Fracture-cavity image generation method and device, equipment and storage medium

Abstract

The invention provides a fracture-cavity image generation method and device, equipment and a storage medium, and the method comprises the steps: obtaining the electrical imaging image data of a to-be-measured stratum, the electrical imaging image data comprising the conductivity values of the to-be-measured stratum corresponding to a plurality of pixel points; according to the conductivity values of the to-be-measured stratum corresponding to a plurality of pixel points in the electric imaging image data, respectively determining imaging porosity values of the to-be-measured stratum corresponding to the plurality of pixel points; and through traversing the imaging porosity values of the to-be-detected stratum corresponding to the plurality of pixel points, comparing the size relationship between the imaging porosity value of the to-be-detected stratum corresponding to each pixel point and the porosity threshold value, and generating a fracture-cavity image. According to the method, the electrical conductivity value in the electrical imaging image is subjected to preset data processing and data comparison, so that the high-quality fracture-cavity image with the image resolution greater than the preset resolution is generated, and data support is provided for subsequent fracture-cavity type identification.

Description

Technical Field

[0001] This application relates to the field of geological exploration and development, and in particular to a method, apparatus, device and storage medium for generating fracture-cavity images. Background Technology

[0002] Well logging imaging images are commonly used to qualitatively identify and quantitatively evaluate the development of formation fractures and pores. A crucial step in quantitatively evaluating formation fracture and pore development using well logging imaging images is segmenting the fracture and pore images from the images. Well logging imaging images include, for example, electrical imaging images and acoustic imaging images.

[0003] In related technologies, application CN117409028A discloses a well logging imaging image segmentation method, which uses a preset sliding window to segment well logging imaging images and determines the fracture and cavity types of formations based on the segmented images. The problem with this image segmentation process is that in tight sections, the target obtained after image segmentation may be background noise rather than a valid fracture and cavity target; that is, the image quality of the fracture and cavity images obtained from image segmentation is low, which may lead to the inability to identify the fracture and cavity type from the image.

[0004] Therefore, how to extract high-quality fracture-cavity images from well logging imaging images is a problem that urgently needs to be solved. Summary of the Invention

[0005] This application provides a method, apparatus, device, and storage medium for generating fracture-cavity images. By performing preset data processing on the conductivity values ​​of the test stratum corresponding to multiple pixels in the electrical imaging image data, the imaging porosity values ​​of the test stratum corresponding to multiple pixels are obtained. By comparing the pixel-level values ​​with a preset porosity threshold, the imaging porosity values ​​of the pixels corresponding to background rock noise are identified and updated, so as to generate a high-quality fracture-cavity image with an image resolution greater than the preset resolution, providing data support for subsequent identification of fracture-cavity types.

[0006] The first aspect of this application provides a method for generating a fracture-vuggy image. The method includes: acquiring electrical imaging image data of a formation to be tested, the electrical imaging image data including electrical conductivity values ​​of the formation to be tested corresponding to multiple pixels; determining imaging porosity values ​​of the formation to be tested corresponding to the multiple pixels based on the electrical conductivity values ​​of the formation to be tested; and generating a fracture-vuggy image by traversing the imaging porosity values ​​of the formation to be tested corresponding to the multiple pixels and comparing the relationship between the imaging porosity value of the formation to be tested corresponding to each pixel and a porosity threshold.

[0007] In some embodiments, the imaging porosity value of the formation to be tested corresponding to multiple pixels is determined based on the electrical conductivity value of the formation to be tested corresponding to multiple pixels, including: acquiring well logging data of the formation to be tested and core experimental data of samples taken from the formation to be tested; determining the resistivity value of the formation to be tested and the porosity value of different geological formations in the formation to be tested based on the well logging data; determining the cementation index of the formation to be tested based on the core experimental data; and determining the imaging porosity value of the formation to be tested corresponding to multiple pixels according to the following formula based on the electrical conductivity value of the formation to be tested, the resistivity value of the formation to be tested, the porosity value of different geological formations in the formation to be tested, and the cementation index:

[0008]

[0009] Where, φ (i,j,k) R represents the imaging porosity value of the stratum under test corresponding to each pixel; m represents the cementation index of the stratum under test; φ represents the porosity value φ of different geological formations in the stratum under test; xo C represents the resistivity value of the formation being measured. (i,j,k) This represents the conductivity value of the geological formation corresponding to each pixel.

[0010] In some embodiments, the method further includes: generating 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; generating a histogram based on the porosity image, wherein the histogram is used to count the number of pixels corresponding to each imaging porosity value in the porosity image;

[0011] Determine the porosity value at the valley point based on the porosity statistical peaks in the histogram;

[0012] Use the valley point porosity value as the porosity threshold; or

[0013] Multiple imaging porosity values ​​in the histogram are used as multiple candidate thresholds. By traversing multiple candidate thresholds, multiple inter-class variances corresponding to multiple candidate thresholds are obtained.

[0014] Compare multiple inter-class variances and determine the candidate threshold with the largest inter-class variance as the porosity threshold;

[0015] Specifically, when traversing each candidate threshold, pixels with imaging porosity values ​​greater than or equal to the candidate threshold are divided into the first group, and pixels with imaging porosity values ​​less than the candidate threshold are divided into the second group. Based on the pixels and imaging porosity values ​​of the first group and the pixels and imaging porosity values ​​of the second group, the inter-class variance corresponding to the candidate threshold is obtained.

[0016] In some embodiments, the method further includes: determining a lower limit value of porosity of the formation to be tested based on core experimental data of samples taken from the formation to be tested.

[0017] In some embodiments, a fracture-vuggy image is generated by traversing the imaging porosity values ​​of the formation to be tested corresponding to multiple pixels and comparing the relationship between the imaging porosity value of the formation to be tested corresponding to each pixel and a porosity threshold. This includes: taking a first pixel among the multiple pixels as an example; if the imaging porosity value of the formation to be tested corresponding to the first pixel is greater than or equal to the porosity threshold, the imaging porosity value of the formation to be tested corresponding to the first pixel is not updated; if the imaging porosity value of the formation to be tested corresponding to the first pixel is less than the porosity threshold, the imaging porosity value of the formation to be tested corresponding to the first pixel is updated to 0; after traversing multiple pixels, a fracture-vuggy image is generated based on the unupdated and updated imaging porosity values ​​of the formation to be tested corresponding to the multiple pixels.

