Pre-stack oil gas detection method and system, electronic equipment and storage medium

Abstract

According to the pre-stack oil and gas detection method and system provided by the embodiment of the invention, the comprehensive fluid detection factor is calculated from the two parameters of the centroid frequency and the bandwidth coefficient and is used for detecting the oil-gas-bearing sandstone, frequency spectrum information can be more fully utilized, the prediction and detection capability of the oil-gas-bearing sandstone is further improved, and the detection efficiency of the oil-gas-bearing sandstone is improved. And the multiplicity and uncertainty of oil-gas-containing sandstone prediction in oil exploration and development are effectively reduced. According to the technology, a good effect is achieved in fluid identification and prediction of a west non-sea-area delta sandstone reservoir, and oil-gas-containing sandstone can be obviously highlighted on a comprehensive oil-gas detection factor data body; the method has a powerful supporting effect on improving the success rate, optimizing the deployment of the well position and reducing the drilling risk of the offshore oil and gas field when the development well and the new block exploratory well are deployed in the next step.

Description

Technical Field

[0001] The present application relates to the technical field of oil and gas reservoir prediction, and in particular to a pre-stack oil and gas detection method, system, electronic equipment and storage medium. Background Art

[0002] Reservoir fluid property detection is one of the key elements in oil and gas field exploration and development. Oil and gas detection based on spectrum difference has been widely used since the 1990s. In the frequency domain, the high-frequency energy absorption of seismic waves by oil and gas-bearing sandstones and the effect of increased low-frequency energy proportion caused by high-frequency energy absorption are typical signs for detecting and predicting oil and gas-bearing sandstones.

[0003] At present, there are mainly the following methods for estimating frequency absorption attenuation:

[0004] 1. Detecting oil and gas through the low-frequency shadows left after the high frequencies caused by oil and gas-bearing rocks are absorbed. Such as Castagna et al. (Instantaneous spectral analysis: detection of low-frequency shadows associated with hydrocarbons: The Leading Edge, 2003, VOL. 22 (2), 120-127), Chen Xuehua et al. (Numerical simulation and detection of low-frequency shadows, Petroleum Geophysical Exploration, 2009, VOL. 44, NO. 3, 298-303); This type of method calculates the attenuation of high-frequency energy of seismic data through time-frequency analysis, on the premise that the oil and gas-bearing sand body has a significant absorption or low-pass filtering effect on the high-frequency energy, resulting in the characteristics of strong low-frequency energy and significantly weakened high-frequency energy.

[0005] Second, using the spectrum ratio method, that is, predicting river channel sand bodies and oil and gas reservoirs by using the ratio of amplitude spectra, such as Zhao Limin et al. (Prediction of river channel sand bodies by using seismic spectrum ratio, China Petroleum Exploration, 2003, Vol. 8 (4): 60-62).

[0006] Third, the spectrum area difference method is used to calculate the change in the spectrum area reduction of the high-frequency part and the increase in the spectrum area of ​​the low-frequency part due to frequency attenuation to identify oil and gas. For example, Diao Rui et al. (Application of absorption attenuation analysis technology based on generalized S transform in oil and gas identification, Petroleum Geophysical Exploration, 2011, Vol. 50(3), 260-265).

[0007] Fourth, by calculating the attenuation gradient value of the high-frequency energy, the fluid properties of the reservoir can be distinguished based on this value. For example, Diao Rui et al. (Application of absorption attenuation analysis technology based on generalized S transform in oil and gas identification, Petroleum Geophysical Exploration, 2011, Vol. 50 (3), 260-265).

