Complex hidden reservoir characterization method and related equipment

Abstract

The invention belongs to the technical field of oil-gas exploration and development, and discloses a complex hidden reservoir characterization method and related equipment, and the method comprises the steps: firstly, extracting layer section attributes from a seismic data attribute body through employing a self-adaptive Gaussian weighting strategy, distributing different weights for a target layer section and a strong reflection layer, thereby suppressing the interference of the strong reflection layer, and obtaining a target layer section; the seismic response of the reservoir is enhanced; then, an equal metric mapping algorithm is used for carrying out fusion analysis on the extracted Gaussian weighted layer section attributes, and the equal metric mapping algorithm is a nonlinear dimension reduction algorithm and can effectively capture a complex nonlinear mapping relation between the layer section attributes and hidden reservoir space distribution features; and therefore, the spatial distribution characteristics of the complex hidden reservoir can be accurately represented.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of oil and gas exploration and development, and particularly relates to a complex subtle reservoir characterization method and related equipment. BACKGROUND

[0002] With the continuous advancement of oil and gas exploration work in China, the current exploration targets have gradually shifted from structural oil and gas reservoirs to lithologic oil and gas reservoirs. However, lithologic oil and gas reservoirs are usually deeply buried, thin in thickness, and have significant lateral heterogeneity in space, bringing greater technical challenges to exploration. In particular, complex subtle reservoirs are easily disturbed by surrounding strong reflection layers due to their small thickness and weak seismic response, and it is difficult to identify the effective response of the reservoir on the seismic profile. The traditional reservoir characterization method has great limitations in depicting the spatial distribution characteristics, and it is difficult to achieve accurate prediction.

[0003] Seismic multi-attribute analysis technology can effectively overcome the problems of multiple solutions and boundary ambiguity of single attribute in reservoir characterization by fusing multiple seismic attributes sensitive to reservoirs, and has been widely used in fine reservoir characterization. Typical methods include attribute analysis methods based on red-green-blue (RGB) fusion technology and multi-attribute analysis techniques based on principal component analysis (PCA), support vector regression (SVR), self-organizing mapping (SOM) and other machine learning methods. However, in the characterization of complex subtle reservoirs, the reservoir seismic response is often obscured by strong reflection layers, making it difficult to accurately identify on conventional seismic profiles and attribute profiles. The above methods still have great technical challenges in the characterization of such reservoirs. SUMMARY

[0004] The application provides a complex subtle reservoir characterization method and related equipment, which solves the problem that the existing seismic multi-attribute analysis technology cannot accurately identify on conventional seismic profiles and attribute profiles.

[0005] To achieve the above object, the application provides the following technical scheme: A complex subtle reservoir characterization method, comprising: extracting an attribute data volume from seismic data; calculating a weight coefficient of an adaptive Gaussian window according to the horizon of the top and bottom of a target interval; extracting an interval attribute from the attribute data volume according to the weight coefficient; performing fusion analysis on the interval attribute according to an equimeasure mapping algorithm to characterize the spatial distribution characteristics of the complex subtle reservoir.

[0006] Preferably, the step of extracting an attribute data volume from seismic data is specifically: reading a three-dimensional seismic data volume; extracting an attribute data volume from the three-dimensional seismic data volume according to a corresponding attribute calculation formula; Among them, the attribute data body includes general seismic attributes composed of root mean square amplitude, average amplitude, average energy, and arc length, instantaneous seismic attributes composed of instantaneous amplitude, instantaneous frequency, and instantaneous phase, and spectral decomposition attributes obtained by time-frequency transformation.

[0007] Preferably, the steps of calculating the weight coefficient of the adaptive Gaussian window according to the top and bottom layers of the target layer are specifically as follows: The window length of the adaptive Gaussian window is calculated based on the top and bottom layers of the target layer; The weight coefficient of the adaptive Gaussian window is then calculated based on the window length of the adaptive Gaussian window.

[0008] Preferably, the window length of the adaptive Gaussian window is calculated as follows:

[0009] in, is the window length of the adaptive Gaussian window, is the bottom layer of the target interval, The horizon at the top of the target interval.

[0010] Preferably, the weight coefficient of the adaptive Gaussian window is calculated as follows:

[0011] in, is the weight coefficient of the adaptive Gaussian window, For interval Equally spaced data within the range, , It is an exponential function with the natural constant e as its base.

