Pre-stack seismic fluid detection method and system

Abstract

This invention discloses a pre-stack seismic fluid detection method and system. The method includes: acquiring pre-stack AVO gathers of the reservoir to be detected; obtaining near-channel and far-channel stacking profiles at different frequencies based on the pre-stack AVO gathers; selecting multiple target segments in the reservoir to be detected, and extracting segment amplitude attributes from the near-channel and far-channel stacking profiles at each frequency for each target segment to obtain near-channel and far-channel amplitude attributes at different frequencies for each target segment; obtaining sensitive frequencies for distinguishing between oil and gas and water based on the near-channel and far-channel amplitude attributes at different frequencies for each target segment; and obtaining the fluid judgment result of the reservoir to be detected based on the sensitive frequencies. Based on the above data processing flow, this invention combines drilled well data and actual seismic data to automatically find the optimal sensitive frequencies for distinguishing between oil and gas and water, and simultaneously projects the oil and gas and water detection results back onto the seismic profiles to make the display results more intuitive.

Description

Technical Field

[0001] This invention relates to the field of petroleum geology technology, and in particular to a method and system for detecting fluids during pre-stack seismic events. Background Technology

[0002] Pre-stack AVO (Amplitude Versus Offset) seismic technology has long been the most widely used technique for seismic detection of fluid properties in reservoirs. To date, AVO technology mainly employs two methods: traditional AVO analysis and frequency-varying AVO analysis. Traditional AVO analysis only considers the rock physical properties of the two sides of a single interface, neglecting the influence of formation thickness. This leads to many unresolved issues in practical applications. To address these issues, many scholars both domestically and internationally have developed frequency-varying AVO technology based on traditional AVO analysis. This technology combines frequency division techniques with AVO analysis to analyze the AVO gradient changes of the seismic reflection amplitude of the target layer within different frequency bands, thus solving the problem of AVO identification in thin interbedded reservoirs. Representative techniques in frequency-varying AVO analysis include: spectral cross-plotting, dispersive AVO analysis, and frequency-varying AVO based on rock physical analysis. Spectral cross-plotting, as an important technique in frequency-varying AVO, has been widely used due to its ease of operation and strong controllability in production practice. The spectral cross-referencing technique was first proposed by Ren in 2007. Considering the influence of thin-layer thickness, pore fluid, and incident angle on the reflected amplitude of seismic waves at different frequencies, he combined AVO (Aspect-Oriented Volatility) and spectral decomposition techniques to propose a new method for fluid detection. This method can quickly distinguish between gas-bearing and water-bearing reservoirs. However, a crucial parameter in these methods is finding the most sensitive seismic frequency, at which the amplitude difference between gas (oil) and water layers is greatest, resulting in lower ambiguity.

[0003] Currently, there is no quantitative evaluation method for determining sensitive frequencies. The search for sensitive frequencies is also done manually and is not closely integrated with well data, lacking interpretive basis. Therefore, to address this problem, this invention combines drilled well data with actual seismic data to automatically find the optimal sensitive frequencies for distinguishing between oil and gas and water. Simultaneously, the oil and gas and water detection results are projected back onto the seismic profile to make the display results more intuitive. Summary of the Invention

[0004] This invention provides a pre-stack seismic fluid detection method and system that combines drilled well data with actual seismic data to automatically identify the optimal sensitive frequency for distinguishing between oil and gas and water, and simultaneously projects the oil and gas and water detection results back onto the seismic profile to make the display results more intuitive.

[0005] Firstly, a pre-stack seismic fluid detection method is provided, comprising: Obtain the pre-stack AVO gather of the reservoir to be tested; Based on the pre-stack AVO gathers, near-path stacking profiles and far-path stacking profiles at different frequencies were obtained. Multiple target segments in the reservoir to be tested are selected, and the amplitude attributes of the near-channel and far-channel stacked profiles corresponding to each frequency at each target segment are extracted to obtain the near-channel and far-channel amplitude attributes at different frequencies at each target segment. Based on the near-field amplitude properties and far-field amplitude properties at different frequencies in each target layer, the sensitive frequencies that distinguish between oil and gas and water are obtained. Based on the sensitive frequency, the fluid determination result of the reservoir to be detected is obtained.

