Fracture-vug image generation method and apparatus, device, and storage medium

Abstract

A fracture-vug image generation method and apparatus, a device, and a storage medium. The method comprises: acquiring electro-imaging image data of a formation under test (S110), the electro-imaging image data comprising conductivity values of the formation under test that correspond to multiple pixel points; on the basis of the conductivity values of the formation under test that correspond to the multiple pixel points in the electro-imaging image data, respectively determining imaging porosity values of the formation under test that correspond to the multiple pixel points (S120); and generating a fracture-vug image by traversing the imaging porosity values of the formation under test that correspond to the multiple pixel points, and comparing the magnitude relationship between the imaging porosity value of the formation under test that corresponds to each pixel point and a porosity threshold (S130). By performing preset data processing and data comparison on conductivity values in an electro-imaging image, the method generates a high-quality fracture-vug image having an image resolution greater than a preset resolution, thereby providing data support for subsequent identification of fracture-vug types.

Description

Method, device and equipment for generating fracture-cave image and storage medium

[0001] The present application claims priority from the Chinese patent application No. 202411260049.0 filed on September 9, 2024, and entitled "Method, device and equipment for generating fracture-cave image and storage medium", the whole content of which is incorporated herein by reference. TECHNICAL FIELD

[0002] The present application relates to the field of geological exploration and development, and in particular to a method, device and equipment for generating a fracture-cave image and a storage medium. BACKGROUND

[0003] Logging imaging images are often used to qualitatively identify and quantitatively evaluate the development of fractures and cavities in a formation. An important step in quantitatively evaluating the development of fractures and cavities in a formation using logging imaging images is to segment a fracture-cave image from the logging imaging images. Logging imaging images include, for example, electrical imaging images, acoustic imaging images, etc.

[0004] In the related art, the application file with the publication number CN117409028A discloses a logging imaging image segmentation method, which uses a preset sliding window to perform image segmentation processing on a logging imaging image, and determines the fracture-cave type of the formation based on the image after the image segmentation processing. The problem with the above image segmentation processing process is that in a dense section, the target obtained after the image segmentation processing may be rock background noise rather than an effective fracture-cave target, i.e., the image quality of the fracture-cave image obtained by the image segmentation is not high, which may result in the inability to identify the fracture-cave type from the image.

[0005] Therefore, how to segment a high-quality fracture-cave image from a logging imaging image is a problem to be solved at present. SUMMARY

[0006] The present application provides a method, device and equipment for generating a fracture-cave image and a storage medium, which obtains imaging porosity values of a formation to be measured corresponding to a plurality of pixel points by performing a preset data processing on the electrical conductivity values of the formation to be measured corresponding to the plurality of pixel points in electrical imaging image data, identifies and updates the imaging porosity values of the pixel points corresponding to background rock noise by performing a pixel-level numerical comparison with a preset porosity threshold value, and generates a high-quality fracture-cave image with an image resolution greater than a preset resolution, thereby providing data support for subsequent identification of a fracture-cave type.

[0007] The first aspect of the application provides a method for generating a fracture-vug image, the method comprising: obtaining electrical imaging image data of a formation to be measured, the electrical imaging image data comprising electrical conductivity values of the formation to be measured corresponding to a plurality of pixel points; determining imaging porosity values of the formation to be measured corresponding to the plurality of pixel points respectively according to the electrical conductivity values of the formation to be measured corresponding to the plurality of pixel points; and generating the fracture-vug image by traversing the imaging porosity values of the formation to be measured corresponding to the plurality of pixel points, comparing the imaging porosity value of the formation to be measured corresponding to each pixel point with a porosity threshold, and comparing the imaging porosity value of the formation to be measured corresponding to each pixel point with a porosity threshold.

[0008] In some embodiments, the determining of the imaging porosity values of the formation to be measured corresponding to the plurality of pixel points respectively according to the electrical conductivity values of the formation to be measured corresponding to the plurality of pixel points comprises: obtaining logging data of the formation to be measured and core experiment data of a sampling sample of the formation to be measured; determining resistivity values of the formation to be measured and porosity values of different geologies in the formation to be measured according to the logging data; determining a cementation exponent of the formation to be measured according to the core experiment data; and determining the imaging porosity values of the formation to be measured corresponding to the plurality of pixel points according to the electrical conductivity values of the formation to be measured corresponding to the plurality of pixel points, the resistivity values of the formation to be measured, the porosity values of different geologies in the formation to be measured, and the cementation exponent according to the following formula:

[0009] wherein φ (i,j,k) represents the imaging porosity value of the formation to be measured corresponding to each pixel point; m represents the cementation exponent of the formation to be measured; φ represents the porosity value φ of different geologies in the formation to be measured; R xo represents the resistivity value of the formation to be measured; C (i,j,k) represents the electrical conductivity value of the formation to be measured corresponding to each pixel point.

[0010] In some embodiments, the method further comprises: generating a porosity image of the formation to be measured according to the imaging porosity values of the formation to be measured corresponding to the plurality of pixel points; generating a histogram according to the porosity image, the histogram being used to count the number of pixel points corresponding to each imaging porosity value in the porosity image;

[0011] determining a valley porosity value according to a porosity statistical spectrum peak in the histogram;

[0012] taking the valley porosity value as the porosity threshold; or

[0013] taking a plurality of imaging porosity values in the histogram as a plurality of candidate thresholds, traversing the plurality of candidate thresholds to obtain a plurality of inter-class variances corresponding to the plurality of candidate thresholds;

[0014] comparing the plurality of inter-class variances to determine a candidate threshold with the largest inter-class variance as the porosity threshold;

[0015] Wherein, when traversing each candidate threshold, the pixel points with the imaging porosity value greater than or equal to the candidate threshold are divided into a first group, and the pixel points with the imaging porosity value less than the candidate threshold are divided into a second group; according to the pixel points and the imaging porosity value of the first group and the pixel points and the imaging porosity value of the second group, the inter-class variance corresponding to the candidate threshold is obtained.

[0016] In some embodiments, the method further comprises: determining the lower limit value of the porosity of the formation under test according to core experiment data of a sampling sample of the formation under test.

[0017] In some embodiments, the generating the fracture-cave image by traversing the imaging porosity values of the formation under test corresponding to the plurality of pixel points and comparing the imaging porosity value of the formation under test corresponding to each pixel point with the porosity threshold value comprises: taking a first pixel point in the plurality of pixel points as an example, if the imaging porosity value of the formation under test corresponding to the first pixel point is greater than or equal to the porosity threshold value, the imaging porosity value of the formation under test corresponding to the first pixel point is not updated; if the imaging porosity value of the formation under test corresponding to the first pixel point is less than the porosity threshold value, the imaging porosity value of the formation under test corresponding to the first pixel point is updated to 0; after traversing the plurality of pixel points, the fracture-cave image is generated according to the imaging porosity values of the formation under test corresponding to the plurality of pixel points that are not updated and the plurality of pixel points that are updated.

[0018] In some embodiments, the generating the fracture-cave image by traversing the imaging porosity values of the formation under test corresponding to the plurality of pixel points and comparing the imaging porosity value of the formation under test corresponding to each pixel point with the porosity threshold value comprises: taking a first pixel point in the plurality of pixel points as an example, if the imaging porosity value of the formation under test corresponding to the first pixel point is greater than or equal to the porosity threshold value, the conductivity value of the formation under test corresponding to the first pixel point is not updated; if the imaging porosity value of the formation under test corresponding to the first pixel point is less than the porosity threshold value, the conductivity value of the formation under test corresponding to the first pixel point is updated to 0; after traversing the plurality of pixel points, the fracture-cave image is generated according to the conductivity values of the formation under test corresponding to the plurality of pixel points that are not updated and the plurality of pixel points that are updated.