[0012] Preferably, the method of extracting layer segment attributes from the data attribute body according to the weight coefficient is:

[0013] in, is the layer attribute, is the weight coefficient of the adaptive Gaussian window, It is the attribute data body.

[0014] Preferably, the steps of performing fusion analysis on the layer attributes according to the isometric mapping algorithm are as follows: For each data point in the layer segment attributes, find the k nearest neighbor points of each data point, and use the Euclidean distance between the nearest neighbor point and the data point as the edge weight to establish a connection graph between the data point and the nearest neighbor point; The shortest path algorithm is used to calculate the shortest manifold distance between any two data points on the connection graph, and a distance matrix is generated; The distance matrix is dimensionally reduced by using a multidimensional scaling algorithm, and each data point in the layer section attribute is mapped to a low-dimensional space, so as to obtain a representation result of the complex hidden reservoir.

[0015] A complex hidden reservoir characterization system comprises: An extraction module is configured to extract an attribute data volume from seismic data; A calculation module is configured to calculate a weight coefficient of an adaptive Gaussian window according to the horizon of the top and bottom of a target layer section; A second extraction module is configured to extract a layer section attribute from the seismic data attribute volume according to the weight coefficient; A characterization module is configured to perform fusion analysis on the layer section attribute according to an isometric mapping algorithm, so as to represent the spatial distribution characteristics of the complex hidden reservoir.

[0016] A computer device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of a complex hidden reservoir characterization method when executing the computer program.

[0017] A computer readable storage medium stores a computer program, and the computer program implements the steps of a complex hidden reservoir characterization method when executed by a processor.

[0018] Compared with the prior art, the present application has the following beneficial effects: the present application provides a complex hidden reservoir characterization method and related equipment, which first extracts a layer section attribute from a seismic data attribute volume using an adaptive Gaussian weighting strategy, assigns different weights to a target layer section and a strong reflection layer, thereby suppressing the interference of the strong reflection layer and enhancing the seismic response of the reservoir; then performs fusion analysis on the extracted Gaussian weighted layer section attribute using an isometric mapping algorithm, the isometric mapping algorithm is a nonlinear dimension reduction algorithm, which can effectively capture the complex nonlinear mapping relationship between the layer section attribute and the spatial distribution characteristics of the hidden reservoir, thereby accurately representing the spatial distribution characteristics of the complex hidden reservoir. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 A complex hidden reservoir characterization method flowchart is provided in the present application; Figure 2 A schematic diagram of a three-dimensional seismic data volume to be interpreted in an embodiment of the present application, which contains a complex hidden reservoir, is provided.

[0020] Figure 3 A schematic diagram of a well tie profile seismic data in an embodiment of the present application is provided.

[0021] Figure 4 A layer slice along layer at 25 ms below the horizon of the amplitude of the original seismic data applied to actual seismic data in the embodiment of the present application.

[0022] Figure 5 A complex hidden reservoir characterization result of seismic multi-attribute analysis applied to actual seismic data in the embodiment of the present application.

[0023] Figure 6 A complex hidden reservoir characterization result of Gaussian weighted seismic multi-attribute analysis applied to actual seismic data in the embodiment of the present application.

[0024] Figure 7 A complex hidden reservoir characterization system block diagram of the present application. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme of the embodiments of the present application will be described clearly and completely below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. The components of the embodiments of the present application described and shown in the drawings can be arranged and designed in various different configurations.

[0026] Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the claimed present application, but only represents selected embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative labor are within the scope of protection of the present application.

[0027] It should be noted that: similar numbers and letters represent similar items in the following drawings, therefore, once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings.

[0028] In order for those skilled in the art to better understand the technical scheme of the present application, the present application will be further described in combination with the drawings below.

[0029] As shown in Figure 1 The present application provides a complex hidden reservoir characterization method, comprising: S1 extracting attribute data volume from seismic data; S2 calculating the weight coefficient of adaptive Gaussian window according to the horizon of the top and bottom of the target layer section; S3 extracting layer section attribute from the seismic data attribute volume according to the weight coefficient; S4 performing fusion analysis on the layer section attribute according to the equimeasure mapping algorithm, and characterizing the spatial distribution characteristics of the complex hidden reservoir.

