Viewing surface porosity determination method and device, equipment and storage medium

Abstract

The invention discloses a view surface porosity determination method, device and equipment and a storage medium. The method comprises the following steps: acquiring a target acoustic imaging image and a target core image corresponding to a coring depth section of a target drilling well; determining first change information of a first view surface porosity corresponding to the target acoustic imaging image and second change information of a second view surface porosity corresponding to the target core image; determining a view surface porosity conversion coefficient between the first view surface porosity and the second view surface porosity based on the first change information and the second change information; and determining a third visual surface porosity of the target drilling well under the full well depth, and determining a target visual surface porosity of the target drilling well under the full well depth based on the visual surface porosity conversion coefficient and the third visual surface porosity. According to the method, the fracture-vug type reservoir visual surface porosity can be determined, and the generation precision of the visual surface porosity can be improved, so that accurate evaluation of the fracture-vug type reservoir is ensured, and prediction of the oil and gas yield of the fracture-vug type reservoir and formulation of an exploitation scheme are facilitated.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method, device, equipment and storage medium for determining visual face ratio. Background Art

[0002] Fracture-vuggy reservoirs, as a new oil and gas exploration target, offer significant exploration potential and development prospects. Fracture-vuggy reservoirs are composed of fractures, solution pores, and caves. Fracture and pore systems play a crucial role in controlling the occurrence and migration of oil and gas. Identifying and evaluating the apparent porosity of pore structures such as pores and fractures in wellbore formations is crucial for predicting oil and gas production and developing recovery plans.

[0003] Currently, traditional methods for determining apparent surface porosity primarily evaluate reservoir fractures and vugs using acoustic imaging logging data to calculate the apparent surface porosity. However, these methods are often affected by instrumentation and measurement conditions, resulting in low precision and inability to accurately evaluate fracture-vug reservoirs, thereby facilitating oil and gas production prediction and development of production plans for these reservoirs. Summary of the Invention

[0004] The present invention provides a method, device, equipment and medium for determining the apparent surface porosity of fracture-vuggy reservoirs, which can improve the generation accuracy of the apparent surface porosity, thereby ensuring accurate evaluation of fracture-vuggy reservoirs and facilitating the prediction of oil and gas production in fracture-vuggy reservoirs and the formulation of production plans.

[0005] According to one aspect of the present invention, a method for determining a visual face ratio is provided, the method comprising:

[0006] Obtaining target acoustic imaging images and target core images corresponding to the target wellbore at the coring depth section;

[0007] Determining first change information of a first apparent surface frequency corresponding to the target acoustic imaging image and second change information of a second apparent surface frequency corresponding to the target core image;

[0008] determining, based on the first change information and the second change information, an apparent face rate conversion coefficient between the first apparent face rate and the second apparent face rate;

[0009] A third apparent surface porosity of the target well at full well depth is determined, and a target apparent surface porosity of the target well at the full well depth is determined based on the apparent surface porosity conversion coefficient and the third apparent surface porosity.

[0010] According to another aspect of the present invention, there is provided a device for determining a visual face ratio, the device comprising:

[0011] An image acquisition module is used to acquire a target acoustic imaging image and a target core image corresponding to the coring depth section of the target drilling;

[0012] a change information determining module, configured to determine first change information of a first apparent surface frequency corresponding to the target acoustic imaging image and second change information of a second apparent surface frequency corresponding to the target core image;

[0013] a conversion coefficient determining module, configured to determine an apparent face ratio conversion coefficient between the first apparent face ratio and the second apparent face ratio based on the first change information and the second change information;

[0014] The target apparent surface porosity determination module is configured to determine a third apparent surface porosity of the target drilling at the full well depth, and determine a target apparent surface porosity of the target drilling at the full well depth based on the apparent surface porosity conversion coefficient and the third apparent surface porosity.

[0015] According to another aspect of the present invention, an electronic device is provided, comprising:

[0016] at least one processor; and

[0017] a memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the method for determining the visual face ratio according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining the visual face ratio according to any embodiment of the present invention when executed.

[0020] The technical solution of the embodiments of the present invention, by acquiring a target acoustic imaging image and a target core image corresponding to the coring depth of the target wellbore, can ensure comprehensive data acquisition while avoiding full-well coring, thereby reducing exploration costs. First change information of a first apparent porosity corresponding to the target acoustic imaging image and second change information of a second apparent porosity corresponding to the target core image are determined, thereby clarifying the fluctuation pattern of porosity at local depth and providing a basis for generating a subsequent conversion coefficient. Based on the first and second change information, an apparent porosity conversion coefficient between the first and second apparent porosity is determined, quantifying the apparent porosity relationship between the acoustic imaging image and the core image. A third apparent porosity of the target wellbore is determined at the full wellbore depth, and based on the apparent porosity conversion coefficient and the third apparent porosity, the target apparent porosity of the target wellbore at the full wellbore depth is determined, thereby improving the accuracy of the apparent porosity of the target wellbore at the full wellbore depth and reducing costs. The target acoustic imaging image and target core image corresponding to the target wellbore at the coring depth section are used to determine the apparent surface porosity conversion coefficient of the target wellbore at the local depth. Then, the data conversion of the apparent surface porosity of the entire wellbore section is achieved based on the apparent surface porosity conversion coefficient at the local depth, thereby improving the accuracy of the apparent surface porosity of the target wellbore at the full wellbore depth.

