Array acoustic wave far-probing signal processing method and electronic device for drilling collision avoidance

Abstract

This application provides a method and electronic device for processing array acoustic remote detection signals for well collision prevention. The method includes: acquiring well logging physical parameters using logging equipment during well logging and transmitting detection acoustic waves towards a target area; acquiring multiple array acoustic waves obtained after reflection or scattering of the detection acoustic waves through formation interfaces within the target area; constructing a three-dimensional signal data volume in a preset three-dimensional spatial coordinate system based on the multiple array acoustic waves; determining a search range based on the well logging physical parameters; traversing the signal points in the three-dimensional signal data volume, and for each signal point, determining at least one cluster based on the number of all signal points within the search range centered on the signal point; selecting a cluster whose shape satisfies a preset shape from the signal points in the at least one cluster as the target cluster; and determining the location of adjacent wells based on the three-dimensional spatial coordinates of the signal points in the target cluster. This application can improve the accuracy of adjacent well identification.

Description

Technical Field

[0001] This application relates to the field of signal processing technology, and in particular to an array acoustic remote detection signal processing method and electronic device for drilling collision prevention. Background Technology

[0002] In oil and gas exploration and development, especially when drilling infill or adjustment wells in cluster well areas with densely packed wellheads, accurate monitoring of the relative position of the well to adjacent drilled wells (i.e., neighboring wells) is crucial for preventing downhole collision accidents. Array acoustic logging, as an effective long-range detection technology, can receive reflected or scattered signals from the wellbore of neighboring wells, making it a key means of achieving neighboring well detection and collision prevention early warning. However, in practical applications, these target signals are often extremely weak and deeply embedded in strong background noise, resulting in a very low signal-to-noise ratio, posing a significant challenge to effective signal extraction.

[0003] Currently, most methods for processing array acoustic logging signals are based on two-dimensional analysis or employ isotropic three-dimensional filtering and clustering techniques. These methods typically treat the signal as a uniformly distributed set in the horizontal and vertical directions, using a search kernel of a fixed shape (such as a sphere) for noise filtering or signal clustering. However, adjacent well reflection signals exhibit significant anisotropic characteristics in real space. Specifically, they show long-range correlation in the depth direction due to the continuous extension of the wellbore, while in the horizontal direction, they are confined to a smaller range due to acoustic attenuation and propagation constraints. Existing isotropic processing methods struggle to adapt to these physical characteristics, easily fragmenting the truly continuous signal or mistakenly clustering vertically distributed noise as valid signals, resulting in low identification accuracy. Summary of the Invention

[0004] The main objective of this application is to propose an array acoustic remote detection signal processing method, device, electronic device, and storage medium for drilling collision prevention, which can solve the problem of low identification accuracy of adjacent wells in the prior art and improve the identification accuracy.

[0005] To achieve the above objectives, a first aspect of this application proposes a method for processing array acoustic remote detection signals for drilling collision prevention, the method comprising: During the logging process, logging physical parameters are collected using logging equipment, and sound waves are emitted into the target area, which refers to the area where the well to be exploited is located. Multiple arrayed acoustic waves are obtained after the probed acoustic waves are reflected or scattered by the geological interface within the target area; Based on the multiple arrayed acoustic waves, a three-dimensional signal data volume is constructed in a preset three-dimensional spatial coordinate system. The three-dimensional signal data volume includes the spatial coordinates of each signal point in the arrayed acoustic waves in the three-dimensional spatial coordinate system and the acoustic wave signal intensity corresponding to the signal point. The search range is determined based on the well logging physical parameters. The signal points in the three-dimensional signal data volume are traversed. For each signal point, at least one cluster is determined based on the number of all signal points within the search range centered on the signal point. Clusters whose shape satisfies a preset shape and whose signal points in at least one cluster are designated as target clusters; The location of adjacent wells is determined based on the three-dimensional spatial coordinates of the signal points in the target cluster.

[0006] In some embodiments, the array acoustic wave is a two-dimensional array acoustic wave, and each of the array acoustic waves corresponds to a different acquisition depth; The step of constructing a three-dimensional signal data volume in a preset three-dimensional spatial coordinate system based on multiple arrayed acoustic waves includes: Multiple array acoustic waves are stacked in depth order to obtain the three-dimensional signal data volume. The three-dimensional spatial coordinate system uses the X-axis and Y-axis to represent the detection range on the horizontal plane and the Z-axis to represent the depth.

[0007] In some embodiments, the logging physical parameters include the logging speed of the logging equipment movement, the data sampling rate of the sound waves, the sound wave frequency, and the detected formation acoustic characteristics. Determining the search range based on the well logging physical parameters includes: The maximum longitudinal length is determined by multiplying the logging rate by the data sampling rate, where the maximum longitudinal length is the maximum search range in depth. The wavelength of the sound wave is calculated based on the sound wave frequency and the acoustic characteristics of the strata. Based on the sound wave wavelength and a preset coefficient, the maximum horizontal length is determined, and the search range on the horizontal plane is determined based on the maximum horizontal length. The search range is constructed based on the maximum vertical length and the maximum horizontal length.

[0008] In some embodiments, the search range is ellipsoidal, the maximum longitudinal length is the longitudinal semi-axis length of the ellipsoid, and the maximum transverse length is the transverse semi-axis length of the ellipsoid.

[0009] In some embodiments, traversing the signal points in the three-dimensional signal data volume, and for each signal point, determining at least one cluster based on the number of all signal points within a search range centered on that signal point, includes: Signal points in the three-dimensional signal data volume whose signal strength is greater than the signal strength threshold are taken as points in the initial signal point set; Obtain multiple connected regions formed by the signal points in the initial signal point set; For each connected region, signal points in sub-regions within the connected region that meet preset conditions are selected as candidate signal points; For each candidate signal point, traverse the search and determine at least one cluster based on the number of all signal points within the search range centered on the candidate signal point.