[0018] In some embodiments, a fracture-vuggy image is generated by traversing the imaging porosity values ​​of the formation to be tested corresponding to multiple pixels and comparing the relationship between the imaging porosity value of the formation to be tested corresponding to each pixel and a porosity threshold. This includes: taking a first pixel among the multiple pixels as an example, if the imaging porosity value of the formation to be tested corresponding to the first pixel is greater than or equal to the porosity threshold, the conductivity value of the formation to be tested corresponding to the first pixel is not updated; if the imaging porosity value of the formation to be tested corresponding to the first pixel is less than the porosity threshold, the conductivity value of the formation to be tested corresponding to the first pixel is updated to 0; after traversing multiple pixels, a fracture-vuggy image is generated based on the conductivity values ​​of the formation to be tested corresponding to the multiple pixels that are not updated and those that are updated.

[0019] A second aspect of this application provides an apparatus for generating an image of a slit hole, the apparatus comprising:

[0020] The acquisition module is used to acquire electrical imaging image data of the formation to be tested. The electrical imaging image data includes the electrical conductivity values ​​of the formation to be tested corresponding to multiple pixels.

[0021] The determination module is used to determine the imaging porosity value of the formation to be tested based on the electrical conductivity value of the formation to be tested corresponding to multiple pixels in the electrical imaging image data.

[0022] The image generation module is used to generate a fracture-cavity image by traversing the imaging porosity values ​​of the formation to be tested corresponding to multiple pixels, comparing the imaging porosity value of the formation to be tested corresponding to each pixel with the porosity threshold.

[0023] A third aspect of this application provides an electronic device, including: a processor, a memory, and a display; the memory is coupled to the processor, the memory is used to store computer program code, the processor calls the computer program code to cause the electronic device to perform the method of the first aspect, and the display is used to display a slit image.

[0024] A fourth aspect of this application provides a readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method of the first aspect.

[0025] The fifth aspect of this application provides a computer program product, including a computer program, which, when executed by a processor, is used to implement the method as described in the first aspect.

[0026] The sixth aspect of this application also provides a chip system applied to an electronic device, the chip system including one or more processors, the one or more processors being used to invoke computer instructions to cause the electronic device to implement the method of the first aspect.

[0027] The seventh aspect of this application also provides a computer program product, including a computer program, which, when executed by a processor, is used to implement the method as described in the first aspect.

[0028] The method, apparatus, device, and storage medium for generating fracture-cavity images provided in this application determine the imaging porosity values ​​of the formation corresponding to multiple pixels in the electrical imaging image data of the formation under test, thereby converting the conductivity values ​​of the pixels into imaging porosity values. Then, by iterating through the imaging porosity values ​​of the formation under test corresponding to multiple pixels, the imaging porosity value of each pixel is compared with a porosity threshold to generate a fracture-cavity image. This method performs preset data processing on the conductivity values ​​of the formation under test corresponding to multiple pixels in the electrical imaging image data to obtain imaging porosity values ​​of the formation under test corresponding to multiple pixels. By comparing these values ​​with a preset porosity threshold at the pixel level, the imaging porosity values ​​of pixels corresponding to background rock noise are identified and updated to generate high-quality fracture-cavity images with an image resolution greater than a preset resolution, thus providing data support for subsequent identification of fracture-cavity types. Attached Figure Description

[0029] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0030] Figure 1 A scenario diagram illustrating a method for generating a seam hole image according to an embodiment of this application;

[0031] Figure 2 This is a flowchart illustrating a method for generating a seam image according to an embodiment of this application.

[0032] Figure 3 Another applicable scenario diagram for the method of generating a seam hole image provided in one embodiment of this application;

[0033] Figure 4 A histogram diagram illustrating a method for generating a slit image according to an embodiment of this application;

[0034] Figure 5 A schematic diagram of a scenario for segmenting a dissolution-cavity type stratum, using a method for generating fracture images according to an embodiment of this application;

[0035] Figure 6 A schematic diagram of a scenario for fracture-type stratum segmentation, illustrating an embodiment of the fracture image generation method of this application.

[0036] Figure 7 A schematic diagram of a scenario for segmenting fracture-dissolution cavity type strata using a method for generating fracture-cavity images according to an embodiment of this application;

[0037] Figure 8 A schematic diagram of a cave-type stratigraphic segmentation example for a method of generating crevice images according to an embodiment of this application;

[0038] Figure 9 A schematic diagram of a scenario illustrating the segmentation of Cretaceous fractured tight sandstone strata in the Kuqa Depression of the Tarim Basin, using a method for generating fracture images according to an embodiment of this application.

[0039] Figure 10 A schematic diagram of a scene illustrating an example of Cambrian dolomite strata segmentation in the Tarim Basin, for which a method for generating slit images according to an embodiment of this application is applicable.

[0040] Figure 11 A schematic diagram of the structure of a device for generating a slit image according to an embodiment of this application;

[0041] Figure 12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0042] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0043] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0044] The terms "first," "second," etc., used in this application are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. In the following descriptions of embodiments, "a plurality of" means two or more, unless otherwise explicitly defined.

[0045] During well logging, electrical imaging logging tools use button electrodes to transmit current into the formation, measuring the micro-conductivity of the formation near the wellbore as it varies with depth, and displaying this information visually as an image. Typically, lighter areas in the image represent low conductivity, while darker areas represent high conductivity. Because different geological bodies near the wellbore have varying conductivity, electrical imaging images reflect geological phenomena such as bedding, fractures, and dissolution cavities in the formation near the wellbore, with a resolution of approximately 5 mm. Electrical imaging images are commonly used for qualitative identification and quantitative evaluation of formation fracture and cavity development. A crucial step in quantitatively evaluating formation fracture and cavity development using electrical imaging images is segmenting the fracture and cavity image from the electrical imaging image.

[0046] In related technologies, the image quality of the slit images segmented from electro-imaging images is not high. That is to say, the image features used to identify the type of slit in the segmented images are not obvious, and the interference noise in the images is relatively large.

[0047] Therefore, this application provides a method, apparatus, device, and storage medium for generating fracture-cavity images. The method includes: determining the imaging porosity values ​​of the formation corresponding to multiple pixels in an electrical imaging image of the formation to be tested, thereby converting the pixel conductivity values ​​into imaging porosity values. Then, by iterating through the imaging porosity values ​​of the formation corresponding to multiple pixels, comparing the imaging porosity value of each pixel with a porosity threshold, a fracture-cavity image is generated. The porosity threshold is used to distinguish the imaging porosity value of the fracture-cavity corresponding to a pixel from the imaging porosity value of the background rock noise corresponding to the pixel.

[0048] The above method performs preset data processing on the electrical conductivity values ​​of the test strata corresponding to multiple pixels in the electrical imaging image data to obtain the imaging porosity values ​​of the test strata corresponding to multiple pixels. By comparing the values ​​with a preset porosity threshold at the pixel level, the imaging porosity values ​​of the pixels corresponding to background rock noise are identified and updated to generate high-quality fracture and cavity images with an image resolution greater than the preset resolution, providing data support for subsequent identification of fracture and cavity types.