[0019] The second aspect of the present application provides a fracture-cave image generation device, the generation device comprising:

[0020] The acquisition module is configured to acquire electrical imaging image data of the formation under test, the electrical imaging image data comprising conductivity values of the formation under test corresponding to a plurality of pixel points;

[0021] The determination module is configured to determine imaging porosity values of the formation under test corresponding to the plurality of pixel points respectively according to the conductivity values of the formation under test corresponding to the plurality of pixel points in the electrical imaging image data;

[0022] The image generation module is configured to traverse the imaging porosity values of the multiple pixel points corresponding to the formation under test, compare the imaging porosity values of each pixel point corresponding to the formation under test with the porosity threshold, and generate the fracture-cave image.

[0023] The third aspect of the present application provides an electronic device, comprising a processor, a memory and a display; the memory is coupled with the processor, and the memory is configured to store computer program code; the processor is configured to invoke the computer program code to enable the electronic device to perform the method of the first aspect; and the display is configured to display the fracture-cave image.

[0024] The fourth aspect of the present application provides a readable storage medium, which stores a computer program; when the computer program is executed by a processor, the method of the first aspect is implemented.

[0025] The fifth aspect of the present application provides a computer program product, comprising a computer program; when the computer program is executed by a processor, the method of the first aspect is implemented.

[0026] The sixth aspect of the present application further provides a chip system, which is applied to an electronic device; the chip system comprises one or more processors; the one or more processors are configured to invoke computer instructions to enable the electronic device to implement the method of the first aspect.

[0027] The seventh aspect of the present application further provides a computer program product, comprising a computer program; when the computer program is executed by a processor, the method of the first aspect is implemented.

[0028] The method, device, equipment and storage medium for generating a fracture-cave image provided by the present application are as follows: according to the electrical conductivity values of multiple pixel points corresponding to the formation under test in the electrical imaging image data of the formation under test, the imaging porosity values of the multiple pixel points corresponding to the formation under test are determined respectively, so as to convert the electrical conductivity values of the pixel points into the imaging porosity values. Then, by traversing the imaging porosity values of the multiple pixel points corresponding to the formation under test, the imaging porosity values of each pixel point corresponding to the formation under test are compared with the porosity threshold, and the fracture-cave image is generated. The above method performs a preset data processing on the electrical conductivity values of the multiple pixel points corresponding to the formation under test in the electrical imaging image data, obtains the imaging porosity values of the multiple pixel points corresponding to the formation under test, performs a pixel-level numerical comparison with the preset porosity threshold, identifies and updates the imaging porosity values of the pixel points corresponding to the background rock noise, so as to generate a high-quality fracture-cave image with an image resolution greater than a preset resolution, and further provide data support for subsequent identification of fracture-cave types. BRIEF DESCRIPTION OF DRAWINGS

[0029] The accompanying drawings, which are incorporated into and form a part of the specification, illustrate one embodiment consistent with the present application and, together with the description, serve to explain the principles of the application.

[0030] Fig. 1 is a scenario diagram of a method for generating a fracture-vug image according to an embodiment of the present application;

[0031] Fig. 2 is a flow diagram of a method for generating a fracture-vug image according to an embodiment of the present application;

[0032] Fig. 3 is another scenario diagram of a method for generating a fracture-vug image according to an embodiment of the present application;

[0033] Fig. 4 is a histogram diagram of a method for generating a fracture-vug image according to an embodiment of the present application;

[0034] Fig. 5 is a scenario diagram of a segmentation example of a dissolution-vug type formation using a method for generating a fracture-vug image according to an embodiment of the present application;

[0035] Fig. 6 is a scenario diagram of a segmentation example of a fracture type formation using a method for generating a fracture-vug image according to an embodiment of the present application;

[0036] Fig. 7 is a scenario diagram of a segmentation example of a fracture-dissolution-vug type formation using a method for generating a fracture-vug image according to an embodiment of the present application;

[0037] Fig. 8 is a scenario diagram of a segmentation example of a cave type formation using a method for generating a fracture-vug image according to an embodiment of the present application;

[0038] Fig. 9 is a scenario diagram of a segmentation example of a fracture-vug type formation of a Cretaceous system in a Kuqa depression of Tarim Basin using a method for generating a fracture-vug image according to an embodiment of the present application;

[0039] Fig. 10 is a scenario diagram of a segmentation example of a Cambrian system of dolomite formation in Tarim Basin using a method for generating a fracture-vug image according to an embodiment of the present application;

[0040] Fig. 11 is a structural diagram of a device for generating a fracture-vug image according to an embodiment of the present application;

[0041] Fig. 12 is a structural diagram of an electronic device according to an embodiment of the present application.

[0042] The above-described figures have shown specific embodiments of the present application, which will be described in more detail hereinafter. These figures and written descriptions are not intended to limit the scope of the concept of the present application by any means, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0043] The exemplary embodiments will be described in detail herein with reference to the attached drawings. In the following description, like reference numerals refer to like elements, unless the context clearly dictates otherwise. The following description of exemplary embodiments is not representative of all possible embodiments consistent with the present application. Rather, it is merely an example of apparatus and methods consistent with some aspects of the present application as detailed in the appended claims.

[0044] The terms "first", "second", and the like, as used herein, are intended to modify any element so described, but do not specifically identify such elements unless otherwise specified. The terms "a number of" and "multiple numbers of", as used herein, are intended to mean two or more, unless otherwise specifically indicated.

[0045] During logging, an electrical imaging logging tool transmits electric current to the formation using button electrodes, measures the micro-conductivity information of the formation near the borehole wall with depth variation, and displays the information in the form of an image. Generally, the light-colored part of the image represents low conductivity, and the dark-colored part represents high conductivity. Because the conductivities of different geological bodies near the borehole wall are different, the electrical imaging image reflects the geological phenomena such as the bedding, fractures, and dissolution pores of the formation near the borehole wall, and the resolution can reach about 5 mm. The electrical imaging image is often used to qualitatively identify and quantitatively evaluate the fracture and pore development of the formation. An important step of quantitatively evaluating the fracture and pore development of the formation using the electrical imaging image is to segment a fracture and pore image from the electrical imaging image.

[0046] In the related art, the image quality of the fracture and pore image segmented from the electrical imaging image is not high, that is, the image features used to identify the fracture and pore types in the segmented image are not obvious, and the interference noise in the image is large.

[0047] In view of this, the present application provides a fracture and pore image generation method, device, equipment, and storage medium. The method comprises: determining imaging porosity values of a to-be-measured formation corresponding to a plurality of pixel points in electrical imaging image data of the to-be-measured formation according to electrical conductivity values of the to-be-measured formation corresponding to the plurality of pixel points, to convert the electrical conductivity values of the pixel points into the imaging porosity values. Then, by traversing the imaging porosity values of the to-be-measured formation corresponding to the plurality of pixel points, comparing the imaging porosity values of the to-be-measured formation corresponding to each pixel point with a porosity threshold, a fracture and pore image is generated. The porosity threshold is used to distinguish the imaging porosity values of the fracture and pore corresponding to the pixel points from the imaging porosity values of the background rock noise corresponding to the pixel points.