[0010] In some embodiments, the preset conditions include: The signal strength of the signal points in the sub-region is within a preset strength range; The density of signal points in the sub-region is greater than the preset density.

[0011] In some embodiments, the step of traversing each candidate signal point, and for each candidate signal point, determining at least one cluster based on the number of all signal points within a search range centered on the candidate signal point, includes: For any of the candidate signal points, the following processing is performed: Obtain the first number of all signal points within the search range centered on the candidate signal point; If the first number is greater than a preset threshold, then the candidate signal points are marked as core signal points; All signal points within the search range centered on the core signal point are used as the first signal point in the initial cluster. For each of the first signal points in the initial cluster, obtain a second number of all signal points within the search range centered on the first signal point; If the second number is greater than the preset threshold, then all signal points within the search range centered on the first signal point are added to the initial cluster as first signal points. Then, the process jumps to the step of obtaining the second number of all signal points within the search range centered on the first signal point for each first signal point in the initial cluster, until all first signal points in all the initial clusters are traversed, and a cluster is obtained.

[0012] In some embodiments, the step of selecting a cluster whose shape satisfies a preset shape from the signal points in the at least one cluster as the target cluster includes: Calculate the vertical extension length and horizontal deviation of each cluster for at least one of the clusters, where the vertical extension length is the coordinate range of all signal points in the cluster in the depth direction, and the horizontal deviation is the standard deviation or maximum distribution radius of the coordinates of all signal points in the cluster on the horizontal plane. Clusters whose vertical extension length is greater than a first threshold and whose horizontal deviation is less than a second threshold are designated as the target clusters.

[0013] In some embodiments, determining the location of adjacent wells based on the three-dimensional spatial coordinates of signal points in the target cluster includes: Obtain the azimuth sequence of signal points in the target cluster as a function of depth; Based on the azimuth sequence, outliers among the signal points in the target cluster are determined; The three-dimensional spatial coordinates of the adjacent well are obtained based on the three-dimensional spatial coordinates of the signal points in the target cluster, excluding the outlier points. The location of the adjacent well is determined based on its three-dimensional spatial coordinates.

[0014] To achieve the above objectives, a second aspect of this application provides an array acoustic remote detection signal processing device for drilling collision prevention, the device comprising: The acquisition module is used to acquire logging physical parameters through logging equipment during the logging process and to transmit detection acoustic waves to the target area, which refers to the area where the well to be exploited is located. The acquisition module is used to acquire multiple arrayed sound waves obtained after the probed sound waves are reflected or scattered by the stratum interface in the target area; A construction module is used to construct a three-dimensional signal data volume in a preset three-dimensional spatial coordinate system based on multiple arrayed acoustic waves. The three-dimensional signal data volume includes the spatial coordinates of each signal point in the arrayed acoustic waves in the three-dimensional spatial coordinate system and the acoustic wave signal intensity corresponding to the signal point. The calculation module is used to determine the search range based on the well logging physical parameters; The traversal module is used to traverse the signal points in the three-dimensional signal data volume. For each signal point, at least one cluster is determined based on the number of all signal points within the search range centered on the signal point. The filtering module is used to select clusters whose shape satisfies a preset shape from the signal points in the at least one cluster as target clusters; The determination module is used to determine the location of adjacent wells based on the three-dimensional spatial coordinates of signal points in the target cluster.

[0015] To achieve the above objectives, a third aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect.

[0016] To achieve the above objectives, a fourth aspect of the present application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect.

[0017] This application proposes an array acoustic remote detection signal processing method, device, electronic equipment, and storage medium for drilling collision prevention. It acquires logging physical parameters during the logging process and transmits detection acoustic waves to the target area where the well to be exploited is located. It obtains array acoustic waves reflected or scattered by the formation interface and constructs a three-dimensional signal data volume containing the spatial coordinates of signal points and the intensity of acoustic signals in a preset three-dimensional spatial coordinate system. Based on the logging physical parameters, a search range is determined, and the signal points in the three-dimensional signal data volume are traversed. At least one cluster is formed based on the number of signal points within the search range centered on each signal point. Target clusters with shapes conforming to preset requirements are selected, and finally, the location of adjacent wells is determined based on the three-dimensional spatial coordinates of the signal points in the target clusters. By dynamically adjusting the search range using physical parameters and using three-dimensional spatial geometric features for clustering and selection, it can adaptively match the physical characteristics of the signals, effectively filter out background noise, and accurately identify continuous adjacent well signals. This achieves high-precision and robust detection of adjacent well locations even in complex noise environments, solving the problem of drilling collision prevention. Attached Figure Description

[0018] Figure 1 This is a schematic flowchart of the array acoustic remote detection signal processing method for drilling collision prevention provided in the embodiments of this application; Figure 2 This is a schematic diagram of the implementation environment architecture of the array acoustic remote detection signal processing method for drilling collision prevention provided in the embodiments of this application; Figure 3 This is a flowchart illustrating the definition of the dynamic anisotropic search kernel (search range) provided in an embodiment of this application. Figure 4 This is a schematic diagram illustrating the physical concepts of drilling environment and adjacent well signals provided in the embodiments of this application; Figure 5 This is a flowchart of identifying valid signals and removing noise provided in an embodiment of this application; Figure 6 This is a schematic diagram of the polar coordinates of the signal strength after preliminary clustering and noise reduction provided in the embodiments of this application; Figure 7 This is a schematic diagram illustrating the effect of the secondary filtering step provided in the embodiments of this application; Figure 8 This is a schematic diagram of the array acoustic remote detection signal processing device for drilling collision prevention provided in the embodiments of this application; Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0020] It should be noted that although functional modules are divided in the device schematic diagram and a 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 aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0022] In oil and gas exploration and development, especially when drilling infill or adjustment wells in congested blocks, accurate monitoring of the relative position of the well to neighboring drilled wells (adjacent wells) is crucial to prevent downhole collisions. Array acoustic logging is an effective long-range detection technique capable of receiving reflected or scattered signals from the walls of adjacent wells.