[0049] Please see Figure 1 , Figure 1This is an application scenario diagram of the method for generating fracture images according to an embodiment of this application. The application scenario includes an electronic device 10, an imaging processor 11, and electrode plates 12. Multiple electrode plates 12 are arranged laterally (in the X direction in the figure) inside the stratum to be measured. Figure 1 The number of horizontally arranged electrode plates is not shown in the figure. Multiple electrode plates 12 are grouped together. Multiple groups of electrode plates 12 are arranged longitudinally (in the Y direction in the figure) inside the formation to be tested. Multiple button electrodes (not shown) are provided on the contact surface between each electrode plate 12 and the formation to be tested. Each electrode button transmits a current signal to the formation to be tested and receives a current signal returned from the formation to be tested. The imaging processor 11 calculates the conductivity value of the formation to be tested corresponding to each electrode button based on the current signals received and transmitted by the electrode buttons, and converts it into the conductivity value of the formation to be tested corresponding to each pixel (i.e., one electrode button can be converted into one corresponding pixel). The electronic device 10 generates a fracture image based on the conductivity value of the formation to be tested corresponding to each pixel using the fracture image generation method provided in this application.

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

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

[0052] It should be noted that, Figure 1 This is merely a schematic diagram illustrating one application scenario provided by an embodiment of this application. This embodiment does not necessarily represent... Figure 1 The document does not limit the actual form of the various devices included, nor does it specify the form of the devices. Figure 1 The interaction methods between devices are limited, and can be set according to actual needs in the specific application of the solution.

[0053] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0054] Figure 2 This is a flowchart illustrating an embodiment of the method for generating a seam hole image provided in this application. The execution entity of this embodiment can be an electronic device. The method in this embodiment can be implemented by software, hardware, or a combination of both. Figure 2 As shown, the method for generating the image of the slit hole may include the following steps:

[0055] Step S110: Acquire electrical imaging image data of the formation to be tested.

[0056] Electrical imaging data includes the electrical conductivity values ​​of the tested formation corresponding to multiple pixels. For example, such as... Figure 1 As shown, the electronic device 10 acquires the conductivity values ​​of the test formation corresponding to multiple pixels from the imaging processor 11. The conductivity value of the test formation corresponding to each pixel can be represented by C(i, j, k). Where C represents the conductivity value, i represents the electrode number, j represents the button electrode number on each electrode, and k represents the sampling point number, 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 being tested. i and j together represent the lateral coordinate (X-axis coordinate) of the button electrode in the two-dimensional coordinate system of the formation being tested. k represents the longitudinal coordinate (Y-axis coordinate) of the button electrode in the formation being tested. The electronic device can calculate the position and conductivity value of the pixel corresponding to the button electrode in the image using C(i, j, k).

[0058] The upper limits 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, j = 1, 2, ..., 24. If the selected imaging logging technology is Extended Range MicroImager Tool (XRMI), then i = 1, 2, ..., 6, j = 1, 2, ..., 25. If the selected imaging logging technology is Micro-Conductivity Imaging (MCI), then i = 1, 2, ..., 6, j = 1, 2, ..., 24.

[0059] Step S120: Determine the imaging porosity value of the formation to be tested corresponding to the multiple pixels based on the conductivity values ​​of the formation to be tested.

[0060] Specifically, based on the conductivity value of the formation to be tested corresponding to each pixel, and combined with a preset conversion algorithm, the imaging porosity value of the formation to be tested corresponding to each pixel is obtained. For example, the conductivity value of the formation to be tested corresponding to the first pixel of a plurality of pixels is used to obtain the imaging porosity value of the formation to be tested corresponding to the first pixel, and so on, until the imaging porosity values ​​of the formation to be tested corresponding to all pixels are obtained.

[0061] Step S130: By traversing the imaging porosity values ​​of the formation to be tested corresponding to multiple pixels, the imaging porosity value of the formation to be tested corresponding to each pixel is compared with the porosity threshold to generate a fracture-cavity image.

[0062] The porosity threshold can be calculated based on core sample data from the stratum being tested. Alternatively, it can be determined by counting the number of pixels and analyzing the distribution of the imaging porosity values ​​corresponding to each pixel, and then using this distribution to determine the porosity threshold. The porosity threshold is used to distinguish between the imaging porosity values ​​of fractures and cavities corresponding to pixels and the imaging porosity values ​​of background rock noise corresponding to pixels.

[0063] Since the porosity values ​​are arranged from largest to smallest as follows: fractures / cavities, background rock noise, and background rock, a porosity threshold is set between that of fractures / cavities and background rock noise. Then, by comparing the imaging porosity value of each pixel corresponding to the tested formation with the porosity threshold, pixels corresponding to fractures / cavities, background rock noise, and background rock can be categorized. This allows the fracture / cavity images generated based on the categorized pixels to more accurately highlight the characteristics of fractures / cavities in the tested formation.

[0064] Fracture-vuggy images are used to identify the types of fractures and vulcanizations in the strata being tested. Fracture-vuggy types include dissolution pores, fractures, fracture-dissolution pores, and caves. In other words, fracture-vuggy images can be dissolution pore images, fracture images, fracture-dissolution pore images, or cave images, etc.

[0065] In an application scenario, combined Figure 1 and Figure 3 As shown, after acquiring electrical imaging image data of the stratum to be measured, the electronic device generates, based on the electrical imaging image data, the following... Figure 3 The conductivity image of the formation to be tested is shown in (1), where cracks and dissolution cavities in the conductivity image appear as dark-colored high conductivity responses. Next, based on the conductivity values ​​of multiple pixels corresponding to the formation to be tested in the electrical imaging image data, the imaging porosity values ​​of the formation to be tested corresponding to multiple pixels are determined respectively. Based on the imaging porosity values ​​corresponding to multiple pixels, a model is generated as shown in (1). Figure 3 The porosity image of the formation to be tested is shown in (2). Finally, by traversing the imaging porosity values ​​of the formation to be tested corresponding to multiple pixels, the relationship between the imaging porosity value of the formation to be tested corresponding to each pixel and the porosity threshold is compared to generate the porosity image shown in (2). Figure 3 The crack and dissolution pores in the crack image (3) are shown as dark high porosity response.