[0008] In terms of patent literature, there are some relevant patents and research results, such as Tian Renfei et al., Oil and Gas Detection Method and Device (CN109991661A), which provides an oil and gas detection method and device, by calculating the time-frequency energy difference between the near-offset signal and the far-offset signal in each seismic trace signal of the pre-stack seismic data, and detecting the oil and gas content of the corresponding target geological layer based on the time-frequency energy difference, highlighting the weak changes in the seismic signal caused by the oil and gas reservoir, and being able to more accurately identify the oil and gas content of the reservoir. The invention mainly calculates the time-frequency energy difference between the near-offset and far-offset signals, does not convert the offset gathers into angle gathers, and simply uses the time-frequency energy difference as the basis for oil and gas detection, without further calculating the relative attenuation relationship of different frequencies and angles and the different fluid properties represented by the attenuation law. For example, Qin Guangsheng et al., a method for oil and gas detection using post-stack seismic data (CN104360382A), the patent divides the high and low frequency parts of the spectrum by weighted average frequency, obtains the energy of the low frequency part by the inverse frequency weighting method, obtains the energy coefficient by the ratio of the energy of the low frequency part of the spectrum to the energy of the high frequency part, and constructs the seismic wave attenuation factor by the energy coefficient and the weighted frequency to indicate the possibility of oil and gas in the formation. The invention has good feasibility in distinguishing oil and gas-bearing strata using the morphology of the spectrum. The disadvantage is that the morphology of the spectrum is relatively diverse, and simply dividing the low frequency energy and high frequency energy may have strong multi-solutions. In addition, when there is more noise in the pure wave data, the energy of the high frequency part will be relatively more, which will affect the calculation results. For example, Zhang Gulan et al., a method for oil and gas detection using the instantaneous frequency attribute of seismic data (CN101923176A), the patent provides a method for detecting oil and gas by calculating the attenuation gradient of the low frequency band, that is, calculating the gradient of the fitting straight line between the values ​​of the frequencies corresponding to 15% and 35% of the total energy of the time spectrum on the time spectrum. This invention focuses on analyzing the favorable oil and gas-bearing areas corresponding to locations with strong absorption attenuation gradients in the low-frequency band. It has good targeting and noise resistance, but does not fully utilize spectral information, especially high-frequency information. In particular, when the spectrum is non-Gaussian or near-Gaussian distribution, inaccurate calculations may occur.

[0009] In terms of journal literature, there are some relevant patents and research results, such as Meng Meichen et al. (Science, Technology and Engineering, 2016, 16 (7): 139-145.), which applies the frequency attenuation gradient attribute to the gas content detection of coal seams in the area according to the characteristics of coal-bearing strata coal-bed methane enrichment. This paper uses the time-frequency method of decomposing wavelet spectrum to effectively extract the main frequency of seismic signals; it analyzes that when seismic waves propagate in coal seams, there will be energy absorption and high-frequency attenuation, and the frequency attenuation gradient attribute can effectively indicate the gas content of coal seams. However, this method only uses the degree of pre-stack frequency attenuation of far, medium and near traces, that is, the overall energy loss of the high-frequency part, without considering the low-frequency energy loss or increase, and only targets gas layers. The analysis method is effective but relatively simple. Qin Xilin (China University of Petroleum, doctoral dissertation, 2019), according to which the frequency attenuation gradient is a quantitative characteristic of the energy attenuation intensity of the high frequency of seismic data. It is believed that in porous medium reservoirs (sandstone reservoirs), high energy attenuation usually indicates oil and gas development areas; in fracture medium reservoirs (carbonate reservoirs), high energy attenuation usually indicates fracture-cavity development areas. The frequency attenuation gradient is obtained by calculating the rate of change of the amplitude spectrum in the high-frequency range with frequency based on time-frequency analysis. This paper examines the attenuation degree of the amplitude spectrum at high frequencies, uses two typical high-frequency energy attenuation gradients to represent the attenuation frequency speed, and predicts the reservoir development area based on the different attenuation speeds caused by different reservoirs to high-frequency energy. This method considers the changes in low-frequency energy and does not consider the differences between the spectra of different incident angle gathers, so there is a certain uncertainty.

[0010] In summary, the patent and journal literature survey shows that the use of spectral information to detect or identify oil and gas sand bodies has good practical significance, but there is still a lot of room for improvement and perfection of methods and technologies, such as the calculation of attenuation gradients when the spectral morphology is irregular, the resolution of the spectrum, the selection of calculation time windows, the use of not only post-stack data but also pre-stack data, and how to make more full use of spectral information, etc. Summary of the invention

[0011] In view of this, the present application provides a pre-stack oil and gas detection method, system, electronic device and storage medium, so as to solve the problems existing in the prior art in seismic exploration and development, such as the difficulty in detecting oil and gas-bearing sandstones, strong multi-solution, and the unclear response, strong concealment and difficulty in identification of oil and gas-bearing sandstones in seismic data. The method comprehensively predicts the oil and gas content of the reservoir by utilizing the centroid frequency differences and bandwidth coefficient differences shown in pre-stack seismic data of oil and gas reservoirs and non-oil and gas layers at different incident angles.