[0023] In particular, cluster well operations in mature oilfields involve densely packed wellheads and complex downhole trajectories. Due to their age, the inclination data from neighboring old wells often contain errors or are no longer reliable, which greatly increases the risk of collisions during the drilling of new wells. Therefore, the industry urgently needs a technology that can detect the location of adjacent wells in real time and accurately during drilling to achieve proactive collision avoidance and early warning, thus ensuring operational safety.

[0024] However, the signal used for this purpose is extremely weak and deeply embedded in strong background noise. Existing signal processing methods have the following limitations: Limitations of 2D processing: Traditional processing methods typically operate on a single 2D depth slice, neglecting the continuity of the signal in depth (vertical direction). As a continuous tubing string, the reflected signal from an adjacent well should exhibit a continuous trajectory in three-dimensional space.

[0025] The blind spots of isotropic filtering: Some 3D processing methods use standard spherical neighborhoods for filtering or clustering. This "isotropic" assumption contradicts the physical reality of signals. It treats signal variations in both the horizontal (X, Y) and vertical (Z) directions equally, thus easily leading to the erroneous segmentation of a real, vertically extending neighboring well signal into multiple unrelated fragments, or misclassifying vertically distributed random noise as a signal.

[0026] Rigidity of static parameters: The parameters (such as search radius) of existing filtering or clustering algorithms are usually static values ​​that are preset based on experience. They cannot adapt to changes in logging speed, formation conditions or noise levels, resulting in poor adaptability and robustness of the algorithm under different working conditions.

[0027] Based on this, embodiments of this application provide an array acoustic remote detection signal processing method, device, electronic device, and storage medium for drilling collision prevention, aiming to provide an intelligent noise reduction and identification method that can fully utilize the three-dimensional spatial distribution characteristics of the signal and adaptively adjust the processing parameters to meet the needs of real-time drilling collision prevention.

[0028] The array acoustic remote detection signal processing method, apparatus, electronic device and storage medium for drilling collision prevention provided in this application are specifically described through the following embodiments. First, the array acoustic remote detection signal processing method for drilling collision prevention in this application embodiment is described.

[0029] The array acoustic remote detection signal processing method for drilling collision prevention provided in this application relates to the field of signal processing technology. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the array acoustic remote detection signal processing method for drilling collision prevention, but is not limited to the above forms.

[0030] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: 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, and distributed computing environments including any of the above systems or devices. This 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. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0031] It should be noted that in all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. In addition, when embodiments of this application require access to sensitive personal information of users, separate permission or consent from the user is obtained through pop-ups or redirection to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0032] Figure 1 This is an optional flowchart of an array acoustic remote detection signal processing method for drilling collision prevention provided in this application embodiment. Figure 1 The method may include, but is not limited to, steps S100 to S700.

[0033] Step S100: During the logging process, logging physical parameters are collected using logging equipment, and sound waves are emitted into the target area, which refers to the area where the well to be mined is located.

[0034] In this embodiment, the implementation environment architecture of the array acoustic remote detection signal processing method for drilling collision prevention is as follows: Figure 2As shown, the core architecture is divided into two main parts: the downhole hardware layer and the surface software and hardware layer. The downhole hardware layer and the surface software and hardware layer achieve bidirectional communication through a dedicated logging data link. This data link can use wired transmission (such as cable) or wireless transmission (such as electromagnetic wave transmission). It is responsible for uploading the logging physical parameters and array acoustic wave data preprocessed by the downhole processor to the surface computing equipment. At the same time, it can transmit the parameter configuration instructions (such as signal strength threshold and clustering preset threshold) issued by the surface operator to the downhole processor, realizing the interaction of instructions and data communication between the downhole and the surface.

[0035] The downhole hardware layer includes near-bit measurement tools and a downhole processor. The near-bit measurement tool, as the core hardware for data acquisition, integrates an acoustic wave transmitting module, an array acoustic wave receiving sensor, and logging parameter acquisition units (such as depth and velocity sensors). Mounted close to the drill bit, it can acquire logging physical parameters in real time, such as logging velocity, data sampling rate, and formation acoustic characteristics. Simultaneously, it transmits probe acoustic waves to the target area and receives array acoustic wave signals reflected / scattered from the formation interface and adjacent wellbore walls, serving as the source of raw data. The downhole processor connects directly to the near-bit measurement tool and has a built-in data preprocessing algorithm. This algorithm performs preliminary noise reduction and format standardization on the acquired raw array acoustic wave signals and logging physical parameters, reducing the volume of raw data. It also has a data caching function to temporarily store preprocessed data, supporting subsequent downhole and surface data transmission and preventing data loss due to data link bandwidth limitations.

[0036] The surface hardware and software layer includes surface computing equipment and an operator interface. The surface computing equipment, as the core computing carrier, incorporates a full-process algorithm module for array acoustic remote sensing signal processing for drilling collision prevention. After receiving preprocessed data transmitted from downhole via a data link, it can automatically execute all steps of the array acoustic remote sensing signal processing method for drilling collision prevention, ultimately calculating the three-dimensional spatial location information of adjacent wells. The operator interface, as the human-computer interaction window, has three main functions: data display, parameter configuration, and result warning. It can display in real time the raw data collected downhole, intermediate results from surface calculations (such as cluster distribution and azimuth sequence), and the final location information of adjacent wells; it supports operators in adjusting algorithm parameters (such as adaptation coefficients and threshold parameters) according to on-site conditions; and it can trigger a collision prevention warning when the distance between an adjacent well and the well to be exploited approaches a safe threshold, guiding on-site drilling operation decisions.

