Method for quantitatively determining fracture parameters through acoustic imaging logging and related device

Abstract

A method for quantitatively determining fracture parameters by acoustic imaging logging and a related device, comprising: acquiring a fracture image by acoustic imaging, and obtaining coordinates of a fracture line in the fracture image according to fracture occurrence interpretation; performing acoustic imaging crack sub-image segmentation along the crack line; on the basis of segmentation of the sub-images, crack parameters are calculated in combination with coordinates of the crack lines in the crack image; and carrying out crack parameter continuous calculation along the depth of the crack image. According to the method, fracture interpretation data based on an acoustic imaging logging data volume is combined with interpreted fracture track data, fracture parameters are extracted from segmentation of acoustic imaging fracture sub-images, and the fracture parameters obtained after segmentation of the acoustic imaging fracture sub-images are calculated; and quantitative evaluation of the fracture parameters of the fractured reservoir is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of well logging for oil and natural gas exploration, and in particular to a method for quantitatively determining crack parameters through acoustic imaging logging and a related device. Background Art

[0002] For oil and gas wells, fractures in the reservoir are important channels for oil and gas production. Acoustic imaging logging can measure the amplitude and propagation time of the reflected waves from the well wall and obtain their images. Acoustic imaging can accurately pick up the inclination and dip of fractures. The inclination and dip of fractures indicate the occurrence of fractures, but they are not helpful for analyzing the contribution of fractures in the reservoir. Fractures are important channels for oil and gas flow. The width, length and proportion of fracture area per unit well section (i.e., surface ratio) of fractures are very important for reservoir evaluation. The above parameters can be used to quantitatively evaluate the contribution of fractures in fractured reservoirs. There is currently no relevant literature on the quantitative calculation of fracture parameters using acoustic imaging logging after combining the picked-up fractures with images. Summary of the invention

[0003] The purpose of the present invention is to provide a method and related device for quantitatively determining fracture parameters by acoustic imaging logging, so as to solve the problem that fracture parameters cannot be used to quantitatively evaluate reservoirs.

[0004] To achieve the above object, the present invention adopts the following technical solutions:

[0005] In a first aspect, the present invention provides a method for quantitatively determining fracture parameters by acoustic imaging logging, comprising:

[0006] Acquire the crack image through acoustic imaging, and obtain the coordinates of the crack line in the crack image based on the crack occurrence interpretation;

[0007] Segmentation of crack sub-images along the crack line by acoustic imaging;

[0008] On the basis of the segmented sub-images, the crack parameters are calculated in combination with the coordinates of the crack lines in the crack image;

[0009] The crack parameters are calculated continuously along the depth of the crack image.

[0010] Optionally, based on the fracture occurrence interpretation, the coordinates of the fracture line in the fracture image are obtained:

[0011] Fracture occurrence refers to the direction, dip and inclination of the fracture;

[0012] Define j as the image data column number, j = 1 ~ col, then the crack line coordinates are:

[0013]

[0014]

[0015] f d is the crack depth, f az is the apparent inclination of the crack, f h is the crack width.

[0016] Optional, acoustic imaging crack sub-image segmentation along the crack line:

[0017] (1) Define the window length as L, extract data in the range of [yL / 2, y+L / 2] along the crack coordinate y at depth, and form an image window containing the crack;

[0018] (2) Along the x-coordinate, extract the data column to form the curve f l , the coordinate index range is [-L, L];

[0019] (3) Calculation curve f l The first derivative f l ′ and the second-order derivative f l ″;

[0020] (4) On the curve f l The minimum amplitude Img0 can be found near the coordinate y;

[0021] (5) Assuming the crack amplitude error is δ, find the minimum retention range [y u ,y d ];

[0022] (6) In [-L, y u ] and [y d , L] to find the final segmentation boundary points that meet the following conditions and obtain the segmentation image of the data column.

[0023] Optionally, in step (6):

[0024] ①If f is satisfied l ″(y j )-f l ″(y j-1 )>0 and f l ″(y k+1 )-f l ″(y k )>0;y j ∈[-L, y u ],y k ∈[y d , L]; then y j ,y k are the dividing points of the upper and lower boundaries respectively;

[0025] ②If satisfied and Where n = 1, 2, ...; but are the dividing points of the upper and lower boundaries respectively;

[0026] Optionally, loop through processes (2) to (6) to complete the crack sub-image segmentation within the image window; loop through processes (1) to (7) to complete the crack sub-image segmentation within all crack windows at depth.

