Reflector identification method based on sound wave detection imaging technology and related equipment

Abstract

The invention provides a reflector identification method based on a sound wave detection imaging technology, which comprises the following steps: acquiring a sound wave logging imaging picture containing noise, inputting a Gaussian clustering number, carrying out Gaussian hybrid clustering on the sound wave logging imaging picture, laying a foundation for carrying out pixel-level identification on a reflector region in the sound wave detection imaging picture, and according to a clustering result, carrying out pixel-level identification on the reflector region in the sound wave detection imaging picture. The method comprises the following steps of: re-assigning values to pixel points of an acoustic logging imaging image according to categories to obtain a plurality of sub-images, screening the sub-images, carrying out image fusion on the screened sub-images, carrying out noise reduction processing on the fused sub-images, and removing noise in the image, so that the exploration and mining efficiency in the actual process is greatly improved; according to the method, the sub-image after noise removal and the acoustic logging imaging image are mapped, a reflector result is obtained, noise reduction processing on the fused sub-image comprises two steps of coarse noise reduction and fine noise reduction, noise in the acoustic logging imaging image can be removed in a targeted mode, and a reflector area is better protected from being influenced by noise removal.

Description

Technical Field

[0001] The present invention belongs to the technical field of acoustic wave long-range detection, and in particular relates to a reflector identification method based on acoustic wave detection imaging technology and related equipment. Background Art

[0002] In recent years, the structural morphology and reservoir physical properties of oil and gas reservoirs have become more complex, and the difficulty of exploration has gradually increased. With the development of acoustic wave remote detection logging technology, the detection capability of geological structures near the well has been improved, especially the promotion of dipole shear wave remote detection technology, which can effectively detect the address near the well and can truly image the complex underground structure. However, the formation reflection amplitude in acoustic wave remote detection is extremely low, the coherence is weak, and it is easily interfered by the direct wave of the wellbore with a strong amplitude. There will also be other types of acoustic wave interference, resulting in noise in the logging data. Reflector identification in acoustic wave logging imaging is crucial in the interpretation of unconventional reservoirs, and can provide guiding information for subsequent fracturing and production. However, the existence of noise and the inconsistency of the size and shape of the reflectors in the acoustic wave logging imaging make reflector identification more difficult.

[0003] With the development of interdisciplinary integration, it has become a trend to combine image processing technology to identify reflectors. Gaussian mixture models are widely used in image processing, and they can effectively segment images through clustering. Image denoising is also a common method in digital image processing. Noise reduction can be used to remove noise from acoustic logging images to highlight reflectors. Common digital image processing denoising methods include mean filtering and Gaussian filtering, but these methods often destroy the details of the image. Therefore, how to develop an accurate reflector identification system without destroying the reflector area information has become a problem that needs to be solved urgently. Summary of the invention

[0004] The present invention provides a reflector recognition method based on acoustic wave detection imaging technology and related equipment, which solves the problem that the existing digital image processing noise reduction method will destroy the detailed information of the image.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A reflector identification method based on acoustic wave detection imaging technology, comprising:

[0007] Get the acoustic logging image containing noise and input the number of Gaussian clusters;

[0008] Gaussian mixture clustering is performed on the acoustic logging imaging image. According to the clustering result, the pixel points of the acoustic logging imaging image are reassigned according to their categories to obtain multiple sub-images, and the sub-images are screened.

[0009] Perform image fusion on the filtered sub-images;

[0010] Perform noise reduction on the fused sub-images;

[0011] The sub-image after noise removal is mapped with the acoustic logging imaging image to obtain the reflector result.

[0012] Preferably, the subgraphs are screened by: screening the subgraphs containing the reflectors according to the re-assignment result, and then screening satisfactory subgraphs from the subgraphs containing the reflectors.

[0013] Preferably, screening a satisfactory subgraph from a subgraph containing reflectors is specifically as follows: determining whether the reflectors in the screened subgraph are connected; if not, it indicates that the input number of Gaussian clusters does not achieve satisfactory results, and the number of Gaussian clusters is re-entered until the reflectors in the subgraph are connected; if so, screening is performed directly.

[0014] Preferably, the denoising process for the fused sub-images includes two steps: coarse denoising and fine denoising.

[0015] Preferably, the coarse noise reduction is specifically:

[0016] Whether the pixel is a noise point is determined based on the number of pixels similar to the pixel in the neighborhood of the fused sub-image, and the pixels that are noise points are removed.