[0030] The step of extracting the attribute data volume from the seismic data to be analyzed comprises: reading a three-dimensional seismic data volume ; extracting a corresponding attribute data volume from the seismic data according to a corresponding attribute calculation formula . The extracted seismic attributes mainly include three categories: general seismic attributes such as root mean square amplitude, average amplitude, average energy, and arc length, instantaneous seismic attributes such as instantaneous amplitude, instantaneous frequency, and instantaneous phase, and spectral decomposition attributes calculated by time-frequency transformation.

[0031] The step of calculating the weight coefficient of the adaptive Gaussian window according to the horizon of the top and bottom of the target layer section comprises: according to the horizon of the top and bottom of the target layer section and calculating the window length of the adaptive Gaussian window ; calculating the corresponding weight coefficient by using the formula , wherein is the equally spaced data in the interval , and .

[0032] The step of extracting the layer section attribute from the seismic data attribute volume according to the weight coefficient comprises: extracting the layer section attribute from the seismic data attribute volume according to the seismic data attribute data volume and the weight coefficient by using the formula .

[0033] The step of performing fusion analysis on the layer section attribute according to the isometric mapping algorithm (ISOMAP) comprises: for each data point in the layer section attribute, finding its nearest k neighbors and establishing a connection graph, wherein the edge weight value of the graph is the Euclidean distance between two data points; calculating the shortest path between any two data points on the neighbor graph by using the shortest path algorithm as the distance between the two points, that is, the shortest manifold distance, to obtain a distance matrix; performing dimension reduction on the distance matrix by using the multidimensional scaling algorithm (MDS) to map the layer section attribute data points to a low-dimensional space, and then obtaining the representation result of the complex hidden reservoir.

[0034] In some embodiments, a complex hidden reservoir characterization method based on Gaussian weighted seismic multi-attribute analysis comprises the following steps: S100, extracting an attribute data volume from seismic data to be analyzed; S200, calculating the weight coefficient of an adaptive Gaussian window according to the horizon of the top and bottom of a target layer section; S300, extracting a layer attribute from the seismic data attribute volume according to the weight coefficient; S400, performing fusion analysis on the layer attribute according to an isometric mapping algorithm (ISOMAP) to represent the spatial distribution characteristics of the complex hidden reservoir.

[0035] The complex hidden reservoir characterization method based on the Gaussian weighted seismic multi-attribute analysis provided by the application first extracts a layer attribute from a seismic data attribute volume using an adaptive Gaussian weighting strategy, assigns different weights to the target layer and the strong reflection layer, thereby suppressing the interference of the strong reflection layer and enhancing the seismic response of the reservoir.

[0036] In related technologies, attribute analysis methods based on red-green-blue (RGB) fusion technology and multi-attribute analysis technologies based on machine learning methods such as principal component analysis (PCA), support vector regression (SVR), and self-organizing mapping (SOM) can effectively solve general reservoir interpretation problems, but still have technical limitations in complex hidden reservoir characterization. Due to the interference of strong reflection layers on the seismic response of complex hidden reservoirs, it is difficult to accurately identify them in conventional seismic profiles and attribute profiles, which limits the applicability of the above methods in such reservoirs. To solve this problem, a Gaussian weighted seismic multi-attribute analysis method is proposed for fine characterization of complex hidden reservoirs, effectively solving the technical defects of existing technologies in accurately identifying and effectively applying such reservoirs.

[0037] Specifically, S100, the step of extracting an attribute data volume from the seismic data to be analyzed includes: S110, reading a three-dimensional seismic data volume ; S120, extracting a corresponding attribute data volume from the seismic data according to a corresponding attribute calculation formula .

[0038] Among them, the extracted seismic attributes mainly include three categories: general seismic attributes such as root mean square amplitude, average amplitude, average energy, and arc length, instantaneous seismic attributes such as instantaneous amplitude, instantaneous frequency, and instantaneous phase, and spectral decomposition attributes calculated by time-frequency transformation.

[0039] By extracting general seismic, instantaneous seismic attributes and spectral decomposition attributes, multi-source information reflecting reservoir geological characteristics can be fully extracted, such as amplitude, frequency, phase, energy, absorption coefficient and other key attribute parameters. These attributes have complementary response characteristics of reservoirs in different scales and frequency ranges, and can more comprehensively reflect the lithology changes, fluid distribution and boundary characteristics of complex and subtle reservoirs.