[0027] Optionally, based on the segmented sub-images, the crack parameters are calculated in combination with the coordinates of the crack lines in the crack image:

[0028] Single crack length:

[0029]

[0030] Single crack width

[0031] Image single pixel area:

[0032] Then the segmented image area is: A F =A pix *Num

[0033] Single crack width:

[0034] The mean amplitude of a single crack:

[0035] Width after tilt correction:

[0036] F widcor =F wid ×sin(90°-f dip )

[0037] In the formula, F len is the crack length, i.e. the line integral along the crack line trajectory; A pix is the single pixel area, RLEV is the depth sampling interval, A mpi is the segmentation target value (i=1, 2, ..., Num), and Num is the number of segmentation target points in the window.

[0038] Optionally, crack parameters are calculated continuously along the image depth

[0039] Fracture face rate

[0040] ① Calculate the total area within the image window

[0041]

[0042] in, is the starting depth, is the end depth, Dcal is the mean well diameter;

[0043] ② Crack surface rate F spor , the sub-image area A′ within the depth window F

[0044]

[0045] Crack length F LEN , depth window The sum of the line integrals of all crack lines within the range;

[0046] Crack apparent width F WID

[0047]

[0048] Crack width F after inclination correction WID_COR

[0049] Calculate the average inclination of the cracks in the image window

[0050]

[0051] Crack width F after inclination correction WID_COR

[0052] F WID_COR =F WID sin(90°-Dip A )

[0053] Crack density F DEN

[0054]

[0055] The mean amplitude of the crack reflected sound wave F AMP

[0056]

[0057] In the formula, A′ mpi Dip is the target value amplitude value for segmentation within the window. A is the average apparent inclination angle of the crack in the depth frame, and x is the depth window Number of cracks within the range, F DEN It is the number of cracks within every 100m depth.

[0058] In a second aspect, the present invention provides a system for quantitatively determining fracture parameters by acoustic imaging logging, comprising:

[0059] A data acquisition module is used to acquire a crack image through acoustic imaging, and obtain the coordinates of the crack line in the crack image according to the crack occurrence interpretation;

[0060] An image segmentation module, used for segmenting crack sub-images along the crack line by acoustic imaging;

[0061] The crack parameter calculation module calculates the crack parameters based on the segmented sub-images and the coordinates of the crack lines in the crack image; and continuously calculates the crack parameters along the depth of the crack image.

[0062] In a third aspect, the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, steps of a method for quantitatively determining fracture parameters by acoustic imaging logging are implemented.

[0063] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of a method for quantitatively determining fracture parameters by acoustic imaging logging.

[0064] Compared with the prior art, the present invention has the following technical effects:

[0065] In order to solve the problem of quantitative calculation of fracture parameters in fractured reservoirs during oil and gas exploration and development, a method for quantitative evaluation of fracture parameters applicable to fractured reservoirs is established. The present invention uses fracture interpretation data based on acoustic imaging logging data, combined with interpreted fracture trajectory data, to extract fracture parameters by segmenting acoustic imaging fracture sub-images, and calculates the fracture parameters after segmentation of acoustic imaging fracture sub-images; the calculated fracture quantitative parameters mainly include single fracture length, single fracture average width, single fracture reflection wave amplitude mean; as well as fracture width, fracture length, fracture surface ratio, fracture density, fracture reflection wave amplitude mean, etc. along the logging depth. Using these parameters, the contribution of fractures to the reservoir can be comprehensively evaluated, and the quantitative evaluation of fractures in fractured reservoirs can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a flow chart of the present invention.

[0067] Figure 2 This is a result diagram of the quantitative processing of crack parameters of the present invention; DETAILED DESCRIPTION

[0068] The present invention is based on the results of fracture interpretation of acoustic imaging logging data. Fracture interpretation can obtain relevant parameters, and define f d is the crack depth, f dip is the apparent inclination angle of the crack, f az is the apparent inclination of the crack, f h is the crack width, f cal is the wellbore diameter at the fracture depth.

[0069] Embodiment 1:

[0070] The implementation process of the method for quantitatively calculating fracture parameters through acoustic imaging logging is as follows: Figure 1 shown.

[0071] (1) Loading acoustic imaging logging data and performing imaging processing; picking up fractures from the processed images and calculating the fracture dip angle;

[0072] (2) Taking the image and the picked cracks as input, calculate the acoustic imaging crack segmentation sub-image;

[0073] (3) Taking the segmented sub-image and the picked cracks as input, calculate the single crack parameters;

[0074] (4) Taking the segmented sub-image and the picked cracks as input, the crack parameters are continuously calculated along the image depth;

[0075] (5) Scale and calibration of crack apparent width and crack surface porosity parameters;

[0076] (6) Program modules implemented based on the previous calculation and processing results;

[0077] (7) Plot the processing results.

