Wellbore trajectory identification method and device, electronic device, and storage medium

Abstract

The embodiments of the present disclosure provide a borehole trajectory recognition method and device, an electronic device, and a storage medium, and relate to the field of artificial intelligence technology. The borehole trajectory recognition method includes: obtaining while-drilling acoustic wave test data; performing format conversion processing on the while-drilling acoustic wave test data to obtain a three-dimensional data body; performing image preprocessing on the three-dimensional data body to obtain a two-dimensional target image; performing feature extraction processing on the two-dimensional target image to obtain feature point data of each two-dimensional target image; obtaining initial borehole trajectory information according to the feature point data; performing probability screening processing on the initial borehole trajectory information to obtain intermediate borehole trajectory information; performing adjacent borehole trajectory recognition processing on the intermediate borehole trajectory information to obtain target borehole trajectory information. The technical solution provided by the embodiments of the present disclosure can reduce the labor cost of borehole collision prevention and improve the efficiency and accuracy of borehole trajectory recognition.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and in particular to a wellbore trajectory recognition method and device, electronic equipment, and storage medium. Background Art

[0002] Offshore platforms are characterized by a large number of wells, small distances between wells, and dense cluster well patterns. With the needs of later oilfield development, the spacing between well patterns is gradually reduced, and the requirements for wellbore trajectories are more stringent. Therefore, in the overall intensified drilling process, it is necessary to identify the wellbore trajectories of the drilled directional wells in order to avoid collisions and avoid obstacles.

[0003] The current wellbore collision avoidance technology uses traditional well trajectory data for data analysis to determine the wellbore trajectory during the drilling process. Specifically, it includes downhole electromagnetic measurement, measurement while drilling technology, manual monitoring and other methods. However, the current wellbore collision avoidance technology consumes a lot of labor costs and has problems of low recognition efficiency and low recognition accuracy. Summary of the invention

[0004] The main purpose of the embodiments of the present disclosure is to provide a wellbore trajectory identification method and device, electronic equipment, and storage medium, which can reduce the labor cost of wellbore collision prevention and improve the efficiency and accuracy of wellbore trajectory identification.

[0005] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present disclosure proposes a wellbore trajectory identification method, comprising:

[0006] Acquire acoustic test data while drilling;

[0007] Performing format conversion processing on the while-drilling acoustic wave test data to obtain a three-dimensional data volume;

[0008] Performing image preprocessing on the three-dimensional data volume to obtain a two-dimensional target image;

[0009] Performing feature extraction processing on the two-dimensional target images to obtain feature point data of each of the two-dimensional target images;

[0010] Obtaining initial wellbore trajectory information according to the feature point data;

[0011] Performing probability screening processing on the initial wellbore trajectory information to obtain intermediate wellbore trajectory information;

[0012] The intermediate wellbore trajectory information is processed for adjacent wellbore trajectory identification to obtain target wellbore trajectory information.

[0013] In some embodiments, the step of performing format conversion processing on the while drilling acoustic wave test data to obtain a three-dimensional data volume includes:

[0014] Performing envelope extraction processing on the while-drilling acoustic wave test data to obtain envelope data;

[0015] Linear interpolation is performed on the envelope data to obtain the three-dimensional data volume.

[0016] In some embodiments, performing image preprocessing on the three-dimensional data volume to obtain a two-dimensional target image includes:

[0017] Performing cross-sectional transformation processing on the three-dimensional data volume to obtain a two-dimensional cross-sectional image;

[0018] The two-dimensional cross-sectional image is subjected to image denoising to obtain the two-dimensional target image.

[0019] In some embodiments, performing feature extraction processing on the two-dimensional target images to obtain feature point data of each of the two-dimensional target images includes:

[0020] Binarization is performed on the two-dimensional target image according to a preset maximum inter-class variance method to obtain a binary image;

[0021] Performing interference removal processing on the binary image according to a preset connected region search algorithm to obtain a binary denoised image;

[0022] Performing symmetrical flipping processing on the binary denoised image to obtain a symmetrical flipped image;

[0023] Calculating the overlapping area of ​​the binary denoised image and the symmetrically flipped image to obtain an intersection image;

[0024] The center point coordinates of the intersection image are used as the feature point data of the two-dimensional target image.

[0025] In some embodiments, obtaining initial wellbore trajectory information according to the feature point data includes:

[0026] Reconstructing the two-dimensional target image to obtain a three-dimensional borehole image;

[0027] The feature point data is connected in the three-dimensional borehole image according to a preset Euclidean distance algorithm to obtain the initial borehole trajectory information.