[0040] More specifically, the step S200 of calculating the weight coefficient of the adaptive Gaussian window according to the horizon of the top and bottom of the target layer section comprises: S210, calculating the horizon of the top and bottom of the target layer section And the window length of the adaptive Gaussian window is calculated ; S220, the corresponding weight coefficient is calculated by the formula , wherein, is the equal-interval data in the interval , and .

[0041] Further, the step S300 of extracting the layer section attribute from the seismic data attribute body according to the weight coefficient comprises: S310, the layer section attribute is extracted from the seismic data attribute body according to the seismic data attribute data body and the weight coefficient by the formula .

[0042] In combination with the adaptive Gaussian weighting strategy to extract the layer section attribute, the interference of the strong reflection layer near the target layer section can be effectively suppressed, the identification ability of the subtle reservoir can be enhanced, and thus the reliability and resolution of the reservoir interpretation can be improved.

[0043] Further, the step S400 of performing fusion analysis on the layer section attribute according to the isometric mapping algorithm (ISOMAP) comprises: S410, for each data point in the layer section attribute, find its nearest k neighbors, and establish a connection graph, wherein the edge weight of the graph is the Euclidean distance between the two data points; S420, the shortest path algorithm is used to calculate the shortest path between any two data points on the neighbor graph as the distance between the two points, i.e. the shortest manifold distance, to obtain a distance matrix; S430, the multidimensional scaling algorithm (MDS) is used to reduce the dimension of the distance matrix, and the layer section attribute data points are mapped to a low-dimensional space, and then the representation result of the complex and subtle reservoir is obtained.

[0044] The present application is not limited to the above-mentioned embodiments. Figure 2The figure shows the 3D seismic data volume and the location of the target interpretation layer. The two points W1 and W2 represent the locations of two wells, which will be used to subsequently verify the effectiveness of the complex subtle reservoir characterization method based on Gaussian-weighted seismic multi-attribute analysis. The straight line AB represents the well-connected profile passing through the two wells.

[0045] Attachment Figure 3 The seismic data of the connected well section of two wells are shown. Due to the interference of the strong reflection layer, the target layer does not show obvious seismic response.

[0046] Attachment Figure 4 It is a layer The slice along the layer at 25ms below shows that a single seismic attribute cannot correctly characterize the spatial distribution characteristics of complex subtle reservoirs.

[0047] Attachment Figure 5 This shows that conventional multi-attribute analysis methods can characterize the spatial distribution characteristics of complex subtle reservoirs to a certain extent, but there are still deficiencies in the accuracy of characterization results and the depiction of reservoir boundaries.

[0048] Attachment Figure 6 The effectiveness and accuracy of the complex hidden reservoir characterization method based on Gaussian weighted seismic multi-attribute analysis of the present invention are verified.

[0049] like Figure 7 As shown, the present invention also provides a complex subtle reservoir characterization system, comprising: Extraction module: used to extract attribute data from seismic data; Calculation module: used to calculate the weight coefficient of the adaptive Gaussian window according to the top and bottom layers of the target layer; A second extraction module is used to extract layer attributes from the seismic data attribute volume according to the weight coefficient; Characterization module: used to perform fusion analysis on the layer attributes according to the isometric mapping algorithm to characterize the spatial distribution characteristics of complex hidden reservoirs.

[0050] An embodiment of the present invention provides a terminal device. The terminal device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of each of the aforementioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of each module / unit in each of the aforementioned device embodiments are implemented.

[0051] The computer program may be divided into one or more modules / units, which are stored in the memory and executed by the processor to accomplish the present invention.

[0052] The terminal device can be a desktop computer, a notebook computer, a palm computer, a cloud server, and the like. The terminal device can include, but is not limited to, a processor and a memory.

[0053] The processor can be a central processing unit (CPU), and can also be other general-purpose processors, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and the like.

[0054] The memory can be used to store the computer program and / or modules, and the processor can realize various functions of the terminal device by running or executing the computer program and / or modules stored in the memory, and calling data stored in the memory.