[0078] Embodiment 2:

[0079] A method for quantitatively determining fracture parameters by acoustic imaging logging, comprising:

[0080] Acquire the crack image through acoustic imaging, and obtain the coordinates of the crack line in the crack image based on the crack occurrence interpretation;

[0081] Segmentation of crack sub-images along the crack line by acoustic imaging;

[0082] On the basis of the segmented sub-images, the crack parameters are calculated in combination with the coordinates of the crack lines in the crack image;

[0083] The crack parameters are calculated continuously along the depth of the crack image.

[0084] Embodiment 3:

[0085] A method for quantitatively determining fracture parameters by acoustic imaging logging, comprising:

[0086] Acquire the crack image through acoustic imaging, and obtain the coordinates of the crack line in the crack image based on the crack occurrence interpretation;

[0087] Segmentation of crack sub-images along the crack line by acoustic imaging;

[0088] On the basis of the segmented sub-images, the crack parameters are calculated in combination with the coordinates of the crack lines in the crack image;

[0089] The crack parameters are calculated continuously along the depth of the crack image.

[0090] Crack line coordinate calculation

[0091] According to the interpretation of the crack occurrence, the coordinates of the crack line in the image are obtained. j is defined as the column number of the image data (j = 1 ~ col), then the coordinates of the crack line are:

[0092]

[0093]

[0094] Embodiment 4:

[0095] A method for quantitatively determining fracture parameters by acoustic imaging logging, comprising:

[0096] Acquire the crack image through acoustic imaging, and obtain the coordinates of the crack line in the crack image based on the crack occurrence interpretation;

[0097] Segmentation of crack sub-images along the crack line by acoustic imaging;

[0098] On the basis of the segmented sub-images, the crack parameters are calculated in combination with the coordinates of the crack lines in the crack image;

[0099] The crack parameters are calculated continuously along the depth of the crack image.

[0100] Crack line coordinate calculation

[0101] According to the interpretation of the crack occurrence, the coordinates of the crack line in the image are obtained. j is defined as the column number of the image data (j = 1 ~ col), then the coordinates of the crack line are:

[0102]

[0103]

[0104] Crack sub-image segmentation along the crack line using acoustic imaging

[0105] (1) Define the window length as L, extract data in the range of [yL / 2, y+L / 2] along the crack coordinate y in 5.1 at depth, and form an image window containing the crack;

[0106] (2) Along the x-coordinate in 5.1, extract the data series to form the curve f l , the coordinate index range is [-L, L];

[0107] (3) Calculation curve f l The first derivative f l ′ and the second-order derivative f l ″;

[0108] (4) On the curve f l The minimum amplitude Img0 can be found near the coordinate y;

[0109] (5) Assuming the crack amplitude error is δ, find the minimum retention range [y u ,y d ];

[0110] (6) In [-L, y u ] and [y d , L], the final segmentation boundary points that meet the following conditions can be found within the range to obtain the segmentation image of the data column;

[0111] ①If f is satisfied l ″(y j )-f l ″(y j-1 )>0 and f l ″(y k+1 )-f l ″(y k )>0;y j ∈[-L, y u ],y k ∈[y d , L]; then y j ,y k are the dividing points of the upper and lower boundaries respectively;

[0112] ②If satisfied and Where n = 1, 2, ...; but are the dividing points of the upper and lower boundaries respectively;

[0113] (7) looping through (2) to (6) to complete the segmentation of the crack sub-image within the image window;

[0114] (8) Repeat the steps (1) to (7) to complete the segmentation of crack sub-images within all crack windows in depth.

[0115] Embodiment 5:

[0116] A method for quantitatively determining fracture parameters by acoustic imaging logging, comprising:

[0117] Acquire the crack image through acoustic imaging, and obtain the coordinates of the crack line in the crack image based on the crack occurrence interpretation;

[0118] Segmentation of crack sub-images along the crack line by acoustic imaging;

[0119] On the basis of the segmented sub-images, the crack parameters are calculated in combination with the coordinates of the crack lines in the crack image;

[0120] The crack parameters are calculated continuously along the depth of the crack image.