[0006] In some embodiments, obtaining the near-path and far-path stacking profiles at different frequencies based on the pre-stack AVO gather includes: Near-path stacking and far-path stacking were performed on the pre-stack AVO gathers of the earthquake, respectively, to obtain the corresponding near-path stacking profile and far-path stacking profile; The near-channel and far-channel superimposed profiles are spectrally decomposed based on a high-resolution time-frequency decomposition algorithm to obtain the near-channel and far-channel superimposed profiles corresponding to each frequency within a preset frequency band.

[0007] In some embodiments, obtaining the sensitive frequency for distinguishing oil and gas from water based on the near-field and far-field amplitude attributes at different frequencies in each target layer includes: For a frequency f, a near-far amplitude intersection diagram is drawn with the near-far amplitude attribute as the abscissa and the far-far amplitude attribute as the ordinate. Each point in the diagram represents the near-far amplitude attribute or the far-far amplitude attribute corresponding to a target layer segment at frequency f. The near- and far-field amplitude cross-plots at each frequency were color-coded by gas testing to obtain gas layer points and water layer points. Based on the point distance between gas layer points and water layer points in the near-far amplitude intersection diagram at each frequency, the sensitive frequency for distinguishing between oil and gas and water is obtained.

[0008] In some embodiments, obtaining the sensitive frequency for distinguishing between oil and gas and water based on the point distance between gas layer points and water layer points in the near-to-far amplitude cross-plot at each frequency includes: Calculate the point distance between each gas layer point and each water layer point in the near-far amplitude intersection diagram at each frequency, accumulate all the calculated point distances, and the frequency corresponding to the maximum accumulated distance value is the sensitive frequency for distinguishing oil and gas from water.

[0009] In some embodiments, obtaining the fluid determination result of the reservoir to be detected based on the sensitive frequency includes: Based on the kernel density estimation method, a prediction map with contour lines is drawn on the near-far amplitude cross plot at the sensitive frequency; The fluid determination result of the reservoir to be detected is obtained based on the prediction map, and the fluid determination result is represented by seismic horizon.

[0010] In some embodiments, the kernel density estimation method plots a prediction map with contour lines on the near-far amplitude cross-plot at the sensitive frequency, including: The probability density distribution of gas and water points in the near- and far-path amplitude intersection diagram at the sensitive frequency is estimated based on the kernel density estimation method. Connecting gas layer points with the same preset probability density value yields gas layer contour lines; connecting water layer points with the same preset probability density value yields water layer contour lines. By setting multiple preset probability density values, a prediction map is obtained, which includes multiple isolines of the gas layer and multiple isolines of the water layer.

[0011] In some embodiments, obtaining the fluid determination result of the reservoir to be detected based on the prediction map includes: Obtain the actual gas layer points and actual water layer points of the reservoir to be detected; If the actual gas layer point falls within the gas layer contour line formed by the target preset probability density value, then the outermost gas layer contour line is set as the gas layer. If the actual water layer point falls within the water layer contour line formed by the target preset probability density value, then the ring formed by the outermost water layer contour line is set as the water layer.

[0012] Secondly, a pre-stack seismic detection fluid system is provided, comprising: The gather acquisition module is used to acquire pre-stack AVO gathers of the reservoir to be detected. The frequency analysis module is communicatively connected to the gather acquisition module and is used to obtain the near-path stacking profile and far-path stacking profile corresponding to different frequencies based on the pre-stack AVO gathers of the earthquake. The attribute extraction module is communicatively connected to the frequency analysis module. It is used to select multiple target segments in the reservoir to be detected, and extract the segment amplitude attributes of the near-channel stacked profile and the far-channel stacked profile corresponding to each frequency at each target segment, so as to obtain the near-channel amplitude attributes and the far-channel amplitude attributes at different frequencies at each target segment. A sensitive frequency acquisition module, communicatively connected to the attribute extraction module, is used to obtain the sensitive frequencies that distinguish between oil and gas and water based on the near-field amplitude attributes and far-field amplitude attributes at different frequencies in each target layer; and, The fluid judgment module is communicatively connected to the sensitive frequency acquisition module and is used to obtain the fluid judgment result of the reservoir to be detected based on the sensitive frequency.

[0013] Thirdly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the pre-stack seismic detection fluid method as described above.

[0014] Fourthly, embodiments of the present invention provide an electronic device, including a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, wherein the processor, when executing the computer program, implements the pre-stack seismic detection fluid method as described above.

