic phase axes of the reservoir section are concentrated and can be clearly distinguished in the time and frequency domain. Different combinations of λ and p values are selected to obtain high-precision seismic trace time-frequency decomposition results.
[0089] 2. Numerical fitting was used to obtain the fitted sub-spectrums at different time points in the time-frequency decomposition results of the seismic traces.
[0090] In practical applications, many flow property extractions are performed directly on time-frequency decomposition data from seismic traces. However, because the subsurface reflection coefficient is not random white noise and the source wavelet varies with depth, the seismic trace spectrum exhibits significant jumps, leading to instability in the extraction of fluid flow properties. Therefore, simulating the wavelet amplitude spectrum can eliminate the influence of the formation reflection coefficient.
[0091] Previous studies have found that the amplitude spectrum of a wavelet is usually smooth and exhibits certain regularities. Rose pointed out that as long as the spectrum of a seismic wavelet is smooth, it can be fitted using a certain mathematical approach. To better represent the wavelet spectrum, this invention proposes the following multi-parameter formula for fitting:
[0092]
[0093] Where k and N are arbitrarily set constants, A is an undetermined parameter, and a n Let f be the polynomial coefficients to be determined with respect to frequency f. Using the seismic trace time spectrum obtained in step 1, the least squares spectral simulation method is used to fit the wavelet spectrum amplitude, i.e., given k and N, the coefficients A and a are calculated. n Put A and a n Substituting into formula (7), we obtain a smooth fitting curve |W(f)| of the earthquake record amplitude spectrum, which is the fitted wavelet amplitude spectrum.
[0094] 3. Extract fluid flow properties at the 70% peak frequency of the fitted sub-spectrum.
[0095] Based on the aforementioned high-precision time-frequency decomposition and wavelet fitting, the flow attribute value at the 70% peak frequency of the low frequency is extracted using formula (4), and this value is used as the overall flow attribute value, which can be understood as a measure of overall flow.
[0096] 4. Predict underground oil and gas sweet spots based on the obtained fluid flow properties.
[0097] Furthermore, the fluid flow properties obtained from the seismic characterization method for predicting underground shale oil flowability are used to predict underground oil and gas sweet spots. Since fluid flowability is better and flow property values are higher at oil and gas sweet spots, areas with high flow property values often indicate oil and gas sweet spots.
[0098] This invention proposes a seismic characterization method for predicting the fluidity of underground shale oil. This method primarily utilizes low-frequency seismic information to characterize reservoir fluidity, and this attribute value can effectively predict the location of actual underground shale oil sweet spots. Steps 1-3 described above constitute the seismic characterization method for predicting underground shale oil fluidity. Based on this, combined with step 4, a shale oil sweet spot detection method based on underground shale oil fluidity (or a shale oil sweet spot detection method based on (fluid) flow properties) can be formed.
[0099] Example 3
[0100] Please refer to Figure 3 One embodiment provides a seismic characterization device for predicting the fluidity of underground shale oil, in the following form:
[0101] 1. Input module
[0102] Input module 01 is used to input post-stack seismic data.
[0103] 2. Transformation Module
[0104] Transformation module 1 is used to perform a generalized S-transform based on the post-stack seismic data to obtain time-frequency domain results.
[0105] 3. Fitting Module
[0106] Fitting module 2 is used to fit the sub-spectrum.
[0107] 4. Extraction Module
[0108] Extraction module 3 is used to extract fluid flow properties as a measure of the overall flowability of shale oil reservoirs based on the time-frequency domain results and the fitted sub-spectrum.
[0109] One embodiment provides a shale oil sweet spot detection device based on the fluidity of underground shale oil, including the aforementioned seismic characterization device for predicting underground shale oil fluidity, and further including a prediction module 4. The prediction module 4 is used to predict underground oil and gas sweet spots based on the fluidity properties obtained from the seismic characterization device for predicting underground shale oil fluidity, wherein a large flow property value indicates an oil and gas sweet spot. Because the fluid fluidity is relatively good and the flow property value is relatively large at oil and gas sweet spots, areas with high flow property values often indicate oil and gas sweet spots.
[0110] It should be noted that the above-mentioned seismic characterization device for predicting the flowability of underground shale oil is used to implement the seismic characterization method for predicting the flowability of underground shale oil in the above embodiments, and each module in the device corresponds to each step in the method.
[0111] Example 4
[0112] 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.
[0113] Example 5
[0114] 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.
[0115] One or more of the aforementioned computer instructions can form a program.
[0116] 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.
[0117] 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.
[0118] Example 6
[0119] Please refer to Figure 4-7 In this embodiment:
[0120] Figure 4 The following is a post-stack seismic profile of a real shale oil field.