[0066] Understandably, background rock noise such as mudstone, induced fractures, and wellbore collapse, along with cracks, dissolution cavities, or caves, generally exhibit high conductivity in conductivity images, making them difficult to distinguish. However, mudstone, induced fractures, and wellbore collapses typically develop in poorer locations within the tested formation, exhibiting lower porosity. Conversely, cracks, dissolution cavities, and caves develop in better locations within the tested formation, exhibiting higher porosity. A porosity image generated by mapping porosity values ​​across multiple pixels can differentiate between high-conductivity targets like fractures and cavities and background rock noise. Furthermore, by analyzing the relationship between the imaging porosity value and a porosity threshold, fractures and cavities are segmented into porosity images, yielding fracture-cavity images.

[0067] It is understandable that, compared with the conductivity image formed by electrical imaging image data, the embodiments of this application generate a fracture image by comparing the imaging porosity value of the stratum under test corresponding to each pixel with the porosity threshold. This avoids segmenting background rock noise with similar conductivity into the fracture image and can better highlight the characteristics of the fractures and cavities in the stratum under test.

[0068] In some embodiments, determining the imaging porosity value of the formation corresponding to multiple pixels in the electrical imaging image data, based on the electrical conductivity value of the formation to be tested, includes the following steps:

[0069] Step S210: Obtain well logging data of the formation to be tested and core experimental data of the sampled formation to be tested.

[0070] A logging device is also installed within the formation to be tested. Electronic equipment acquires logging data of the formation based on the logging device, including acoustic velocity, neutron porosity, and / or natural gamma rays.

[0071] Core test data comes from core samples taken from reservoirs within the formation being tested. These samples are fully saturated with fluid, and the resistivity and porosity values ​​are measured using measuring instruments. In other words, core test data includes the resistivity and porosity values ​​of the sampled material. The reservoir is used to store fluids such as oil and gas.

[0072] Step S220: Determine the resistivity value of the formation to be tested and the porosity value of different geological features in the formation to be tested based on the well logging data.

[0073] Specifically, the appropriate porosity model is selected based on the type of well logging data, and the porosity values ​​of different geological formations in the tested strata are obtained based on the well logging data and the porosity model. For example, the porosity values ​​of different geological formations in the tested strata are obtained based on the acoustic velocity and the corresponding time-averaging equation. It can be understood that the porosity values ​​of different geological formations in the tested strata include the porosity values ​​of mudstone (a type of background rock noise) and karst caves, etc.

[0074] The logging apparatus includes a dual-lateral logging instrument, which measures the real-time resistivity of the formation to be tested. The average resistivity value can be calculated from the real-time resistivity value, and this average resistivity value (also known as the flushed zone resistivity) can be used as the resistivity value of the formation to be tested. For example, the real-time resistivity value is the shallow lateral resistivity value.

[0075] Step S230: Determine the cementation index of the stratum to be tested based on the core test data.

[0076] Specifically, the resistivity and porosity values ​​of the samples taken from the core test data are substituted into Archie's Formula, and the cementation index is fitted by the least squares method.

[0077] It is understandable that when the strata to be tested are clastic rock strata, the cementation index of the clastic rock strata is the average cementation index of each region of the clastic rock strata. When the strata to be tested are heterogeneous carbonate rock strata, due to the uneven distribution of fractures and cavities in heterogeneous carbonate rock strata, the cementation index of heterogeneous carbonate rock strata can be the average value, or each region of the heterogeneous carbonate rock strata can correspond to a cementation index.

[0078] Step S240: Based on the electrical conductivity value of the test stratum corresponding to multiple pixel points, the resistivity value of the test stratum, the porosity value of different geological features in the test stratum, and the cementation index of the test stratum, determine the imaging porosity value of the test stratum corresponding to multiple pixel points.

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

[0080]

[0081] Where, φ (i,j,k) The image porosity value of the formation to be tested corresponds to each pixel; m represents the cementation index of the formation to be tested; φ represents the porosity value φ of different geological formations in the formation to be tested, calculated from conventional well logging data; R xo C represents the resistivity value of the formation being measured. (i,j,k) This represents the conductivity value of the geological formation corresponding to each pixel.

[0082] It is understandable that different geological phenomena with similar electrical conductivity in the strata under test can be converted using formula (1) to obtain the imaging porosity values ​​corresponding to different geological phenomena. For example, mudstone (a type of background rock noise) and karst caves are both highly conductive and not easily distinguishable in terms of electrical conductivity values. However, in the porosity values ​​φ calculated from conventional well logging data, the porosity value φ of karst caves is large, while that of mudstone is small. The mudstone imaging porosity value φ after φ constraint conversion in formula (1) is...(i,j,k) Small, the imaging porosity value φ of the cave. (i,j,k) Large. Thus, by constraining the porosity values ​​φ of different geological phenomena in the stratum to be tested, fractures and cavities and background rock noise can be distinguished. Similarly, after converting fractures, cavities, background rock noise and background rock through formula (1), the imaging porosity values ​​corresponding to fractures, cavities, background rock noise and background rock can be obtained respectively, and these three imaging porosity values ​​are arranged from large to small as fractures, cavities, background rock noise and background rock.

[0083] For example, formula (1) can be derived by the following method:

[0084] First, according to Archie's formula The conversion yields formula (2):

[0085]

[0086] Where φ represents the porosity value of different geological formations in the strata being tested, with units of v / v. R mf The resistivity of the mud filtrate is expressed in ohm·m. (S) xo R represents the water saturation of the flushing belt, expressed in v / v. xo denoted as ρ, where ρ is the resistivity value of the formation to be measured, in ohm·m. ρ 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.

[0087] It is understandable that formula (2) reflects the correlation between the porosity value of different geological formations in the test stratum and the resistivity value of the test stratum. Based on this correlation, the correlation between the resistivity value of the test stratum corresponding to the pixel point and the imaging porosity value of the test stratum corresponding to the pixel point can be calculated. The formula for expressing the correlation between the pixel points is shown in (3).

[0088]

[0089] 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.

[0090] 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).

[0091]

[0092] 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.

[0093] 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.

[0094]

[0095] 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).

[0096] 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.

[0097] 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:

[0098] 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.

[0099] Specifically, based on the spatial location of multiple pixels and the corresponding imaging porosity values ​​of multiple pixels, a model is generated as follows: Figure 3 The porosity image of the formation to be tested is shown in (1).

[0100] Step S320: Generate a histogram based on the porosity image.

[0101] Histograms are used to count the number of pixels corresponding to each imaging porosity value in a porosity image.

[0102] For example, Figure 4This is a histogram generated from the porosity image. The horizontal axis of the histogram represents the imaging porosity value corresponding to each pixel in the porosity image, and the vertical axis represents the number of pixels corresponding to each imaging porosity value in the porosity image.

[0103] Step S330: Determine the porosity value at the valley point based on the porosity statistical peaks in the histogram.