[0012] In a first aspect, an embodiment of the present application provides a pre-stack oil and gas detection method, the method comprising the following operations:

[0013] Superimpose the data of different ranges of incident angles and obtain the time-frequency spectrum of the seismic traces in time series through data processing;

[0014] Calculate the centroid frequency f for the instantaneous spectrum S(f) at each time sampling point t s , and obtain the centroid frequency f that varies with time s (t), by calculating the ratio of the centroid frequency obtained by stacking the data of different ranges of incidence angle gathers, the centroid frequency attenuation rate D is obtained f (t);

[0015] Calculate the bandwidth coefficient σ for the instantaneous spectrum S(f) s The bandwidth coefficient attenuation rate D is obtained by calculating the bandwidth coefficient by stacking the data of different ranges of incident angle gathers. σ (t);

[0016] Using the centroid frequency decay rate D f (t) and bandwidth coefficient attenuation rate D σ (t) Construction of comprehensive fluid detection factor H d , respectively using the centroid frequency attenuation rate D f (t), bandwidth coefficient attenuation rate D σ (t) and the comprehensive fluid detection factor H d Detect oil and gas content and make comprehensive predictions of oil and gas.

[0017] Preferably, the different ranges of incident angle gather data are far-angle and near-angle gather data.

[0018] Preferably, the centroid frequency f s The calculation of is as follows:

[0019]

[0020] Preferably, the centroid frequency attenuation rate D f (t) is calculated as follows:

[0021]

[0022] Among them, f sf (t), f sn (t) are the centroid frequencies calculated from the superposition data of the gathers with different ranges of incident angles.

[0023] Preferably, the bandwidth coefficient σ s The calculation of is as follows:

[0024] Assume the decibel threshold coefficient of the spectrum is b, and the calculation formula is:

[0025]

[0026] Among them, Smax is the largest value in S(f), and S(f) is set according to the decibel threshold value:

[0027]

[0028] The bandwidth factor is calculated by the following formula:

[0029]

[0030] Preferably, the bandwidth coefficient attenuation rate D σ (t) is calculated as follows:

[0031]

[0032] Among them, σ sf (t),σ sn (t) is the bandwidth coefficient calculated from the superposition data of the gathers with different ranges of incident angles.

[0033] Preferably, the comprehensive fluid detection factor H d The calculation of is as follows:

[0034] H d =D σ D f .

[0035] In a second aspect, an embodiment of the present application further provides a pre-stack oil and gas detection system, the system comprising:

[0036] The gather data stacking module is used to stack gather data with different ranges of incident angles and obtain the seismic trace time-frequency spectrum of the time series through data processing;

[0037] The centroid frequency attenuation rate calculation module is used to calculate the centroid frequency f for the instantaneous spectrum S(f) at each time sampling point t s , and obtain the centroid frequency f that varies with time s (t), the centroid frequency attenuation rate D is obtained by calculating the centroid frequency by stacking data from different ranges of incident angle gathers f (t);

[0038] The bandwidth coefficient attenuation rate calculation module is used to calculate the bandwidth coefficient σ for the instantaneous spectrum S(f) s The bandwidth coefficient attenuation rate D is obtained by calculating the bandwidth coefficient by stacking the data of different ranges of incident angle gathers. σ (t);

[0039] Comprehensive fluid detection factor calculation module, used to use the centroid frequency attenuation rate D f (t) and bandwidth coefficient attenuation rate D σ (t) Construction of comprehensive fluid detection factor H d, respectively using the centroid frequency attenuation rate D f (t), bandwidth coefficient attenuation rate D σ (t) and the comprehensive fluid detection factor H d Detect oil and gas content and make comprehensive predictions of oil and gas.