[0015] Compared with the prior art, the advantages of the present invention are as follows: (1) By combining drilled well data and actual seismic data, the optimal sensitive frequency for distinguishing oil and gas from water is automatically identified; (2) The results of sensitive frequency selection are entirely data-driven and are not affected by human factors; (3) The generated prediction map, which includes contour lines, can guide the identification of gas layers with a high probability of occurrence, thereby minimizing the risk. Attached Figure Description

[0016] Figure 1 This is a schematic flowchart of an embodiment of the pre-stack seismic detection fluid method of the present invention; Figure 2 This is a schematic diagram of amplitude attribute extraction at the target layer segment in this invention; Figure 3 This is a schematic diagram of the sensitive frequency selection process of the present invention; Figure 4 This is a schematic diagram showing the distance between the gas layer point and the water layer point in this invention; Figure 5 This invention includes a contour map and a fluid discrimination diagram; Figure 6 This is a schematic diagram of the fluid detection cross-section of the present invention; Figure 7 This is a schematic flowchart of an embodiment of a pre-stack seismic detection fluid method of the present invention. Detailed Implementation

[0017] Referring now to specific embodiments of the invention, examples of which are illustrated in the accompanying drawings. Although the invention will be described in conjunction with specific embodiments, it will be understood that it is not intended to limit the invention to the described embodiments. Rather, it is intended to cover variations, modifications, and equivalents included within the spirit and scope of the invention as defined by the appended claims. It should be noted that the method steps described herein can be implemented by any functional block or functional arrangement, and any functional block or functional arrangement can be implemented as a physical entity or a logical entity, or a combination of both.

[0018] To enable those skilled in the art to better understand the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] Note: The examples described below are merely specific examples and are not intended to limit the embodiments of the present invention to the specific steps, values, conditions, data, order, etc. Those skilled in the art can utilize the concept of the present invention to construct more embodiments not mentioned herein by reading this specification.

[0020] Please see Figure 1 The present invention provides a flowchart of a pre-stack seismic fluid detection method, which includes: Step S100: Obtain the pre-stack AVO gather of the reservoir to be detected; AVO gathers refer to a set of seismic waveform data obtained from observing the same point underground at different offsets; pre-stack AVO gathers refer to common center point gathers that have not yet undergone stacking processing and contain information on amplitude variation with offset.

[0021] Step S200: Based on the pre-stack AVO gathers, obtain the near-path stacking profiles and far-path stacking profiles corresponding to different frequencies, including: Step S210: Perform near-path stacking and far-path stacking on the pre-stack AVO gathers of the earthquake respectively to obtain near-path stacking profiles and far-path stacking profiles. Specifically, pre-stack AVO gathers from earthquakes are stacked. Near-channel gathers are receivers with smaller offsets, closer to the hypocenter; far-channel gathers are receivers with larger offsets, farther from the hypocenter. The near-channel stacking profile is obtained by stacking gathers with smaller offsets, and the far-channel stacking profile is obtained by stacking gathers with larger offsets. The stacking process involves summing and averaging the amplitudes at different offsets for each sampling point. The purpose of stacking is to suppress random noise and enhance effective reflected signals through coherent superposition of multiple signals, thereby improving the signal-to-noise ratio while preserving AVO information at different angles.

[0022] Step S220: Based on the high-resolution time-frequency decomposition algorithm, the near-channel stacking profile and the far-channel stacking profile are spectrally decomposed to obtain the near-channel stacking profile and the far-channel stacking profile corresponding to each frequency within the preset frequency band. Specifically, a high-resolution time-frequency decomposition algorithm is used to obtain amplitude volumes of different frequencies from the aforementioned near-path and far-path stacked profiles, thus revealing the effective frequency band of the seismic event. The high-resolution time-frequency decomposition algorithm primarily employs wavelet transform. Wavelet transform, through multi-scale analysis and time-frequency localization, converts the time-domain signal to the frequency domain, matching different frequency components of the signal using wavelets of different scales. The wavelet transform formula is as follows: ; in, Let a be the wavelet basis function with scale a and flatness b; In the formula, 'a' controls the frequency; the larger 'a' is, the lower the frequency; 'b' controls the time position; W x (a, b) are the results after wavelet transform; x(t) is the seismic signal in the time domain, which corresponds to the superimposed near-channel and far-channel seismic data. t is the independent variable of the signal x(t), representing continuous time points.