[0055] The modules / units integrated in the terminal device, if realized in the form of software function units and sold or used as independent products, can be stored in a computer readable storage medium. Based on this understanding, all or part of the processes in the above-mentioned embodiment methods can also be completed by a computer program instructing related hardware, and the computer program can be stored in a computer readable storage medium. The computer program can realize the steps of the above-mentioned various method embodiments when executed by a processor. The computer program includes computer program code, which can be in the form of source code, object code, executable files or some intermediate forms, etc. The computer readable medium can include any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the computer readable medium can include or exclude contents 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.

[0056] Although the embodiments of the present application have been described above with reference to the accompanying drawings, the present application is not limited to the above-described specific embodiments and areas of application, and the above-described specific embodiments are merely illustrative and instructive, but are not restrictive. Those skilled in the art can make various modifications under the teachings of the specification, and such modifications are also within the scope of the present application protected by the claims.

Claims

1. A complex and subtle reservoir characterization method, characterized in that: include: Extract attribute data volumes from seismic data; Calculate the weight coefficient of the adaptive Gaussian window according to the top and bottom layers of the target layer; extracting layer segment attributes from the attribute data volume according to the weight coefficient; The layer attributes are fused and analyzed based on the isometric mapping algorithm to characterize the spatial distribution characteristics of complex hidden reservoirs.

2. A complex subtle reservoir characterization method according to claim 1, characterized in that: The steps for extracting attribute data volumes from seismic data are as follows: Read 3D seismic data volume; extracting an attribute data volume from the three-dimensional seismic data volume according to a corresponding attribute calculation formula; Among them, the attribute data body includes general seismic attributes composed of root mean square amplitude, average amplitude, average energy, and arc length, instantaneous seismic attributes composed of instantaneous amplitude, instantaneous frequency, and instantaneous phase, and spectral decomposition attributes obtained by time-frequency transformation.

3. A complex subtle reservoir characterization method according to claim 1, characterized in that: The steps for calculating the weight coefficient of the adaptive Gaussian window according to the top and bottom layers of the target layer are as follows: The window length of the adaptive Gaussian window is calculated based on the top and bottom layers of the target layer; The weight coefficient of the adaptive Gaussian window is then calculated based on the window length of the adaptive Gaussian window.

4. A complex subtle reservoir characterization method according to claim 3, characterized in that: The calculation method of the window length of the adaptive Gaussian window is: in, is the window length of the adaptive Gaussian window, is the bottom layer of the target interval, The horizon at the top of the target interval.

5. A complex subtle reservoir characterization method according to claim 4, characterized in that: The calculation method of the weight coefficient of the adaptive Gaussian window is: in, is the weight coefficient of the adaptive Gaussian window, For interval Equally spaced data within the range, , It is an exponential function with the natural constant e as its base.

6. A complex subtle reservoir characterization method according to claim 1, characterized in that: The method for extracting layer segment attributes from the data attribute body according to the weight coefficient is: in, is the layer attribute, is the weight coefficient of the adaptive Gaussian window, It is the attribute data body.

7. A complex subtle reservoir characterization method according to claim 1, characterized in that: The specific steps of performing fusion analysis on the layer attributes according to the isometric mapping algorithm are as follows: For each data point in the layer segment attributes, find the k nearest neighbor points of each data point, and use the Euclidean distance between the nearest neighbor point and the data point as the edge weight to establish a connection graph between the data point and the nearest neighbor point; Use the shortest path algorithm to calculate the shortest manifold distance between any two data points on the connection graph and generate a distance matrix; The distance matrix is ​​reduced in dimensionality using a multidimensional scaling algorithm, and each data point in the layer attributes is mapped to a low-dimensional space to obtain a characterization result of a complex hidden reservoir.

8. A complex and subtle reservoir characterization system, characterized in that: include: Extraction module: used to extract attribute data from seismic data; Calculation module: used to calculate the weight coefficient of the adaptive Gaussian window according to the top and bottom layers of the target layer; A second extraction module is used to extract layer attributes from the seismic data attribute volume according to the weight coefficient; Characterization module: used to perform fusion analysis on the layer attributes according to the isometric mapping algorithm to characterize the spatial distribution characteristics of complex hidden reservoirs.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the steps of the complex subtle reservoir characterization method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the complex subtle reservoir characterization method according to any one of claims 1 to 7 are implemented.

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