[0040] In a third aspect, an embodiment of the present application further provides an electronic device, including:

[0041] processor;

[0042] Memory;

[0043] And a computer program, wherein the computer program is stored in the memory, and the computer program includes instructions, which, when executed by the processor, enable the electronic device to perform the pre-stack oil and gas detection method.

[0044] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the pre-stack oil and gas detection method.

[0045] In the embodiment of the present application, by calculating the comprehensive fluid detection factor from the two parameters of the centroid frequency and the bandwidth coefficient, the detection of oil and gas sandstone can make fuller use of the spectrum information, further improve the prediction and detection capabilities of oil and gas sandstone, and effectively reduce the multi-solution and uncertainty of the prediction of oil and gas sandstone in oil exploration and development. This technology has achieved good results in the fluid identification and prediction of delta sandstone reservoirs in the West African waters. The oil and gas sandstone can be clearly highlighted on the comprehensive oil and gas detection factor data body, which has a strong supporting role in improving the success rate and optimizing the deployment of wells and reducing the drilling risks of offshore oil and gas fields when deploying development wells and exploration wells in new blocks in the next step. The method of this application can more accurately identify and predict oil and gas formations. The method is based on the multi-angle analysis of the spectrum, and in order to avoid the "masking" effect of post-stack data on spectrum changes, the pre-stack angle channel superposition data is used for analysis, which further improves the accuracy and effectiveness of the method. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0047] Figure 1 A flow chart of a pre-stack oil and gas detection method provided in an embodiment of the present application;

[0048] Figure 2 The time-frequency spectrum diagram of the stacked data of the near- and far-angle gathers provided by the embodiment of the present application, with the near-angle on the left and the far-angle on the right;

[0049] Figure 3 A schematic diagram of the frequency attenuation rate, bandwidth coefficient attenuation rate and comprehensive fluid detection factor change index provided in the embodiment of the present application;

[0050] Figure 4 The root mean square amplitude attribute map along the target layer provided in the embodiment of the present application;

[0051] Figure 5 A schematic diagram of comprehensive fluid detection factors extracted along the target layer provided in an embodiment of the present application;

[0052] Figure 6 A block diagram of a pre-stack oil and gas detection system provided in an embodiment of the present application;

[0053] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to better understand the technical solution of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0055] It should be clear that the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0056] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0057] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0058] like Figure 1 As shown, the embodiment of the present invention discloses a pre-stack oil and gas detection method, including the following operations:

[0059] S1, superimpose the data of different ranges of incident angles and obtain the time-frequency spectrum of the seismic traces of the time series through processing;

[0060] The pre-stack pure wave gather data is converted to obtain the incident angle gather data, and angle gathers of different ranges are selected for superposition to obtain incident angle superposition data of different ranges. For efficiency and calculation effect, the embodiment of the present invention only calculates two ranges of incident angles (near and far angles) for superposition, and the selection of the angle range is determined according to the conditions and quality of the pre-stack data. Under normal circumstances, gather data of two ranges of 5-15 and 20-35 degrees can be selected for superposition. If the superposition angle range is too small, it is easy to cause a decrease in the signal-to-noise ratio. If the range is too large, the difference between the near and far angle superposition data is not obvious.

[0061] The generalized S transform is performed on the gather data of the two ranges, as follows:

[0062]

[0063] Among them, λ and p are time window scale adjustment coefficients. When λ=1, p=1, the above formula is a standard S transform.

[0064] The time-frequency spectrum of a seismic record trace is obtained by using the generalized S transform: S(t, f).

[0065] S2, calculate the centroid frequency of the instantaneous spectrum at each time sampling point, obtain the centroid frequency that changes with time, and obtain the frequency attenuation rate by calculating the centroid frequency through superposition of data of different ranges of incident angle gathers;

[0066] For the instantaneous spectrum S(f) at a certain time sampling point t, the centroid frequency of the spectrum can be calculated according to the centroid shift method, as shown in the following formula:

[0067]

[0068] For each instantaneous spectrum, the centroid frequency is calculated according to the above formula to obtain the centroid frequency f that changes with time. s (t), the centroid frequencies calculated for the stacked data of the far-angle and near-angle gathers are denoted as f sf (t) and f sn (t), the ratio of the two is recorded as the frequency attenuation rate D f (t):