[0023] Step S300: Select multiple target segments in the reservoir to be tested, and extract the segment amplitude attributes of the near-channel stacked profile and the far-channel stacked profile corresponding to each frequency at each target segment to obtain the near-channel amplitude attributes and far-channel amplitude attributes at different frequencies at each target segment.

[0024] Specifically, in this embodiment of the invention, stratigraphic amplitude attribute extraction is a key technology in seismic exploration data processing and geological interpretation. It refers to extracting quantitative attributes related to amplitude from seismic data within a specific stratigraphic segment to characterize the lithology, physical properties, fluid properties, or structural features of the strata. It primarily involves indirectly inferring subsurface geological information by quantifying the amplitude response of seismic waves.

[0025] Based on well seismic calibration, the phase axis of a target geological segment in the reservoir to be tested is picked up. After picking, the phase axis (a complete peak or trough) information is included by opening time windows at the top and bottom. The seismic amplitude attribute Si of all n sampling points of the phase axis is read, and the root mean square amplitude is calculated to obtain the amplitude attribute S of the target segment. The calculation formula is shown in the following formula. For the root mean square amplitude extraction process, please refer to [link to relevant documentation]. Figure 2 As shown.

[0026] .

[0027] Therefore, based on the above method, the near-field amplitude attributes and far-field amplitude attributes at different frequencies at each target segment can be obtained.

[0028] Step S400: Based on the near-field amplitude attributes and far-field amplitude attributes at different frequencies in each target layer, obtain the sensitive frequencies that distinguish between oil and gas and water, including: Step S410: For a frequency f, draw a near-far amplitude intersection diagram with the near-path amplitude attribute as the abscissa and the far-path amplitude attribute as the ordinate. Each point in the diagram represents the near-path amplitude attribute or the far-path amplitude attribute corresponding to a target layer at frequency f. According to step S300 above, the near-path amplitude attributes and far-path amplitude attributes at different frequencies corresponding to all drilled well points can be obtained. For example... Figure 3 As shown, the near-channel amplitude attributes and far-channel amplitude attributes at different frequencies are cross-plotted to obtain near-channel and far-channel amplitude cross-plots for each frequency f. The purpose of this step is to find the sensitive frequency, ensuring that the amplitude difference between the air and water layers in the near-channel and far-channel superimposed profiles is maximized at this frequency, thereby reducing the ambiguity of fluid detection.

[0029] Step S420: Color-coding the near-field and far-field amplitude cross-plots at each frequency using gas testing to obtain gas layer points and water layer points; Specifically, the near- and far-field amplitude cross-plots at each frequency are color-coded using gas testing. If it is a gas layer, it is red; if it is a water layer, it is blue. In practice, numerical codes are used to represent the colors for subsequent labeling of gas and water layer types.

[0030] Step S430: Based on the point distances between gas layer points and water layer points in the near- and far-field amplitude cross-plots at each frequency, obtain the sensitive frequencies for distinguishing between oil and gas and water, including: Calculate the point distance between each gas layer point and each water layer point in the near-far amplitude intersection diagram at each frequency, accumulate all the calculated point distances, and the frequency corresponding to the maximum accumulated distance value is the sensitive frequency for distinguishing oil and gas from water.

[0031] Specifically, in this embodiment of the invention, the sum of distances between all gas layer points and water layer points is calculated using the Euclidean distance formula, and the distances between all point pairs are accumulated iteratively. The following are detailed steps and formula explanations: Assume there are n gas layer points, denoted as A1, A2, ..., A n A i = (x i y i There are m points in the water layer, denoted as B1, B2, ..., B. m B j =(u j v j ),like Figure 4 As shown, two points A i = (xi y i ) and B j =(u j v j The Euclidean distance d between ) ij for: ; Calculate the distances between all n×m pairs of points and sum them up. This can be achieved through a double iteration: ① Initialization: Set a variable D. sum =0, used to accumulate distance. ② Outer iteration: For each point A in the first series. i (i from 1 to n); Inner iteration: For each point Bj in the second series (j from 1 to m): calculate the Euclidean distance dij between Ai and Bj, and then... ij Add to D sum Above. ③ After double iteration, D sum This is the sum of distances between all pairs of points.