[0021] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 This is a flow chart of a method for determining visual face ratio according to the first embodiment of the present invention;

[0024] Figure 2 This is an example diagram of a target core image according to the first embodiment of the present invention;

[0025] Figure 3 This is an example diagram of a target core image after threshold segmentation according to the first embodiment of the present invention;

[0026] Figure 4 This is an example diagram of the relationship between the first view face ratio and the second view face ratio according to the first embodiment of the present invention;

[0027] Figure 5 This is a flow chart of a method for determining visual face ratio according to a second embodiment of the present invention;

[0028] Figure 6 This is an example diagram of an acoustic imaging logging data processing result according to the second embodiment of the present invention;

[0029] Figure 7 This is an example diagram of an acoustic imaging logging data processing result after core image adjustment according to the second embodiment of the present invention;

[0030] Figure 8 2 is a schematic structural diagram of a device for determining visual face ratio according to a third embodiment of the present invention;

[0031] Figure 9 2 is a schematic structural diagram of an electronic device for implementing the method for determining the visual face ratio according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0033] It should be noted that the terms "first," "second," "target," and the like in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the numbers used in this way are interchangeable where appropriate so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or apparatus.

[0034] Example 1

[0035] Figure 1A flowchart of a method for determining the apparent surface ratio is provided for the first embodiment of the present invention. This embodiment is applicable to the case of calculating the apparent surface ratio of a drilling area. The method can be executed by a device for determining the apparent surface ratio, which can be implemented in the form of hardware and / or software. The device for determining the apparent surface ratio can be configured in an electronic device. Figure 1 As shown, the method includes:

[0036] S110 , obtaining a target acoustic imaging image and a target core image corresponding to the coring depth section of the target well.

[0037] The target wellbore may refer to a specific wellbore in a fracture-vuggy reservoir area where visual surface surface analysis is required. The coring depth section may refer to a specific depth range within which core samples are obtained using a coring tool (e.g., a core drill bit) in the target wellbore. The target acoustic imaging image may refer to an image generated by scanning the wellbore wall using an acoustic imaging logging tool (e.g., an ultrasonic imager) in the coring depth section, reflecting macroscopic structures such as cracks and holes in the wellbore wall. The target core image may refer to an image of a core sample obtained by a drilling coring tool, which may be obtained using technologies such as CT scanning to reveal the pore structure within the core.

[0038] Specifically, in the coring depth section of the target well, an acoustic imaging logging tool (such as an ultrasonic imager) can be used to scan the wellbore wall to generate a high-resolution two-dimensional acoustic image. The two-dimensional acoustic image is then subjected to acceleration correction and image enhancement processing to obtain a target acoustic imaging image, which can reflect the macroscopic structure of the wellbore wall cracks, holes, etc. within the coring depth section. The full-diameter core of the coring depth section can be obtained using the drilling coring tool, and a CT image of the core surface can be obtained using CT scanning. The CT image is then subjected to noise reduction and contrast enhancement processing to obtain the target core image. Acoustic imaging provides continuous macroscopic information of the wellbore wall, and the core image reveals microscopic pore details. The combination of the two reduces the error of a single data source, while avoiding coring the entire well section and obtaining cores only at key depth sections, thereby reducing exploration costs.

[0039] For example, a certain oilfield is a fracture-vuggy reservoir, primarily developed in carbonate rocks. The target well is an exploration well in this oilfield, and the target interval (coring depth) is a carbonate formation. Acoustic imaging logging is performed using an acoustic imaging logging tool. The two-dimensional images obtained by logging are preprocessed (acceleration correction and image enhancement) to obtain the target acoustic imaging image. To better characterize the reservoir space characteristics, further coring is performed at the coring depth. CT scans are performed on the full-diameter cores obtained through drilling and coring. This non-destructive process provides surface images, vertical slice images, and horizontal slice images of the core, describing the core's bedding structure, cross-sectional structure, and pore-fracture distribution. The target acoustic imaging image is an image of the wellbore formation, with a depth resolution of approximately 3 mm. In order to calibrate the surface porosity calculation of acoustic imaging logging, the core CT image needs to match the acoustic imaging logging image. Therefore, a core surface CT image with an image resolution of 1.25 mm is selected and preprocessed (noise reduction and contrast enhancement) to obtain the target core image.

[0040] Exemplarily, "obtaining a target acoustic imaging image corresponding to the target drilling in the coring depth section" in S110 may include: obtaining an initial acoustic imaging image corresponding to the target drilling in the coring depth section; performing acceleration correction processing on the initial acoustic imaging image to obtain a corrected initial acoustic imaging image; and performing image enhancement processing on the corrected initial acoustic imaging image to obtain a target acoustic imaging image.