[0069]

[0070] S3, calculating the bandwidth coefficient for the instantaneous spectrum, and obtaining the bandwidth coefficient attenuation rate by calculating the bandwidth coefficient through superposition of data of different ranges of incident angle gathers;

[0071] Use the correlation algorithm to calculate the bandwidth coefficient, assuming the decibel threshold coefficient of the spectrum is b, and the calculation formula is:

[0072]

[0073] Among them, S max is the largest value in S(f), and S(f) is set according to the decibel threshold value:

[0074]

[0075] The bandwidth factor is calculated by the following formula:

[0076]

[0077] The bandwidth coefficients calculated for the stacked data of far-angle and near-angle gathers are denoted as σ sf (t) and σ sn (t), the ratio of the two is recorded as the bandwidth coefficient attenuation rate:

[0078]

[0079] S4. Using the centroid frequency attenuation rate and the bandwidth coefficient attenuation rate to construct a comprehensive fluid detection factor, respectively using the centroid frequency attenuation rate, the bandwidth coefficient attenuation rate and the comprehensive fluid detection factor to detect oil and gas content, and comprehensively predict oil and gas;

[0080] The comprehensive fluid detection factor is constructed using the centroid frequency and bandwidth coefficient:

[0081] H d =D σ D f

[0082] At this time, through three variables, the frequency attenuation rate D f , bandwidth coefficient attenuation rate D σ And the comprehensive fluid detection factor H d By testing the oil and gas content separately, the most sensitive one can be selected as the priority result.

[0083] A wellbore pre-stack seismic trace of a well that actually encountered multiple oil and gas reservoirs was selected for testing. The generalized S transform was first used to calculate the time-frequency spectrum of the wellbore far-angle and near-angle gather stack data, such as Figure 2 As shown in the figure, the position marked by the dotted line is the position of the oil-bearing sandstone reservoir. It can be seen that due to the absorption of oil and gas sandstone, the main energy distribution of the time-frequency spectrum moves from high frequency to low frequency, and the bandwidth also changes accordingly. However, since it is not intuitive to observe from the time-frequency spectrum and the signal-to-noise ratio is low, the time-frequency spectrum cannot effectively describe the main frequency and bandwidth, so the prediction result cannot be used.

[0084] Figure 3The three far- and near-channel spectrum change indicators calculated by the time-spectrum frequency are frequency attenuation rate, bandwidth coefficient attenuation rate and comprehensive fluid detection factor. The three variables can respond to oil and gas sandstones to a certain extent. From shallow to deep, they correspond to three oil-bearing sandstones (about 700ms, 1220ms, and 1750ms), corresponding to strata below 2000ms.

[0085] In the embodiment of the present invention, a Tertiary delta sandstone reservoir G layer under development in the West African offshore block is selected for prediction. The reservoir is buried at a depth of about 950-1050 meters, and the time corresponds to about 1130-1230ms. Figure 4 is the root mean square amplitude attribute extracted along the target layer. This attribute cannot be consistent with the actual drilling results. G-5 and G-12 are both industrial oil wells, but there is no response on this map. The strong reflection in the northern part of the oil field is located in the low part of the structure. Well G-2 saw water after a short production cycle, indicating that the strong reflection in the low part of the structure is more likely to be the reflection of water-bearing sandstone.

[0086] Figure 5 It is the comprehensive fluid detection factor attribute extracted along the target layer. The factor has a high degree of consistency with the actual drilling. The coupling degree with the fault is very high in the high part of the structure, which shows that the fault has a good sealing effect on oil and gas. The oil and gas-bearing sandstone predicted by this factor can well guide the deployment of development wells and evaluation wells, especially in the north of G-5 well and the south of G-4 well, where there are still good areas for deploying drilling. This result is difficult to achieve through amplitude attributes and post-stack data.