[0028] In some embodiments, the performing probability screening processing on the initial wellbore trajectory information to obtain the intermediate wellbore trajectory information includes:

[0029] Acquire the feature quantity of the feature point data in the initial wellbore trajectory information;

[0030] Perform probability calculation according to the number of features to obtain probability information corresponding to the initial wellbore trajectory information;

[0031] The intermediate wellbore trajectory information is screened out from the initial wellbore trajectory information according to the probability information.

[0032] In some embodiments, performing adjacent wellbore trajectory identification processing on the intermediate wellbore trajectory information to obtain target wellbore trajectory information includes:

[0033] Obtaining nearby wellbore trajectory record information;

[0034] The target wellbore trajectory information in the intermediate wellbore trajectory information is identified according to the adjacent wellbore trajectory record information.

[0035] To achieve the above-mentioned purpose, the second aspect of the present disclosure proposes a wellbore trajectory identification device, comprising:

[0036] A while-drilling acoustic wave test data acquisition module, used to acquire while-drilling acoustic wave test data;

[0037] A format conversion module, used for performing format conversion processing on the while-drilling acoustic wave test data to obtain a three-dimensional data body;

[0038] An image preprocessing module, used for performing image preprocessing on the three-dimensional data volume to obtain a two-dimensional target image;

[0039] A feature extraction module, used to perform feature extraction processing on the two-dimensional target image to obtain feature point data of each of the two-dimensional target images;

[0040] An initial wellbore trajectory generating module, used to obtain initial wellbore trajectory information according to the feature point data;

[0041] A probability screening module is used to perform probability screening on the initial wellbore trajectory information to obtain intermediate wellbore trajectory information;

[0042] The adjacent wellbore trajectory identification module is used to perform adjacent wellbore trajectory identification processing on the intermediate wellbore trajectory information to obtain target wellbore trajectory information.

[0043] To achieve the above objective, a third aspect of the present disclosure provides an electronic device, including:

[0044] at least one memory;

[0045] at least one processor;

[0046] at least one program;

[0047] The program is stored in a memory, and the processor executes at least one program to implement the method of the present disclosure as described in the first aspect above.

[0048] To achieve the above objective, a fourth aspect of the present disclosure provides a storage medium, which is a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute:

[0049] As the method according to the first aspect above.

[0050] The wellbore trajectory recognition method and device, electronic device, and storage medium proposed in the embodiments of the present disclosure first obtain the while-drilling acoustic wave test data, then perform format conversion processing on the while-drilling acoustic wave test data to obtain a three-dimensional data body, and then perform image preprocessing to obtain a two-dimensional target image, and then perform feature extraction processing on the two-dimensional target image to obtain feature point data, and obtain initial wellbore trajectory information based on the feature point data, and finally perform probability screening processing and adjacent wellbore trajectory recognition processing to obtain target wellbore trajectory information. The technical solution provided by the embodiments of the present disclosure can reduce the labor cost of wellbore collision prevention and improve the efficiency and accuracy of wellbore trajectory recognition. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a flow chart of the wellbore trajectory identification method provided by the embodiment of the present disclosure.

[0052] Figure 2 yes Figure 1 Flowchart of step S120 in .

[0053] Figure 3 yes Figure 1 Flowchart of step S130 in .

[0054] Figure 4 yes Figure 1 Flowchart of step S140 in .

[0055] Figure 5 yes Figure 1 Flowchart of step S150 in FIG.

[0056] Figure 6 yes Figure 1 Flowchart of step S160 in .

[0057] Figure 7 yes Figure 1 Flowchart of step S170 in .

[0058] Figure 8 It is a module block diagram of a wellbore trajectory identification device provided in an embodiment of the present disclosure.

[0059] Fig. 9 It is a schematic diagram of the hardware structure of the electronic device provided by the embodiment of the present disclosure.

[0060] Figure numerals: while-drilling acoustic test data acquisition module 810, format conversion module 820, image preprocessing module 830, feature extraction module 840, initial wellbore trajectory generation module 850, probability screening module 860, adjacent wellbore trajectory identification module 870, processor 901, memory 902, input / output interface 903, communication interface 904, bus 905. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present application 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 application and are not used to limit the present application.

[0062] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0063] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present invention belongs. The terms used herein are only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0064] Offshore platforms are characterized by a large number of wells, small distances between wells, and dense cluster well patterns. With the needs of later oilfield development, the spacing between well patterns is gradually reduced, and the requirements for wellbore trajectories are more stringent. Therefore, in the overall intensified drilling process, it is necessary to identify the wellbore trajectories of the drilled directional wells in order to avoid collisions and avoid obstacles.