[0041] The initial acoustic imaging image may refer to a high-resolution two-dimensional acoustic image without any processing, which is generated by scanning the well wall using an acoustic imaging logging tool (such as an ultrasonic imager).

[0042] Specifically, an acoustic imaging logging tool can be used to obtain an initial acoustic imaging image corresponding to the target wellbore at the coring depth. Since the "recording depth" corresponding to the initial acoustic imaging image is generally recorded at regular depth intervals at the wellhead, the actual depth of the downhole instrument (acoustic imaging logging tool) along the well axis is not completely consistent with the "recording depth," resulting in a certain deviation. Acceleration correction can be used to calculate the true depth of the instrument throughout the entire wellbore section, and image data resampling and image correction are performed to obtain a corrected initial acoustic imaging image. Image enhancement processing is performed on the corrected initial acoustic imaging image, and histogram accumulation is performed on the corrected initial acoustic imaging image values. The acoustic imaging display color scale extremes are set. Based on the linear correspondence between the data extremes (excluding outliers) and the color scale extremes, a scale factor is calculated to scale the data to the color scale values, thereby obtaining the target acoustic imaging image. This makes the target acoustic imaging image more clearly reflect the formation information.

[0043] For example, because the acoustic imaging logging tool may move at a non-uniform speed downhole, resulting in deviations in the depth of the acoustic imaging image and the appearance of stretched images, after acquiring the initial acoustic imaging image corresponding to the coring depth section of the target wellbore, acceleration correction processing can be performed to calculate the true depth of the acoustic imaging logging tool throughout the entire wellbore section. Image data resampling is then performed to ensure that the initial acoustic imaging image reflects more accurate formation information. Image enhancement processing is then performed on the corrected initial acoustic imaging image to obtain the target acoustic imaging image, thereby making the target acoustic imaging image more clearly reflect formation information.

[0044] It should be noted that image enhancement includes static enhancement and dynamic enhancement. Among them, static image enhancement uses the acceleration-corrected acoustic imaging image of the coring depth segment for histogram accumulation, removes the outliers at both ends of the data, obtains the extreme values ​​of the data by statistics, sets the extreme values ​​of the acoustic imaging display color scale, and calculates the scale coefficient based on the linear correspondence between the extreme values ​​of the data and the extreme values ​​of the color scale to complete the static color calibration. The specific formula is:

[0045]

[0046] Among them, log is the acoustic imaging image value (which can refer to the pixel value), log max is the maximum value of the acoustic imaging image, log min is the minimum value of the acoustic imaging image, col is the acoustic imaging color value, col max is the maximum value of the acoustic imaging color scale, col min It is the minimum value of the acoustic imaging color scale.

[0047] Dynamic image enhancement is to select a sliding window length in the entire coring depth section, perform static color calibration within the window length range, then move the window according to a certain step length, and repeat the static color calibration until the color calibration of the entire well section is completed.

[0048] S120 , determining first change information of a first apparent surface frequency corresponding to the target acoustic imaging image and second change information of a second apparent surface frequency corresponding to the target core image.

[0049] The first apparent surface porosity may refer to the apparent surface porosity obtained by analyzing the target acoustic imaging image, reflecting the macroscopic distribution of pores in the wellbore wall. The apparent surface porosity may refer to the area ratio of structures such as pores and cracks in rock or soil on a two-dimensional observation surface. The first change information may refer to the changing trend of the first apparent surface porosity within the coring depth segment, such as the result of linear regression or sliding window statistics, which may be a curve showing the change of apparent surface porosity with depth. The second apparent surface porosity may refer to the apparent surface porosity obtained by analyzing the target core image, reflecting the microscopic distribution of pores within the core. The second change information may refer to the changing trend of the second apparent surface porosity within the coring depth segment.

[0050] Specifically, an image processing algorithm can be used to identify the fracture and hole area in the target acoustic imaging image, calculate the fracture and hole area ratio, obtain the first apparent surface frequency, and analyze the change trend of the first apparent surface frequency with depth in the coring depth section to obtain the first change information. An image processing algorithm (such as threshold segmentation) can be used to analyze the target core image (such as Figure 2 The target core image after segmentation can be shown as Figure 3 Calculate the area ratio of the fracture-cavity region in the target core image to obtain the second apparent porosity. Analyze the variation trend of the second apparent porosity with depth within the coring depth segment to obtain the second variation information. The first and second variation information can clarify the fluctuation pattern of the apparent porosity at local depth, thus providing a basis for conversion coefficient modeling.

[0051] For example, a depth interval may be set and the apparent porosity may be calculated based on a core image threshold.

[0052] The core image has depth information. The depth interval is set and the first viewpoint surface frequency is calculated based on the image threshold. The specific formula is:

[0053]

[0054] Among them, mkl c is the first view face ratio of the core image, if() is a logical judgment, cp i is the core image value, cut is the image threshold, n is the number of data points of the core image at a depth, and i is the i-th data point of the core image at a depth.

[0055] S130 : Determine a visual face ratio conversion coefficient between the first visual face ratio and the second visual face ratio based on the first change information and the second change information.