[0037] In this embodiment, during the logging process, logging physical parameters are collected in real time using logging equipment such as near-bit measurement tools. These physical parameters form the basis for subsequent determination of the search range and signal identification, and include, but are not limited to: logging velocity, data sampling rate, emission frequency of the probe sound wave (sound wave frequency), and formation acoustic characteristics of the target area (such as formation average sound velocity). Simultaneously, probe sound waves are emitted from the logging equipment towards the target area, which is the underground space surrounding the well to be exploited. This area includes the formation surrounding the well itself and the wellbore trajectories of any potentially adjacent drilled wells. The frequency and intensity of the probe sound waves are pre-set based on the formation conditions of the target area and the expected detection distance, ensuring that the sound waves can effectively propagate to the wellbore walls of potential adjacent wells and generate reflected or scattered signals.

[0038] Step S200: Obtain multiple arrayed sound waves after the probed sound waves are reflected or scattered by the stratum interface in the target area.

[0039] In this embodiment, during the propagation of the sound waves underground, reflection or scattering occurs when they encounter interfaces between different media (such as formation bedding planes or adjacent well walls). The receiving array on the logging equipment receives these reflected or scattered signals, forming multiple array sound wave data. The array sound waves carry spatial location information and signal strength information along the propagation path, with each array sound wave corresponding to the sound wave reflection or scattering response at a specific spatial location within the detection range.

[0040] Step S300: Based on the multiple arrayed acoustic waves, a three-dimensional signal data volume is constructed in a preset three-dimensional spatial coordinate system. The three-dimensional signal data volume includes the spatial coordinates of each signal point in the arrayed acoustic waves in the three-dimensional spatial coordinate system and the acoustic wave signal intensity corresponding to the signal point.

[0041] In this embodiment, to fully utilize the continuity of the signal in depth, multiple two-dimensional array acoustic wave data collected in depth order are stacked and converted into a three-dimensional signal data volume. Specifically, a preset three-dimensional spatial coordinate system, such as a Cartesian coordinate system, is established, where the X and Y axes represent the horizontal detection range (i.e., radial distance and azimuth), and the Z axis represents the logging depth. Each array acoustic wave data is mapped to this coordinate system, such that the three-dimensional signal data volume includes the spatial coordinates (x, y, z) of each signal point in the array acoustic wave in the three-dimensional spatial coordinate system and the acoustic wave signal intensity corresponding to the signal point.

[0042] Step S400: Determine the search range based on the well logging physical parameters.

[0043] In this embodiment, based on the collected well logging physical parameters, the geometric parameters of an ellipsoidal search range (search kernel) for clustering are dynamically calculated. The search range is a three-dimensional spatial search boundary adapted to the signal distribution characteristics of adjacent wells, and its geometry and size are dynamically adjusted according to the well logging conditions.

[0044] Specifically, the vertical search range (Z-axis direction) is determined based on the logging rate and data sampling rate. For example, its value can be proportional to the product of the logging rate and the sampling time interval to ensure that signal points belonging to the same physical target in continuous sampling can be connected. The horizontal search range (X-axis and Y-axis directions) is determined based on the acoustic frequency and formation acoustic characteristics. For example, its value can be a function of acoustic frequency, formation rock type, and expected detection distance.

[0045] Step S500: Traverse the signal points in the three-dimensional signal data volume. For each signal point, determine at least one cluster based on the number of all signal points within the search range centered on the signal point.

[0046] In this embodiment, a density-based spatial clustering algorithm (such as the DBSCAN algorithm) is employed to process the three-dimensional signal data volume using a dynamic search range. All signal points in the three-dimensional signal data volume are traversed one by one. For each currently traversed signal point, the number of other signal points contained within the search range is counted, using that signal point as the center and the search range as the boundary. If this number reaches a preset minimum neighbor number (pre-set based on signal strength and noise level), the current signal point is designated as the core point, and all signal points within the search range are grouped together. Then, using each signal point within this group as the new center, the above search and counting process is repeated, incorporating newly discovered signal points that meet the criteria into the group, until no new signal points can be incorporated, forming a complete cluster. This process continues, traversing ungrouped signal points in the three-dimensional signal data volume according to the above rules, sequentially forming multiple clusters, ultimately resulting in at least one cluster.

[0047] Step S600: The cluster whose shape satisfies the preset shape formed by the signal points in the at least one cluster is taken as the target cluster.

[0048] In this embodiment, based on the physical distribution characteristics of adjacent wells, preset shape judgment conditions for clusters are established. These preset shapes must satisfy the inherent characteristics of adjacent well signals: "continuous vertical extension and concentrated horizontal distribution." Specifically, this includes: the vertical extension length of the cluster in the Z-axis direction is greater than a preset length threshold (e.g., 10 meters), and the horizontal deviation in the XY plane is less than a preset deviation threshold (e.g., 2 meters). At least one cluster is subjected to shape judgment, and clusters that meet the above preset shape conditions are identified as target clusters. These target clusters represent the set of signal points representing adjacent well signals. Clusters that do not meet the conditions are considered noise and discarded.

[0049] Step S700: Determine the location of the adjacent well based on the three-dimensional spatial coordinates of the signal points in the target cluster.

[0050] In this embodiment, the three-dimensional spatial coordinates of all signal points in the target cluster are extracted. By calculating the centroid coordinates of the cluster (i.e., the average of the X, Y, and Z coordinates of all signal points) or fitting the spatial trajectory of the cluster, the three-dimensional spatial location information of the adjacent well (including its lateral plane position and longitudinal depth range) is obtained. This location information is then output to the surface control system to provide real-time and accurate adjacent well location data for drilling collision prevention and early warning.