[0017] Preferably, fine denoising is performed on the sub-image after coarse denoising. Specifically, the sub-image after coarse denoising is first marked with connected domains, the number of pixels in the connected domains, i.e., the area of ​​the connected domains, is counted, and the area of ​​the connected domains is compared with a preset threshold. A connected domain smaller than the threshold is judged as a noise area, and the connected domain is removed to finally obtain a sub-image after fine denoising.

[0018] Preferably, mapping the noise-removed sub-image with the acoustic logging imaging image is specifically performed as follows:

[0019] The sub-image after noise removal is binarized, and the sub-image after binarization is multiplied pixel by pixel with the acoustic logging imaging image to obtain the reflector.

[0020] A reflector recognition system based on acoustic wave detection imaging technology, comprising:

[0021] Acquisition module: used to obtain the acoustic logging imaging image containing noise and input the number of Gaussian clusters;

[0022] Screening module: used to perform Gaussian mixture clustering on the acoustic logging imaging image. According to the clustering results, the pixel points of the acoustic logging imaging image are reassigned according to their categories to obtain multiple sub-images, and the sub-images are screened.

[0023] Fusion module: fuse the filtered sub-images;

[0024] Denoising module: performs denoising on the fused sub-images;

[0025] Mapping module: Map the noise-removed sub-image with the acoustic logging imaging image to obtain the reflector result.

[0026] A computer device comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the reflector identification method based on acoustic wave detection imaging technology are implemented.

[0027] A computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of a reflector identification method based on acoustic wave detection imaging technology are implemented.

[0028] Compared with the prior art, the present invention has the following beneficial effects: the present invention provides a reflector identification method based on acoustic wave detection imaging technology, obtains an acoustic wave logging imaging image containing noise, inputs the number of Gaussian clusters, performs Gaussian mixture clustering on the acoustic wave logging imaging image, and lays a foundation for pixel-level identification of the reflector area in the acoustic wave detection imaging image; according to the clustering result, the pixel points of the acoustic wave logging imaging image are reassigned according to their categories to obtain multiple sub-images, the sub-images are screened, the screened sub-images are image fused, the fused sub-images are denoised, the noise in the image is removed, and the exploration and mining efficiency in the actual process is greatly improved; the sub-image after noise removal is mapped with the acoustic wave logging imaging image to obtain the reflector result.

[0029] Furthermore, the denoising process for the fused sub-image includes two steps: coarse denoising and fine denoising, which can specifically remove the noise in the acoustic logging imaging image and better protect the reflector area from being affected by the denoising. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 This is a flow chart of a reflector identification method based on acoustic wave detection imaging technology of the present invention;

[0031] Figure 2 This is a block diagram of a reflector recognition system based on acoustic wave detection imaging technology of the present invention;

[0032] Figure 3 This is a flow chart of the reflector identification process according to an embodiment of the present invention;

[0033] Figure 4 A schematic diagram of Gaussian clustering according to an embodiment of the present invention;

[0034] Figure 5 This is an acoustic logging imaging diagram containing noise according to an embodiment of the present invention;

[0035] Figure 6 This is the reflector recognition result of the embodiment of the present invention. DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0037] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0038] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0039] In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. indicate an orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is usually placed when in use, it is only for the convenience of describing the present invention and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In addition, the terms "first", "second", etc. are only used to distinguish the description, and cannot be understood as indicating or implying relative importance.

[0040] In addition, if the term "horizontal" appears, it does not mean that the component must be absolutely horizontal, but can be slightly tilted. For example, "horizontal" only means that its direction is more horizontal than "vertical", which does not mean that the structure must be completely horizontal, but can be slightly tilted.

[0041] In the description of the embodiments of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal connection of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0042] In order to enable those skilled in the art to better understand the technical solution of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings.

[0043] like Figure 1 The present invention provides a reflector identification method based on acoustic wave detection imaging technology, comprising:

[0044] S101 obtains an acoustic logging image containing noise and inputs the number of Gaussian clusters;

[0045] S102 performs Gaussian mixture clustering on the acoustic logging imaging image, and according to the clustering result, reassigns the pixel points of the acoustic logging imaging image according to the category to which they belong, obtains multiple sub-images, and screens the sub-images;

[0046] S103 performs image fusion on the filtered sub-images;

[0047] S104 performs noise reduction processing on the fused sub-images;

[0048] S105 maps the noise-removed sub-image with the acoustic logging imaging image to obtain a reflector result.