[0048] The method preforms preset data processing on the electrical conductivity values of the to-be-measured stratum corresponding to the plurality of pixel points in the electrical imaging image data, obtains the imaging porosity values of the to-be-measured stratum corresponding to the plurality of pixel points, performs pixel-level numerical comparison with the preset porosity threshold, identifies and updates the imaging porosity values of the pixel points corresponding to the background rock noise, and generates a high-quality fracture-cave image with an image resolution greater than the preset resolution, thereby providing data support for subsequent identification of fracture-cave types.

[0049] Referring to FIG. 1, FIG. 1 is an application scenario diagram of the method for generating a fracture-cave image according to an embodiment of the present application. The application scenario includes an electronic device 10, an imaging processor 11, and a plurality of polar plates 12. The plurality of polar plates 12 are arranged horizontally (e.g., in the X direction in the figure) in the to-be-measured stratum (the number of the plurality of polar plates arranged horizontally is not shown in FIG. 1) and form a group. A plurality of groups of polar plates 12 are arranged vertically (e.g., in the Y direction in the figure) in the to-be-measured stratum. Each polar plate 12 is provided with a plurality of button electrodes (not shown in the figure) on the contact surface with the to-be-measured stratum. Each button electrode conducts a current signal to the to-be-measured stratum and receives a current signal returned from the to-be-measured stratum. The imaging processor 11 calculates the electrical conductivity values of the positions of the to-be-measured stratum corresponding to each button electrode based on the current signals received and transmitted by the button electrodes, and converts the electrical conductivity values into the electrical conductivity values of the positions of the to-be-measured stratum corresponding to each pixel point (i.e., one button electrode can be converted into one corresponding pixel point). The electronic device 10 generates a fracture-cave image by using the method for generating a fracture-cave image provided in the present application based on the electrical conductivity values of the to-be-measured stratum corresponding to each pixel point.

[0050] In some embodiments, the imaging processor can be integrated into the electronic device.

[0051] In some embodiments, the electronic device includes a server, a mobile phone, a tablet computer, a desktop computer, a laptop computer, a notebook computer, an ultra-mobile personal computer (UMPC), or the like.

[0052] It should be noted that FIG. 1 is only a schematic diagram of an application scenario provided by an embodiment of the present application, and the actual form of each device included in FIG. 1 is not limited, nor is the interaction mode between the devices in FIG. 1 limited. In the specific application of the scheme, the actual needs can be set according to the actual needs.

[0053] The technical solutions of the present application and how the technical solutions of the present application solve the above technical problems will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes can not be described in detail in some embodiments. The embodiments of the present application will be described below with reference to the accompanying drawings.

[0054] FIG. 2 is a flowchart of an embodiment of a method for generating a fracture image according to the present disclosure. The method can be implemented by software, hardware, or a combination of software and hardware. As shown in FIG. 2, the method for generating a fracture image can include the following steps:

[0055] At step S110, the electrical imaging data of the formation under test is obtained.

[0056] The electrical imaging data includes electrical conductivity values of the formation under test corresponding to a plurality of pixels. For example, as shown in FIG. 1, the electronic device 10 obtains the electrical conductivity values of the formation under test corresponding to a plurality of pixels from the imaging processor 11. The electrical conductivity value of the formation under test corresponding to each pixel can be represented as C(i, j, k). Wherein C represents the electrical conductivity value, i represents the serial number of the electrode plate, j represents the serial number of the button electrode on each electrode plate, and k represents the serial number of the sampling point, k = 1, 2, …, n.

[0057] It can be understood that i, j, and k can represent the spatial position of each button electrode in the formation under test. i and j together represent the horizontal coordinate (X-axis coordinate) of the button electrode in the two-dimensional coordinate system of the formation under test. k represents the vertical coordinate (Y-axis coordinate) of the button electrode in the formation under test. The electronic device can convert the position and electrical conductivity value of the pixel corresponding to the button electrode in the image through C(i, j, k).

[0058] Wherein the upper limit values of i and j are related to the selected imaging logging technology. If the selected imaging logging technology is fullbore microresistivity imaging (FMI), then i = 1, 2, …, 8, and j = 1, 2, …, 24. If the selected imaging logging technology is extended range micro imager tool (XRMI), then i = 1, 2, …, 6, and j = 1, 2, …, 25. If the selected imaging logging technology is micro-conductivity imaging (MCI), then i = 1, 2, …, 6, and j = 1, 2, …, 24.

[0059] At step S120, the imaging porosity values of the formation under test corresponding to a plurality of pixels are determined respectively according to the electrical conductivity values of the formation under test corresponding to a plurality of pixels.

[0060] Specifically, according to the conductivity value of the pixel point corresponding to the formation to be measured, the imaging porosity value of the pixel point corresponding to the formation to be measured is obtained by combining the preset conversion algorithm. For example, the conductivity value of the first pixel point corresponding to the formation to be measured is obtained according to the conversion algorithm, and the imaging porosity value of the first pixel point corresponding to the formation to be measured is obtained. Similarly, until the imaging porosity value of all pixel points corresponding to the formation to be measured is obtained.

[0061] Step S130: By traversing the imaging porosity values of the formation to be measured corresponding to the plurality of pixel points, the size relationship between the imaging porosity value of the formation to be measured corresponding to each pixel point and the porosity threshold value is compared, and a fracture-cave image is generated.

[0062] The porosity threshold value can be calculated according to the core experiment data of the sampling sample of the formation to be measured. Alternatively, after the number of pixel points and the imaging porosity values of the formation to be measured corresponding to the pixel points are counted, the distribution of the imaging porosity values is analyzed, and the porosity threshold value is determined according to the distribution of the imaging porosity values. The porosity threshold value is used to distinguish the imaging porosity value of the fracture-cave corresponding to the pixel point from the imaging porosity value of the background rock noise corresponding to the pixel point.

[0063] Since the porosity values are arranged from large to small as fracture-cave, background rock noise and background rock. The porosity threshold value is limited between the fracture-cave and the background rock noise, and then by comparing the size relationship between the imaging porosity value of the formation to be measured corresponding to each pixel point and the porosity threshold value, the pixel points corresponding to the fracture-cave, the pixel points corresponding to the background rock noise and the pixel points corresponding to the background rock can be classified. The fracture-cave image generated according to the classified pixel points can more accurately highlight the characteristics of the fracture-cave of the formation to be measured.

[0064] The fracture-cave image is used to identify the fracture-cave type of the formation to be measured. The fracture-cave type includes dissolution pore type, fracture type, fracture-dissolution pore type and cave type, etc. That is, the fracture-cave image can be a dissolution pore image, a fracture image, a fracture-dissolution pore image or a cave image, etc.

[0065] In one application scenario, in combination with FIG. 1 and FIG. 3, after the electronic device obtains the electrical imaging image data of the formation to be measured, the electrical conductivity image of the formation to be measured shown in (1) of FIG. 3 is generated according to the electrical imaging image data, wherein the fractures and the dissolution cavities on the electrical conductivity image are dark high-conductivity responses. Then, according to the electrical conductivity values of the formation to be measured corresponding to the plurality of pixel points in the electrical imaging image data, the imaging porosity values of the formation to be measured corresponding to the plurality of pixel points are respectively determined, and according to the imaging porosity values corresponding to the plurality of pixel points, the porosity image of the formation to be measured shown in (2) of FIG. 3 is generated. Finally, by traversing the imaging porosity values of the formation to be measured corresponding to the plurality of pixel points, the size relationship between the imaging porosity value of the formation to be measured corresponding to each pixel point and the porosity threshold value is compared, and the fracture-cavity image shown in (3) of FIG. 3 is generated, wherein the fractures and the dissolution cavities on the fracture-cavity image are dark high-porosity responses.

