A visual logging video depth calibration method based on collar data and inter-frame motion estimation

Abstract

The invention discloses a visual logging video depth calibration method based on coupling data and inter-frame motion estimation, and relates to the technical field of storage-type visual logging in the petroleum and petrochemical industry. The visual logging depth calibration method has higher accuracy than the existing distance measurement, and solves the problems of logging cable elongation and time asynchronism. The method automatically identifies casing couplings and records the position of the image where the coupling appears in the video by using edge detection technology, feature parameter extraction, feature matching and intelligent discrimination. The method intelligently and automatically extracts coupling parameters, and calculates the distance from the coupling to the camera by using the proportional relationship between the camera imaging distance and the imaging size. The method interpolates coupling data by using a method based on inter-frame motion estimation to obtain depth data between couplings. The method automatically merges the accurately calibrated data with the video, and performs intelligent matching to ensure data accuracy.

Description

Technical Field

[0001] The invention relates to the technical field of storage-type visual logging in the petroleum and petrochemical industry, and in particular to a visual logging video depth calibration method based on coupling data and inter-frame motion estimation. Background Art

[0002] The storage-type visual logging technology equipment can store the downhole video in the internal memory of the downhole instrument, and the surface instrument records the length of the wireline downhole and stores the depth information. The downhole video and the surface depth information are recorded synchronously based on time. After the logging operation is completed, the depth information recorded on the ground is synchronized and synthesized on the video screen.

[0003] The traditional visual logging video depth calibration method has the following problems: 1. The depth recording and video recording time must be strictly synchronized, otherwise the time error will cause depth error; 2. The steel wire has a certain amount of elongation, and there is an error between the depth recorded by the ground instrument and the actual depth, and the error will accumulate with the increase of depth; 3. The elongation of the steel wire is related to the downhole depth and the weight of the downhole instrument, which is difficult to calibrate accurately; 4. After measurement, depth correction is required based on the marked short casing depth data or casing coupling data. Chinese patent CN201410429511.5 discloses a method and device for processing time-depth data of logging equipment, the method comprising: obtaining drill motion state data and data sampling time when the logging equipment is used for logging operation; determining a first operation window time according to the drill motion state data and the data sampling time; correcting the first operation window time using pre-acquired video data to obtain a second operation window time; obtaining time-depth data based on the second operation window time, attribute data of the logging equipment and the data sampling time. This patented technology is complex to operate and difficult to implement, the logging equipment is easily damaged underground, and the logging video depth calibration cannot be performed; therefore, the demand for improving the accuracy of visual logging video depth calibration is becoming increasingly prominent. Summary of the invention

[0004] The present invention provides a visual logging video depth calibration method based on coupling data and inter-frame motion estimation, the purpose of which is to solve the problem of large errors in traditional visual logging video depth calibration methods; this method does not require a ground depth recording device, can not only automatically calibrate the video depth according to the coupling data table, but also greatly improves the accuracy of visual logging video depth calibration.

[0005] The technical solution of the present invention is: a visual logging video depth calibration method based on collar data and inter-frame motion estimation, comprising the following steps:

[0006] Step 1: Collect well logging video;

[0007] Step 2: Read the logging video, identify the casing collar, and record the frame number of the image where the collar appears in the video;

[0008] Step 3: Extract the coupling parameters: calculate the distance from the coupling to the camera;

[0009] Step 4: Fuse the identified coupling parameters with the coupling data table data to calibrate the coupling depth information;

[0010] Step 5: Interpolate the collar data through the inter-frame motion estimation method to obtain the depth data between the collars.

[0011] Further preferably, the method further comprises the following steps:

[0012] Step 6: Export the depth information and synchronize the depth information to the logging video for display.

[0013] Preferably, the first step is to use an underground high-definition video acquisition device to collect video.

[0014] Preferably, the downhole high-definition video acquisition instrument has a pressure resistance of 80 MPa and a temperature resistance of 160°C.

[0015] Preferably, in step 2, automatic identification or computer-assisted manual identification of the casing coupling is adopted.

[0016] Preferably, the specific steps of identifying the casing coupling in step 2 are:

[0017] (a) Extract the image and convert it into a grayscale image;

[0018] (b) extracting feature parameters using edge detection technology, matching them with the collar image, and automatically determining whether a collar appears in the image;

[0019] (c) Record the frame number of the collar image, count the identified collars, and output the counting result.

[0020] Preferably, the distance from the coupling to the camera in step three is calculated using the proportional relationship between the camera imaging distance and the imaging size.

[0021] Preferably, the coupling depth in the coupling data table in step 4 is the depth of each coupling on the oil pipe recorded before entering the well.