[0032] Step S500: Based on the sensitive frequency, obtain the fluid judgment result of the reservoir to be detected, including: Step S510, based on the kernel density estimation method, plots a prediction map with contour lines on the near-far amplitude cross-plot at the sensitive frequency, including: Step S511: Estimate the probability density distribution of gas layer points and water layer points in the near-far amplitude intersection diagram at the sensitive frequency based on the kernel density estimation method; Specifically, the kernel density estimation method is used to estimate the probability density distribution of these points on the near-field and far-field amplitude cross plots. The basic idea of ​​kernel density estimation is to place a small "kernel" (usually a Gaussian kernel, i.e., a normal distribution curve) at each data point, and then superimpose all kernels to form a smooth, continuous probability density function. ② Construct a grid: Create a dense grid within the range of x and y values. Each intersection of the grid is a coordinate point. ③ Calculate the density value of the grid points: For each point in the grid, calculate the probability density value of that point according to the kernel density estimation function. The specific estimation method is as follows: Given a two-dimensional dataset containing n points: (x1, y1), (x2, y2), ..., (xn, yn), we want to estimate the probability density at any point (x, y). The steps are: ① Choose a kernel function: Typically, a Gaussian kernel (i.e., a normal distribution) is chosen. For two-dimensional data, a two-dimensional Gaussian kernel is used. ② Choose the bandwidth: Bandwidth controls the width of the kernel function, i.e., the range of influence of each data point. The choice of bandwidth greatly affects the estimation result. ③ Calculate the contribution of each data point to the target point (x, y): For each data point (xi, yi), calculate the value of the Gaussian kernel centered at that point at (x, y). This value represents the contribution of that data point to the probability density of the target point. ④ Summation and averaging: Summate the contributions of all data points to the target point (x, y), then divide by the number of data points n to obtain the estimated probability density of the target point.

[0033] For two-dimensional data, the formula for using the two-dimensional Gaussian kernel function is as follows: ; Where h is the bandwidth and (xi, yi) is the i-th data point.

[0034] Therefore, the probability density estimate at the target point (x, y) is: .

[0035] This results in a two-dimensional array representing the probability density of each point on the entire grid.

[0036] Step S512: Connect gas layer points with the same preset probability density value to obtain gas layer contour lines; connect water layer points with the same preset probability density value to obtain water layer contour lines. Specifically, contour lines are curves that connect points with the same probability density value. Several specific density values ​​are selected (e.g., levels determined by percentiles), and then the points corresponding to these density values ​​are found in the grid, and these points are connected to form a smooth curve. Contour lines are generated based on preset probability density values, such as... Figure 5 The display shows contour lines for 70% and 90% probabilities. Finally, the corresponding probability density values ​​are marked on the contour lines.

[0037] Step S513: Finally, multiple preset probability density values ​​are set to obtain a prediction map including multiple contour lines of the gas layer and multiple contour lines of the water layer.

[0038] Step S520: Obtain the fluid judgment result of the reservoir to be detected based on the prediction map, and represent the fluid judgment result using seismic horizons, including: Obtain the actual gas layer points and actual water layer points of the reservoir to be detected; If the actual gas layer point falls within the gas layer contour line formed by the target preset probability density value, then the outermost gas layer contour line is set as the gas layer. If the actual water layer point falls within the water layer contour line formed by the target preset probability density value, then the ring formed by the outermost water layer contour line is set as the water layer.

[0039] Specifically, in this embodiment of the invention, the gas (water) layer points of the sensitive frequency of the actual seismic data of the reservoir to be detected are projected onto the aforementioned prediction chart, such as... Figure 5 As shown, if point 1 (gas layer point) falls within the circle where the gas layer is 90% complete, it is predicted to be a gas layer; if point 2 (water layer point) falls within the circle where the water layer is 90% complete, it is predicted to be a water layer.

[0040] The results of gas and water layers are then expressed using seismic horizons, such as... Figure 6 As shown in the figure, the red layers are the predicted gas layers, and the blue layers are the predicted water layers.

[0041] See also Figure 7 As shown in the embodiment of the present invention, a pre-stack seismic fluid detection method is provided. The key points of this method are: combining pre-stack seismic data with well test (gas) data from drilled wells to identify sensitive seismic frequencies, and creating a probability contour map to clarify the range of near-path and far-path amplitudes corresponding to different probability contour lines at the sensitive frequencies. This map is then projected back onto the cross-section in stratigraphic form.

[0042] Under the constraints of pre-stack seismic data and well test (gas) data, a two-dimensional probability density function is used to draw contour lines with the same probability. The relationship between actual data points and contour lines is compared and analyzed, and fluid identification is performed on points that fall within the specified contour lines.