[0051] This embodiment fully utilizes the continuity characteristics of adjacent well signals by constructing a three-dimensional signal data volume and performing spatial clustering. By dynamically defining the anisotropic search range based on physical parameters, the algorithm model matches the real spatial form of the signal, effectively suppressing random noise. The core parameters of the search range are derived from real-time logging physical parameters, which can be dynamically adjusted according to changes in drilling conditions and formation environment, overcoming the shortcomings of poor adaptability of static parameter algorithms and improving reliability under different operating conditions.

[0052] In some embodiments, the array acoustic wave is a two-dimensional array acoustic wave, and each array acoustic wave corresponds to a different acquisition depth. Step S200 may include, but is not limited to, step S210: Step S210: Stack the multiple array acoustic waves in depth order to obtain the three-dimensional signal data volume. The three-dimensional spatial coordinate system uses the X-axis and Y-axis to represent the detection range on the horizontal plane and the Z-axis to represent the depth.

[0053] In this embodiment, the array acoustic wave is a two-dimensional array acoustic wave, each two-dimensional array acoustic wave corresponds to a unique acquisition depth, and the acquisition depths of all two-dimensional array acoustic waves are distributed sequentially along the logging advance direction, that is, as the logging equipment extends underground, the acquisition is carried out in the order of shallow to deep or deep to shallow.

[0054] Each two-dimensional array acoustic wave is essentially a two-dimensional signal matrix (such as a W×H pixel matrix, where W and H are the preset number of pixels in the horizontal detection dimension). Each element in the matrix corresponds to the acoustic signal intensity at a specific spatial location within the horizontal detection range (XY plane). That is, the rows and columns of the two-dimensional array acoustic wave correspond one-to-one with the X-axis and Y-axis of the preset three-dimensional spatial coordinate system, respectively. The numerical values ​​of the matrix elements quantize the acoustic signal intensity at the XY coordinate (such as normalized to a grayscale value of 0-255). The acquisition depth corresponding to each two-dimensional array acoustic wave is a fixed value of the Z-axis of the two-dimensional signal matrix in the three-dimensional spatial coordinate system.

[0055] Specifically, a pre-defined three-dimensional spatial coordinate system is first established. In this coordinate system, the X-axis and Y-axis together define the detection range on the horizontal plane. Specifically, the X-axis and Y-axis can represent the horizontal plane coordinates centered on the wellbore, used to describe the radial distance and azimuth angle of the signal point relative to the wellbore. The Z-axis is defined as the depth axis, used to represent the position of the logging instrument in the vertical direction, i.e., the corresponding acquisition depth. Next, the acquired multiple two-dimensional array acoustic waves are stacked in depth order. The two-dimensional data layers representing different depths are arranged sequentially according to the size of the Z-axis coordinates to form a three-dimensional data structure.

[0056] This embodiment, through a construction method that stacks the data sequentially according to depth, perfectly matches the acquisition logic of well logging operations and the longitudinal continuous distribution characteristics of adjacent well tubing strings. This allows the constructed three-dimensional signal data volume to fully retain the continuous extension characteristics of adjacent well signals in the depth direction, providing a data foundation that is more in line with physical reality for subsequent cluster analysis and shape selection.

[0057] In some embodiments, the logging physical parameters include the logging speed of the logging equipment, the data sampling rate of the sound waves, the sound wave frequency, and the detected formation acoustic characteristics; step S400 may include, but is not limited to, steps S410 to S440: Step S410: Determine the maximum longitudinal length based on the product of the logging rate and the data sampling rate, where the maximum longitudinal length is the maximum search range in depth; Step S420: Calculate the sound wave wavelength based on the sound wave frequency and the acoustic characteristics of the strata; Step S430: Determine the maximum horizontal length based on the sound wave wavelength and the preset coefficient, and determine the search range on the horizontal plane based on the maximum horizontal length; Step S440: Construct the search range based on the maximum vertical length and the maximum horizontal length.

[0058] In this embodiment, logging speed refers to the average speed at which logging equipment equipped with acoustic wave transmitting and receiving modules (such as near-bit measurement tools) moves along the wellbore depth direction (Z-axis direction) during the logging process, measured in m / s. This parameter is acquired in real time by the depth measurement module built into the logging equipment, or calculated from the total moving depth and total time recorded during the logging process. Its core function is to reflect the equipment's moving efficiency in the depth direction, providing a basis for the vertical search range.

[0059] The data sampling rate refers to the frequency at which acoustic signals are acquired, measured in Hertz (Hz), which is the number of times an acoustic signal is acquired per second. This parameter is a preset system parameter of the logging equipment and can be directly retrieved from the equipment configuration information. Its core function is to determine the time interval between two adjacent signal acquisitions and, in conjunction with the logging speed, calculate the signal distribution interval along the depth direction.

[0060] The acoustic frequency refers to the center frequency of the sound waves emitted by the logging equipment towards the target area, measured in Hertz (Hz). This parameter is a preset transmission parameter, pre-set according to the formation density of the target area, the expected detection distance, and other operating conditions. It can be obtained from the parameters of the equipment's transmission module, and its core function is to determine the propagation characteristics of the sound waves (such as wavelength and attenuation rate).

[0061] The core of formation acoustic characteristics refers to the average sound wave propagation velocity in the target area's formation, measured in meters per second (m / s). Auxiliary parameters such as the formation sound wave attenuation coefficient may also be included. This parameter is obtained through inversion calculations of the acquired array acoustic signals, or by retrieving existing formation acoustic data from adjacent wells in the target area. It can also be obtained in real-time by a dedicated formation sound velocity measurement module. Its core function is to reflect the propagation law of sound waves in the formation, providing a basis for the lateral search range.