[0121] Crack line coordinate calculation

[0122] According to the interpretation of the crack occurrence, the coordinates of the crack line in the image are obtained. j is defined as the column number of the image data (j = 1 ~ col), then the coordinates of the crack line are:

[0123]

[0124]

[0125] Crack sub-image segmentation along the crack line using acoustic imaging

[0126] (1) Define the window length as L, extract data in the range of [yL / 2, y+L / 2] along the crack coordinate y in 5.1 at depth, and form an image window containing the crack;

[0127] (2) Along the x-coordinate in 5.1, extract the data series to form the curve f l , the coordinate index range is [-L, L];

[0128] (3) Calculation curve f l The first derivative f l ′ and the second-order derivative f l ″;

[0129] (4) On the curve f l The minimum amplitude Img0 can be found near the coordinate y;

[0130] (5) Assuming the crack amplitude error is δ, find the minimum retention range [y u ,y d ];

[0131] (6) In [-L, y u ] and [y d , L], the final segmentation boundary points that meet the following conditions can be found within the range to obtain the segmentation image of the data column;

[0132] ①If f is satisfied l ″(y j )-f l ″(y j-1 )>0 and f l ″(y k+1 )-f l ″(y k )>0;y j ∈[-L, y u ],y k ∈[yd , L]; then y j ,y k are the dividing points of the upper and lower boundaries respectively;

[0133] ②If satisfied and Where n = 1, 2, ...; but are the dividing points of the upper and lower boundaries respectively;

[0134] (7) looping through (2) to (6) to complete the segmentation of the crack sub-image within the image window;

[0135] (8) Repeat the steps (1) to (7) to complete the segmentation of crack sub-images within all crack windows in depth.

[0136] Based on the segmented sub-images, the crack parameters are calculated by combining the picked crack trajectory lines.

[0137] (1) Single crack length:

[0138]

[0139] (2) Single crack width

[0140] Image single pixel area:

[0141] Then the segmented image area is: A F =A pix *Num

[0142] Single crack width:

[0143] (3) Mean value of single crack amplitude:

[0144] (4) Width after tilt correction:

[0145] F widcor =F wid ×sin(90°-f dip )

[0146] In the formula, F len is the crack length, i.e. the line integral along the crack line trajectory; A pix is the single pixel area, RLEV is the depth sampling interval, A mpi is the segmentation target value (i=1, 2, ..., Num), and Num is the number of segmentation target points in the window.

[0147] Continuous calculation of crack parameters along the image depth

[0148] (1) Crack surface ratio

[0149] ① Calculate the total area within the image window

[0150]

[0151] in, is the starting depth, is the end depth, D cal is the mean well diameter;

[0152] ② Crack surface rate F spor , the sub-image area A′ within the depth window F

[0153]

[0154] (2) Crack length F LEN , depth window The sum of the line integrals of all crack lines within the range;

[0155] (3) Crack apparent width F WID

[0156]

[0157] (4) Crack width F after inclination correction WID_COR

[0158] ① Calculate the average inclination of the cracks in the image window

[0159]

[0160] ② Crack width F after inclination correction WID_COR

[0161] F WID_COR =F WID sin(90°-Dip A )

[0162] (5) Crack density F DEN

[0163]

[0164] (6) Mean amplitude of crack reflected sound waves F AMP

[0165]

[0166] In the formula, A′ mpi Dip is the target value amplitude value for segmentation within the window. A is the average apparent inclination angle of the crack in the depth frame, and x is the depth window Number of cracks within the range, F DENIt is the number of cracks within every 100m depth.

[0167] Embodiment 6:

[0168] Specifically, in order to achieve the invention objectives, the present invention relates to the following four processes:

[0169] Crack line coordinate calculation

[0170] According to the interpretation of the crack occurrence, the coordinates of the crack line in the image are obtained. j is defined as the column number of the image data (j = 1 ~ col), then the coordinates of the crack line are:

[0171]

[0172]

[0173] Crack sub-image segmentation along the crack line using acoustic imaging

[0174] (1) Define the window length as L, extract data in the range of [yL / 2, y+L / 2] along the crack coordinate y in 5.1 at depth, and form an image window containing the crack;

[0175] (2) Along the x-coordinate in 5.1, extract the data series to form the curve f l , the coordinate index range is [-L, L];

[0176] (3) Calculation curve f l The first derivative f l ′ and the second-order derivative f l ″;

[0177] (4) On the curve f l The minimum amplitude Img0 can be found near the coordinate y;

[0178] (5) Assuming the crack amplitude error is δ, find the minimum retention range [y u ,y d ];