[0065] The current wellbore collision prevention technology uses traditional well trajectory data for data analysis to determine the wellbore trajectory during the drilling process. Specifically, it includes downhole electromagnetic measurement, measurement while drilling technology, manual monitoring and other methods. However, the current wellbore collision prevention technology consumes a lot of labor costs and has problems of low recognition efficiency and low recognition accuracy, which slows down the drilling implementation process and brings hidden dangers to the safe production of drilling.

[0066] If wellside information is obtained through acoustic logging while drilling, and real-time image processing and analysis of the wellside information is performed to obtain wellbore trajectory data, collision avoidance and obstacle avoidance can be achieved. In this way, acoustic logging while drilling technology will achieve technological innovation in drilling applications and provide technical support for the safe implementation of drilling operations.

[0067] Based on this, the embodiments of the present disclosure provide a method and device for identifying a wellbore trajectory, an electronic device, and a storage medium. The wellbore trajectory identification method and device, an electronic device, and a storage medium are firstly obtained, and then the wellbore trajectory identification data is format converted to obtain a three-dimensional data body. Then, image preprocessing is performed to obtain a two-dimensional target image. Then, feature extraction is performed on the two-dimensional target image to obtain feature point data. Initial wellbore trajectory information is obtained according to the feature point data. Finally, probability screening processing and adjacent wellbore trajectory identification processing are performed to obtain target wellbore trajectory information. The technical solution provided by the embodiments of the present disclosure can reduce the labor cost of wellbore collision prevention and improve the efficiency and accuracy of wellbore trajectory identification.

[0068] The disclosed embodiments provide a borehole trajectory recognition method that can realize three-dimensional borehole trajectory recognition through acoustic logging data, utilize image processing technology, utilize image processing methods, and adopt image preprocessing methods to remove noise and interference parts of the borehole image, and utilize image segmentation methods in combination with the strong signal strength and symmetrical crescent characteristics of the borehole image to extract the position of the crescent center point as the possible position of the borehole trajectory, and complete the recognition of the three-dimensional borehole trajectory by connecting the crescent center point and obtaining the top n trajectory lines with the highest probability as the borehole trajectory.

[0069] The embodiments of the present disclosure provide a wellbore trajectory identification method and device, an electronic device, and a storage medium, which are specifically described through the following embodiments. First, the wellbore trajectory identification method in the embodiments of the present disclosure is described.

[0070] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0071] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0072] The borehole trajectory recognition method provided by the embodiment of the present disclosure relates to the field of artificial intelligence technology, and in particular to the field of image processing technology. The borehole trajectory recognition method provided by the embodiment of the present disclosure can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, or a smart watch, etc.; the server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms; the software can be an application that implements the borehole trajectory recognition method, etc., but is not limited to the above forms.

[0073] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0074] The disclosed embodiment proposes a wellbore trajectory identification method, including: acquiring while-drilling acoustic wave test data; performing format conversion processing on the while-drilling acoustic wave test data to obtain a three-dimensional data body; performing image preprocessing on the three-dimensional data body to obtain a two-dimensional target image; performing feature extraction processing on the two-dimensional target image to obtain feature point data of each two-dimensional target image; obtaining initial wellbore trajectory information according to the feature point data; performing probability screening processing on the initial wellbore trajectory information to obtain intermediate wellbore trajectory information; performing adjacent wellbore trajectory identification processing on the intermediate wellbore trajectory information to obtain target wellbore trajectory information.

[0075] Figure 1 is an optional flow chart of the wellbore trajectory identification method provided by the embodiment of the present disclosure, Figure 1 The method may include but is not limited to steps S110 to S170, specifically including:

[0076] S110, obtaining while-drilling acoustic wave test data;

[0077] S120, performing format conversion processing on the while-drilling acoustic wave test data to obtain a three-dimensional data volume;

[0078] S130, performing image preprocessing on the three-dimensional data volume to obtain a two-dimensional target image;

[0079] S140, performing feature extraction processing on the two-dimensional target image to obtain feature point data of each two-dimensional target image;

[0080] S150, obtaining initial wellbore trajectory information according to the feature point data;

[0081] S160, performing probability screening processing on the initial wellbore trajectory information to obtain intermediate wellbore trajectory information;

[0082] S170, performing adjacent wellbore trajectory identification processing on the intermediate wellbore trajectory information to obtain target wellbore trajectory information.

[0083] In step S110, the while-drilling acoustic wave test data is the original data collected by the while-drilling acoustic wave logging instrument, and the data format of the while-drilling acoustic wave test data is the las format.

[0084] In step S120, the format conversion process is to convert the LWD acoustic test data in las format into a three-dimensional data body in raw format, specifically including: extracting the envelope of the wellbore trajectory signal of the las file through Fourier transform of the collected logging data, and then converting the las data into a three-dimensional data body in raw format by linear difference.