[0121] Figure 5 The results of time-frequency analysis of arbitrary seismic trace data on post-stack earthquakes (a) and their generalized S-transform (b) are shown. It can be seen that when the parameters are selected appropriately, the generalized S-transform has good resolution in the time-frequency domain.
[0122] Figure 6 The profile results of the fluid flow properties of Well 1 are shown. It can be seen that the high fluidity property value area corresponds well with the oil-bearing section of Well 1 (boxed area).
[0123] Figure 7The profile results of the fluid flow properties of Well 2 are shown. It can be seen that the high fluidity property area corresponds well with the oil-bearing section of the actual Well 2 (boxed area).
[0124] in, Figure 6 , Figure 7 The results indicate that fluidity properties can be used to predict the sweet spot of underground oil and gas reservoirs.
[0125] 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 seismic characterization method for predicting the fluidity of underground shale oil, characterized in that, The fluidity at the frequency corresponding to 70% of the peak frequency at the low-frequency end is used as a measure of the overall fluidity of shale oil reservoirs.
2. A seismic characterization method for predicting the fluidity of underground shale oil, characterized in that, include: S1. Perform a generalized S-transform based on post-stack seismic data to obtain time-frequency domain results; S2. Fitting the sub-spectrum; S3. Based on the time-frequency domain results and the fitted sub-spectrum, extract the fluid flow properties as a measure of the overall flowability of the shale oil reservoir.
3. The seismic characterization method for predicting the fluidity of underground shale oil according to claim 2, characterized in that, In step S1, a generalized S-transform is performed on each trace of the post-stack seismic data to obtain the time-frequency domain results of the corresponding trace.
4. The seismic characterization method for predicting the fluidity of underground shale oil according to claim 3, characterized in that, In step S1, the generalized S-transform of formula (6) is used to decompose the seismic trace into time and frequency domains to obtain high-precision time and frequency domain results of the seismic trace. Where S(τ,f) is the time spectrum of the signal, h(t) is the seismic signal to be analyzed, f is the frequency, t is the time, τ is the time point of analysis, i is the imaginary unit, and the parameters λ and p together adjust the rate at which the length of the time window changes with the frequency. Among them, based on the different signals, taking the energy concentration of the main seismic phase axis in the reservoir section and the ability to be clearly distinguished in the time and frequency domain as the standard, different combinations of λ and p values are selected to obtain high-precision seismic trace time-frequency decomposition results.
5. The seismic characterization method for predicting the fluidity of underground shale oil according to claim 2, characterized in that, In step S2, the sub-wavelengths at different time points are obtained by numerical fitting method on the time-frequency decomposition results of the seismic trace.
6. The seismic characterization method for predicting the fluidity of underground shale oil according to claim 5, characterized in that, The sub-wavelength spectrum was fitted using the multi-parameter formula (7). Where k and N are arbitrarily set constants, A is an undetermined parameter, and a n The polynomial coefficients with respect to frequency f are to be determined. Specifically, using the seismic trace time spectrum obtained in step S1, the least squares spectral simulation method is employed to fit the wavelet spectrum amplitude, i.e., given k and N; the coefficients A and a are calculated. n and A and a n Substituting into formula (7) yields a smooth fitting curve |W(f)| of the earthquake record amplitude spectrum, which is the fitted wavelet amplitude spectrum.
7. The seismic characterization method for predicting the fluidity of underground shale oil according to claim 2, characterized in that, In step S3, fluid flow properties are extracted at the 70% peak frequency of the fitted sub-spectrum. Specifically, the flow property value at the 70% peak frequency of the sub-spectrum is extracted using formula (4). Where F is the fluid flow rate of the reservoir, R is the reflection coefficient of the fast P-wave, ω is the angular frequency of the seismic signal, and C is a function of the angular frequency ω.
8. The seismic characterization method for predicting the fluidity of underground shale oil according to claim 2, characterized in that, The seismic characterization method for predicting the fluidity of underground shale oil also includes: S01. Input the post-stack seismic data.
9. Based on the flow properties obtained by the seismic characterization method for predicting the flowability of underground shale oil according to any one of claims 1 or 2-8, a sweet spot for underground oil and gas is predicted, wherein, A high flow property value indicates an oily or gaseous dessert.
10. A seismic characterization device for predicting the flowability of underground shale oil, characterized in that, include: The transformation module is used to perform a generalized S-transform based on post-stack seismic data to obtain time-frequency domain results. The fitting module is used to fit the sub-spectrum; and The extraction module is used to extract fluid flow properties as a measure of the overall flowability of shale oil reservoirs based on the time-frequency domain results and the fitted sub-spectrum.
11. The seismic characterization device for predicting the fluidity of underground shale oil according to claim 10, characterized in that, The seismic characterization device for predicting underground shale oil flow also includes: The input module is used to input the post-stack seismic data.
12. 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 or 2-8.
13. 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 claim 1 or any one of claims 2-8.