[0056] The apparent surface frequency conversion coefficient may refer to a coefficient used to convert the apparent surface frequency under acoustic imaging data into the apparent surface frequency under core scale.

[0057] Specifically, the first change information and the second change information can be depth-aligned within the coring depth segment, and the first change information and the second change information can be correlated and analyzed to determine the apparent surface ratio conversion coefficient between the first apparent surface ratio and the second apparent surface ratio, thereby quantifying the relationship between the apparent surface ratio between the acoustic imaging image and the core image.

[0058] Exemplarily, S130 may include: using the first change information as a reference, performing data fitting on the first change information and the second change information, and obtaining a visual aspect ratio conversion coefficient between the first change information and the second change information.

[0059] Specifically, the first change information can be used as a benchmark, and the first change information and the second change information can be fitted using algorithms such as linear regression and polynomial fitting. The statistical relationship between the first apparent face frequency and the second apparent face frequency can be analyzed, and the apparent face frequency conversion coefficient (such as a scaling factor or a nonlinear function) between the first change information and the second change information can be determined, thereby helping to improve the apparent face frequency accuracy under the acoustic imaging image.

[0060] S140 , determining a third apparent surface porosity of the target drilling well at the full well depth, and determining a target apparent surface porosity of the target drilling well at the full well depth based on the apparent surface porosity conversion coefficient and the third apparent surface porosity.

[0061] The full well depth may refer to the total depth range of the target drilling, covering the entire well section. The third apparent surface porosity may refer to the apparent surface porosity calculated from the acoustic imaging image at the full well depth. The target apparent surface porosity may refer to the calibrated apparent surface porosity for the entire well section, obtained by calibrating the third apparent surface porosity using the apparent surface porosity conversion coefficient.

[0062] Specifically, an initial acoustic imaging image of the target well at full well depth is obtained, preprocessed, and based on the preprocessed initial acoustic imaging image, a third apparent porosity of the target well at full well depth is determined. The apparent porosity conversion coefficient is used as an adjustment factor to numerically adjust the third apparent porosity, resulting in an adjusted target apparent porosity of the target well at full well depth. This improves the accuracy of the apparent porosity and reduces costs.

[0063] Exemplarily, S140 may include: determining a third apparent surface frequency of the target well at the full well depth based on an acoustic imaging image of the target well at the full well depth; performing numerical conversion on the third apparent surface frequency based on an apparent surface frequency conversion coefficient to determine a target apparent surface frequency of the target well at the full well depth.

[0064] Specifically, based on the acoustic imaging image of the target well at full well depth, an image processing algorithm can be used to identify the pore area in the acoustic imaging image, calculate the pore area ratio, and obtain the third apparent porosity corresponding to each depth at full well depth. The apparent porosity conversion coefficient is applied to the third apparent porosity, and the third apparent porosity is numerically adjusted to obtain the adjusted target apparent porosity for the target well at full well depth. This can provide a more accurate apparent porosity at full well depth, thereby ensuring accurate evaluation of fracture-vuggy reservoirs and facilitating the prediction of oil and gas production in fracture-vuggy reservoirs and the formulation of production plans.

[0065] For example, the relationship between the apparent surface ratio of the acoustic imaging image (the first apparent surface ratio) and the apparent surface ratio of the core image (the second apparent surface ratio) can be expressed as follows: Figure 4As shown in the figure, the slope of the straight line is the apparent face rate conversion coefficient. According to the apparent face rate conversion coefficient, the third apparent face rate is corrected / adjusted, which can be expressed as:

[0066] mkl=k*mkl w

[0067] Among them, mkl is the target face rate, k is the face rate conversion coefficient, mkl w is the third view face rate.

[0068] In this embodiment, by acquiring the target acoustic imaging image and target core image corresponding to the target wellbore at the coring depth, comprehensive data acquisition can be ensured while avoiding full-well coring, thereby reducing exploration costs. First change information for the first apparent porosity corresponding to the target acoustic imaging image and second change information for the second apparent porosity corresponding to the target core image are determined, thereby clarifying the porosity fluctuation pattern at the local depth and providing a basis for generating a subsequent conversion coefficient. Based on the first and second change information, an apparent porosity conversion coefficient between the first and second apparent porosity is determined, quantifying the apparent porosity relationship between the acoustic imaging image and the core image. A third apparent porosity is determined for the target wellbore at the full wellbore depth, and based on the apparent porosity conversion coefficient and the third apparent porosity, the target apparent porosity for the target wellbore at the full wellbore depth is determined, thereby improving the accuracy of the apparent porosity for the target wellbore at the full wellbore depth and reducing costs. The present invention determines the apparent surface porosity conversion coefficient of the target well at the local depth through the target acoustic imaging image and the target core image corresponding to the coring depth section of the target well, and then realizes the data conversion of the apparent surface porosity of the entire well section according to the apparent surface porosity conversion coefficient at the local depth, thereby improving the accuracy of the apparent surface porosity of the target well at the full well depth.