[0043] This invention also provides a pre-stack seismic detection fluid system, comprising: The gather acquisition module is used to acquire pre-stack AVO gathers of the reservoir to be detected. The frequency analysis module is communicatively connected to the gather acquisition module and is used to obtain the near-path stacking profile and far-path stacking profile corresponding to different frequencies based on the pre-stack AVO gathers of the earthquake. The attribute extraction module is communicatively connected to the frequency analysis module. It is used to select multiple target segments in the reservoir to be detected, and extract the segment amplitude attributes of the near-channel stacked profile and the far-channel stacked profile corresponding to each frequency at each target segment, so as to obtain the near-channel amplitude attributes and the far-channel amplitude attributes at different frequencies at each target segment. A sensitive frequency acquisition module, communicatively connected to the attribute extraction module, is used to obtain the sensitive frequencies that distinguish between oil and gas and water based on the near-field amplitude attributes and far-field amplitude attributes at different frequencies in each target layer; and, The fluid judgment module is communicatively connected to the sensitive frequency acquisition module and is used to obtain the fluid judgment result of the reservoir to be detected based on the sensitive frequency.

[0044] Specifically, this embodiment corresponds one-to-one with the above method embodiments. The functions of each module have been described in detail in the corresponding method embodiments, so they will not be repeated here.

[0045] In summary, the present invention has the following beneficial effects: (1) By combining drilled well data and actual seismic data, the optimal sensitive frequency for distinguishing oil and gas from water is automatically identified; (2) The results of sensitive frequency selection are entirely data-driven and are not affected by human factors; (3) The generated prediction map, which includes contour lines, can guide the identification of gas layers with a high probability of occurrence, thereby minimizing the risk.

[0046] Based on the same inventive concept, embodiments of this application also provide a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements all or part of the method steps of the above method.

[0047] The present invention can implement all or part of the processes in the above methods, or it can be accomplished by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc. It should be noted that the content contained in the computer-readable medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium does not include electrical carrier signals and telecommunication signals.

[0048] Based on the same inventive concept, embodiments of this application also provide an electronic device, including a memory and a processor. The memory stores a computer program that runs on the processor. When the processor executes the computer program, it implements all or part of the method steps described above.

[0049] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the computer device, connecting all parts of the computer device through various interfaces and lines.

[0050] Memory can be used to store computer programs and / or modules. The processor performs various functions of the computer device by running or executing the computer programs and / or modules stored in the memory, and by accessing data stored in the memory. Memory can primarily include a program storage area and a data storage area. The program storage area can store the operating system and at least one application program required for a function (e.g., sound playback, image playback, etc.); the data storage area can store data created based on the use of the mobile phone (e.g., audio data, video data, etc.). Furthermore, memory can include high-speed random access memory, and can also include non-volatile memory, such as hard disks, RAM, plug-in hard disks, SmartMedia Cards (SMC), Secure Digital (SD) cards, Flash Cards, at least one disk storage device, flash memory device, or other volatile solid-state storage devices.

[0051] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, servers, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0052] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), servers, and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0053] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0054] These computer program instructions 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 of the function specified in one or more boxes.

[0055] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A pre-stack seismic fluid detection method, characterized in that, include: Obtain the pre-stack AVO gather of the reservoir to be tested; Based on the pre-stack AVO gathers, near-path stacking profiles and far-path stacking profiles at different frequencies were obtained. Multiple target segments in the reservoir to be tested are selected, and the amplitude attributes of the near-channel and far-channel stacked profiles corresponding to each frequency at each target segment are extracted to obtain the near-channel and far-channel amplitude attributes at different frequencies at each target segment. Based on the near-field amplitude properties and far-field amplitude properties at different frequencies in each target layer, the sensitive frequencies that distinguish between oil and gas and water are obtained. Based on the sensitive frequency, the fluid determination result of the reservoir to be detected is obtained.

2. The pre-stack seismic detection fluid method as described in claim 1, characterized in that, The process of obtaining near-path and far-path stacking profiles at different frequencies based on the pre-stack AVO gathers includes: Near-path stacking and far-path stacking were performed on the pre-stack AVO gathers of the earthquake, respectively, to obtain the corresponding near-path stacking profile and far-path stacking profile; The near-channel and far-channel superimposed profiles are spectrally decomposed based on a high-resolution time-frequency decomposition algorithm to obtain the near-channel and far-channel superimposed profiles corresponding to each frequency within a preset frequency band.