[0062] In this embodiment, the definition process of the dynamic anisotropic search kernel (search range) is as follows: Figure 3 As shown, based on externally input or data-extracted physical parameters, the three semi-axis parameters of an ellipsoidal search kernel are dynamically calculated for subsequent clustering steps. First, the maximum longitudinal length is determined by the product of the logging velocity and the data sampling rate. Specifically, the logging velocity reflects the speed at which the instrument moves within the wellbore, and the data sampling rate reflects the frequency of data acquisition. Their product represents the vertical distance (i.e., depth interval) traveled by the logging equipment between two consecutive data acquisitions. To ensure that signal points belonging to the same physical target but slightly related at consecutive sampling depths are included in the same search neighborhood during clustering, this product is used as the basis for calculation to determine the maximum longitudinal length, which is defined as the maximum search range in the depth (Z-axis) direction.

[0063] Then, the sound wave wavelength is calculated based on the sound wave frequency and the formation acoustic properties. Formation acoustic properties typically refer to the speed at which sound waves propagate in the current formation medium (formation sound speed). According to the principles of physics, the sound wave wavelength is equal to the formation sound speed divided by the sound wave frequency. This wavelength reflects the fundamental scale of the sound wave signal in horizontal space.

[0064] Subsequently, the maximum lateral length is determined based on the sound wave wavelength and a preset coefficient. Considering the distribution characteristics of sound wave energy, the search range on the horizontal plane should not be too large to avoid introducing excessive noise, nor too small to avoid signal interruption. Therefore, the calculated sound wave wavelength is multiplied by a preset coefficient (this coefficient can be set based on experience in actual application scenarios, such as 0.5, 1.0, etc.) to obtain the maximum lateral length. This maximum lateral length determines the search range on the horizontal plane (XY plane).

[0065] Finally, a search range is constructed based on the maximum longitudinal length and the maximum transverse length. The resulting search range is an anisotropic three-dimensional region (e.g., an ellipsoidal region), with its longitudinal dimension determined based on well logging motion parameters and its transverse dimension determined based on acoustic propagation parameters. This ensures that the shape of the search kernel closely matches the signal from the adjacent well to be detected at the physical level.

[0066] In a specific embodiment, assuming an average logging velocity v_log of 1800 m / h (i.e., 0.5 m / s) and a data sampling interval Δt of 0.2 s, the depth increment corresponding to one sampling point is 0.1 m. In this case, the longitudinal half-axis eps_z can be set to 1.5 * 0.1 m = 0.15 m to ensure the connection of sampling gaps caused by instrument jitter or signal fluctuations. Simultaneously, if the acoustic frequency f is known to be 10 kHz and the formation average sound velocity v_rock is 3000 m / s, the acoustic wavelength is approximately 0.3 m. In this case, the transverse half-axis eps_x and eps_y can be set to a reasonable multiple of this wavelength, for example, 0.5 * 0.3 m = 0.15 m. In this way, the shape of the search kernel is no longer fixed but adaptively adjusted according to logging conditions and formation environment.

[0067] This embodiment transforms the search range from static empirical parameters to dynamic variables driven by real-time physical observations, enabling the algorithm to understand and adapt to its working environment, thus fundamentally improving robustness. By binding the longitudinal scale to instrument kinematics and the lateral scale to wave physics, the constructed ellipsoidal search range is highly consistent with the real spatial attenuation and distribution patterns of reflected signals from adjacent wells in terms of morphology, resolving the inherent contradictions of the isotropic model.

[0068] In some embodiments, the search range is ellipsoidal, the maximum longitudinal length is the longitudinal semi-axis length of the ellipsoid, and the maximum transverse length is the transverse semi-axis length of the ellipsoid.

[0069] In this embodiment, to more accurately adapt to the anisotropic characteristics of adjacent well signals in spatial distribution (i.e., extending longer in the depth direction and relatively concentrated in the horizontal direction), the search range is specifically constructed as an ellipsoid. The physical concepts of drilling environment and adjacent well signals are as follows: Figure 4 As shown, Figure 4 The figure shows the idealized spatial distribution of a neighboring well signal (red ellipsoid) within the detection range, along with the drilling wellbore (central black cylinder). This figure visually illustrates the anisotropic physical characteristics of the neighboring well signal, which extends continuously in the depth (Z-axis) direction but is localized in the horizontal (XY plane) direction, providing a theoretical basis for the use of an ellipsoidal search kernel in this embodiment.

[0070] The geometric parameters of the ellipsoidal search range are mapped to the calculated maximum longitudinal and lateral lengths as follows: First, the maximum longitudinal length calculated based on the logging rate and data sampling rate is defined as the longitudinal semi-axis length of the ellipsoid. This ensures that, in the depth direction (Z-axis), the search neighborhood can cover the distance determined by instrument movement and sampling intervals, thereby effectively connecting longitudinally continuous signal points.

[0071] Secondly, the maximum lateral length calculated based on the sound wave frequency and the acoustic characteristics of the strata is defined as the lateral semi-axis length of the ellipsoid. This limits the search radius in the horizontal plane (XY plane) to match the wavelength characteristics of the sound wave signal.

[0072] By determining the longitudinal and transverse semi-axis lengths of the ellipsoid based on the maximum longitudinal and transverse lengths respectively, the resulting ellipsoidal search range can physically and self-consistently cover the signal points. Compared to a spherical search range, it can more accurately eliminate random noise in the horizontal direction and retain continuous signals in the depth direction.

[0073] This embodiment solves the core problem of mismatch between isotropic shapes such as spheres and signal features by clearly defining the search range as an ellipsoidal shape, accurately matching the anisotropic distribution characteristics of adjacent well signals that are vertically continuous and horizontally localized, thereby improving the targeting of signal clustering.

[0074] In some embodiments, step S500 may include, but is not limited to, steps S510 to S540: Step S510: Select the signal points in the three-dimensional signal data volume whose signal strength is greater than the signal strength threshold as points in the initial signal point set; Step S520: Obtain multiple connected regions formed by the signal points in the initial signal point set; Step S530: For each connected region, select the signal points in the sub-regions that meet the preset conditions within the connected region as candidate signal points. Step S540: Traverse each candidate signal point, and for each candidate signal point, determine at least one cluster based on the number of all signal points within the search range centered on the candidate signal point.