[0069] Example 2

[0070] Figure 5 This is a flowchart of a method for determining visual face ratio provided in Example 2 of the present invention. This embodiment, based on the above embodiments, optimizes the step of "determining first change information of the first visual face ratio corresponding to the target acoustic imaging image." Explanations of terms identical or corresponding to those in the above embodiments are omitted here.

[0071] See also Figure 5 Another method for determining the visual face ratio provided by this embodiment specifically includes the following steps:

[0072] S210 , obtaining a target acoustic imaging image and a target core image corresponding to the coring depth section of the target well.

[0073] S220 , performing binarization processing on the target acoustic imaging image to generate an initial binary image.

[0074] The initial binary image may refer to a black and white image obtained by binarizing the target acoustic imaging image.

[0075] Specifically, if the original acoustic imaging image is in color, it must first be converted to grayscale. An appropriate image threshold (grayscale value) can be selected to convert the grayscale image into a binary image. This binarization process simplifies the complex target acoustic imaging image into a black and white binary image, highlighting the contrast between the crack and the background, facilitating subsequent analysis and processing.

[0076] Exemplarily, S220 may include: determining a grayscale image corresponding to the target acoustic imaging image, and performing threshold segmentation on the grayscale image based on grayscale values ​​of a plurality of pixels in the grayscale image to obtain an initial binary image.

[0077] The grayscale image may refer to an image obtained by graying the target acoustic imaging image.

[0078] Specifically, the color target acoustic imaging image can be converted into a grayscale image, providing a unified data format for subsequent threshold segmentation. Grayscale processing simplifies image information, highlights the geological features (such as holes and cracks) reflected by changes in acoustic wave intensity, and provides a clearer data basis for subsequent threshold segmentation. According to the grayscale value difference of pixels in the grayscale image, the image is segmented into two parts according to the image threshold: the crack hole (foreground) and the background (wellbore matrix). The part greater than the image threshold (foreground part) is the crack hole information, and the part less than the image threshold (background part) is the background information. Image binarization processing can highlight the crack hole information in the acoustic imaging image and generate an initial binary image. Through binarization processing, complex acoustic imaging images can be simplified into black and white binary images, significantly reducing the amount of data and improving processing efficiency.

[0079] For example, the image threshold value based on which the grayscale image is segmented can be determined by analyzing the grayscale image using a threshold segmentation method. Specifically:

[0080] Set a certain window length and step size to divide the grayscale image, determine the image threshold within the window length range, move the window according to the step size, and repeat the threshold calculation. The maximum inter-class variance method is used for threshold segmentation. The core idea of ​​this method is to divide the image into foreground and background parts by setting a threshold, and traverse all possible thresholds to maximize the inter-class variance between the two parts. The specific formula is:

[0081]

[0082] Wherein, c is the image threshold, σ(c) is the inter-class variance, p1(c) is the distribution frequency of the acoustic imaging image foreground part, p2(c) is the distribution frequency of the acoustic imaging image background part, m1(c) is the data mean value of the acoustic imaging image foreground part, and m2(c) is the data mean value of the acoustic imaging image background part. All possible threshold values are traversed, and the threshold value when the inter-class variance σ(c) is maximum is the final threshold value.

[0083] For example, the acoustic imaging image can be mainly divided into background and fracture-vug, the part (foreground part) greater than the image threshold is fracture-vug information, and the part (background part) less than the image threshold is background information, image binarization processing is performed, and fracture-vug information in the acoustic imaging image is highlighted. The specific formula is:

[0084]

[0085] Wherein, bina is the binary image value, col is the gray value of the pixel point, and c is the image threshold.

[0086] S230, performing abnormal area segmentation on the initial binary image to obtain a target binary image.

[0087] Wherein, the abnormal area can refer to an area in the initial binary image that is incorrectly identified as a pore due to noise, image distortion or other factors. The target binary image can refer to an image obtained after the initial binary image is subjected to abnormal area segmentation, and the image does not contain abnormal areas.

[0088] Specifically, the abnormal area in the initial binary image is removed to obtain the target binary image, so that false pores in the acoustic imaging caused by signal interference or irregular shape of the well wall can be effectively removed, and the accuracy of the porosity calculation is significantly improved.

[0089] For example, as shown in Figure 6 , the acoustic imaging image is divided into a background part and a foreground part, the background part is removed, and the foreground part is subjected to abnormal area segmentation to obtain a target binary image. Figure 6The following diagrams show the processing results of acoustic imaging logging data. Track 1 shows the gamma curve and caliper curve, Track 2 shows depth, Track 3 shows the static amplitude image, Track 4 shows the dynamic amplitude image, Track 5 shows the time image, Track 6 shows the fracture characteristics, Track 7 shows depth, Track 8 shows the binary image before denoising (i.e., the initial binary image), Track 9 shows the binary image after denoising (i.e., the target binary image), and Track 10 shows the apparent surface porosity curve. In the acoustic imaging image, black spots represent holes, and black sinusoidal curves represent fractures. Tool eccentricity can also be observed, and the calculated apparent surface porosity is mainly distributed between 0.05 and 0.25. Denoising can be used to remove anomalous areas in the initial binary image to prevent them from affecting the apparent surface porosity calculation. It should be noted that when eccentricity or diameter expansion occurs, the downhole tool deviates from the wellbore axis, resulting in unequal paths for the acoustic signal to reach and reflect from the wellbore wall. This manifests as distinct vertical dark bands in the acoustic imaging image, representing anomalous areas. By observing the initial binary image, if obvious vertical dark bands (i.e., abnormal areas) are found, the pixel values ​​where the vertical dark bands are located can be set to 0, thus converting the abnormal areas to background areas. A noise binary image is then generated. The initial values ​​of the noise image are all 1, and the image values ​​at the locations of the vertical dark bands are 0.