[0066] It can be understood that the background rock noise such as shale, induced fractures and borehole collapse and the fractures, dissolution cavities or caves have generally high-conductivity characteristics on the electrical conductivity image, and it is difficult to distinguish them on the electrical conductivity image. However, the shale, induced fractures and borehole collapse generally develop in poor positions of the formation to be measured and have small porosity. The fractures, dissolution cavities and caves develop in good positions of the formation to be measured and have large porosity. The porosity image generated by the imaging porosity values corresponding to the plurality of pixel points can distinguish the fracture-cavity targets and the background rock noise with high conductivity. Then, by comparing the size relationship between the imaging porosity value and the porosity threshold value, the fracture-cavity is segmented from the porosity image to obtain the fracture-cavity image.

[0067] It can be understood that, compared with the electrical conductivity image formed by the electrical imaging image data, the fracture image generated by comparing the size relationship between the imaging porosity value of the formation to be measured corresponding to each pixel point and the porosity threshold value can avoid segmenting the background rock noise with similar conductivity into the fracture-cavity image, and can highlight the characteristics of the fracture-cavity of the formation to be measured.

[0068] In some embodiments, according to the electrical conductivity values of the formation to be measured corresponding to the plurality of pixel points in the electrical imaging image data, the imaging porosity values of the formation to be measured corresponding to the plurality of pixel points are respectively determined, including the following steps:

[0069] Step S210: Obtain the logging data of the formation to be measured and the core experiment data of the sampling sample of the formation to be measured.

[0070] The logging device is also installed in the formation to be measured. The electronic device obtains the logging data of the formation to be measured according to the logging device, and the logging data includes acoustic velocity, neutron porosity and / or natural gamma ray, etc.

[0071] The core experiment data is obtained from a core experiment, specifically, a sampling sample selected from a reservoir in the formation to be measured is fully saturated with a fluid, and a resistivity value and a porosity value of the saturated sampling sample are measured by a measuring instrument. That is, the core experiment data includes the resistivity value and the porosity value of the sampling sample. The reservoir is used to store fluids such as oil and gas.

[0072] Step S220: determining a resistivity value of the formation to be measured and porosity values of different geologies in the formation to be measured according to the logging data.

[0073] Specifically, a corresponding porosity model is selected according to the type of the logging data, and the porosity values of different geologies in the formation to be measured are obtained according to the logging data and the porosity model. For example, the porosity values of different geologies in the formation to be measured are obtained according to the acoustic wave velocity and a time average equation corresponding to the acoustic wave velocity. It can be understood that the porosity values of different geologies in the formation to be measured include the porosity value of mudstone (one of background rock noise) and the porosity value of a solution cavity, etc.

[0074] The logging device includes a dual laterolog instrument, which is used to measure a real-time resistivity value of the formation to be measured. The average resistivity value can be calculated according to the real-time resistivity value, and the average resistivity value (also referred to as a flushed zone resistivity) can be used as the resistivity value of the formation to be measured. For example, the real-time resistivity value is a shallow measurement resistivity value.

[0075] Step S230: determining a cementation exponent of the formation to be measured according to the core experiment data.

[0076] Specifically, the resistivity value and the porosity value of the sampling sample in the core experiment data are brought into Archie's Formula, and the cementation exponent is fitted by a least square method.

[0077] It can be understood that when the formation to be measured is a clastic rock formation, the cementation exponent of the clastic rock formation is an average value of the cementation exponents of each region of the clastic rock formation. When the formation to be measured is a non-uniform carbonate rock formation, the cementation exponents of the non-uniform carbonate rock formation can be average values or each region of the non-uniform carbonate rock formation corresponds to a cementation exponent, because the fracture and cavity distribution of the non-uniform carbonate rock formation is not uniform.

[0078] Step S240: determining an imaging porosity value of the formation to be measured corresponding to each of the plurality of pixel points according to the conductivity value of the formation to be measured corresponding to the plurality of pixel points, the resistivity value of the formation to be measured, the porosity values of different geologies in the formation to be measured, and the cementation exponent of the formation to be measured.

[0079] For example, the imaging porosity value of the formation to be measured corresponding to each pixel point can be calculated according to the following formula (1) (i.e., a conversion algorithm).

[0080] wherein, φ (i,j,k) represents the imaging porosity value of the measured formation corresponding to each pixel point; m represents the cementation index of the measured formation; φ represents the porosity value of different geologies in the measured formation, which is calculated from conventional logging data; R xo represents the resistivity value of the measured formation; C (i,j,k) represents the conductivity value of the measured formation corresponding to each pixel point.

[0081] It can be understood that different geological phenomena with similar conductivity in the measured formation can be converted by formula (1) to obtain the imaging porosity values corresponding to different geological phenomena. For example, mudstone (one kind of background rock noise) and cave are both high conductivity, and it is not easy to distinguish them in terms of conductivity value. However, in the porosity value φ calculated from conventional logging data, the porosity value φ of the cave is large, and the porosity value φ of the mudstone is small. The imaging porosity value φ of the mudstone converted by φ in formula (1) is small, and the imaging porosity value φ of the cave is large. In this way, by the constraint of the porosity value φ of different geological phenomena in the measured formation, the fracture-cave and the background rock noise can be distinguished. Similarly, after the fracture-cave, the background rock noise and the background rock are converted by formula (1), the imaging porosity values corresponding to the fracture-cave, the background rock noise and the background rock can be obtained respectively, and the three imaging porosity values are arranged from large to small as fracture-cave, background rock noise and background rock. (i,j,k) (i,j,k)

[0082] Exemplarily, formula (1) can be derived according to the following method:

[0083] Firstly, according to the Archie formula , the conversion can obtain formula (2):

[0084] wherein, φ represents the porosity value of different geologies in the measured formation, the unit is v / v. R mf is the mud filtrate resistivity, the unit is ohm·m. S xo is the flushed zone water saturation, the unit is v / v. R xo is the resistivity value of the measured formation, the unit is ohm·m. a is the formation factor coefficient in the Archie formula, m is the cementation index, b is the resistivity increase factor coefficient, and n is the saturation index.

[0085] It can be understood that formula (2) reflects the correlation between the porosity value of different geologies in the measured formation and the resistivity value of the measured formation, and according to the correlation, the correlation between the resistivity value of the measured formation corresponding to the pixel point and the imaging porosity value of the measured formation corresponding to the pixel point can be converted, and the expression formula of the correlation corresponding to the pixel point is shown in formula (3). ​​

[0086] The difference from formula (2) is that φ m (i,j,k) R represents the imaging porosity value of the formation to be measured corresponding to the pixel. xoi This represents the resistivity value of the formation to be measured corresponding to the pixel.

[0087] Next, based on formula (3) and the reciprocal relationship between conductivity and resistivity... The correlation between the imaging porosity values ​​is converted into the correlation between the electrical conductivity value of the formation to be tested corresponding to the pixel and the imaging porosity value of the formation to be tested corresponding to the pixel, thus obtaining formula (4).

[0088] The difference from formula (3) is that C (i,j,k) This represents the conductivity value of the formation to be measured corresponding to the pixel.

[0089] After obtaining formula (4), in formula (4) φ m (i,j,k) Multiply both the numerator and denominator of the relative expression (i.e., on the right side of formula (4)) by the resistivity value R of the formation being measured. xo Formula (5) is obtained.

[0090] Substituting equation (2) into equation (5), R mf For the resistivity of mud filtrate, S xo By eliminating the water saturation of the flushing zone, a is the formation factor coefficient in Archie's formula, m is the cementation index, b is the resistivity increase factor coefficient, and n is the saturation index, we obtain formula (1).