[0087] The embodiment of the present invention calculates the comprehensive fluid detection factor from the two parameters of centroid frequency and bandwidth coefficient for the detection of oil and gas sandstone, which can make fuller use of spectrum information, further improve the prediction and detection capabilities of oil and gas sandstone, and effectively reduce the multi-solution and uncertainty of oil and gas sandstone prediction in oil exploration and development. This technology has achieved good results in fluid identification and prediction of delta sandstone reservoirs in the West African waters. Oil and gas sandstone can be clearly highlighted in the comprehensive oil and gas detection factor data body, which has a strong supporting role in improving the success rate and optimizing the deployment of wells and exploration wells in new blocks in the next step, and reducing the drilling risks of offshore oil and gas fields.

[0088] Corresponding to the above-mentioned embodiment, the present application also provides a pre-stack oil and gas detection system.

[0089] See also Figure 6 , which is a structural block diagram of a pre-stack oil and gas detection system provided in this application. Figure 6 As shown, it mainly includes the following modules:

[0090] The gather data stacking module is used to stack gather data with different ranges of incident angles and obtain the seismic trace time-frequency spectrum of the time series through data processing;

[0091] The centroid frequency attenuation rate calculation module is used to calculate the centroid frequency f for the instantaneous spectrum S(f) at each time sampling point t s , and obtain the centroid frequency f that varies with time s (t), the centroid frequency attenuation rate D is obtained by calculating the centroid frequency by stacking data from different ranges of incident angle gathers f (t);

[0092] The bandwidth coefficient attenuation rate calculation module is used to calculate the bandwidth coefficient σ for the instantaneous spectrum S(f) s The bandwidth coefficient attenuation rate D is obtained by calculating the bandwidth coefficient by stacking the data of different ranges of incident angle gathers. σ (t);

[0093] Comprehensive fluid detection factor calculation module, used to use the centroid frequency attenuation rate D f (t) and bandwidth coefficient attenuation rate D σ (t) Construction of comprehensive fluid detection factor H d , respectively using the centroid frequency attenuation rate D f (t), bandwidth coefficient attenuation rate D σ (t) and the comprehensive fluid detection factor H d Detect oil and gas content and make comprehensive predictions of oil and gas.

[0094] It should be pointed out that the specific contents involved in the embodiments of the present application can be found in the description of the above method embodiments, and for the sake of brevity, they will not be repeated here.

[0095] Corresponding to the above embodiment, an embodiment of the present application further provides an electronic device.

[0096] See also Figure 7 , is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 7 As shown, the electronic device 700 may include: a processor 701, a memory 702 and a communication unit 703. These components communicate via one or more buses. Those skilled in the art may understand that the electronic device structure shown in the figure does not constitute a limitation on the embodiments of the present application. It may be a bus structure or a star structure, and may include more or fewer components than shown in the figure, or combine certain components, or arrange components differently.

[0097] The communication unit 703 is used to establish a communication channel so that the electronic device can communicate with other devices.

[0098] The processor 701 is the control center of the electronic device. It uses various interfaces and lines to connect various parts of the entire electronic device. It runs or executes software programs and / or modules stored in the memory 702, and calls data stored in the memory to perform various functions of the electronic device and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 701 can include only a central processing unit (CPU). In the embodiment of the present application, the CPU can be a single computing core or multiple computing cores.

[0099] The memory 702 is used to store the execution instructions of the processor 701. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0100] When the execution instructions in the memory 702 are executed by the processor 701 , the electronic device 700 is enabled to execute part or all of the steps in the above method embodiment.

[0101] Corresponding to the above embodiment, the embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium may store a program, wherein when the program is running, the device where the computer-readable storage medium is located may be controlled to perform some or all of the steps in the above method embodiment. In a specific implementation, the computer-readable storage medium may be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0102] Corresponding to the above-mentioned embodiment, the embodiment of the present application further provides a computer program product, which includes executable instructions. When the executable instructions are executed on a computer, the computer executes some or all of the steps in the above-mentioned method embodiment.

[0103] In the embodiments of the present application, "at least one" refers to one or more, and "plurality" refers to two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone. Among them, A and B can be singular or plural. The character " / " generally indicates that the previous and subsequent associated objects are in an "or" relationship. "At least one of the following" and similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0104] Those of ordinary skill in the art will appreciate that the various units and algorithm steps described in the embodiments disclosed herein can be implemented in a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0105] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0106] In several embodiments provided in the present application, any function can be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as an independent product. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., and other media that can store program codes.