[0075] In this embodiment, to improve the efficiency of the clustering algorithm and further suppress background noise, the 3D signal data volume is preprocessed and coarsely screened before the final clustering. First, threshold filtering is performed on the constructed 3D signal data volume. Since the 3D data volume contains a large amount of background noise data, and real, effective adjacent well reflection signals usually have high acoustic signal intensity, a signal intensity threshold is set (e.g., retaining the top 2% of grayscale values ​​among all data points). All signal points in the 3D signal data volume are traversed, and signal points with signal intensities greater than this threshold are selected to form the initial signal point set. This step effectively removes most of the low-intensity background noise, significantly reducing the amount of data processed subsequently.

[0076] Secondly, spatial topology analysis is performed on the initial signal point set. In three-dimensional space, real adjacent well signals often cluster together, forming spatial regions of a certain volume. Therefore, a connected component analysis algorithm is used to perform connected component analysis on the initial signal point set, identifying multiple independent connected regions. Each connected region represents a set of spatially adjacent and high-intensity signal points, which are highly likely to be potential regions of adjacent well signals. A connected region refers to a group of high-intensity signal points that are adjacent to each other (e.g., horizontally, vertically, or diagonally) on the same two-dimensional depth slice, similar to bright spots connected in a horizontal signal map; in contrast, there are isolated bright spots (discrete noise, residual noise that was not completely removed after thresholding).

[0077] Subsequently, a refined representative point extraction process is performed for each connected region. Considering that a connected region may contain many redundant points, directly clustering all points would still be computationally intensive. Therefore, for each connected region, signal points in sub-regions that meet preset conditions are selected as candidate signal points. These preset conditions could be, for example, calculating the geometric center (centroid) or centroid of the signal intensity distribution of the connected region and extracting the sub-regions located near this center where the local signal intensity is highest; or extracting the core sub-region with the highest signal point density within the connected region. In this way, the vast connected regions are simplified into a few representative feature points (i.e., candidate signal points), achieving secondary dimensionality reduction of the data.

[0078] Finally, clustering is performed based on the filtered candidate signal point set. For each candidate signal point, the search range (e.g., an ellipsoidal search kernel) determined in the previous embodiments is traversed to calculate the number of other candidate signal points within the search range centered on that candidate signal point. Based on density connectivity principles (e.g., the DBSCAN algorithm logic), these candidate signal points are divided into at least one cluster. Because the candidate signal point set has been filtered through the previous steps, most of the discrete noise has been eliminated and the data size has been significantly reduced, thus enabling the rapid and accurate acquisition of clusters corresponding to adjacent well trajectories.

[0079] This embodiment first eliminates a large number of low-intensity noise points by using a signal strength threshold, then eliminates discrete interference points by using connected component analysis, and finally extracts core candidate signal points. This allows subsequent traversal clustering to be performed only on a small number of high-value candidate points, significantly reducing the computational load and adapting to the working conditions of real-time logging.

[0080] In some embodiments, the preset conditions include: The signal strength of the signal points in the sub-region is within a preset strength range; The density of signal points in the sub-region is greater than the preset density.

[0081] In this embodiment, to reduce subsequent computation and avoid redundant calculations, the algorithm does not retain all pixels within a connected region. Instead, it extracts the most representative location point from each connected region. There are two extraction methods: one is to calculate the geometric center (centroid) of the region, and the other is to find the center of the part with the highest signal intensity (brightest) within the region. Considering physical realities (such as a limited number of adjacent wells and differences in signal intensity), preset rules are set to filter the extracted feature points. For example, candidate points are sorted according to signal intensity or the area of ​​the connected region, retaining only the strongest few (e.g., a maximum of two per slice), further eliminating residual interference signals.

[0082] In this embodiment, the brightest sub-regions with concentrated energy and dense distribution within the connected area are selected using a dual quantization condition of signal strength range and signal point density. Weak signal clutter at the edges of the connected area is eliminated by using a preset strength range, ensuring that the signal energy within the sub-region matches the characteristics of reflected signals from adjacent wells (the reflected signal intensity from adjacent wells is moderate and stable; excessively high intensity may indicate formation abrupt changes or interference, while excessively low intensity indicates noise). Sparsely distributed signal portions within the connected area are eliminated by using a preset density, ensuring that the signal point distribution within the sub-region is sufficiently dense (adjacent well walls are continuous structures, and reflected signals will inevitably exhibit a dense distribution on the horizontal plane; sparse portions are mostly clutter interference).

[0083] This embodiment effectively avoids centroid shift caused by irregular area shape or internal noise points by focusing on the high-intensity, high-density core area.

[0084] In some embodiments, step S540 may include, but is not limited to, steps S541 to S545: For any of the candidate signal points, the following processing is performed: Step S541: Obtain the first number of all signal points within the search range centered on the candidate signal point; Step S542: If the first number is greater than a preset threshold, then the candidate signal points are marked as core signal points. Step S543: Take all signal points within the search range centered on the core signal point as the first signal point in the initial cluster; Step S544: For each first signal point in the initial cluster, obtain the second number of all signal points within the search range centered on the first signal point; Step S545: If the second number is greater than the preset threshold, then all signal points within the search range centered on the first signal point are added to the initial cluster as first signal points. Then, the process jumps to the step of obtaining the second number of all signal points within the search range centered on the first signal point for each first signal point in the initial cluster, until all first signal points in all the initial clusters are traversed to obtain a cluster.