[0091] It is understandable that formula (1) reflects the conductivity value C of the stratum to be measured corresponding to the pixel. (i,j,k) The imaging porosity value φ of the formation to be measured corresponding to the pixel. (i,j,k) The correlation between them. After the electronic device obtains the conductivity value of the test stratum corresponding to each pixel, it inputs it into formula (1) and outputs the imaging porosity value of the test stratum corresponding to each pixel.

[0092] In some embodiments, the method for generating the pore image further includes calculating a porosity threshold, and there are various ways to calculate the porosity threshold. In one embodiment, the calculation of the porosity threshold includes the following steps:

[0093] Step S310: Generate a porosity image of the formation to be tested based on the imaging porosity values ​​of the formation to be tested corresponding to multiple pixels.

[0094] Specifically, according to the spatial positions of the plurality of pixel points and the plurality of imaging porosity values corresponding to the plurality of pixel points, a porosity image of the formation to be measured as shown in (1) of FIG. 3 is generated.

[0095] Step S320: generating a histogram according to the porosity image.

[0096] The histogram is used to count the number of pixel points corresponding to each imaging porosity value in the porosity image.

[0097] For example, FIG. 4 is a histogram generated according to the porosity image. The abscissa of the histogram represents the imaging porosity value corresponding to each pixel point in the porosity image, and the ordinate represents the number of pixel points corresponding to each imaging porosity value in the porosity image.

[0098] Step S330: determining the valley porosity value according to the porosity statistical spectrum peak in the histogram.

[0099] Step S340: taking the valley porosity value as the porosity threshold.

[0100] Specifically, referring to FIG. 4, the valley porosity value between the two porosity statistical spectrum peaks in the porosity histogram image is determined , which is taken as the preset porosity threshold.

[0101] It can be understood that the porosity statistics of the background rock and noise calculation and the porosity statistics of the effective fracture and dissolution hole calculation are both Gaussian distribution, and the superposition of them will have two statistical spectrum peaks. The valley porosity corresponding to the low valley of the superposition spectrum peak is the preset porosity threshold, which can more accurately distinguish the pixel points corresponding to the fractures and holes and the background rock and noise.

[0102] In another embodiment, the Otsu method, i.e., the maximum between-class variance method (OTSU), can also be used to determine the porosity threshold. Specifically, the calculation of the porosity threshold includes the following steps:

[0103] Step S410: taking the plurality of imaging porosity values in the histogram as a plurality of candidate thresholds, traversing the plurality of candidate thresholds to obtain a plurality of between-class variances corresponding to the plurality of candidate thresholds;

[0104] Step S420: comparing the plurality of between-class variances to determine that the candidate threshold with the maximum between-class variance is the porosity threshold.

[0105] When each candidate threshold is traversed, the pixel points with imaging porosity values greater than or equal to the candidate threshold are divided into a first group, and the pixel points with imaging porosity values less than the candidate threshold are divided into a second group. According to the pixel points and imaging porosity values of the first group and the pixel points and imaging porosity values of the second group, the between-class variance corresponding to the candidate threshold is obtained.

[0106] It can be understood that the inter-class variance is a statistical quantity for measuring the difference between the first group of pixel points and the second group of pixel points. Taking the candidate threshold value with the maximum inter-class variance as the porosity threshold value means that the difference between the imaging porosity values of the two groups of pixel points is maximized, which is conducive to segmenting the first group of pixel points and the second group of pixel points, that is, it is conducive to more clearly segmenting the pixel points belonging to the fracture-vug and the pixel points belonging to the background rock noise and the background rock.

[0107] In still another embodiment, the calculation of the porosity threshold value comprises the following steps:

[0108] Step S510: determining the lower limit value of the porosity of the formation to be measured according to the core experiment data of the sampling sample of the formation to be measured.

[0109] The core experiment data further includes the irreducible water saturation and the permeability. Specifically, under a certain pressure, one end of the sampling sample is injected, and the outflow is collected from the other end. By measuring the inflow and outflow and the pressure difference between the two ends of the sampling sample, the permeability is calculated in combination with Darcy's law. The sampling sample is placed in a centrifuge, and under the action of centrifugal force, the movable water is discharged, and the irreducible water remains in the pores of the sampling sample. By measuring the weight change before and after centrifugation, the irreducible water saturation of the sampling sample can be calculated.

[0110] Then, the correlation between the porosity, the permeability and the irreducible water saturation is established. When the irreducible water saturation is a certain value, for example, 80%, the porosity corresponding to the irreducible water saturation is the lower limit value of the porosity of the formation to be measured.

[0111] Step S520: taking the lower limit value of the porosity as the porosity threshold value.

[0112] It can be understood that the sampling sample comes from the reservoir, and according to the lower limit value of the porosity of the sampling sample, the lower limit value of the porosity of the reservoir can be obtained. In the formation to be measured, there is a non-reservoir, and the porosity of the non-reservoir is lower than that of the reservoir. Compared with the reservoir, the productivity of the fracture-vug in the non-reservoir is reduced, and the economic benefit is reduced. Taking the lower limit value of the porosity of the reservoir as the porosity threshold value can divide the reservoir and the non-reservoir (the non-reservoir also includes the background rock and the background rock noise) so as to focus on analyzing the fracture-vug of the reservoir with high productivity and high economic benefit.

[0113] In some embodiments, by traversing the imaging porosity values of the formation to be measured corresponding to a plurality of pixel points, comparing the size relationship between the imaging porosity value of the formation to be measured corresponding to each pixel point and the porosity threshold value, a fracture-vug image is generated, and there are various embodiments. In one embodiment, it comprises the following steps:

[0114] Step S610: Taking a first pixel point in the plurality of pixel points as an example, if the imaging porosity value of the first pixel point corresponding to the formation under test is greater than or equal to the porosity threshold, the imaging porosity value of the first pixel point corresponding to the formation under test is not updated.

[0115] It can be understood that the imaging porosity value greater than or equal to the porosity threshold indicates that the pixel point corresponding to the imaging porosity value is a pixel point corresponding to a fracture-cave, and the imaging porosity value remains unchanged and is displayed as dark color in the fracture-cave image.

[0116] Step S620: If the imaging porosity value of the first pixel point corresponding to the formation under test is less than the porosity threshold, the imaging porosity value of the first pixel point corresponding to the formation under test is updated to 0.

[0117] It can be understood that the imaging porosity value less than the porosity threshold indicates that the pixel point corresponding to the imaging porosity value is a pixel point corresponding to background rock noise or background rock, and the imaging porosity value is updated to 0 and is not displayed in the fracture-cave image.

[0118] Step S630: After traversing the plurality of pixel points, a fracture-cave image is generated according to the imaging porosity values of the plurality of pixel points corresponding to the formation under test which are not updated and updated.

[0119] It can be understood that the pixel point corresponding to the fracture-cave in the generated fracture-cave image is displayed as dark color, and the background rock noise and the background rock are not displayed. According to the imaging porosity values of the plurality of pixel points corresponding to the formation under test which are not updated and updated, the fracture-cave image is generated, and the generated fracture-cave image is a porosity image.