[0022] Preferably, the actual depth of the coupling in step 4 is equal to the coupling depth in the coupling data table minus the distance from the coupling to the camera.

[0023] Preferably, the specific steps of interpolating the buttock data by the method of inter-frame motion estimation in step 5 are:

[0024] (a) Using the position of the collar to perform eccentricity correction and expansion transformation on the logging video;

[0025] (b) In the expanded video, two adjacent frames are extracted and the two images are registered using the autocorrelation method;

[0026] (c) Calculate the longitudinal offset Dp of the two images using linear interpolation: When the frame number fn satisfies fm<fn≤fx,

[0027]

[0028] Wherein, Dx is the corresponding logging depth of the frame number fx, and Dm is the corresponding logging depth of the frame number fm;

[0029] (d) Continuously calculate to obtain the total longitudinal offset;

[0030] (e) The logging depth value corresponding to each frame of image is calculated based on the longitudinal offset of the total two images, the total offset and the collar depth data.

[0031] Compared with the prior art, the present invention has the following advantages:

[0032] (1) The present invention proposes a new visual logging depth calibration method, which has higher accuracy than the existing distance measurement method and solves the problems of logging cable elongation and time asynchrony.

[0033] (2) The present invention utilizes edge detection technology, feature parameter extraction, feature matching and intelligent discrimination to automatically identify casing couplings and record the position of the image where the coupling appears in the video.

[0034] (3) The present invention intelligently and automatically extracts the coupling parameters and calculates the distance from the coupling to the camera using the proportional relationship between the camera imaging distance and the imaging size.

[0035] (4) The present invention uses a method based on inter-frame motion estimation to interpolate the collar data to obtain depth data between the collars.

[0036] (5) The present invention automatically merges the accurately calibrated data with the video and performs intelligent matching to ensure data accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0038] Figure 1 It is a schematic diagram of the system flow of the present invention;

[0039] Figure 2 It is an image of a certain frame in the logging video;

[0040] Figure 3 It is the coupling parameter table in step 3;

[0041] Figure 4 This is the data fusion table in step 4. DETAILED DESCRIPTION

[0042] The present invention provides a visual logging video depth calibration method based on coupling data and inter-frame motion estimation. The method does not require a ground depth recording device, can not only automatically calibrate the video depth according to the coupling data table, but also greatly improves the accuracy of the visual logging video depth calibration.

[0043] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0044] Embodiment 1

[0045] The present invention provides a visual logging video depth calibration method based on collar data and inter-frame motion estimation, comprising the following steps:

[0046] Step 1: Collect well logging video;

[0047] Step 2: Read the logging video, identify the casing collar, and record the frame number of the image where the collar appears in the video;

[0048] Step 3: Extract the coupling parameters: calculate the distance from the coupling to the camera;

[0049] Step 4: Fuse the identified coupling parameters with the coupling data table data to calibrate the coupling depth information;

[0050] Step 5: Interpolate the collar data through the inter-frame motion estimation method to obtain the depth data between the collars.

[0051] It is worth noting that the visual logging video depth calibration method does not require a ground depth recording device. It can not only automatically calibrate the video depth according to the coupling data table, but also greatly improve the accuracy of the visual logging video depth calibration. Since the ground depth recording device is abandoned, the visual logging video depth calibration method solves the problems of logging cable elongation and time asynchrony.

[0052] Embodiment 2

[0053] Embodiment 2 includes all the technical features of Embodiment 1, specifically including:

[0054] The present invention provides a visual logging video depth calibration method based on collar data and inter-frame motion estimation, comprising the following steps:

[0055] Step 1: Collect well logging video;

[0056] Step 2: Read the logging video, identify the casing collar, and record the frame number of the image where the collar appears in the video;

[0057] Step 3: Extract the coupling parameters: calculate the distance from the coupling to the camera;

[0058] Step 4: Fuse the identified coupling parameters with the coupling data table data to calibrate the coupling depth information;

[0059] Step 5: Interpolate the collar data through the inter-frame motion estimation method to obtain the depth data between the collars.

[0060] It is worth noting that the visual logging video depth calibration method does not require a ground depth recording device. It can not only automatically calibrate the video depth according to the coupling data table, but also greatly improve the accuracy of the visual logging video depth calibration. Since the ground depth recording device is abandoned, the visual logging video depth calibration method solves the problems of logging cable elongation and time asynchrony.

[0061] In addition, the second embodiment further provides a visual logging video depth calibration method based on coupling data and inter-frame motion estimation, which is defined as follows, and specifically includes the following steps:

[0062] Step 6: Export the depth information and synchronize the depth information to the logging video for display.