[0049] The sub-graph screening is specifically as follows: according to the result of the re-assignment, the sub-graph containing the reflector is screened, and then a satisfactory sub-graph is screened from the sub-graph containing the reflector.

[0050] The specific method of screening a satisfactory subgraph from a subgraph containing reflectors is as follows: determine whether the reflectors in the screened subgraph are connected. If not, it means that the input number of Gaussian clusters does not achieve a satisfactory effect. In this case, the number of Gaussian clusters is re-entered until the reflectors in the subgraph are connected. If so, the subgraph is screened directly.

[0051] The denoising process for the fused sub-images includes two steps: coarse denoising and fine denoising.

[0052] The coarse noise reduction is specifically as follows:

[0053] Whether the pixel is a noise point is determined based on the number of pixels similar to the pixel in the neighborhood of the fused sub-image, and the pixels that are noise points are removed.

[0054] The fine denoising process for the sub-image after the coarse denoising is as follows: first, the connected domain of the sub-image after the coarse denoising is marked, the number of pixels in the connected domain, i.e., the area of ​​the connected domain, is counted, and the area of ​​the connected domain is compared with a preset threshold. If the connected domain is smaller than the threshold, it is judged as a noise area, and the connected domain is removed, so as to finally obtain the sub-image after the fine denoising.

[0055] The sub-image after noise removal is mapped to the acoustic logging imaging image as follows:

[0056] The sub-image after noise removal is binarized, and the sub-image after binarization is multiplied pixel by pixel with the acoustic logging imaging image to obtain the reflector.

[0057] Example:

[0058] like Figure 1 The working process of the reflector identification of the present invention is shown as follows:

[0059] 1) The user selects a noisy acoustic logging image and inputs the number of Gaussian clusters;

[0060] 2) Perform Gaussian clustering on the acoustic logging image, such as Figure 4 As shown, according to the clustering results, the pixel points of the acoustic logging imaging image are reassigned according to the category they belong to;

[0061] 3) According to the result of re-assignment, the sub-image containing the reflector is selected to form a preliminary background removal effect;

[0062] 4) Determine whether the reflectors in the screened subgraph are connected. If not, it means that the input number of Gaussian clusters does not achieve a satisfactory effect. Then re-enter the number of Gaussian clusters and repeat steps 2 to 4.

[0063] 5) Fusing the selected multiple valid sub-images into a single single-channel grayscale sub-image;

[0064] 6) Performing rough noise reduction processing on the sub-image obtained in step 5, mainly judging whether the pixel is a noise point based on the number of points similar to the pixel in the pixel neighborhood, and the basis for judgment is that the number of color-similar points in the pixel neighborhood of the reflector is greater than that around the noise;

[0065] 7) Perform fine denoising on the sub-image after coarse denoising. First, mark the connected domain of the sub-image after coarse denoising, count the number of pixels in the connected domain, that is, the area of ​​the connected domain, and compare the area of ​​the connected domain with a preset threshold. If the connected domain is smaller than the threshold, it is judged as a noise area, and the connected domain is removed to finally obtain the sub-image after fine denoising.

[0066] 8) Map the denoised image to the original image by binarizing the noise-removed sub-image, and then map the binarized image to the original image by multiplying them pixel by pixel to obtain the reflector. Figure 5 As shown in the figure, the results of identifying the reflectors in the acoustic wave detection imaging are as follows: Figure 6 shown.

[0067] like Figure 2 As shown, the present invention also provides a reflector recognition system based on acoustic wave detection imaging technology, comprising:

[0068] Acquisition module: used to obtain the acoustic logging imaging image containing noise and input the number of Gaussian clusters;

[0069] Screening module: used to perform Gaussian mixture clustering on the acoustic logging imaging image. According to the clustering results, the pixel points of the acoustic logging imaging image are reassigned according to their categories to obtain multiple sub-images, and the sub-images are screened.

[0070] Fusion module: fuse the filtered sub-images;

[0071] Denoising module: performs denoising on the fused sub-images;

[0072] Mapping module: Map the noise-removed sub-image with the acoustic logging imaging image to obtain the reflector result.