[0104] Step S340: Use the valley point porosity value as the porosity threshold.

[0105] Specifically, please refer to Figure 4 Determine the porosity value at the valley point between two porosity statistical peaks in the porosity histogram image. This is used as a preset porosity threshold.

[0106] It is understandable that the porosity statistics calculated from background rock and noise, as well as the porosity statistics calculated from effective fractures and dissolution cavities, all follow a Gaussian distribution. Their superposition will result in two statistical peaks, and the superposition of these peaks yields the porosity at the valley points corresponding to the troughs. This is the preset porosity threshold, which allows for a more accurate distinction between the pixels corresponding to the cracks and the background rock noise.

[0107] In another implementation, the Otsu method—Otsu inter-class variance method (OTSU) can also be used to determine the porosity threshold. Specifically, the calculation of the porosity threshold includes the following steps:

[0108] Step S410: Use multiple imaging porosity values ​​in the histogram as multiple candidate thresholds, traverse multiple candidate thresholds, and obtain multiple inter-class variances corresponding to multiple candidate thresholds;

[0109] Step S420: Compare multiple inter-class variances and determine the candidate threshold with the largest inter-class variance as the porosity threshold;

[0110] When traversing each candidate threshold, pixels with imaging porosity values ​​greater than or equal to the candidate threshold are divided into the first group, and pixels with imaging porosity values ​​less than the candidate threshold are divided into the second group. Based on the pixels and imaging porosity values ​​of the first group and the second group, the inter-class variance corresponding to the candidate threshold is obtained.

[0111] Understandably, inter-class variance is a statistic that measures the difference between the first group of pixels and the second group of pixels. Using the candidate threshold with the largest inter-class variance as the porosity threshold means that the difference between the imaging porosity values ​​of the two groups of pixels is maximized, which is beneficial for segmenting the first group of pixels and the second group of pixels. In other words, it is beneficial for more clearly segmenting pixels belonging to crevices and holes, and pixels belonging to background rock noise and background rocks.

[0112] In another embodiment, the calculation of the porosity threshold includes the following steps:

[0113] Step S510: Determine the lower limit of porosity of the formation to be tested based on the core test data of the sampled strata.

[0114] The core experiment data also includes bound water saturation and permeability. Specifically, under a certain pressure, water is injected into one end of the sample, while the outflow is collected from the other end. Permeability is calculated by measuring the inflow and outflow rates and the pressure difference between the two ends of the sample, combined with Darcy's law. The sample is then placed in a centrifuge; under centrifugal force, mobile water is expelled, while bound water remains in the pores of the sample. The bound water saturation of the sample can be calculated by measuring the weight change before and after centrifugation.

[0115] Next, the correlation between porosity, permeability, and bound water saturation is established. When the bound water saturation is at a certain value, such as 80%, the porosity corresponding to the bound water saturation is the lower limit of the porosity of the formation to be tested.

[0116] Step S520: Use the lower limit of porosity as the porosity threshold.

[0117] Understandably, the sampled material originates from the reservoir, and the lower limit of the reservoir's porosity can be derived from the lower limit of the sample's porosity. Within the tested formation, non-reservoir layers exist, with lower porosity than the reservoir layers. Compared to reservoir layers, the productivity and economic benefits of fractures and voids in non-reservoir layers are reduced. Using the lower limit of reservoir porosity as a porosity threshold allows for the differentiation between reservoir and non-reservoir layers (non-reservoir layers also include background rocks and background rock noise), enabling focused analysis of fractures and voids in reservoirs with high productivity and economic benefits.

[0118] In some embodiments, a fracture-vuggy image is generated by iterating through the imaging porosity values ​​of the formation to be tested corresponding to multiple pixels and comparing the relationship between the imaging porosity value of the formation to be tested corresponding to each pixel and a porosity threshold. This can be achieved through various implementations. One implementation includes the following steps:

[0119] Step S610: Taking the first pixel among multiple pixels as an example, if the imaging porosity value of the test stratum corresponding to the first pixel is greater than or equal to the porosity threshold, the imaging porosity value of the test stratum corresponding to the first pixel is not updated.

[0120] It is understandable that if the imaging porosity value is greater than or equal to the porosity threshold, it means that the pixel corresponding to the imaging porosity value is the pixel corresponding to the slit. If the imaging porosity value remains unchanged, it will be displayed as dark in the slit image.

[0121] Step S620: If the imaging porosity value of the formation to be tested corresponding to the first pixel is less than the porosity threshold, update the imaging porosity value of the formation to be tested corresponding to the first pixel to 0.

[0122] It is understandable that if the imaging porosity value is less than the porosity threshold, it means that the pixel corresponding to the imaging porosity value is background rock noise or a pixel corresponding to the background rock. The imaging porosity value is updated to 0 and is not displayed in the crevice image.

[0123] Step S630: After traversing multiple pixels, generate a fracture-cavity image based on the imaging porosity values ​​of the strata to be tested corresponding to the multiple pixels that are not updated and those that are updated.

[0124] Understandably, in the generated fracture-cavity image, the pixels corresponding to the fractures are displayed in dark colors, while background rock noise and background rock are not displayed. Based on the imaging porosity values ​​of the tested strata corresponding to multiple pixels, both before and after updating, a fracture-cavity image is generated; this generated image is a porosity image.

[0125] For example, such as Figure 3 In the porosity image of (2), each pixel has a corresponding imaging porosity value of the formation to be tested. Compare whether the imaging porosity value of the formation to be tested corresponding to each pixel is greater than or equal to the porosity threshold. Taking the first pixel in the porosity image as an example, if the imaging porosity value of the formation to be tested corresponding to the first pixel is greater than or equal to the porosity threshold, the imaging porosity value of the formation to be tested corresponding to the first pixel is not updated (that is, the imaging porosity value of the first pixel remains unchanged). If the imaging porosity value of the formation to be tested corresponding to the first pixel is less than the porosity threshold, the imaging porosity value of the formation to be tested corresponding to the first pixel is updated to 0. After traversing multiple pixels, generate the imaging porosity value of the formation to be tested corresponding to the multiple pixels that are not updated and the updated pixels. Figure 3 (3) Image of the suture hole. In another embodiment, the following steps are included:

[0126] Step S710: Taking the first pixel among multiple pixels as an example, if the imaging porosity value of the test stratum corresponding to the first pixel is greater than or equal to the porosity threshold, the electrical conductivity value of the test stratum corresponding to the first pixel is not updated.

[0127] It is understandable that if the imaging porosity value is greater than or equal to the porosity threshold, it means that the pixel corresponding to the imaging porosity value is the pixel corresponding to the slit, the electrical conductivity value remains unchanged, and it appears dark in the slit image.