[0063] Among them, Example 2 explains in detail the step one of well logging video acquisition. Specifically, step one is to use a downhole high-definition video acquisition instrument to perform video acquisition.

[0064] Among them, the underground high-definition video acquisition instrument can withstand a pressure of 80MPa and a temperature of 160℃.

[0065] Embodiment 3

[0066] Embodiment 3 includes all the technical features of Embodiment 2, specifically including:

[0067] The present invention provides a visual logging video depth calibration method based on collar data and inter-frame motion estimation, comprising the following steps:

[0068] Step 1: Collect well logging video;

[0069] Step 2: Read the logging video, identify the casing collar, and record the frame number of the image where the collar appears in the video;

[0070] Step 3: Extract the coupling parameters: calculate the distance from the coupling to the camera;

[0071] Step 4: Fuse the identified coupling parameters with the coupling data table data to calibrate the coupling depth information;

[0072] Step 5: interpolate the collar data by using the inter-frame motion estimation method to obtain the depth data between the collars;

[0073] Step 6: Export the depth information and synchronize the depth information to the logging video for display.

[0074] Among them, Example 3 provides a detailed explanation of identifying the casing coupling in step 2, and the specific steps are:

[0075] (a) Extract the image and convert it into a grayscale image;

[0076] (b) extracting feature parameters using edge detection technology, matching them with the collar image, and automatically determining whether a collar appears in the image;

[0077] (c) Record the frame number of the collar image, count the identified collars, and output the counting result.

[0078] It is worth noting that the visual logging video depth calibration method uses edge detection technology, feature parameter extraction, feature matching and intelligent discrimination to automatically identify casing collars and record the position of the image where the collar appears in the video.

[0079] Embodiment 4

[0080] Embodiment 4 includes all the technical features of Embodiment 3, specifically including:

[0081] The present invention provides a visual logging video depth calibration method based on collar data and inter-frame motion estimation, comprising the following steps:

[0082] Step 1: Use underground high-definition video acquisition equipment to collect video;

[0083] Step 2: Read the logging video, use edge detection technology, feature parameter extraction, feature matching and intelligent discrimination to automatically identify the casing collar, and record the frame number of the image where the collar appears in the video;

[0084] like Figure 2 As shown, the specific steps include:

[0085] (a) Extract the image and convert it into a grayscale image;

[0086] (b) extracting feature parameters using edge detection technology, matching them with the collar image, and automatically determining whether a collar appears in the image;

[0087] (c) Record the frame number of the collar image, count the identified collars, and output the counting result.

[0088] Step 3: Extract the coupling parameters: calculate the distance from the coupling to the camera;

[0089] Step 4: Fuse the identified coupling parameters with the coupling data table data to calibrate the coupling depth information;

[0090] Step 5: interpolate the collar data by using the inter-frame motion estimation method to obtain the depth data between the collars;

[0091] Step 6: Export the depth information and synchronize the depth information to the logging video for display.

[0092] Among them, embodiment 4 explains step 3 and step 4 in detail. Specifically, in step 3, the distance from the coupling to the camera is calculated using the proportional relationship between the camera imaging distance and the imaging size.

[0093] The coupling depth in the coupling data table in step 4 is the depth of each coupling on the oil pipe recorded before entering the well. The actual depth of the coupling is equal to the coupling depth in the coupling data table minus the distance from the corresponding coupling to the camera recorded in step 3.

[0094] It is worth noting that the visual logging video depth calibration method does not require a ground depth recording device. It can not only automatically calibrate the video depth according to the coupling data table, but also greatly improve the accuracy of the visual logging video depth calibration. The visual logging video depth calibration method can automatically extract the coupling parameters and calculate the distance from the coupling to the camera using the proportional relationship between the camera imaging distance and the imaging size.

[0095] Embodiment 5

[0096] Embodiment 4 includes all the technical features of Embodiment 3, specifically including:

[0097] The present invention provides a visual logging video depth calibration method based on collar data and inter-frame motion estimation, comprising the following steps:

[0098] Step 1: Use underground high-definition video acquisition equipment to collect video;

[0099] Step 2: Read the logging video, use edge detection technology, feature parameter extraction, feature matching and intelligent discrimination to automatically identify the casing collar, and record the frame number of the image where the collar appears in the video;

[0100] like Figure 2 As shown, the specific steps include:

[0101] (a) Extract the image and convert it into a grayscale image;

[0102] (b) extracting feature parameters using edge detection technology, matching them with the collar image, and automatically determining whether a collar appears in the image;

[0103] (c) Record the frame number of the collar image, count the identified collars, and output the counting result.