[0073] A terminal device is provided in one embodiment of the present invention. 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 in the above-mentioned method embodiments are implemented. Alternatively, when the processor executes the computer program, the functions of the modules / units in the above-mentioned device embodiments are implemented.

[0074] The computer program may be divided into one or more modules / units, and the one or more modules / units are stored in the memory and executed by the processor to accomplish the present invention.

[0075] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0076] The processor can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0077] The memory may be used to store the computer programs and / or modules, and the processor implements various functions of the terminal device by running or executing the computer programs and / or modules stored in the memory and calling the data stored in the memory.

[0078] If the module / unit integrated in the terminal device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), electric carrier signal, telecommunication signal and software distribution medium. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0079] Although the embodiments of the present invention are described above in conjunction with the accompanying drawings, the present invention is not limited to the above-mentioned specific embodiments and application fields, and the above-mentioned specific embodiments are only illustrative and instructive, rather than restrictive. Under the guidance of the specification, a person skilled in the art can also make many forms without departing from the scope of protection of the claims of the present invention, all of which belong to the scope of protection of the present invention.

Claims

1. A reflector identification method based on acoustic wave detection imaging technology, characterized in that: include: Get the acoustic logging image containing noise and input the number of Gaussian clusters; Gaussian mixture clustering is performed on the acoustic logging imaging image. According to the clustering result, the pixel points of the acoustic logging imaging image are reassigned according to their categories to obtain multiple sub-images, and the sub-images are screened. Perform image fusion on the filtered sub-images; Perform noise reduction on the fused sub-images; The sub-image after noise removal is mapped with the acoustic logging imaging image to obtain the reflector result.

2. The reflector identification method based on acoustic wave detection imaging technology according to claim 1 is characterized in that: The sub-graphs are screened specifically as follows: based on the result of the re-assignment, the sub-graphs containing the reflectors are screened, and then satisfactory sub-graphs are screened from the sub-graphs containing the reflectors.

3. The reflector identification method based on acoustic wave detection imaging technology according to claim 2 is characterized in that: The specific method of screening a satisfactory subgraph from a subgraph containing reflectors is as follows: determine whether the reflectors in the screened subgraph are connected. If not, it means that the input number of Gaussian clusters does not achieve a satisfactory effect. In this case, the number of Gaussian clusters is re-entered until the reflectors in the subgraph are connected. If so, the subgraph is screened directly.

4. The reflector identification method based on acoustic wave detection imaging technology according to claim 1 is characterized in that: The denoising process for the fused sub-images includes two steps: coarse denoising and fine denoising.

5. The reflector identification method based on acoustic wave detection imaging technology according to claim 4 is characterized in that: The coarse noise reduction is specifically as follows: Whether the pixel is a noise point is determined based on the number of pixels similar to the pixel in the neighborhood of the fused sub-image, and the pixels that are noise points are removed.

6. The reflector identification method based on acoustic wave detection imaging technology according to claim 5 is characterized in that: The fine denoising process for the sub-image after the coarse denoising is as follows: first, the connected domain of the sub-image after the coarse denoising is marked, the number of pixels in the connected domain, i.e., the area of ​​the connected domain, is counted, and the area of ​​the connected domain is compared with a preset threshold. If the connected domain is smaller than the threshold, it is judged as a noise area, and the connected domain is removed, so as to finally obtain the sub-image after the fine denoising.

7. The reflector identification method based on acoustic wave detection imaging technology according to claim 1 is characterized in that: The sub-image after noise removal is mapped to the acoustic logging imaging image as follows: The sub-image after noise removal is binarized, and the sub-image after binarization is multiplied pixel by pixel with the acoustic logging imaging image to obtain the reflector.

8. A reflector recognition system based on acoustic wave detection imaging technology, characterized in that: include: Acquisition module: used to obtain the acoustic logging imaging image containing noise and input the number of Gaussian clusters; Screening module: used to perform Gaussian mixture clustering on the acoustic logging imaging image. According to the clustering results, the pixel points of the acoustic logging imaging image are reassigned according to their categories to obtain multiple sub-images, and the sub-images are screened. Fusion module: fuse the filtered sub-images; Denoising module: performs denoising on the fused sub-images; Mapping module: Map the noise-removed sub-image with the acoustic logging imaging image to obtain the reflector result.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the reflector identification method based on acoustic wave detection imaging technology as described in 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 a reflector identification method based on acoustic wave detection imaging technology as described in any one of claims 1 to 7 are implemented.

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