CN117974754A - Method, electronic equipment and medium for quantitatively identifying reservoir karst cave based on image processing - Google Patents
Method, electronic equipment and medium for quantitatively identifying reservoir karst cave based on image processing Download PDFInfo
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Abstract
The invention provides a method for quantitatively identifying a reservoir karst cave based on image processing, electronic equipment and a medium, wherein the method comprises the following steps: preprocessing the imported seismic attribute data volume; searching for a local maximum of the data gradient, thereby identifying an edge of the target body; filling the target body according to the edge detection result and the connected domain rule; extracting the outermost peripheral edge of the filling target body and removing smaller abnormal bodies to obtain the final target body form; marking the target body and calculating the geometric parameters of the target body. The method combines the image field technology with the carbonate karst cave type reservoir quantitative evaluation to explore the benefits, and obtains the geometrical parameter information of the reservoir karst cave by means of filtering, edge detection and the like, wherein the geometrical parameter information comprises the number, the size and the position of the karst cave, and the development degree and the distribution rule of the reservoir karst cave can be well represented.
Description
Technical Field
The invention belongs to the technical field of oil reservoir geophysics, and particularly relates to a quantitative identification method, electronic equipment and medium for a reservoir karst cave based on image processing.
Background
The carbonate fracture-cavity type oil reservoir resource reserves in China are rich, and the method has great exploration and development potential. The scale of the deep carbonate resource in the Tahe oil field is quite considerable, the estimated content reaches 8% of the domestic carbonate reservoir resource content, and the Tahe oil field is the research object of a plurality of expert scholars. The Tahe Otto carbonate reservoir is a typical karst fracture-cave reservoir, and the karst cave and the cracks are very developed, wherein the karst cave is taken as the main part, but the shape and the spatial distribution of the Tahe Otto carbonate reservoir are relatively strong in heterogeneity due to the stress and the corrosion, if the karst cave is relatively difficult to predict only through geological research, the accurate identification and quantitative description of the characteristics of the karst cave have important theoretical and practical significance for understanding the development rule of the carbonate reservoir karst cave of the Tahe oil field.
At present, in the aspects of carbonate reservoir karst cave identification and quantitative analysis, a great deal of work is carried out by the former, mainly the quantitative interpretation research is carried out by utilizing the seismic geological data of each scale of a rock core, well logging, earthquake and the like, and at the scale of the rock core, automatic detection and calibration and pickup calculation of each parameter are carried out on the karst cave mainly through full-borehole micro-resistivity imaging (FEI); in the logging scale, the vertical height of the karst cave is calibrated through logging data, but the horizontal width of the karst cave is difficult to obtain; in the seismic scale, the former builds a karst cave volume correction quantity edition based on a large number of model forward experiments, and quantitatively estimates the volume of an effective karst cave body, but the geometric parameter quantitative analysis work for the karst cave is mainly developed on the FEI well logging image, and the similar quantitative interpretation technology based on image processing is less in the aspect of seismic data, so that the searching of an effective karst cave identification and quantitative evaluation method is necessary.
Disclosure of Invention
The invention aims to provide a quantitative identification method of a reservoir karst cave based on image processing, which utilizes image processing technologies such as filtering, edge detection, hole filling and the like to carry out finer identification and measurement on the reservoir karst cave, so as to obtain more accurate karst cave quantity, size and distribution information and provide data support for subsequent quantitative evaluation of the karst cave type carbonate reservoir.
In order to achieve the above purpose, the invention provides a quantitative identification method of a reservoir karst cave based on image processing, which comprises the following steps:
Preprocessing the imported seismic attribute data volume;
Searching for a local maximum of the data gradient, thereby identifying an edge of the target body;
Filling the target body according to the edge detection result and the connected domain rule;
Extracting the outermost peripheral edge of the filling target body and removing smaller abnormal bodies to obtain the final target body form;
marking the target body and calculating the geometric parameters of the target body.
Further, preprocessing the imported seismic attribute data volume includes:
obtaining a data body to be processed through image graying conversion;
carrying out Gaussian filtering treatment to suppress interference noise of data;
and performing target image segmentation on the attribute data in a given threshold range.