[0104] Step 3: Extract the coupling parameters: calculate the distance from the coupling to the camera;

[0105] Step 4: Fuse the identified coupling parameters with the coupling data table data to calibrate the coupling depth information;

[0106] Step 5: interpolate the collar data by using the inter-frame motion estimation method to obtain the depth data between the collars;

[0107] Step 6: Export the depth information and synchronize the depth information to the logging video for display.

[0108] Among them, the fifth embodiment explains in detail the interpolation of the buttock data by the method of inter-frame motion estimation in step five, and the specific steps are:

[0109] (a) Using the position of the collar to perform eccentricity correction and expansion transformation on the logging video;

[0110] (b) In the expanded video, two adjacent frames are extracted and the two images are registered using the autocorrelation method;

[0111] (c) Calculate the longitudinal offset Dp of the two images using linear interpolation: When the frame number fn satisfies fm<fn≤fx,

[0112]

[0113] Wherein, Dx is the corresponding logging depth of the frame number fx, and Dm is the corresponding logging depth of the frame number fm;

[0114] (d) Continuously calculate to obtain the total longitudinal offset;

[0115] (e) The logging depth value corresponding to each frame of image is calculated based on the longitudinal offset of the total two images, the total offset and the collar depth data.

[0116] The present invention provides a visual logging video depth calibration method based on coupling data and inter-frame motion estimation. The method does not require a ground depth recording device, can not only automatically calibrate the video depth according to a coupling data table, but also greatly improves the accuracy of the visual logging video depth calibration. The present invention interpolates the coupling data using a method based on inter-frame motion estimation to obtain depth data between couplings. The visual logging video depth calibration method automatically merges the accurately calibrated data with the video, and performs intelligent matching to ensure data accuracy.

[0117] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A visual logging video depth calibration method based on collar data and inter-frame motion estimation, characterized in that: The steps include: Step 1: Collect well logging video; Step 2: Read the logging video, identify the casing collar, and record the frame number of the image where the collar appears in the video; Step 3: Extract the coupling parameters and calculate the distance from the coupling to the camera; Step 4: The identified coupling parameters are integrated with the coupling data table data to calibrate the coupling depth information; the coupling depth in the coupling data table is the depth of each coupling on the oil pipe recorded before entering the well; the actual coupling depth is equal to the coupling depth in the coupling data table minus the distance from the coupling to the camera; Step 5: interpolate the collar data by using the inter-frame motion estimation method to obtain the depth data between the collars; Among them, the specific steps for interpolating the butt collar data are: (a) Using the position of the collar to perform eccentricity correction and expansion transformation on the logging video; (b) In the expanded video, two adjacent frames are extracted and the two images are registered using the autocorrelation method; (c) Calculate the longitudinal offset Dp of the two images using linear interpolation: When the frame number fn satisfies fm<fn≤fx, Wherein, Dx is the corresponding logging depth of the frame number fx, and Dm is the corresponding logging depth of the frame number fm; (d) Continuously calculate to obtain the total longitudinal offset; (e) The logging depth value corresponding to each frame of image is calculated based on the longitudinal offset of the total two images, the total offset and the collar depth data.

2. A visual logging video depth calibration method based on collar data and inter-frame motion estimation according to claim 1, characterized in that: The following steps are also included: Step 6: Export the depth information and synchronize the depth information to the logging video for display.

3. The method for visual logging video depth calibration based on coupling data and inter-frame motion estimation according to claim 1, characterized in that: The first step is to use an underground high-definition video acquisition device to collect video.

4. The method for visual logging video depth calibration based on coupling data and inter-frame motion estimation according to claim 3, characterized in that: The underground high-definition video acquisition instrument has a pressure resistance of 80MPa and a temperature resistance of 160°C.

5. The method for visual logging video depth calibration based on coupling data and inter-frame motion estimation according to claim 1, characterized in that: In the step 2, automatic identification or computer-assisted manual identification of the casing coupling is adopted.

6. A visual logging video depth calibration method based on collar data and inter-frame motion estimation according to claim 1 or 5, characterized in that: The specific steps for identifying the casing coupling in step 2 are: (a) Extract the image and convert it into a grayscale image; (b) extracting feature parameters using edge detection technology, matching them with the collar image, and automatically determining whether a collar appears in the image; (c) Record the frame number of the collar image, count the identified collars, and output the counting result.

7. The method for visual logging video depth calibration based on coupling data and inter-frame motion estimation according to claim 1, characterized in that: In the step 3, the distance from the coupling to the camera is calculated using the proportional relationship between the camera imaging distance and the imaging size.

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