[0120] For example, as shown in the porosity image of (2) of FIG. 3, each pixel point has a corresponding imaging porosity value of the formation under test. It is compared whether the imaging porosity value of each pixel point corresponding to the formation under test is greater than or equal to the porosity threshold. Taking a first pixel point in the porosity image as an example, if the imaging porosity value of the first pixel point corresponding to the formation under test is greater than or equal to the porosity threshold, the imaging porosity value of the first pixel point corresponding to the formation under test is not updated (i.e., the first pixel point remains unchanged in the imaging porosity value). If the imaging porosity value of the first pixel point corresponding to the formation under test is less than the porosity threshold, the imaging porosity value of the first pixel point corresponding to the formation under test is updated to 0. After traversing the plurality of pixel points, a fracture-cave image of (3) of FIG. 3 is generated according to the imaging porosity values of the plurality of pixel points corresponding to the formation under test which are not updated and updated. In another embodiment, the following steps are included:

[0121] Step S710: Taking a first pixel point in the plurality of pixel points as an example, if the imaging porosity value of the first pixel point corresponding to the formation under test is greater than or equal to the porosity threshold, the conductivity value of the first pixel point corresponding to the formation under test is not updated.

[0122] It can be understood that the imaging porosity value greater than or equal to the porosity threshold value indicates that the pixel point corresponding to the imaging porosity value is the pixel point corresponding to the fracture-vug, and the conductivity value remains unchanged, and the fracture-vug image is displayed in dark color.

[0123] Step S720: If the imaging porosity value of the first pixel point corresponding to the formation under test is less than the porosity threshold value, the conductivity value of the first pixel point corresponding to the formation under test is updated to 0.

[0124] It can be understood that the imaging porosity value less than the porosity threshold value indicates that the pixel point corresponding to the imaging porosity value is the pixel point corresponding to the fracture-vug, and the updated conductivity value is 0, and the fracture-vug image is not displayed.

[0125] Step S730: After traversing the plurality of pixel points, the fracture-vug image is generated according to the conductivity values of the plurality of pixel points corresponding to the formation under test before and after the update.

[0126] It can be understood that the pixel point corresponding to the fracture-vug in the generated fracture-vug image is displayed in dark color, and the background rock noise and the background rock are not displayed. The fracture-vug image is generated according to the conductivity values of the plurality of pixel points corresponding to the formation under test before and after the update, and the generated fracture-vug image is the conductivity image.

[0127] For example, as shown in the conductivity image of (1) of FIG. 3, each pixel point has a corresponding conductivity value of the formation under test, and the conductivity value corresponds to the imaging porosity value of the formation under test. It is compared whether the imaging porosity value of each pixel point corresponding to the formation under test is greater than or equal to the porosity threshold value. For example, the first pixel point in the conductivity image, if the imaging porosity value of the first pixel point corresponding to the formation under test is greater than or equal to the porosity threshold value, the conductivity value of the first pixel point corresponding to the formation under test is not updated (that is, the first pixel point remains unchanged). If the imaging porosity value of the first pixel point corresponding to the formation under test is less than the porosity threshold value, the conductivity value of the first pixel point corresponding to the formation under test is updated to 0. After traversing the plurality of pixel points, the fracture-vug image of (3) of FIG. 3 is generated according to the conductivity values of the plurality of pixel points corresponding to the formation under test before and after the update.

[0128] It can be understood that the fracture-vug image can be generated from the conductivity values of the plurality of pixel points corresponding to the formation under test before and after the update, and the conductivity value of the generated fracture-vug image can be used to study the electrical properties of the fracture-vug while presenting the fracture-vug characteristics. The fracture-vug image can also be generated from the imaging porosity values of the plurality of pixel points corresponding to the formation under test, and the imaging porosity value of the generated fracture-vug image is not affected by other properties, and the results of the fracture-vug of a small size can be more intuitively observed.

[0129] It can be understood that the generation method of the fracture-cave image composed of all or part of the above embodiments converts the conductivity into the imaging porosity value through formula (1), and the imaging porosity values corresponding to the fracture-cave, the background rock noise and the background rock can be obtained respectively, and the three imaging porosity values are arranged from large to small as the fracture-cave, the background rock noise and the background rock. Then, the imaging porosity or conductivity value of the pixel point greater than the imaging porosity threshold value is determined to be unchanged, and the imaging porosity or conductivity value of the other pixel points is set to 0, so that the fracture-cave image can only display the fracture-cave characteristics, that is, the fracture-cave image can be segmented.

[0130] The generation method of the fracture-cave image composed of all or part of the above embodiments can be applied to various to-be-measured strata, including the Ordovician fracture-cave type carbonate rock strata in the Tarim Basin as shown in FIGS. 5 to 8. The fracture-cave type carbonate rock strata are further divided into a dissolution pore-cave type stratum, a fracture type stratum and a fracture-dissolution pore-cave type stratum. That is, FIG. 5 is a segmentation example of the dissolution pore-cave type stratum, FIG. 6 is a segmentation example of the fracture type stratum, FIG. 7 is a segmentation example of the fracture-dissolution pore-cave type stratum, and FIG. 8 is a segmentation example of the cave type stratum.

[0131] The generation method of the fracture-cave image composed of all or part of the above embodiments is also applicable to other lithological strata with secondary fracture-cave development without additional conductive minerals, such as fracture type tight sandstone and fracture-cave type dolomite. For example, FIG. 9 shows a segmentation example of the Cretaceous fracture type tight sandstone stratum in the Kuqa Depression of the Tarim Basin, and FIG. 10 shows a segmentation example of the Cambrian dolomite stratum in the Tarim Basin.

[0132] Specifically, taking the dissolution pore-cave type stratum segmentation example shown in FIG. 5 as an example, the process of generating the dissolution pore-cave image by the generation method of the fracture-cave image composed of all or part of the above embodiments is described.

[0133] As shown in FIG. 5, after the electronic device obtains the electrical imaging image data of the dissolution pore-cave type stratum, the conductivity image (i.e., a static image) of the dissolution pore-cave type stratum shown in the second row is generated according to the electrical imaging image data. The static image is used to show the characteristics of the dissolution pore-cave type stratum at a certain depth. Then, the electrical imaging image data is subjected to shallow resistivity scaling processing, and the scaling image shown in the fifth row is generated according to the scaled electrical imaging image data. Then, the imaging porosity values of the dissolution pore-cave type stratum corresponding to a plurality of pixel points are determined according to the conductivity values of the dissolution pore-cave type stratum corresponding to the plurality of pixel points in the electrical imaging image data, and the porosity image shown in the sixth row is generated according to the spatial positions of the plurality of pixel points and the corresponding imaging porosity values. Finally, the imaging porosity values of the dissolution pore-cave type stratum corresponding to the plurality of pixel points are traversed, the size relationship between the imaging porosity value of the dissolution pore-cave type stratum corresponding to each pixel point and the porosity threshold value is compared, and the segmented porosity image shown in the seventh row (the segmented porosity image is the generated dissolution pore-cave image) is generated.

[0134] The first track is a natural gamma curve, which is used to determine the lithology of the dissolution pore hole type formation. The third track is a conductivity image (i.e., a dynamic image) of the dissolution pore hole type formation, and the dynamic image is used to show the characteristics of the dissolution pore hole type formation at different depths. The fourth track is a depth track, which is used to represent the depths of the dissolution pore hole type formation corresponding to the second, third, fifth, sixth, and seventh tracks.

[0135] It can be understood that, compared with the second track static image formed by the electro-imaging image data, the seventh track dissolution pore hole image generated by the generation method of the fracture-cave image composed of all or part of the embodiments can highlight the dissolution pore hole characteristics of the dissolution pore hole type formation, that is, the dissolution pore hole target can be better segmented.

[0136] The fracture-cave image generation process of the segmentation example shown in FIGS. 6-10 is consistent with the principle of the example shown in FIG. 5, and will not be described here.