[0085] In step S130, the image preprocessing is specifically to convert the three-dimensional data volume into multiple cross-sectional images, and remove the noise of the two-dimensional cross-sectional images by filtering.

[0086] In step S140, the maximum inter-class variance method is used to obtain a binary image for the two-dimensional cross-sectional grayscale image, the interference area is removed by the connected region search method, the denoised binary image is symmetrically flipped along the center point of the image, the intersection of the denoised binary image and the symmetrically flipped image is obtained, the intersection part is retained, and the center point of the area is obtained, and the center point is used as the feature point of the wellbore trajectory.

[0087] In step S150, the two-dimensional borehole image is reconstructed to generate a three-dimensional borehole image, and the borehole position points closest to the center point are connected from top to bottom according to the Euclidean distance, and isolated points and borehole points with distant positions are removed.

[0088] In step S160, multiple wellbore trajectory lines are generated after the connection. The total number of intersections between each wellbore trajectory line and the wellbore trajectory point can be calculated, and the total number of each intersection point is divided by the total number of wellbore points. The first (n) wellbore trajectories with the highest probability are retained as the identified wellbore trajectories.

[0089] In step S170, the first n wellbore trajectories are manually interacted with through a visualization interface to identify the wellbore trajectories of adjacent wells. The identification of three-dimensional wellbore trajectories provides technical support for guiding the next step of drilling construction and the safe implementation of drilling operations.

[0090] The wellbore trajectory identification method proposed in the embodiment of the present disclosure first obtains the while-drilling sonic test data, then performs format conversion processing on the while-drilling sonic test data to obtain a three-dimensional data body, then performs image preprocessing to obtain a two-dimensional target image, then performs feature extraction processing on the two-dimensional target image to obtain feature point data, obtains initial wellbore trajectory information based on the feature point data, and finally performs probability screening processing and adjacent wellbore trajectory identification processing to obtain target wellbore trajectory information. The technical solution provided by the embodiment of the present disclosure can reduce the labor cost of wellbore collision prevention and improve the efficiency and accuracy of wellbore trajectory identification.

[0091] In some embodiments, format conversion is performed on the while drilling sonic test data to obtain a three-dimensional data volume, including: envelope extraction is performed on the while drilling sonic test data to obtain envelope data; and linear interpolation is performed on the envelope data to obtain a three-dimensional data volume.

[0092] Figure 2 is a flowchart of step S120 in some embodiments, Figure 2 The illustrated step S120 includes but is not limited to steps S210 to S220:

[0093] S210, performing envelope extraction processing on the while-drilling acoustic wave test data to obtain envelope data;

[0094] S220, performing linear interpolation processing on the envelope data to obtain a three-dimensional data volume.

[0095] In step S210, the envelope extraction process includes: taking the while-drilling acoustic wave test data as the initial signal, obtaining the Fourier transform of the initial signal, constructing an analytical signal according to the Hilbert transform, and then obtaining the complex envelope of the initial signal as envelope data.

[0096] In a specific embodiment, the envelope extraction process specifically includes:

[0097] (1) Extracting the envelope using Fourier transform: Let the Fourier transform of x(t) be X(jw); then Substituting -w for w gives: That is, the Fourier transform of x*(t) is X*(-jw). If x(t) is a real signal, x(t)=x*(t). After Fourier transform of both sides, we can get X(jw)=X*(-jw). The Fourier transform of a real signal contains negative frequencies. If we want to remove the negative frequencies but keep the total power unchanged, we can set Where u(t) is a step function, omitting "j" in the formula and performing inverse Fourier transform to obtain:

[0098]

[0099] (2) Construct an analytical signal to transform a real signal into a complex signal. Let the original signal be the real part and the signal after Hilbert transform be the imaginary part, and we get: This signal is the Fourier transformed signal obtained in (1). Then substitute x(t)=A(t)cos(w0t+θ(t)) into the analytical signal and we get:

[0100]

[0101] Obtain the absolute value of the analytical signal to obtain the envelope of the wellbore trajectory:

[0102] In step S220, linear interpolation refers to an interpolation method in which the interpolation function is a first-order polynomial, and the interpolation error at the interpolation node is zero. Compared with other interpolation methods, such as parabola interpolation, linear interpolation is simple and convenient. The geometric meaning of linear interpolation is to use a straight line passing through point A and point B in the overview diagram to approximate the original function. By performing linear interpolation on the envelope data, a three-dimensional data body can be obtained.

[0103] In some embodiments, image preprocessing is performed on a three-dimensional data volume to obtain a two-dimensional target image, including: performing cross-sectional conversion processing on the three-dimensional data volume to obtain a two-dimensional cross-sectional image; performing image denoising processing on the two-dimensional cross-sectional image to obtain a two-dimensional target image.