[0090] S240 : Determine first change information of the first viewing face ratio according to the target binarized image.

[0091] Specifically, the total area of ​​white pixels (representing fractures and cavities) in the target binary image corresponding to each depth point is counted and then divided by the total area of ​​the image to obtain the first apparent porosity at that depth point (calculated as: V1 = white pixel area / total pixel area). Along the depth direction, the statistical characteristics of the first apparent porosity are calculated, the rate of change of the apparent porosity is analyzed, and a porosity-with-depth curve is generated. This determines the first change information of the first apparent porosity, providing an important data foundation for reservoir heterogeneity analysis, helping to accurately determine the conversion coefficient of the apparent porosity, and thus improving the accuracy of porosity evaluation throughout the wellbore.

[0092] Exemplarily, S240 may include: dividing the target binary image into multiple sub-binary images based on a mapping relationship between pixel points and coring depths in the target binary image, and determining the first apparent surface frequency corresponding to each sub-binary image; and determining first change information of the first apparent surface frequency corresponding to the target acoustic imaging image based on the first apparent surface frequency corresponding to each sub-binary image.

[0093] The mapping relationship may refer to the correspondence between pixels in the target binary image and the actual well depth. A sub-binary image may refer to dividing the entire target binary image into multiple continuous small images along the depth direction. Each small image is called a sub-binary image, and each sub-binary image corresponds to a depth interval of the same depth in the well section.

[0094] Specifically, the depth record data of the drilling engineering can be used to establish an accurate correspondence between each pixel row (or column) in the target binary image and the actual well depth. For example, a fixed interval (such as 0.1 meters) of well depth corresponding to each row of pixels in the image can be set. According to the research requirements or the length of the coring depth section, the entire target binary image is divided into a plurality of continuous sub-binary images along the depth direction. Each sub-image corresponds to a specific depth interval of the well section. For example, the entire coring depth section (such as 10 meters) can be divided into 100 sub-images, and each sub-image corresponds to a well depth of 0.1 meters. For each sub-binary image, the total number of white pixels (representing fractures and caves) in the sub-binary image is counted respectively. The number of white pixels in each sub-binary image is divided by the total number of pixels in the sub-binary image to obtain the first apparent porosity corresponding to the sub-binary image. The calculation formula is: V1 = white pixel number / total pixel number. Through depth mapping, the accurate correspondence between each sub-binary image and the well depth is ensured, and the apparent porosity analysis has clear geological significance. According to the first apparent porosity corresponding to each sub-binary image, a curve is fitted to represent the linear trend of the apparent porosity change with the depth, and the curve is determined as the first change information of the first apparent porosity corresponding to the target acoustic imaging image. By converting the change trend of the apparent porosity into a quantifiable index (such as the trend slope, the change rate, etc.), objective comparison and analysis are facilitated.

[0095] For example, in a sub-binary image, the part with a value of 1 is the fracture and cave image, and the first apparent porosity is calculated by calculating the proportion of the fracture and cave image. The specific formula is:

[0096]

[0097] wherein, mkl w is the first apparent porosity, nois i is the image value corresponding to the i th data point in the noise binary image, m is the number of data points of the acoustic imaging image at a depth, i is the i th data point of the acoustic imaging image at a depth, bina i is the image value corresponding to the i th data point in the sub-binary image.

[0098] S250, determining the second change information of the second apparent porosity corresponding to the target core image.

[0099] S260, determining the apparent porosity conversion coefficient between the first apparent porosity and the second apparent porosity based on the first change information and the second change information.

[0100] S270, determining the third apparent porosity of the target well at the full well depth, and determining the target apparent porosity of the target well at the full well depth based on the apparent porosity conversion coefficient and the third apparent porosity.

[0101] The technical solution of this embodiment generates an initial binary image by binarizing the target acoustic imaging image, thereby highlighting the contrast between the pores and the background, facilitating subsequent analysis, and reducing the amount of calculation. The initial binary image is segmented into abnormal areas to obtain a target binary image, which can effectively remove pseudo-pores caused by signal interference or irregularities in the acoustic imaging, retain the geometric characteristics of the real pores, and improve the accuracy of porosity calculation. The first change information of the first apparent surface porosity is determined based on the target binary image, and the apparent surface porosity is converted into a quantifiable continuous curve, providing a data basis for reservoir heterogeneity analysis. The present invention realizes efficient processing of acoustic imaging images and quantitative characterization of porosity through binarization processing, abnormal area segmentation and change information extraction, provides a benchmark for subsequent comparative analysis with core data based on the first change information, and supports the accurate determination of the conversion coefficient.