Further, a Canny detection operator is utilized to find the local maximum of the data gradient.
Further, the strong edge and the weak edge are detected using the double threshold, and if the weak edge is communicated with the strong edge, the weak edge and the strong edge are simultaneously output.
Further, an 8-way rule is adopted when filling the target body.
Further, calculating the geometric parameters of the target volume includes the width, height, and center point position of the target volume.
Further, a coordinate value of the minimum circumscribed rectangle of the target body is obtained through calculation according to the connected domain, the length and the width of the rectangle are obtained according to the coordinate, and the position of the center point is calculated.
Further, a reference object with a known size is given, and the size information of the target body is calculated according to the proportional relation between the actual size of the reference object and the number of pixels.
The invention also provides an electronic device comprising:
A memory storing executable instructions;
And the processor runs the executable instructions in the memory to realize the method for quantitatively identifying the reservoir karst cave based on the image processing.
The invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which when executed by a processor implements a method for quantitative identification of a reservoir karst cave based on image processing as described above.
The invention provides a quantitative identification method of a reservoir karst cave based on image processing. The method applies the image processing technology to the seismic interpretation data, and obtains the geometrical parameter information of the karst cave of the reservoir by means of filtering, edge detection and the like, wherein the geometrical parameter information comprises the number, the size and the position of the karst cave, and the development degree and the distribution rule of the karst cave of the reservoir can be well represented.
Drawings
The foregoing and other objects, features and advantages of the invention will be apparent from the following more particular descriptions of exemplary embodiments of the invention as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the invention.
FIG. 1 is a flow chart of karst cave quantitative identification according to an embodiment of the invention.
Fig. 2 is a 4-way schematic diagram according to an embodiment of the invention.
Fig. 3 is an 8-way schematic diagram according to an embodiment of the invention.
Fig. 4 is a gaussian filter diagram according to an embodiment of the present invention.
Fig. 5 is a Canny edge detection graph in accordance with an embodiment of the present invention.
FIG. 6 is a karst cave filling diagram according to an embodiment of the present invention.
FIG. 7 is a karst cave profile according to an embodiment of the present invention.
FIG. 8 is a karst cave parameter information diagram according to an embodiment of the present invention.
Detailed Description
Preferred embodiments of the present invention will be described in more detail below. While the preferred embodiments of the present invention are described below, it should be understood that the present invention may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
The invention provides a quantitative identification method of a reservoir karst cave based on image processing, which utilizes image processing technologies such as filtering, edge detection, hole filling and the like to carry out finer identification and measurement on the reservoir karst cave, obtain more accurate karst cave quantity, size and distribution information and provide data support for subsequent quantitative evaluation of the karst cave type carbonate reservoir.
The invention is further described below with reference to the drawings and specific examples, which are not intended to be limiting. It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other.
Example 1
Referring to fig. 1, the embodiment provides a method for quantitatively identifying a karst cave in a reservoir based on image processing, which includes: preprocessing the imported seismic attribute data volume; searching for a local maximum of the data gradient, thereby identifying an edge of the target body; filling the target body according to the edge detection result and the connected domain rule; extracting the outermost peripheral edge of the filling target body and removing smaller abnormal bodies to obtain the final target body form; marking the target body and calculating the geometric parameters of the target body.
Specifically, the method comprises the first step of preprocessing an imported seismic attribute data body, firstly obtaining the data body to be processed through image graying conversion, then carrying out Gaussian filtering processing, suppressing interference noise of data, and then carrying out target image segmentation on the attribute data by a given threshold range, thereby laying a foundation for subsequent target edge detection and connected domain calibration.
And secondly, searching a local maximum value of the data gradient by using a Canny detection operator, so as to identify the edge of the target body. In the embodiment, the strong edge and the weak edge are detected by using the double threshold values, if the weak edge is communicated with the strong edge, the weak edge and the strong edge are output simultaneously, and compared with other methods, the Canny method is not easy to be interfered by noise, so that the real edge of the target body can be detected more accurately.
The Canny algorithm has the following advantages: low error rate, all edges should be found, and no spurious response; edge points should be well located and the located edges must be as close as possible to the real edges; a single edge point response, meaning that there is only one single edge point location, the detector should not point out multiple pixel edges.