[0179] (6) In [-L, y u ] and [y d , L], the final segmentation boundary points that meet the following conditions can be found within the range to obtain the segmentation image of the data column;

[0180] ①If f is satisfied l ″(y j )-f l ″(y j-1 )>0 and f l ″(y k+1 )-f l ″(yk )>0;y j ∈[-L, y u ],y k ∈[y d , L]; then y j ,y k are the dividing points of the upper and lower boundaries respectively;

[0181] ②If satisfied and Where n = 1, 2, ...; but are the dividing points of the upper and lower boundaries respectively;

[0182] (7) looping through (2) to (6) to complete the segmentation of the crack sub-image within the image window;

[0183] (8) looping through the processes (1) to (7) to complete the segmentation of crack sub-images within all crack windows at depth;

[0184] Based on the segmented sub-images, the crack parameters are calculated by combining the picked crack trajectory lines.

[0185] (1) Single crack length:

[0186]

[0187] (2) Single crack width

[0188] Image single pixel area:

[0189] Then the segmented image area is: A F =A pix *Num

[0190] Single crack width:

[0191] (3) Mean value of single crack amplitude:

[0192] (4) Width after tilt correction:

[0193] F widcor =F wid ×sin(90°-f dip )

[0194] In the formula, F len is the crack length, i.e. the line integral along the crack line trajectory; A pix is the single pixel area, RLEV is the depth sampling interval, A mpi is the segmentation target value (i=1, 2, ..., Num), and Num is the number of segmentation target points in the window.

[0195] Continuous calculation of crack parameters along the image depth

[0196] (1) Crack surface ratio

[0197] ① Calculate the total area within the image window

[0198]

[0199] in, is the starting depth, is the end depth, D cal is the mean well diameter;

[0200] ② Crack surface rate F spor , the sub-image area A′ within the depth window F

[0201]

[0202] (2) Crack length F LEN , depth window The sum of the line integrals of all crack lines within the range;

[0203] (3) Crack apparent width F WID

[0204]

[0205] (4) Crack width F after inclination correction WID_COR

[0206] ① Calculate the average inclination of the cracks in the image window

[0207]

[0208] ② Crack width F after inclination correction WID_COR

[0209] F WID_COR =F WID sin(90°-Dip A )

[0210] (5) Crack density F DEN

[0211]

[0212] (6) Mean amplitude of crack reflected sound waves F AMP

[0213]

[0214] In the formula, A′ mpi Dip is the target value amplitude value for segmentation within the window.A is the average apparent inclination angle of the crack in the depth frame, and x is the depth window Number of cracks within the range, F DEN It is the number of cracks within every 100m depth.

[0215] Scale of crack apparent width and crack surface ratio

[0216] The apparent width of the fracture calculated from the image is obtained after the signal is amplified and also needs to be calibrated by the core.

[0217] F widl =f(F wid )

[0218] but

[0219] f is a calibration function of the core fracture width parameter and the acoustic imaging apparent width parameter.

[0220] In yet another embodiment of the present invention, a system for quantitatively determining fracture parameters by acoustic imaging logging is provided, which can be used to implement the above-mentioned method for quantitatively determining fracture parameters by acoustic imaging logging. Specifically, the system for quantitatively determining fracture parameters by acoustic imaging logging includes:

[0221] A data acquisition module is used to acquire a crack image through acoustic imaging, and obtain the coordinates of the crack line in the crack image according to the crack occurrence interpretation;

[0222] An image segmentation module, used for segmenting crack sub-images along the crack line by acoustic imaging;

[0223] The crack parameter calculation module calculates the crack parameters based on the segmented sub-images and the coordinates of the crack lines in the crack image; and continuously calculates the crack parameters along the depth of the crack image.

[0224] The division of modules in the embodiments of the present invention is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional module in each embodiment of the present invention may be integrated into one processor, or may exist physically separately, or two or more modules may be integrated into one module. The above-mentioned integrated modules may be implemented in the form of hardware or in the form of software functional modules.

[0225] In another embodiment of the present invention, a computer device is provided, the computer device includes a processor and a memory, the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a method for quantitatively determining fracture parameters by acoustic imaging logging.

[0226] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the method for quantitatively determining fracture parameters by acoustic imaging logging in the above embodiment.

[0227] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0228] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0229] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0230] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0231] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for quantitatively determining fracture parameters by acoustic imaging logging, characterized in that: include: Acquire the crack image through acoustic imaging, and obtain the coordinates of the crack line in the crack image based on the crack occurrence interpretation; Segmentation of crack sub-images along the crack line by acoustic imaging; On the basis of the segmented sub-images, the crack parameters are calculated in combination with the coordinates of the crack lines in the crack image; The crack parameters are calculated continuously along the depth of the crack image.