[0102] For example, Figure 7 This figure shows the processing results of acoustic imaging logging data based on core image calibration (adjustment). Track 1 shows the gamma curve and caliper curve, Track 2 shows depth, Track 3 shows the static amplitude image, Track 4 shows the dynamic amplitude image, Track 5 shows the denoised binary image, Track 6 shows depth, Track 7 shows the apparent porosity, and Track 8 shows the calibrated target apparent porosity. Comparing Tracks 7 and 8, the calibrated apparent porosity of acoustic imaging logging is more consistent with the core image apparent porosity than the original apparent porosity (the third apparent porosity). This method improves the accuracy of apparent porosity calculations in acoustic imaging logging.

[0103] Example 3

[0104] Figure 8 This is a schematic diagram of the structure of a device for determining the face ratio of a visual face provided by the third embodiment of the present invention. Figure 8 As shown, the apparatus includes: an image acquisition module 310, a change information determination module 320, a conversion coefficient determination module 330 and a target visual face ratio determination module 340;

[0105] The image acquisition module 310 is used to acquire the target acoustic imaging image and target core image corresponding to the coring depth section of the target well;

[0106] A change information determination module 320 is configured to determine first change information of a first apparent surface frequency corresponding to the target acoustic imaging image and second change information of a second apparent surface frequency corresponding to the target core image;

[0107] a conversion coefficient determining module 330 for determining a visual face ratio conversion coefficient between the first visual face ratio and the second visual face ratio based on the first change information and the second change information;

[0108] The target visual face porosity determination module 340 is configured to determine a third visual face porosity of the target wellbore at a full well depth, and determine a target visual face porosity of the target wellbore at the full well depth based on the visual face porosity conversion coefficient and the third visual face porosity.

[0109] In the embodiment, the target acoustic imaging image and the target core image corresponding to the coring depth section of the target wellbore are acquired, so that the full well coring is avoided, the comprehensiveness of data acquisition is ensured, and the exploration cost is reduced. The first change information of the first visual face porosity corresponding to the target acoustic imaging image and the second change information of the second visual face porosity corresponding to the target core image are determined, so that the fluctuation rule of the porosity at the local depth is determined, and a basis for generating the conversion coefficient is provided. The visual face porosity conversion coefficient between the first visual face porosity and the second visual face porosity is determined based on the first change information and the second change information, so that the relationship between the acoustic imaging image and the core image is quantified. The third visual face porosity of the target wellbore at the full well depth is determined, and the target visual face porosity of the target wellbore at the full well depth is determined based on the visual face porosity conversion coefficient and the third visual face porosity, so that the accuracy of the visual face porosity of the target wellbore at the full well depth is improved, and the cost is reduced. The visual face porosity conversion coefficient of the target wellbore at the local depth is determined by the target acoustic imaging image and the target core image corresponding to the coring depth section of the target wellbore, and then the data conversion of the full well visual face porosity is realized according to the visual face porosity conversion coefficient at the local depth, so that the accuracy of the visual face porosity of the target wellbore at the full well depth is improved.

[0110] Optionally, the image acquisition module 310 comprises:

[0111] The target acoustic imaging image acquisition unit is configured to acquire an initial acoustic imaging image corresponding to the coring depth section of the target wellbore, perform acceleration correction processing on the initial acoustic imaging image to acquire a corrected initial acoustic imaging image, and perform image enhancement processing on the corrected initial acoustic imaging image to obtain a target acoustic imaging image.

[0112] Optionally, the change information determination module 320 comprises:

[0113] The initial binary image generation unit is configured to perform binaryzation processing on the target acoustic imaging image to generate an initial binary image.

[0114] The target binary image generation unit is configured to perform abnormal region segmentation on the initial binary image to obtain a target binary image.

[0115] The first change information determination unit is configured to determine the first change information of the first visual face porosity according to the target binary image.

[0116] Optionally, the initial binary image generation unit is specifically configured to: determine a gray-scale image corresponding to the target acoustic imaging image, and perform threshold segmentation on the gray-scale image based on gray-scale values of a plurality of pixel points in the gray-scale image to obtain an initial binary image.

[0117] Optionally, the first change information determination unit is specifically configured to: divide the target binary image into a plurality of sub-binary images based on a mapping relationship between pixel points and coring depths in the target binary image, and determine a first face hole ratio corresponding to each sub-binary image; and determine first change information of the first face hole ratio corresponding to the target acoustic imaging image based on the first face hole ratio corresponding to each sub-binary image.

[0118] Optionally, the conversion coefficient determination module 330 is specifically configured to: take the first change information as a reference, perform data fitting on the first change information and the second change information to obtain a face hole ratio conversion coefficient between the first change information and the second change information.