Thirdly, the situation that abnormal values possibly exist in the detected edge image and affect subsequent geometric parameter calculation is caused, so that the object is required to be filled according to an edge detection result and a connected domain setting, wherein the connected domain refers to an adjacent pixel point which is the same as a gray value of a target point, the point and the target point are mutually communicated, and in image processing, the connected rule is generally divided into two types, namely 4 connected and 8 connected. 4 connectivity refers to connectivity of 4 points in the horizontal and vertical directions of the target point (as shown in fig. 2), and 8 connectivity refers to connectivity of 8 points in the horizontal, vertical, or diagonal directions (as shown in fig. 3). In this embodiment, in order to ensure the accuracy of hole filling, an 8-connection rule is adopted.
And fourthly, extracting the outermost peripheral edge of the filling target body and removing smaller abnormal bodies to obtain the final target body form.
And fifthly, marking the target body and calculating geometric parameters of the target body, including the width, the height and the center point position of the target body. Specifically, firstly, a coordinate value of a minimum circumscribed rectangle of a target body is obtained through calculation according to a connected domain, and then the length and the width of the rectangle are obtained according to the coordinate. In order to obtain the size information, a reference object with a known size needs to be given, and the size information of other targets can be obtained according to the proportional relation between the actual size of the reference object and the number of pixels.
The embodiment provides a quantitative identification method of a reservoir karst cave based on image processing, which applies an image processing technology to quantitative interpretation work of earthquakes, carries out finer identification and measurement on the karst cave through technical means such as Gaussian filtering, edge detection, hole calibration and the like, finally obtains more accurate karst cave position parameters and size parameters, explores the benefits of combining the image field technology with quantitative evaluation of the carbonate karst cave reservoir, and provides a certain data reference for the next step of earthquake interpretation work.
Example two
The embodiment takes the seismic data of the impedance attribute of a reservoir in a certain area of a Tahe oil field as a research basis, and describes the implementation method and the obtained practical effect.
Firstly, converting a gray level diagram of a data body, and then, performing Gaussian filtering to weaken the influence of other interference information, so as to obtain the gray level diagram shown in fig. 4. Next, the karst cave edge in the image was characterized using the Canny edge detection method, and the result is shown in fig. 5. In some cases, the detected edge image may have abnormal values in the target, so that the karst cave needs to be filled according to the connectivity rule, in this embodiment, an 8-connection rule is adopted, and all the connected parts of the karst cave with 1 pixels are filled to 1, so that a filling diagram shown in fig. 6 is obtained. Next, the outermost contour of the filled karst cave is extracted and part of abnormal values are removed, so that morphological characteristics of the karst cave are obtained, as shown in fig. 7. And finally, marking all the identified karst cave, and calculating according to the connected domain to obtain the pixel information of the length, width and center point of the minimum circumscribed rectangle of the karst cave. If the actual size and position information of the karst cave are to be obtained, a standard reference is set in the image, in this embodiment, it is assumed that the size of the leftmost karst cave, i.e. the first karst cave, is known, geometric parameter information of the remaining four karst caves is calculated according to the ratio of the actual size of the karst cave to the number of pixels, and the final result is shown in fig. 8, where table 1 is the statistical parameter value of the karst cave.
TABLE 1 karst cave parameter information statistics
| Karst cave numbering | Center position (m) | Hole length (m) | Hole width (m) |
| 1 | (145.0，227.5) | 75.0 | 12.0 |
| 2 | (222.2，216.3) | 95.8 | 20.9 |
| 3 | (490.0，170.0) | 70.0 | 16.0 |
| 4 | (534.0，95.2) | 68.6 | 14.1 |
| 5 | (890.0，67.5) | 105.0 | 16.0 |
Example III
The present embodiment provides an electronic device, including:
A memory storing executable instructions;
A processor, the processor executing the executable instructions in the memory to implement the method for quantitative identification of a reservoir karst cave based on image processing provided above, the method comprising: preprocessing the imported seismic attribute data volume; searching for a local maximum of the data gradient, thereby identifying an edge of the target body; filling the target body according to the edge detection result and the connected domain rule; extracting the outermost peripheral edge of the filling target body and removing smaller abnormal bodies to obtain the final target body form; marking the target body and calculating the geometric parameters of the target body.