[0137] Through the fracture image generation process shown in FIG. 6, it can be understood that, compared with the second track static image formed by the electro-imaging image data, the seventh track fracture image generated by the generation method of the fracture-cave image composed of all or part of the embodiments can highlight the fracture characteristics of the fracture formation, that is, the fracture target can be better segmented.

[0138] Through the fracture and dissolution pore hole image generation process shown in FIG. 7, it can be understood that, compared with the second track static image formed by the electro-imaging image data, the seventh track fracture and dissolution pore hole image generated by the generation method of the fracture-cave image composed of all or part of the embodiments can highlight the fracture and dissolution pore hole characteristics of the fracture-dissolution pore hole type formation, that is, the fracture and dissolution pore hole target can be better segmented.

[0139] Through the cave image generation process shown in FIG. 8, it can be understood that, compared with the second track static image formed by the electro-imaging image data, the seventh track cave image generated by the generation method of the fracture-cave image composed of all or part of the embodiments can highlight the cave characteristics of the cave type formation, that is, the cave target can be better segmented.

[0140] Through the fracture image generation process shown in FIG. 9, it can be understood that, compared with the second track static image formed by the electro-imaging image data, the seventh track fracture image generated by the generation method of the fracture-cave image composed of all or part of the embodiments can highlight the fracture characteristics of the Kuqu depression fractured tight sandstone formation, that is, the fracture target can be better segmented.

[0141] Through the fracture and dissolution pore image generation process shown in FIG. 10, it can be understood that, compared with the second static image formed by the electrical imaging image data, the seventh fracture and dissolution pore image generated by the generation method of the fracture and dissolution pore image composed of all or part of the embodiments can highlight the fracture and dissolution pore characteristics of the Cambrian dolomite formation in the Tarim Basin better, that is, the fracture and dissolution pore target can be better segmented out.

[0142] In FIGS. 9 and 10, the first one is added with a natural potential curve, which is used to identify sandstone and mudstone layers.

[0143] FIG. 11 is a structural schematic diagram of an embodiment one of a fracture and dissolution pore image generation device provided by the present application. The device 40 can be integrated in the electronic device 10 in the method embodiments described above, or can be realized by the electronic device 10 in the method embodiments described above. As shown in FIG. 11, the generation device 40 includes an acquisition module 41, a determination module 42 and an image generation module 43.

[0144] The acquisition module 41 is configured to acquire electrical imaging image data of a to-be-measured formation, the electrical imaging image data including electrical conductivity values of the to-be-measured formation corresponding to a plurality of pixel points.

[0145] The determination module 42 is configured to determine imaging porosity values of the to-be-measured formation corresponding to the plurality of pixel points respectively according to the electrical conductivity values of the to-be-measured formation corresponding to the plurality of pixel points in the electrical imaging image data.

[0146] The image generation module 43 is configured to generate a fracture and dissolution pore image by traversing the imaging porosity values of the to-be-measured formation corresponding to the plurality of pixel points, comparing a size relationship between the imaging porosity value of the to-be-measured formation corresponding to each pixel point and a porosity threshold value.

[0147] In some embodiments, the acquisition module 41 is further configured to acquire logging data of the to-be-measured formation and core experiment data of a sampling sample of the to-be-measured formation. The electrical resistivity value of the to-be-measured formation and the porosity values of different geologies in the to-be-measured formation are determined according to the logging data. The cementation index of the to-be-measured formation is determined according to the core experiment data. The imaging porosity values of the to-be-measured formation corresponding to the plurality of pixel points are determined according to the electrical conductivity values of the to-be-measured formation corresponding to the plurality of pixel points in the electrical imaging image data, the electrical resistivity value of the to-be-measured formation, the porosity values of different geologies in the to-be-measured formation and the cementation index of the to-be-measured formation according to the following formula:

[0148] wherein, φ (i,j,k) represents the imaging porosity value of the to-be-measured formation corresponding to each pixel point; m represents the cementation index of the to-be-measured formation; φ represents the porosity values of different geologies in the to-be-measured formation; R xo represents the electrical resistivity value of the to-be-measured formation; C (i,j,k) represents the electrical conductivity value of the to-be-measured formation corresponding to each pixel point.

[0149] In some embodiments, the determining module 42 is further configured to generate a porosity image of the formation under test according to the imaging porosity values of the plurality of pixels corresponding to the formation under test. A histogram is generated according to the porosity image, the histogram is used to count the number of pixels corresponding to each imaging porosity value in the porosity image; a valley point porosity value is determined according to a porosity statistical peak in the histogram; the valley point porosity value is taken as the porosity threshold value; or a plurality of imaging porosity values in the histogram are taken as a plurality of candidate threshold values, a plurality of inter-class variances corresponding to the plurality of candidate threshold values are obtained by traversing the plurality of candidate threshold values; the plurality of inter-class variances are compared, and a candidate threshold value with the largest inter-class variance is determined as the porosity threshold value; wherein, when each candidate threshold value is traversed, the pixels with imaging porosity values greater than or equal to the candidate threshold value are divided into a first group, and the pixels with imaging porosity values less than the candidate threshold value are divided into a second group; the inter-class variance corresponding to the candidate threshold value is obtained according to the pixels and imaging porosity values of the first group and the pixels and imaging porosity values of the second group.

[0150] In some embodiments, the determining module 42 is further configured to determine a lower limit value of the porosity of the formation under test according to core experiment data of a sampling sample of the formation under test; the core experiment data includes a porosity value, a permeability and a bound water saturation of the sampling sample. The lower limit value of the porosity is taken as the porosity threshold value.

[0151] In some embodiments, the image generating module 43 is further configured to take a first pixel in the plurality of pixels as an example, if the imaging porosity value of the formation under test corresponding to the first pixel is greater than or equal to the porosity threshold value, the imaging porosity value of the formation under test corresponding to the first pixel is not updated. If the imaging porosity value of the formation under test corresponding to the first pixel is less than the porosity threshold value, the imaging porosity value of the formation under test corresponding to the first pixel is updated to 0. After traversing the plurality of pixels, the fracture-cave image is generated according to the imaging porosity values of the formation under test corresponding to the plurality of pixels which are not updated and updated.

[0152] In some embodiments, the image generating module 43 is further configured to take a first pixel in the plurality of pixels as an example, if the imaging porosity value of the formation under test corresponding to the first pixel is greater than or equal to the porosity threshold value, the conductivity value of the formation under test corresponding to the first pixel is not updated. If the imaging porosity value of the formation under test corresponding to the first pixel is less than the porosity threshold value, the conductivity value of the formation under test corresponding to the first pixel is updated to 0. After traversing the plurality of pixels, the fracture-cave image is generated according to the conductivity values of the formation under test corresponding to the plurality of pixels which are not updated and updated.

[0153] The determining apparatus 40 provided in the embodiments is configured to implement the technical solutions in any of the foregoing method embodiments, and has similar implementation principles and technical effects, which will not be described herein again.

[0154] Fig. 12 is a structural schematic diagram of an electronic device provided in the present application. As shown in Fig. 12, the electronic device 10 comprises:

[0155] a processor 11, a memory 12, a bus 14 and a display 13;

[0156] The memory 12 is configured to store computer program codes of the processor 11.

[0157] The processor 11 is configured to execute the technical solutions of the electronic device in any one of the preceding method embodiments by executing the computer program codes.

[0158] Optionally, the memory 12 can be independent or integrated with the processor 11.

[0159] The memory 12 and the display 13 are connected with the processor 11 through the bus 14 and complete communication with each other.

[0160] Optionally, the display 13 is at least configured to display a seam hole image.