[0128] Step S720: If the imaging porosity value of the formation to be tested corresponding to the first pixel is less than the porosity threshold, update the electrical conductivity value of the formation to be tested corresponding to the first pixel to 0.

[0129] It is understandable that if the imaging porosity value is less than the porosity threshold, it means that the pixel corresponding to the imaging porosity value is the pixel corresponding to the slit, and the updated conductivity value is 0, so it is not displayed in the slit image.

[0130] Step S730: After traversing multiple pixels, generate a fracture-cavity image based on the conductivity values ​​of the strata to be tested corresponding to the multiple pixels that are not updated and those that are updated.

[0131] Understandably, in the generated fracture-cavity image, the pixels corresponding to the fractures are displayed in dark colors, while background rock noise and background rock are not displayed. Based on the conductivity values ​​of the tested strata corresponding to multiple pixels, both before and after updating, a fracture-cavity image is generated; the generated fracture-cavity image is a conductivity image.

[0132] For example, such as Figure 3 In the conductivity image of (1), each pixel has a corresponding conductivity value of the formation to be tested, and an imaging porosity value of the formation to be tested corresponding to the conductivity value. Compare whether the imaging porosity value of the formation to be tested corresponding to each pixel is greater than or equal to the porosity threshold. Taking the first pixel in the conductivity image as an example, if the imaging porosity value of the formation to be tested corresponding to the first pixel is greater than or equal to the porosity threshold, the conductivity value of the formation to be tested corresponding to the first pixel is not updated (that is, the conductivity value of the first pixel remains unchanged). If the imaging porosity value of the formation to be tested corresponding to the first pixel is less than the porosity threshold, the conductivity value of the formation to be tested corresponding to the first pixel is updated to 0. After traversing multiple pixels, generate a value based on the conductivity values ​​of the formation to be tested corresponding to the multiple pixels that are not updated and those that are updated. Figure 3 (3) Image of the slit.

[0133] Understandably, fracture-vuggy images can be generated from the electrical conductivity values ​​of multiple pixels corresponding to the tested formation, both before and after updating. Fracture-vuggy images generated from conductivity values ​​not only present the characteristics of fractures and vuggies but also allow for the study of their electrical properties. Alternatively, fracture-vuggy images can be generated from the imaging porosity values ​​of multiple pixels corresponding to the tested formation, both before and after updating. Fracture-vuggy images generated from imaging porosity values ​​are unaffected by other properties and allow for more intuitive observation of small-sized fractures and vuggies.

[0134] It is understood that the method for generating the crevice image composed of all or part of the above embodiments converts the conductivity into an imaging porosity value using formula (1), thereby obtaining the imaging porosity values ​​corresponding to the crevice, background rock noise, and background rock, respectively. These three imaging porosity values ​​are arranged from largest to smallest as follows: crevice, background rock noise, and background rock. Next, the imaging porosity or conductivity value of pixels with values ​​greater than the imaging porosity threshold is kept unchanged, while the imaging porosity or conductivity value of other pixels is set to 0. This results in a crevice image that only displays the crevice features, i.e., the crevice image can be segmented.

[0135] The method for generating fracture-cavity images, comprising all or part of the above embodiments, can be applied to various strata to be tested, including, for example... Figures 5 to 8 The Ordovician fracture-cavity carbonate strata shown are found in the Tarim Basin. These strata are further divided into dissolution-cavity strata, fracture strata, and fracture-dissolution-cavity strata. Figure 5 This is an example of dissolution-cavity type stratigraphic segmentation. Figure 6 This is an example of fractured strata segmentation. Figure 7 Example of fracture-dissolution-cavity type stratigraphic segmentation, attached Figure 8 Example of cave-type stratigraphic segmentation.

[0136] The method for generating fracture-cavity images, comprising all or part of the above embodiments, is also applicable to other lithological formations without additional conductive minerals, such as fractured tight sandstone and fracture-cavity dolomite, where secondary fractures and cavities are developed. Figure 9 The example shown is a fragmentation of Cretaceous fractured tight sandstone strata in the Kuqa Depression of the Tarim Basin. Figure 10 The example shown is a segmentation of Cambrian dolomite strata in the Tarim Basin.

[0137] Specifically, with Figure 5 Taking the example of dissolution-cavity type stratigraphic segmentation shown, this paper illustrates the process of generating dissolution-cavity images using a method that combines all or part of the above embodiments.

[0138] like Figure 5 As shown, after acquiring electrical imaging image data of the dissolution-porosity formation, the electronic device generates a conductivity image (i.e., a static image) of the dissolution-porosity formation, as shown in the second channel, based on the electrical imaging image data. The static image is used to display the characteristics of the dissolution-porosity formation at a certain depth. Next, shallow resistivity calibration processing is performed on the electrical imaging image data, and a calibration image, as shown in the fifth channel, is generated based on the calibrated electrical imaging image data. Then, based on the conductivity values ​​of the dissolution-porosity formation corresponding to multiple pixels in the electrical imaging image data, the imaging porosity values ​​of the dissolution-porosity formation corresponding to multiple pixels are determined. Based on the spatial location of multiple pixels and their corresponding imaging porosity values, a porosity image, as shown in the sixth channel, is generated. Finally, by traversing the imaging porosity values ​​of the dissolution-porosity formation corresponding to multiple pixels, the imaging porosity value of the dissolution-porosity formation corresponding to each pixel is compared with the porosity threshold, generating a segmented porosity image, as shown in the seventh channel (the segmented porosity image is the generated dissolution-porosity image).

[0139] The first track is a natural gamma curve, used to determine the lithology of dissolution-porosity strata. The third track is a conductivity image (i.e., a dynamic image) of the dissolution-porosity strata, used to demonstrate the characteristics of dissolution-porosity strata at different depths. The fourth track is a depth track, used to represent the depth of the dissolution-porosity strata corresponding to the second, third, fifth, sixth, and seventh tracks.

[0140] Understandably, compared to the second static image formed by electro-imaging image data, the seventh dissolution cavity image generated by the method of generating crevice and cavity images composed of all or part of the embodiments can better highlight the dissolution cavity characteristics of dissolution cavity type strata, that is, it can better segment the dissolution cavity target.

[0141] Figures 6 to 10 The process of generating the seam hole image in the segmentation example shown is similar to... Figure 5 The principle behind the examples shown is the same, so it will not be repeated here.

[0142] pass Figure 6 The fracture image generation process shown illustrates that, compared to the second static image formed by electro-imaging image data, the seventh fracture image generated by the fracture image generation method composed of all or part of the embodiments can better highlight the fracture characteristics of the fractured strata, that is, it can better segment the fracture target.