[0107] The above is only a specific implementation of the present application. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. The protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A pre-stack oil and gas detection method, characterized in that: The method comprises the following operations: Superimpose the data of different ranges of incident angles and obtain the time-frequency spectrum of the seismic traces in time series through data processing; Calculate the centroid frequency f for the instantaneous spectrum S(f) at each time sampling point t s , and obtain the centroid frequency f that varies with time s (t), by calculating the ratio of the centroid frequency obtained by stacking the data of different ranges of incidence angle gathers, the centroid frequency attenuation rate D is obtained f (t); Calculate the bandwidth coefficient σ for the instantaneous spectrum S(f) s The bandwidth coefficient attenuation rate D is obtained by calculating the bandwidth coefficient by stacking the data of different ranges of incident angle gathers. σ (t); Using the centroid frequency decay rate D f (t) and bandwidth coefficient attenuation rate D σ (t) Construction of comprehensive fluid detection factor H d , respectively using the centroid frequency attenuation rate D f (t), bandwidth coefficient attenuation rate D σ (t) and the comprehensive fluid detection factor H d Detect oil and gas content and make comprehensive predictions of oil and gas.

2. A pre-stack oil and gas detection method according to claim 1, characterized in that: The different ranges of incident angle gather data are far-angle and near-angle gather data.

3. A pre-stack oil and gas detection method according to claim 1, characterized in that: The centroid frequency f s The calculation of is as follows:

4. A pre-stack oil and gas detection method according to claim 1, characterized in that: The centroid frequency decay rate D f (t) is calculated as follows: Among them, f sf (t), f sn (t) are the centroid frequencies calculated from the superposition data of the gathers with different ranges of incident angles.

5. A pre-stack oil and gas detection method according to claim 1, characterized in that: The bandwidth coefficient σ s The calculation of is as follows: Assume the decibel threshold coefficient of the spectrum is b, and the calculation formula is: Among them, S max is the largest value in D(f), and D(f) is set according to the decibel threshold value: The bandwidth factor is calculated by the following formula:

6. A pre-stack oil and gas detection method according to claim 1, characterized in that: The bandwidth coefficient attenuation rate D σ (t) is calculated as follows: Among them, σ sf (t),σ sn (t) is the bandwidth coefficient calculated from the superposition data of the gathers with different ranges of incident angles.

7. A pre-stack oil and gas detection method according to claim 1, characterized in that: The comprehensive fluid detection factor H d The calculation of is as follows: H d =D σ D f 。 8. A pre-stack oil and gas detection system, characterized in that: The system comprises: The gather data stacking module is used to stack gather data with different ranges of incident angles and obtain the seismic trace time-frequency spectrum of the time series through data processing; The centroid frequency attenuation rate calculation module is used to calculate the centroid frequency f for the instantaneous spectrum S(f) at each time sampling point t s , and obtain the centroid frequency f that varies with time s (t), the centroid frequency attenuation rate D is obtained by calculating the centroid frequency by stacking data from different ranges of incident angle gathers f (t); The bandwidth coefficient attenuation rate calculation module is used to calculate the bandwidth coefficient σ for the instantaneous spectrum S(f) s The bandwidth coefficient attenuation rate D is obtained by calculating the bandwidth coefficient by stacking the data of different ranges of incident angle gathers. σ (t); Comprehensive fluid detection factor calculation module, used to use the centroid frequency attenuation rate D f (t) and bandwidth coefficient attenuation rate D σ (t) Construction of comprehensive fluid detection factor H d , respectively using the centroid frequency attenuation rate D f (t), bandwidth coefficient attenuation rate D σ (t) and the comprehensive fluid detection factor H d Detect oil and gas content and make comprehensive predictions of oil and gas.

9. An electronic device, characterized in that: include: processor; Memory; And a computer program, wherein the computer program is stored in the memory, and the computer program includes instructions, which, when executed by the processor, enable the electronic device to execute a pre-stack oil and gas detection method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute a pre-stack oil and gas detection method according to any one of claims 1 to 7.

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