[0104] Figure 3 is a flowchart of step S130 in some embodiments, Figure 3 The illustrated step S130 includes but is not limited to steps S310 to S320:

[0105] S310, performing cross-section conversion processing on the three-dimensional data volume to obtain a two-dimensional cross-section image;

[0106] S320, performing image denoising processing on the two-dimensional cross-sectional image to obtain a two-dimensional target image.

[0107] In step S310 , the cross-section conversion process is to obtain a cross section of the three-dimensional data volume according to the longitudinal high end to obtain at least two two-dimensional cross-section images.

[0108] In step S320, the purpose of the image denoising process is to remove isolated points and noise from the image. The image denoising process includes but is not limited to median filtering and Gaussian filtering.

[0109] In some embodiments, feature extraction processing is performed on the two-dimensional target image to obtain feature point data of each two-dimensional target image, including: binarizing the two-dimensional target image according to a preset maximum inter-class variance method to obtain a binary image; performing interference removal processing on the binary image according to a preset connected region search algorithm to obtain a binary denoised image; symmetrically flipping the binary denoised image to obtain a symmetrically flipped image; calculating the overlapping area of ​​the binary denoised image and the symmetrically flipped image to obtain an intersection image; and using the center point coordinates of the intersection image as the feature point data of the two-dimensional target image.

[0110] Figure 4 is a flowchart of step S140 in some embodiments, Figure 4 The illustrated step S140 includes but is not limited to steps S410 to S450:

[0111] S410, binarizing the two-dimensional target image according to a preset maximum inter-class variance method to obtain a binary image;

[0112] S420, performing interference removal processing on the binary image according to a preset connected region search algorithm to obtain a binary denoised image;

[0113] S430, performing symmetrical flipping processing on the binary denoised image to obtain a symmetrical flipped image;

[0114] S440, calculating the overlapping area of ​​the binary denoised image and the symmetrically flipped image to obtain an intersection image;

[0115] S450, taking the center point coordinates of the intersection image as feature point data of the two-dimensional target image.

[0116] In step S410, the binarization of the image is to set the grayscale value of the pixel points on the image to 0 or 255, so that the entire image presents an obvious black and white visual effect. The binarization process is: all pixel points on the image with grayscale values ​​greater than or equal to the preset grayscale threshold are judged as target objects, and their grayscale values ​​are set to 255 to represent the foreground; pixel points with grayscale values ​​less than the preset grayscale threshold are excluded from the target object area, and their grayscale values ​​are set to 0 to represent the background, wherein the preset grayscale threshold is usually determined based on manual experience and specific tasks.

[0117] The following is a specific example to illustrate the preset grayscale threshold determination process and the binarization process;

[0118] A plane rectangular coordinate system is established in the initial processing image. The crack points of the two-dimensional cracks contained in the initial processing image can find their corresponding coordinates (x, y) in the plane rectangular coordinate system. Assume that the initial processing image is F = {f(x, y)}, and the initial processing image contains the target and the background, where f(x, y) represents the gray value at any point (x, y) in the initial processing image, and f(x, y) is the gray value set G L An element in G, f(x,y)∈G L ={0,1,2,…,L-1}, L represents the L gray levels in the initial processed image, the preset gray threshold is set to t, and the preset gray threshold divides each pixel {f(x,y)} into two categories, namely, the dark area C1 category less than the preset gray threshold t and the bright area C2 category greater than the preset gray threshold t. represents the intra-class variance, represents the between-class variance, and They are all functions of the preset grayscale threshold t, as shown below:

[0119]

[0120]

[0121]

[0122] Where: a1 and a2 are the ratios of pixels in category C1 and category C2 to the total pixels of the image, μ j and C j The mean and variance of the class, μ0 and is the mean and variance of the image F = {f(x,y)}. When the variance within the class is minimized, or the variance between classes is maximized, the optimal preset grayscale threshold is obtained.

[0123] The grayscale values ​​of the pixels in the preprocessed image are compared with the optimal preset grayscale threshold, and the pixels above the preset grayscale threshold are marked as 1, and the pixels below the preset grayscale threshold are marked as 0. Therefore, through the binarization process, the grayscale value of the pixels in the two-dimensional borehole area as the target is set to 255, and the grayscale value of the pixels as the background is set to 0, and a secondary processed image is obtained in which the two-dimensional borehole area is white and the borehole area as the background is black.

[0124] In step S420, a connected region search algorithm is used to search for connectivity between different regions in the image, remove noise information in the image, and obtain a binary denoised image.

[0125] In steps S430 to S450, the binary denoised image is symmetrically flipped to obtain a symmetrically flipped image, and then the overlapping part of the above two images is calculated to obtain an intersection image, which is an image including the overlapping part of the two images. Then, the center point of the area of ​​the intersection image is obtained, and the center point of the area is used as the feature point data of the two-dimensional target image.