[0143] pass Figure 7 The process of generating images of fractures and dissolution cavities shown can be understood to be that, compared with the second static image formed by electro-imaging image data, the seventh fracture and dissolution cavity image generated by the fracture and cavity image generation method composed of all or part of the embodiments can better highlight the fracture and dissolution cavity characteristics of fracture-dissolution cavity type strata, that is, it can better segment the fracture and dissolution cavity targets.

[0144] pass Figure 8 The cave image generation process shown illustrates that, compared to the second static image formed by electro-imaging image data, the seventh cave image generated by the method of generating crevice-cavity images composed of all or part of the embodiments can better highlight the cave features of cave-type strata, that is, can better segment the cave target.

[0145] pass Figure 9 The crack image generation process shown can be understood to be that, compared with the second static image formed by electro-imaging image data, the seventh crack image generated by the crack image generation method composed of all or part of the embodiments can better highlight the crack characteristics of the cracked tight sandstone strata in the Kuqa Depression, that is, it can better segment the crack target.

[0146] pass Figure 10The process of generating images of cracks and dissolution cavities shown can be understood to be that, compared with the second static image formed by electro-imaging image data, the seventh crack and dissolution cavity image generated by the crack and cavity image generation method composed of all or part of the embodiments can better highlight the crack and dissolution cavity characteristics of the Cambrian dolomite strata in the Tarim Basin, that is, it can better segment the crack and dissolution cavity targets.

[0147] in, Figure 9 and Figure 10 The first line in the diagram includes a spontaneous potential curve, used to identify sandstone and mudstone layers.

[0148] Figure 11 This is a schematic diagram of an embodiment of the apparatus for generating seam images provided in this application. The apparatus 40 can be integrated into the electronic device 10 in the above method embodiments, or it can be implemented using the electronic device 10 in the above method embodiments. Figure 11 As shown, the generating device 40 includes an acquisition module 41, a determination module 42, and an image generation module 43.

[0149] The acquisition module 41 is used to acquire electrical imaging image data of the formation to be tested, which includes the electrical conductivity values ​​of the formation to be tested corresponding to multiple pixels.

[0150] The determination module 42 is used to determine the imaging porosity value of the formation to be tested corresponding to multiple pixels in the electrical imaging image data, based on the electrical conductivity value of the formation to be tested corresponding to multiple pixels.

[0151] The image generation module 43 is used to generate a fracture-cavity image by traversing the imaging porosity values ​​of the formation to be tested corresponding to multiple pixels, comparing the imaging porosity value of the formation to be tested corresponding to each pixel with the porosity threshold.

[0152] In some embodiments, the acquisition module 41 is further configured to acquire well logging data of the formation to be tested and core experimental data of samples taken from the formation to be tested. Based on the well logging data, the resistivity value of the formation to be tested and the porosity values ​​of different geological formations within the formation are determined. Based on the core experimental data, the cementation index of the formation to be tested is determined. Based on the conductivity value, resistivity value, porosity values ​​of different geological formations within the formation to be tested, and cementation index of the formation to be tested corresponding to multiple pixels in the electrical imaging image data, the imaging porosity value of the formation to be tested corresponding to multiple pixels is determined according to the following formula:

[0153]

[0154] Where, φ (i,j,k) R represents the imaging porosity value of the stratum under test corresponding to each pixel; m represents the cementation index of the stratum under test; φ represents the porosity value φ of different geological formations in the stratum under test;xo C represents the resistivity value of the formation being measured. (i,j,k) This represents the conductivity value of the geological formation corresponding to each pixel.

[0155] In some embodiments, the determining module 42 is further configured to 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. A histogram is generated based on the porosity image, and the histogram is used to count the number of pixels corresponding to each imaging porosity value in the porosity image. Based on the porosity statistical peaks in the histogram, valley porosity values ​​are determined. The valley porosity values ​​are used as porosity thresholds. Alternatively, multiple imaging porosity values ​​in the histogram are used as multiple candidate thresholds, and the multiple candidate thresholds are iterated to obtain multiple inter-class variances corresponding to the multiple candidate thresholds. The multiple inter-class variances are compared, and the candidate threshold with the largest inter-class variance is determined as the porosity threshold. Specifically, when iterating through each candidate threshold, 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 based on the pixels and imaging porosity values ​​of the first group and the pixels and imaging porosity values ​​of the second group.

[0156] In some embodiments, the determining module 42 is further configured to determine a lower limit value of porosity of the formation to be tested based on core experimental data of the sampled stratum; the core experimental data includes the porosity value, permeability, and bound water saturation of the sampled stratum. The lower limit value of porosity is used as a porosity threshold.

[0157] In some embodiments, the image generation module 43 is further configured to, taking a first pixel among multiple pixels as an example, if the imaging porosity value of the formation to be tested corresponding to the first pixel is greater than or equal to a porosity threshold, not update the imaging porosity value of the formation to be tested corresponding to the first pixel. If the imaging porosity value of the formation to be tested corresponding to the first pixel is less than the porosity threshold, update the imaging porosity value of the formation to be tested corresponding to the first pixel to 0. After traversing multiple pixels, a fracture-cavity image is generated based on the imaging porosity values ​​of the formation to be tested corresponding to the multiple pixels that are not updated and those that are updated.

[0158] In some embodiments, the image generation module 43 is further configured to, taking a first pixel among multiple pixels as an example, if the imaging porosity value of the formation to be tested corresponding to the first pixel is greater than or equal to a porosity threshold, not update the electrical conductivity value of the formation to be tested corresponding to the first pixel. If the imaging porosity value of the formation to be tested corresponding to the first pixel is less than the porosity threshold, update the electrical conductivity value of the formation to be tested corresponding to the first pixel to 0. After traversing multiple pixels, a fracture-cavity image is generated based on the electrical conductivity values ​​of the formation to be tested corresponding to the multiple pixels that are not updated and those that are updated.

[0159] The determining device 40 provided in this embodiment is used to execute the technical solution in any of the foregoing method embodiments. Its implementation principle and technical effect are similar, and will not be described again here.

[0160] Figure 12 This is a schematic diagram of the structure of an electronic device provided in this application. Figure 12 As shown, the electronic device 10 includes:

[0161] Processor 11, memory 12, bus 14 and display 13;

[0162] The memory 12 is used to store the computer program code of the processor 11;

[0163] The processor 11 is configured to execute the technical solution of the electronic device in any of the foregoing method embodiments by executing the computer program code.

[0164] Optionally, the memory 12 can be either standalone or integrated with the processor 11.

[0165] The memory 12 and the display 13 are connected to the processor 11 via the bus 14 and communicate with each other.