[0161] Optionally, the memory 12 can include a random access memory (RAM) and can also include a non-volatile memory, such as at least one disk memory.

[0162] The bus 14 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, only one thick line is shown in the figure, but it does not mean that there is only one bus or only one type of bus.

[0163] The processor described above can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0164] The electronic device 10 is configured to execute the technical solutions of the electronic device in any one of the preceding method embodiments, and the implementation principles and technical effects are similar, which will not be described here.

[0165] The embodiment of the present application further provides a readable storage medium, which stores a computer program, and the computer program is executed by a processor to realize the technical solution provided by any of the foregoing embodiments.

[0166] The embodiment of the present application further provides a chip system, which is applied to an electronic device and includes one or more processors, and the one or more processors are used to call computer instructions to enable the electronic device to realize the technical solution provided by any of the foregoing embodiments.

[0167] The embodiment of the present application further provides a computer program product, which includes a computer program, and the computer program is executed by a processor to realize the technical solution provided by any of the foregoing method embodiments.

[0168] Those skilled in the art can understand that all or part of the steps of the foregoing method embodiments can be completed by using a program instruction related hardware. The foregoing program can be stored in a computer readable storage medium. When the program is executed, the steps of the foregoing method embodiments are executed; and the foregoing storage medium includes: a ROM, a RAM, a magnetic disk or an optical disk and various storage media that can store program codes.

[0169] Finally, it should be noted that: the foregoing embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for part or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method of generating a fracture-vug image, characterized by, The method comprises: obtaining electrical imaging image data of a formation to be measured, the electrical imaging image data comprising electrical conductivity values of the formation to be measured corresponding to a plurality of pixels; determining imaging porosity values of the formation to be measured corresponding to the plurality of pixels respectively according to the electrical conductivity values of the formation to be measured corresponding to the plurality of pixels; generating a fracture-cave image by traversing the imaging porosity values of the formation to be measured corresponding to the plurality of pixels, comparing the imaging porosity value of the formation to be measured corresponding to each pixel with a porosity threshold, and generating the fracture-cave image.

2. The method of claim 1, wherein, The electrical conductivity values of the formation to be measured corresponding to the plurality of pixels, the determination of the imaging porosity values of the formation to be measured corresponding to the plurality of pixels respectively comprises: obtaining logging data of the formation to be measured and core experiment data of a sampling sample of the formation to be measured; determining resistivity values of the formation to be measured and porosity values of different geologies in the formation to be measured according to the logging data; determining cementation indexes of the formation to be measured according to the core experiment data; According to the conductivity value of the formation to be measured corresponding to the plurality of pixel points, the resistivity value of the formation to be measured, the porosity value of different geologies in the formation to be measured, and the cementation index of the formation to be measured, the imaging porosity value of the formation to be measured corresponding to the plurality of pixel points is determined according to the following formula: wherein, φ (i,j,k) represents the imaging porosity value of the formation to be measured corresponding to each pixel point; m represents the cementation index of the formation to be measured; φ represents the porosity value of different geologies in the formation to be measured; R xo represents the resistivity value of the formation to be measured; C (i,j,k) represents the conductivity value of the formation to be measured corresponding to each pixel point.

3. The method according to claim 1 or 2, characterized in that, The method further comprises: generating a porosity image of the formation to be measured according to the imaging porosity values of the formation to be measured corresponding to the plurality of pixels; generating a histogram according to the porosity image, the histogram being used to count the number of pixels corresponding to each imaging porosity value in the porosity image; determining a valley porosity value according to a porosity statistical peak in the histogram; taking the valley porosity value as the porosity threshold; or taking a plurality of imaging porosity values in the histogram as a plurality of candidate thresholds, traversing the plurality of candidate thresholds to obtain a plurality of inter-class variances corresponding to the plurality of candidate thresholds; comparing the plurality of inter-class variances to determine a candidate threshold with the largest inter-class variance as the porosity threshold; wherein, when each candidate threshold is traversed, pixels with imaging porosity values greater than or equal to the candidate threshold are divided into a first group, and pixels with imaging porosity values less than the candidate threshold are divided into a second group; the inter-class variance corresponding to the candidate threshold is obtained according to the pixels and imaging porosity values of the first group and the pixels and imaging porosity values of the second group.

4. The method according to any one of claims 1 to 3, characterized in that, The method further comprises: determining a lower limit value of porosity of the formation to be measured according to the core experiment data of the sampling sample of the formation to be measured; taking the lower limit value of porosity as the porosity threshold.

5. The method according to any one of claims 1 to 4, characterized in that, The generation of the fracture-cave image by traversing the imaging porosity values of the formation to be measured corresponding to the plurality of pixels, comparing the imaging porosity value of the formation to be measured corresponding to each pixel with the porosity threshold, and generating the fracture-cave image comprises: taking a first pixel in the plurality of pixels as an example, if the imaging porosity value of the formation to be measured corresponding to the first pixel is greater than or equal to the porosity threshold, the imaging porosity value of the formation to be measured corresponding to the first pixel is not updated; if the imaging porosity value of the formation to be measured corresponding to the first pixel is less than the porosity threshold, the imaging porosity value of the formation to be measured corresponding to the first pixel is updated to 0; after the plurality of pixels are traversed, the fracture-cave image is generated according to the imaging porosity values of the formation to be measured corresponding to the plurality of pixels that are not updated and updated.

6. The method according to any one of claims 1 to 4, characterized in that, The generating the fracture-cave image by traversing the imaging porosity values of the multiple pixel points corresponding to the formation to be measured, comparing the imaging porosity values of each pixel point corresponding to the formation to be measured with the porosity threshold, comprises: Taking a first pixel point in the multiple pixel points as an example, if the imaging porosity value of the formation to be measured corresponding to the first pixel point is greater than or equal to the porosity threshold, the conductivity value of the formation to be measured corresponding to the first pixel point is not updated; If the imaging porosity value of the formation to be measured corresponding to the first pixel point is less than the porosity threshold, the conductivity value of the formation to be measured corresponding to the first pixel point is updated to 0; After traversing the multiple pixel points, the fracture-cave image is generated according to the conductivity values of the formation to be measured corresponding to the multiple pixel points which are not updated and updated.

7. An apparatus for generating a fracture image, comprising: The generating device comprises: An acquisition module configured to acquire electrical imaging image data of a formation to be measured, the electrical imaging image data comprising conductivity values of the formation to be measured corresponding to multiple pixel points; A determination module configured to determine imaging porosity values of the formation to be measured corresponding to the multiple pixel points respectively according to the conductivity values of the formation to be measured corresponding to the multiple pixel points in the electrical imaging image data; An image generation module configured to generate a fracture-cave image by traversing the imaging porosity values of the multiple pixel points corresponding to the formation to be measured, comparing the imaging porosity values of each pixel point corresponding to the formation to be measured with the porosity threshold.

8. An electronic device, comprising: Comprise: A processor, a memory and a display; The memory is coupled with the processor, and the memory is configured to store computer program code, and the processor is configured to invoke the computer program code to enable the electronic device to execute the method according to any one of claims 1 to 6; The display is configured to display the fracture-cave image obtained according to the method of any one of claims 1 to 6.

9. A readable storage medium, characterized by, The computer program is stored on the readable storage medium, and the computer program is executed by the processor to implement the method of any one of claims 1 to 6.

10. A computer program product, characterised in that, The computer program is stored on the readable storage medium, and the computer program is executed by the processor to implement the method of any one of claims 1 to 6. The computer program is stored on the readable storage medium, and the computer program is executed by the processor to implement the method of any one of claims 1 to 6.

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