[0126] In some embodiments, initial borehole trajectory information is obtained based on feature point data, including: reconstructing a two-dimensional target image to obtain a three-dimensional borehole image; and connecting feature point data in the three-dimensional borehole image according to a preset Euclidean distance algorithm to obtain initial borehole trajectory information.

[0127] Figure 5 is a flowchart of step S150 in some embodiments, Figure 5 The illustrated step S150 includes but is not limited to steps S510 to S520:

[0128] S510, reconstructing the two-dimensional target image to obtain a three-dimensional borehole image;

[0129] S520, performing line connection processing on the feature point data in the three-dimensional borehole image according to a preset Euclidean distance algorithm to obtain initial borehole trajectory information.

[0130] In step S510, the two-dimensional target images are reconstructed according to their original longitudinal height information in the three-dimensional data volume to obtain a three-dimensional borehole image for generating a trajectory.

[0131] In step S520, the feature point data obtained are connected vertically from top to bottom according to the Euclidean distance between the wellbore position points closest to the two points. While connecting the lines, isolated points and wellbore points with farther positions are removed according to the Euclidean distance algorithm to obtain at least two initial wellbore trajectory information, which is the initially drawn wellbore trajectory.

[0132] Specifically, Euclidean distance, also known as Euclidean distance, is used to measure the absolute distance between two points. It is also the absolute distance between two points in multidimensional space. It can be understood as: the real distance between two points in m-dimensional space, or the natural length of a vector. In two-dimensional and three-dimensional space, the Euclidean distance is the actual distance between two points. The calculation formula is as follows:

[0133] In some embodiments, the initial wellbore trajectory information is subjected to probability screening processing to obtain intermediate wellbore trajectory information, including: obtaining the number of features of feature point data in the initial wellbore trajectory information; performing probability calculation based on the number of features to obtain probability information corresponding to the initial wellbore trajectory information; and screening out the intermediate wellbore trajectory information from the initial wellbore trajectory information based on the probability information.

[0134] Figure 6 is a flowchart of step S160 in some embodiments, Figure 6 The illustrated step S160 includes but is not limited to steps S610 to S630:

[0135] S610, obtaining the feature quantity of feature point data in the initial wellbore trajectory information;

[0136] S620, performing probability calculation according to the number of features to obtain probability information corresponding to the initial wellbore trajectory information;

[0137] S630, filtering out intermediate wellbore trajectory information from the initial wellbore trajectory information according to the probability information.

[0138] In step S610 , the number of features is the number of feature points included in the feature point data.

[0139] In step S620, each initial wellbore trajectory information has a certain number of features. The number of features corresponding to each initial wellbore trajectory information is divided by the total number of all trajectory features to obtain probability information corresponding to each initial wellbore trajectory information.

[0140] In step S630, the initial wellbore trajectory information is sorted according to the probability information, and the order is from large to small. A trajectory retention threshold is set. The trajectory retention threshold is the number of trajectories to be selected, denoted as n. The n initial wellbore trajectory information with the largest probability information is screened out as the intermediate wellbore trajectory information according to the trajectory retention threshold.

[0141] In some embodiments, the intermediate wellbore trajectory information is processed for adjacent wellbore trajectory identification to obtain target wellbore trajectory information, including: obtaining adjacent wellbore trajectory record information; and identifying the target wellbore trajectory information in the intermediate wellbore trajectory information according to the adjacent wellbore trajectory record information.

[0142] Figure 7 is a flowchart of step S170 in some embodiments, Figure 7 The illustrated step S170 includes but is not limited to steps S710 to S720:

[0143] S710, obtaining adjacent wellbore trajectory record information;

[0144] S720, identifying target wellbore trajectory information in the intermediate wellbore trajectory information according to the adjacent wellbore trajectory record information.

[0145] In step S710, the neighboring wellbore trajectory record information is a record of the wellbore estimation near the measurement area. The neighboring wellbore trajectory record information is obtained by retrieving it from historical engineering data. The neighboring wellbore trajectory record information includes the azimuth information of the neighboring wellbore, the rough outline of the neighboring wellbore, etc.

[0146] In step S720, artificial intelligence recognition is performed on the intermediate wellbore trajectory information based on the data features reflected by the adjacent wellbore trajectory record information to obtain correct target wellbore trajectory information, and the target wellbore trajectory information is used to guide subsequent construction.

[0147] It should be noted that in addition to the artificial intelligence recognition method, it is also possible to manually interact with the target wellbore trajectory information by using the display of a visual interface and data analysis and manually screening the intermediate wellbore trajectory information based on construction experience.