2. The method for quantitatively determining fracture parameters by acoustic imaging logging according to claim 1, characterized in that: According to the interpretation of the fracture occurrence, the coordinates of the fracture line in the fracture image are obtained: Fracture occurrence refers to the direction, dip and inclination of the fracture; Define j as the image data column number, j = 1 ~ col, then the crack line coordinates are: f d is the crack depth, f az is the apparent inclination of the crack, f h is the crack width.

3. The method for quantitatively determining fracture parameters by acoustic imaging logging according to claim 2, characterized in that: Segmentation of crack sub-images along the crack line by acoustic imaging: (1) Define the window length as L, extract data in the range of [yL / 2, y+L / 2] along the crack coordinate y at depth, and form an image window containing the crack; (2) Along the x-coordinate, extract the data column to form the curve f l , the coordinate index range is [-L,L]; (3) Calculation curve f l The first derivative f l ′ and the second-order derivative f l "; (4) On the curve f l The minimum amplitude Img0 can be found near the coordinate y; (5) Assuming the crack amplitude error is δ, find the minimum retention range [y u ,y d ]; (6) In [-L,y u ] and [y d ,L] to find the final segmentation boundary points that meet the following conditions and obtain the segmentation image of the data column.

4. The method for quantitatively determining fracture parameters by acoustic imaging logging according to claim 3, characterized in that: In step (6): ①If f is satisfied l "9y j )-f l "(y j-1 )>0 and f l "(y k+1 )-f l "(y k )>0;y j ∈[-L,y u ],y k ∈[y d ,L]; then y j ,y k are the dividing points of the upper and lower boundaries respectively; ②If satisfied and Where n = 1, 2…; but are the dividing points of the upper and lower boundaries respectively.

5. The method for quantitatively determining fracture parameters by acoustic imaging logging according to claim 3, characterized in that: The process (2) to (6) is repeated to complete the crack sub-image segmentation in the image window; the process (1) to (7) is repeated to complete the crack sub-image segmentation in all crack windows in depth.

6. The method for quantitatively determining fracture parameters by acoustic imaging logging according to claim 1, characterized in that: On the basis of the segmented sub-images, the crack parameters are calculated by combining the coordinates of the crack lines in the crack image: Single crack length: Single crack width Image single pixel area: Then the segmented image area is: A F =A pix *Num Single crack width: The mean value of single crack amplitude: Width after tilt correction: F widcor =F wid ×sin(90°-f dip ) In the formula, F len is the crack length, i.e. the line integral along the crack line trajectory; A pix is the single pixel area, RLEV is the depth sampling interval, A mpi is the segmentation target value (i=1,2,…,Num), and Num is the number of segmentation target points in the window.

7. The method for quantitatively determining fracture parameters by acoustic imaging logging according to claim 1, characterized in that: Continuous calculation of crack parameters along the image depth Fracture face rate ① Calculate the total area within the image window in, is the starting depth, is the end depth, D cal is the mean well diameter; ② Crack surface rate F spor , the sub-image area A′ within the depth window F Crack length F LEN , depth window The sum of the line integrals of all crack lines within the range; Crack apparent width F WID Crack width F after inclination correction WID_COR Calculate the average inclination of the cracks in the image window Crack width F after inclination correction WID_COR F WID_COR =F WID sin(90°-Dip A ) Crack density F DEN The mean amplitude of the crack reflected sound wave F AMP In the formula, A ′ mpi Dip is the amplitude value of the segmentation target value within the window. A is the average apparent inclination angle of the crack in the depth frame, and x is the depth window Number of cracks within the range, F DEN It is the number of cracks within every 100m depth.

8. A system for quantitatively determining fracture parameters by acoustic imaging logging, characterized in that: include: A data acquisition module is used to acquire a crack image through acoustic imaging, and obtain the coordinates of the crack line in the crack image according to the crack occurrence interpretation; An image segmentation module, used for segmenting crack sub-images along the crack line by acoustic imaging; The crack parameter calculation module calculates the crack parameters based on the segmented sub-images and the coordinates of the crack lines in the crack image; and continuously calculates the crack parameters along the depth of the crack image.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method for quantitatively determining fracture parameters by acoustic imaging logging as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method for quantitatively determining fracture parameters by acoustic imaging logging as claimed in any one of claims 1 to 7 are implemented.

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