[0119] Optionally, the target face hole ratio determination module 340 is specifically configured to: determine a third face hole ratio of the target well at a full well depth based on an acoustic imaging image of the target well at the full well depth; and perform numerical conversion on the third face hole ratio based on the face hole ratio conversion coefficient to determine a target face hole ratio of the target well at the full well depth.

[0120] The above device can execute the face hole ratio determination method provided by any embodiment of the present application, and has corresponding function modules and beneficial effects for executing the face hole ratio determination method.

[0121] Embodiment Four

[0122] Figure 9 is a structural schematic diagram of an electronic device for implementing the face hole ratio determination method of the embodiments of the present application. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections, and relationships, and their functions, are merely examples and are not intended to limit the implementations described and / or claimed in this document.

[0123] As Figure 9As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by the at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12 and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0124] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0125] The processor 11 can be various general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the method for determining the visual face rate.

[0126] In some embodiments, the visual face ratio determination method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the visual face ratio determination method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the visual face ratio determination method in any other appropriate manner (for example, by means of firmware).

[0127] In particular, according to an embodiment of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present invention includes a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication unit 19, or installed from the storage unit 18, or installed from the ROM 12. When the computer program is executed by the processor 11, the above-described functions defined in the method for determining the visual face ratio according to the embodiment of the present invention are performed.

[0128] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0129] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0130] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0131] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0132] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0133] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0134] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0135] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for determining a visual face ratio, characterized in that: include: Obtaining target acoustic imaging images and target core images corresponding to the target wellbore at the coring depth section; Determining first change information of a first apparent surface frequency corresponding to the target acoustic imaging image and second change information of a second apparent surface frequency corresponding to the target core image; determining, based on the first change information and the second change information, an apparent face rate conversion coefficient between the first apparent face rate and the second apparent face rate; A third apparent surface porosity of the target well at full well depth is determined, and a target apparent surface porosity of the target well at the full well depth is determined based on the apparent surface porosity conversion coefficient and the third apparent surface porosity.

2. The method according to claim 1, characterized in that The step of obtaining a target acoustic imaging image corresponding to the target wellbore at the coring depth section includes: Obtaining the initial acoustic imaging image corresponding to the target well at the coring depth section; The initial acoustic imaging image is subjected to acceleration correction processing to obtain the corrected initial acoustic imaging image, and the corrected initial acoustic imaging image is subjected to image enhancement processing to obtain a target acoustic imaging image.

3. The method according to claim 1, characterized in that The determining of first change information of a first visual face frequency corresponding to the target acoustic imaging image includes: performing binarization processing on the target acoustic imaging image to generate an initial binary image; Performing abnormal region segmentation on the initial binary image to obtain a target binary image; First change information of a first-viewing face ratio is determined according to the target binarized image.

4. The method according to claim 3, characterized in that The binarization processing of the acoustic imaging image to generate an initial binary image includes: A grayscale image corresponding to the target acoustic imaging image is determined, and threshold segmentation is performed on the grayscale image based on grayscale values ​​of a plurality of pixels in the grayscale image to obtain an initial binary image.

5. The method according to claim 3, characterized in that The determining first change information of the first viewing angle face ratio according to the target binary image includes: Based on the mapping relationship between the pixel points and the coring depth in the target binary image, the target binary image is divided into a plurality of sub-binary images, and the first view face ratio corresponding to each sub-binary image is determined respectively; Based on the first apparent face frequency corresponding to each sub-binary image, first change information of the first apparent face frequency corresponding to the target acoustic imaging image is determined.

6. The method according to claim 1, characterized in that The determining, based on the first change information and the second change information, a visual face rate conversion coefficient between the first visual face rate and the second visual face rate includes: The first change information is used as a reference, and data fitting is performed on the first change information and the second change information to obtain a visual aspect ratio conversion coefficient between the first change information and the second change information.

7. The method according to claim 1, characterized in that The determining of a third apparent surface porosity of the target drilling well at the full well depth, and determining a target apparent surface porosity of the target drilling well at the full well depth based on the apparent surface porosity conversion coefficient and the third apparent surface porosity, comprises: determining a third apparent surface frequency of the target well at the full well depth based on the acoustic imaging image of the target well at the full well depth; The third apparent surface ratio is numerically converted based on the apparent surface ratio conversion coefficient to determine a target apparent surface ratio of the target drilling well at the full well depth.

8. A device for determining a visual face ratio, characterized in that: include: An image acquisition module is used to acquire a target acoustic imaging image and a target core image corresponding to the coring depth section of the target drilling; a change information determining module, configured to determine first change information of a first apparent surface frequency corresponding to the target acoustic imaging image and second change information of a second apparent surface frequency corresponding to the target core image; a conversion coefficient determining module, configured to determine an apparent face ratio conversion coefficient between the first apparent face ratio and the second apparent face ratio based on the first change information and the second change information; The target apparent surface porosity determination module is configured to determine a third apparent surface porosity of the target drilling at the full well depth, and determine a target apparent surface porosity of the target drilling at the full well depth based on the apparent surface porosity conversion coefficient and the third apparent surface porosity.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to perform the visual face ratio determination method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining the visual face ratio according to any one of claims 1 to 7 when executed.

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