[0148] The disclosed embodiment proposes a wellbore trajectory identification device, including: a while-drilling sonic test data acquisition module, used to acquire while-drilling sonic test data; a format conversion module, used to perform format conversion processing on the while-drilling sonic test data to obtain a three-dimensional data body; an image preprocessing module, used to perform image preprocessing on the three-dimensional data body to obtain a two-dimensional target image; a feature extraction module, used to perform feature extraction processing on the two-dimensional target image to obtain feature point data of each two-dimensional target image; an initial wellbore trajectory generation module, used to obtain initial wellbore trajectory information according to the feature point data; a probability screening module, used to perform probability screening processing on the initial wellbore trajectory information to obtain intermediate wellbore trajectory information; and a neighboring wellbore trajectory identification module, used to perform neighboring wellbore trajectory identification processing on the intermediate wellbore trajectory information to obtain target wellbore trajectory information.

[0149] See also Figure 8 , Figure 8 The figure illustrates a wellbore trajectory recognition device of an embodiment, which includes: a while-drilling acoustic wave test data acquisition module 810, a format conversion module 820, an image preprocessing module 830, a feature extraction module 840, an initial wellbore trajectory generation module 850, a probability screening module 860, and an adjacent wellbore trajectory recognition module 870, wherein the while-drilling acoustic wave test data acquisition module 810 is connected to the format conversion module 820, the format conversion module 820 is connected to the image preprocessing module 830, the image preprocessing module 830 is connected to the feature extraction module 840, the feature extraction module 840 is connected to the initial wellbore trajectory generation module 850, the initial wellbore trajectory generation module 850 is connected to the probability screening module 860, and the probability screening module 860 is connected to the adjacent wellbore trajectory recognition module 870.

[0150] The specific implementation of the wellbore trajectory identification device of this embodiment is basically consistent with the specific implementation of the wellbore trajectory identification method described above, and belongs to the same inventive concept, and will not be repeated here.

[0151] The present disclosure also provides an electronic device, including:

[0152] at least one memory;

[0153] at least one processor;

[0154] at least one program;

[0155] The program is stored in the memory, and the processor executes the at least one program to implement the wellbore trajectory identification method disclosed in the present invention. The electronic device can be any intelligent terminal including a mobile phone, a tablet computer, a personal digital assistant (PDA), a car computer, etc.

[0156] See also Fig. 9 , Fig. 9 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0157] The processor 901 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present disclosure;

[0158] The memory 902 can be implemented in the form of ROM (Read Only Memory), static storage device, dynamic storage device or RAM (Random Access Memory). The memory 902 can store operating systems and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 902, and the processor 901 is used to call and execute the wellbore trajectory identification method of the embodiment of the present disclosure;

[0159] Input / output interface 903, used to implement information input and output;

[0160] Communication interface 904, used to realize communication interaction between the device and other devices, which can be realized by wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.); and

[0161] A bus 905 that transmits information between various components of the device (e.g., the processor 901, the memory 902, the input / output interface 903, and the communication interface 904);

[0162] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0163] The embodiment of the present disclosure further provides a storage medium, which is a computer-readable storage medium. The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the above-mentioned wellbore trajectory identification method.

[0164] The wellbore trajectory identification method and device, electronic device, and storage medium proposed in the embodiments of the present disclosure first obtain the while-drilling acoustic wave test data, then perform format conversion processing on the while-drilling acoustic wave test data to obtain a three-dimensional data body, and then perform image preprocessing to obtain a two-dimensional target image, and then perform feature extraction processing on the two-dimensional target image to obtain feature point data, and obtain initial wellbore trajectory information based on the feature point data, and finally perform probability screening processing and adjacent wellbore trajectory identification processing to obtain target wellbore trajectory information. The technical solution provided by the embodiments of the present disclosure reduces the labor cost of wellbore collision prevention and improves the efficiency and accuracy of wellbore trajectory identification.

[0165] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0166] The embodiments described in the embodiments of the present disclosure are intended to more clearly illustrate the technical solutions of the embodiments of the present disclosure and do not constitute a limitation on the technical solutions provided by the embodiments of the present disclosure. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of the present disclosure are also applicable to similar technical problems.

[0167] It can be understood by those skilled in the art that Figure 1-7 The technical solutions shown in the figure do not constitute a limitation on the embodiments of the present disclosure, and may include more or fewer steps than those shown in the figure, or a combination of certain steps, or different steps.

[0168] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0169] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0170] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0171] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0172] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0173] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0174] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0175] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0176] The preferred embodiments of the present disclosure are described above with reference to the accompanying drawings, but the scope of the rights of the present disclosure is not limited thereto. Any modification, equivalent substitution and improvement made by those skilled in the art without departing from the scope and essence of the present disclosure should be within the scope of the rights of the present disclosure.