[0166] Optionally, the display 13 is used to display at least an image of the slit.

[0167] Optionally, memory 12 may include random access memory (RAM) or non-volatile memory, such as at least one disk drive.

[0168] Bus 14 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0169] The processors mentioned above can be general-purpose processors, including central processing units (CPUs), network processors (NPs), etc.; they can also be digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0170] The electronic device 10 is used to execute the technical solution of the electronic device in any of the aforementioned method embodiments. Its implementation principle and technical effect are similar, and will not be described again here.

[0171] This application also provides a readable storage medium storing a computer program thereon, which, when executed by a processor, implements the technical solutions provided in any of the foregoing embodiments.

[0172] This application also provides a chip system applied to an electronic device. The chip system includes one or more processors, which are used to invoke computer instructions to enable the electronic device to implement the technical solutions provided in any of the foregoing embodiments.

[0173] This application also provides a computer program product, including a computer program, which, when executed by a processor, is used to implement the technical solutions provided in any of the foregoing method embodiments.

[0174] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0175] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for generating an image of a slit hole, characterized in that, The method includes: Acquire electrical imaging data of the formation to be tested, wherein the electrical imaging data includes the electrical conductivity values ​​of the formation to be tested corresponding to multiple pixels; Based on the electrical conductivity values ​​of the formation to be tested corresponding to the plurality of pixels, the imaging porosity values ​​of the formation to be tested corresponding to the plurality of pixels are determined respectively. By iterating through the imaging porosity values ​​of the test formation corresponding to the multiple pixels, and comparing the relationship between the imaging porosity value of the test formation corresponding to each pixel and the porosity threshold, a fracture-cavity image is generated.

2. The method according to claim 1, characterized in that, The conductivity values ​​of the formation to be tested corresponding to the plurality of pixels are used to determine the imaging porosity values ​​of the formation to be tested corresponding to the plurality of pixels, including: Acquire well logging data of the formation to be tested and core experimental data of the sampled specimens from the formation to be tested; The resistivity value of the formation to be tested and the porosity value of different geological features in the formation to be tested are determined based on the well logging data. The cementation index of the stratum to be tested was determined based on the core test data. Based on the electrical conductivity value of the test stratum corresponding to the plurality of pixels, the resistivity value of the test stratum, the porosity value of different geological features in the test stratum, and the cementation index of the test stratum, the imaging porosity value of the test stratum corresponding to the plurality of pixels is determined according to the following formula: Where, φ (i,j,k) The image porosity value of the stratum under test corresponding to each pixel; m represents the cementation index of the stratum under test; φ represents the porosity value of different geological formations in the stratum under test; R xo C represents the resistivity value of the formation being measured. (i,j,k) This represents the electrical conductivity value of the stratum being tested corresponding to each pixel.

3. The method according to claim 1 or 2, characterized in that, The method further includes: Based on the imaging porosity values ​​of the formation to be tested corresponding to the plurality of pixels, a porosity image of the formation to be tested is generated. A histogram is generated based on the porosity image, and the histogram is used to count the number of pixels corresponding to each imaging porosity value in the porosity image; The porosity value at the valley point is determined based on the porosity statistical peaks in the histogram. The valley point porosity value is used as the porosity threshold; or Multiple imaging porosity values ​​in the histogram are used as multiple candidate thresholds. By traversing multiple candidate thresholds, multiple inter-class variances corresponding to multiple candidate thresholds are obtained. Compare multiple inter-class variances and determine the candidate threshold with the largest inter-class variance as the porosity threshold; Specifically, when traversing each candidate threshold, 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; based on the pixels and imaging porosity values ​​of the first group and the pixels and imaging porosity values ​​of the second group, the inter-class variance corresponding to the candidate threshold is obtained.

4. The method according to any one of claims 1 to 3, characterized in that, The method further includes: Based on the core test data of the sampled strata to be tested, the lower limit value of the porosity of the strata to be tested is determined; The lower limit of porosity is used as the porosity threshold.

5. The method according to any one of claims 1 to 4, characterized in that, The step of generating a fracture-vuggy image by traversing the imaging porosity values ​​of the formation to be tested corresponding to the plurality of pixels, comparing the imaging porosity value of the formation to be tested corresponding to each pixel with the porosity threshold, includes: Taking the first pixel among the plurality of pixels as an example, if the imaging porosity value of the test stratum corresponding to the first pixel is greater than or equal to the porosity threshold, the imaging porosity value of the test stratum corresponding to the first pixel is not updated. If the imaging porosity value of the formation to be tested corresponding to the first pixel is less than the porosity threshold, update the imaging porosity value of the formation to be tested corresponding to the first pixel to 0. After traversing all the pixels, the fracture image is generated based on the imaging porosity values ​​of the strata to be tested corresponding to the pixels that are not updated or have been updated.

6. The method according to any one of claims 1 to 4, characterized in that, The step of generating a fracture-vuggy image by traversing the imaging porosity values ​​of the formation to be tested corresponding to the plurality of pixels, comparing the imaging porosity value of the formation to be tested corresponding to each pixel with the porosity threshold, includes: Taking the first pixel among the plurality of pixels as an example, if the imaging porosity value of the test stratum corresponding to the first pixel is greater than or equal to the porosity threshold, the conductivity value of the test stratum corresponding to the first pixel is not updated. If the imaging porosity value of the formation to be tested corresponding to the first pixel is less than the porosity threshold, update the conductivity value of the formation to be tested corresponding to the first pixel to 0. After traversing all the pixels, the fracture image is generated based on the conductivity values ​​of the formation to be tested corresponding to the pixels that are not updated or have been updated.

7. An apparatus for generating images of seams, characterized in that, The generating apparatus includes: The acquisition module is used to acquire electrical imaging image data of the formation to be tested, wherein the electrical imaging image data includes the electrical conductivity values ​​of the formation to be tested corresponding to multiple pixels. The determination module is used to determine the imaging porosity value of the formation to be tested corresponding to the plurality of pixels in the electrical imaging image data, respectively. The image generation module is used to generate a fracture-cavity image by traversing the imaging porosity values ​​of the test strata corresponding to the multiple pixels, comparing the imaging porosity value of the test strata corresponding to each pixel with the porosity threshold.

8. An electronic device, characterized in that, include: Processor, memory, and display; The memory is coupled to the processor, the memory being used to store computer program code, and the processor calling the computer program code to cause the electronic device to perform the method as described in any one of claims 1 to 6; The display is used to display an image of the suture hole obtained according to any one of claims 1 to 6.

9. A readable storage medium, characterized in that, The readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, is used to implement the method as described in any one of claims 1 to 6.

Images