Example IV
The present embodiment provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a method for quantitative identification of a reservoir karst cave based on image processing as provided above, the method comprising: preprocessing the imported seismic attribute data volume; searching for a local maximum of the data gradient, thereby identifying an edge of the target body; filling the target body according to the edge detection result and the connected domain rule; extracting the outermost peripheral edge of the filling target body and removing smaller abnormal bodies to obtain the final target body form; marking the target body and calculating the geometric parameters of the target body.
The computer-readable storage medium described above includes, but is not limited to: optical storage media (e.g., CD-ROM and DVD), magneto-optical storage media (e.g., MO), magnetic storage media (e.g., magnetic tape or removable hard disk), media with built-in rewritable non-volatile memory (e.g., memory card), and media with built-in ROM (e.g., ROM cartridge).
In summary, the invention provides a quantitative identification method of a reservoir karst cave based on image processing, which applies an image processing technology to quantitative interpretation work of earthquakes, carries out finer identification and measurement on the karst cave through technical means such as Gaussian filtering, edge detection, hole calibration and the like, and finally obtains more accurate karst cave position parameters and size parameters.
The foregoing description of embodiments of the invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described.
Claims (10)
1. The quantitative identification method of the reservoir karst cave based on the image processing is characterized by comprising the following steps of:
Preprocessing the imported seismic attribute data volume;
Searching for a local maximum of the data gradient, thereby identifying an edge of the target body;
Filling the target body according to the edge detection result and the connected domain rule;
Extracting the outermost peripheral edge of the filling target body and removing smaller abnormal bodies to obtain the final target body form;
marking the target body and calculating the geometric parameters of the target body.
2. The image processing-based reservoir karst cave quantitative identification method of claim 1, wherein preprocessing the imported seismic attribute data volume comprises:
obtaining a data body to be processed through image graying conversion;
carrying out Gaussian filtering treatment to suppress interference noise of data;
and performing target image segmentation on the attribute data in a given threshold range.
3. The image processing-based reservoir karst cave quantitative identification method of claim 1, wherein a Canny detection operator is utilized to find a local maximum of a data gradient.
4. A method for quantitative identification of a reservoir karst cave based on image processing according to claim 3, wherein the strong and weak edges are detected using a dual threshold, and if the weak edge is connected to the strong edge, the weak edge and the strong edge are outputted at the same time.
5. The quantitative identification method of the reservoir karst cave based on image processing according to claim 1, wherein 8-way rule is adopted when filling the target body.
6. The image processing-based reservoir karst cave quantitative recognition method of claim 1, wherein calculating geometric parameters of the target body comprises width, height and center point position of the target body.
7. The quantitative identification method of the reservoir karst cave based on image processing according to claim 6, wherein the coordinate value of the minimum circumscribed rectangle of the target body is obtained according to the connected domain, the length and width of the rectangle are obtained according to the coordinate, and the position of the central point is calculated.
8. The quantitative identification method of the reservoir karst cave based on image processing according to claim 7, wherein a reference object with a known size is given, and size information of a target body is calculated according to a proportional relation between the actual size of the reference object and the number of pixels.
9. An electronic device, the electronic device comprising:
A memory storing executable instructions;
A processor executing the executable instructions in the memory to implement the image processing-based reservoir cave quantitative identification method of any of claims 1-8.
10. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the image processing based reservoir karst cave quantitative identification method of any one of claims 1 to 8.
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| CN118131340A (en) * | 2024-05-07 | 2024-06-04 | 山东省煤田地质局物探测量队 | Mine geophysical prospecting data analysis method based on electrical prospecting technology |
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| CN118131340A (en) * | 2024-05-07 | 2024-06-04 | 山东省煤田地质局物探测量队 | Mine geophysical prospecting data analysis method based on electrical prospecting technology |
| CN118131340B (en) * | 2024-05-07 | 2024-07-16 | 山东省煤田地质局物探测量队 | Mine geophysical prospecting data analysis method based on electrical prospecting technology |
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