Claims

1. A method for identifying a wellbore trajectory, characterized in that: include: Acquire acoustic test data while drilling; Performing envelope extraction processing on the while-drilling acoustic wave test data to obtain envelope data; Performing linear interpolation processing on the envelope data to obtain a three-dimensional data volume; Performing image preprocessing on the three-dimensional data volume to obtain a two-dimensional target image; Binarization is performed on the two-dimensional target image according to a preset maximum inter-class variance method to obtain a binary image; Performing interference removal processing on the binary image according to a preset connected region search algorithm to obtain a binary denoised image; Performing symmetrical flipping processing on the binary denoised image to obtain a symmetrical flipped image; Calculating the overlapping area of ​​the binary denoised image and the symmetrically flipped image to obtain an intersection image; Using the center point coordinates of the intersection image as the feature point data of the two-dimensional target image; Obtaining initial wellbore trajectory information according to the feature point data; wherein the initial wellbore trajectory information includes a plurality of wellbore trajectory lines; Obtaining the number of intersections between each wellbore trajectory line and all the wellbore trajectory lines, and obtaining the number of features of the feature point data in the initial wellbore trajectory information; According to the feature quantity of the initial wellbore trajectory information, a percentage calculation is performed on the sum of the feature quantities of all the initial wellbore trajectory information to obtain probability information corresponding to the initial wellbore trajectory information; filtering out intermediate wellbore trajectory information from the initial wellbore trajectory information according to the probability information; The intermediate wellbore trajectory information is processed for adjacent wellbore trajectory identification to obtain target wellbore trajectory information.

2. The method according to claim 1, characterized in that The performing image preprocessing on the three-dimensional data volume to obtain a two-dimensional target image includes: Performing cross-sectional transformation processing on the three-dimensional data volume to obtain a two-dimensional cross-sectional image; The two-dimensional cross-sectional image is subjected to image denoising to obtain the two-dimensional target image.

3. The method according to claim 1, characterized in that The obtaining of initial wellbore trajectory information according to the feature point data includes: Reconstructing the two-dimensional target image to obtain a three-dimensional borehole image; The feature point data is connected in the three-dimensional borehole image according to a preset Euclidean distance algorithm to obtain the initial borehole trajectory information.

4. The method according to any one of claims 1 to 3, characterized in that: The step of performing adjacent wellbore trajectory identification processing on the intermediate wellbore trajectory information to obtain target wellbore trajectory information includes: Obtaining nearby wellbore trajectory record information; The target wellbore trajectory information in the intermediate wellbore trajectory information is identified according to the adjacent wellbore trajectory record information.

5. A wellbore trajectory identification device, characterized in that: include: A while-drilling acoustic wave test data acquisition module, used to acquire while-drilling acoustic wave test data; A format conversion module is used to perform envelope extraction processing on the while-drilling acoustic wave test data to obtain envelope data; perform linear interpolation processing on the envelope data to obtain a three-dimensional data body; An image preprocessing module, used for performing image preprocessing on the three-dimensional data volume to obtain a two-dimensional target image; A feature extraction module, used for performing binarization processing on the two-dimensional target image according to a preset maximum inter-class variance method to obtain a binary image; Performing interference removal processing on the binary image according to a preset connected region search algorithm to obtain a binary denoised image; Performing symmetrical flipping processing on the binary denoised image to obtain a symmetrical flipped image; Calculating the overlapping area of ​​the binary denoised image and the symmetrically flipped image to obtain an intersection image; using the center point coordinates of the intersection image as the feature point data of the two-dimensional target image; An initial wellbore trajectory generation module is used to obtain initial wellbore trajectory information according to the feature point data; wherein the initial wellbore trajectory information includes multiple wellbore trajectory lines A probability screening module is used to obtain the number of intersections between each wellbore trajectory line and all the wellbore trajectory lines to obtain the number of features of the feature point data in the initial wellbore trajectory information; perform percentage calculation on the sum of the number of features of all the initial wellbore trajectory information according to the number of features of the initial wellbore trajectory information to obtain probability information corresponding to the initial wellbore trajectory information; and screen out intermediate wellbore trajectory information from the initial wellbore trajectory information according to the probability information; The adjacent wellbore trajectory identification module is used to perform adjacent wellbore trajectory identification processing on the intermediate wellbore trajectory information to obtain target wellbore trajectory information.

6. An electronic device, characterized in that: include: at least one memory; at least one processor; at least one program; The program is stored in the memory, and the processor executes at least one of the programs to implement: A method as claimed in any one of claims 1 to 4.

7. A storage medium, the storage medium being a computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute: the method according to any one of claims 1 to 4.

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