3. The pre-stack seismic detection fluid method as described in claim 1, characterized in that, The method of obtaining sensitive frequencies to distinguish between oil and gas and water based on near-field and far-field amplitude attributes at different frequencies in each target layer includes: For a frequency f, a near-far amplitude intersection diagram is drawn with the near-far amplitude attribute as the abscissa and the far-far amplitude attribute as the ordinate. Each point in the diagram represents the intersection point between the near-far amplitude attribute and the far-far amplitude attribute corresponding to a target layer at frequency f. All intersection points in the near-far amplitude intersection diagrams at each frequency are color-coded by gas testing to obtain gas layer points and water layer points. Based on the point distance between gas layer points and water layer points in the near-far amplitude intersection diagram at each frequency, the sensitive frequency for distinguishing between oil and gas and water is obtained.

4. The pre-stack seismic detection fluid method as described in claim 3, characterized in that, The method of obtaining the sensitive frequencies for distinguishing between oil and gas and water based on the point distances between gas and water layers in the near- and far-field amplitude cross-plots at various frequencies includes: Calculate the point distance between each gas layer point and each water layer point in the near-far amplitude intersection diagram at each frequency, accumulate all the calculated point distances, and the frequency corresponding to the maximum accumulated distance value is the sensitive frequency for distinguishing oil and gas from water.

5. The pre-stack seismic detection fluid method as described in claim 3, characterized in that, The step of obtaining the fluid judgment result of the reservoir to be detected based on the sensitive frequency includes: Based on the kernel density estimation method, a prediction map with contour lines is drawn on the near-far amplitude cross plot at the sensitive frequency; The fluid determination result of the reservoir to be detected is obtained based on the prediction map, and the fluid determination result is represented by seismic horizon.

6. The pre-stack seismic detection fluid method as described in claim 5, characterized in that, The kernel density estimation method is used to plot a prediction map with contour lines on the near-far amplitude cross-plot at the sensitive frequency, including: The probability density distribution of gas and water points in the near- and far-path amplitude intersection diagram at the sensitive frequency is estimated based on the kernel density estimation method. Connecting gas layer points with the same preset probability density value yields gas layer contour lines; connecting water layer points with the same preset probability density value yields water layer contour lines. By setting multiple preset probability density values, a prediction map is obtained, which includes multiple isolines of the gas layer and multiple isolines of the water layer.

7. The pre-stack seismic detection fluid method as described in claim 5, characterized in that, The step of obtaining the fluid judgment result of the reservoir to be detected based on the prediction map includes: Obtain the actual gas layer points and actual water layer points of the reservoir to be detected; If the actual gas layer point falls within the gas layer contour line formed by the target preset probability density value, then the outermost gas layer contour line is set as the gas layer. If the actual water layer point falls within the water layer contour line formed by the target preset probability density value, then the ring formed by the outermost water layer contour line is set as the water layer.

8. A pre-stack seismic detection fluid system, characterized in that, The gather acquisition module is used to acquire pre-stack AVO gathers of the reservoir to be detected. The frequency analysis module is communicatively connected to the gather acquisition module and is used to obtain the near-path stacking profile and far-path stacking profile corresponding to different frequencies based on the pre-stack AVO gathers of the earthquake. The attribute extraction module is communicatively connected to the frequency analysis module. It is used to select multiple target segments in the reservoir to be detected, and extract the segment amplitude attributes of the near-channel stacked profile and the far-channel stacked profile corresponding to each frequency at each target segment, so as to obtain the near-channel amplitude attributes and the far-channel amplitude attributes at different frequencies at each target segment. The sensitive frequency acquisition module is communicatively connected to the attribute extraction module and is used to obtain the sensitive frequency that distinguishes oil and gas from water based on the near-field amplitude attribute and far-field amplitude attribute at different frequencies in each target layer. as well as, The fluid judgment module is communicatively connected to the sensitive frequency acquisition module and is used to obtain the fluid judgment result of the reservoir to be detected based on the sensitive frequency.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the pre-stack seismic detection fluid method as described in any one of claims 1 to 7.

10. An electronic device comprising a storage medium, a processor, and a computer program stored in the storage medium and executable on the processor, characterized in that, When the processor runs the computer program, it implements the pre-stack seismic detection fluid method as described in any one of claims 1 to 7.

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