[0085] In this embodiment, the filtered set of candidate signal points is traversed. For any candidate signal point, the following processing is performed: First, a first number of all signal points centered on the candidate signal point and within the search range (e.g., an ellipsoidal search range) determined in the aforementioned embodiments is obtained. This first number reflects the signal density of the current point in its local neighborhood. The first number is compared with a preset threshold (i.e., the minimum number of neighborhood points). If the first number is greater than the preset threshold, it indicates that the point is located in a signal-dense region and is qualified to become the core of a cluster, so the candidate signal point is marked as a core signal point. Once a point is determined to be a core signal point, all signal points within the search range of the core signal point (including the core signal point itself) are grouped into a set as the first signal point in the initial cluster.

[0086] To incorporate all connected dense signal points into a single cluster, the initial cluster needs to be iteratively expanded. For each first signal point in the initial cluster, the following processing is performed: The second number of all signal points within the search range centered on the first signal point is obtained; it is determined whether the second number is greater than a preset threshold; if the second number is greater than the preset threshold, it indicates that the first signal point is also a core point (or a density-reachable point) and can connect to more signal points. In this case, all signal points within the search range of the first signal point are merged into the initial cluster as new first signal points.

[0087] Whenever a new first signal point is added to the initial cluster, the system jumps back to the processing step of "each first signal point in the initial cluster" to continue performing neighborhood determination and expansion on the newly added point. This process is repeated until all first signal points in the initial cluster have been traversed and no new signal points meeting the density condition have been added. At this point, the initial cluster stops growing and finally forms a complete cluster containing all interconnected signal points.

[0088] This embodiment can effectively aggregate spatially adjacent and high-density signal points together to form clusters representing adjacent well trajectories, while automatically eliminating isolated noise points that do not meet the density requirements.

[0089] In some embodiments, step S600 may include, but is not limited to, steps S610 to S620: Step S610: Calculate the vertical extension length and horizontal deviation of each cluster for at least one of the clusters. The vertical extension length is the coordinate range of all signal points in the cluster in the depth direction, and the horizontal deviation is the standard deviation or maximum distribution radius of the coordinates of all signal points in the cluster on the horizontal plane. Step S620: The clusters whose vertical extension length is greater than the first threshold and whose horizontal deviation is less than the second threshold are selected as the target clusters.

[0090] In this embodiment, after obtaining the clustering results, the process of identifying valid signals and removing noise through geometric feature analysis and decision-making is as follows: Figure 5 As shown.

[0091] For each cluster C_i, calculate its geometric features. Extract the coordinates of all signal points within the cluster along the depth direction (i.e., the Z-axis). By comparing these coordinates, identify the maximum and minimum depth coordinates, and calculate the difference between them; this difference yields the vertical extension length L_z of the cluster. The vertical extension length characterizes the continuous distribution range of the cluster in the vertical direction. Extract the coordinates of all signal points within the cluster along the horizontal plane (i.e., the XY plane). For these horizontal coordinates, calculate the horizontal deviation σ_xy, an index reflecting the degree of dispersion. The horizontal deviation can be calculated using one of the following two methods: Standard deviation: Calculates the standard deviation of the coordinates of all signal points on the horizontal plane relative to their centroids to quantify the density of the clustering of signal points; Maximum distribution radius: Calculate the farthest distance of all signal points from a certain reference point (such as the centroid) on the horizontal plane, which is the maximum distribution radius, to limit the horizontal distribution range of the signal points.

[0092] The calculated geometric parameters are compared with preset thresholds to determine the target clusters. Since adjacent wells typically exhibit slender, tubular signals with a certain depth extension, while noise interference often manifests as isolated point clusters (without vertical extension) or layered interfaces (excessively wide horizontal distribution), a first threshold T_z and a second threshold T_xy are preset. The first threshold is used to determine the vertical extension length, representing the minimum continuous depth range (e.g., 10 meters, 20 meters, or longer) that an effective adjacent well signal should possess, filtering out short clusters that are too fragmented in depth and do not form a continuous trajectory. The second threshold is used to determine the horizontal deviation, representing the maximum allowable horizontal dispersion range (e.g., 1 meter, 2 meters) of the effective adjacent well signal within a local well section, filtering out wide clusters that are excessively diffused on the horizontal plane and do not conform to the local straight or gradually changing characteristics of the wellbore. The first and second thresholds can be dynamically adjusted according to the distribution of adjacent wells in the target area, the performance of logging equipment, and formation conditions, adapting to diverse cluster well operation scenarios such as shallow and deep formations.

[0093] Only clusters that simultaneously meet the conditions of "vertical extension length greater than the first threshold" and "horizontal deviation less than the second threshold" are identified as target clusters whose shapes meet the preset requirements.

[0094] This embodiment locks in the effective signal features that are continuous in the vertical direction by extending the vertical length, and limits the spatial range of the lateral concentration by limiting the horizontal deviation. The two work together to eliminate both the short noise clusters that are discrete in the vertical direction and the interference clusters that are diffused in the lateral direction, which can greatly reduce the false judgment rate. This dual constraint based on geometry can effectively eliminate the reflection signals of geological bedding interfaces and other non-target interference, and accurately lock in the true location of adjacent wells.

[0095] In some embodiments, step S700 may include, but is not limited to, steps S710 to S740: Step S710: Obtain the azimuth sequence of signal points in the target cluster as a function of depth; Step S720: Based on the azimuth sequence, determine the outliers among the signal points in the target cluster; Step S730: Obtain the three-dimensional spatial coordinates of the adjacent well based on the three-dimensional spatial coordinates of the signal points in the target cluster other than the outlier points; Step S740: Determine the location of the adjacent well based on its three-dimensional spatial coordinates.

[0096] In this embodiment, the azimuth sequence of signal points in the target cluster as a function of depth is first obtained. All signal points in the target cluster are sorted according to their depth coordinates (Z-axis). For each signal point, based on its horizontal coordinates (X, Y) in the three-dimensional coordinate system, the azimuth angle of that point relative to a reference direction (such as true north or the main direction of the logging instrument) is calculated. This yields a set of azimuth sequences as a function of depth. This sequence visually reflects the azimuth orientation of adjacent well trajectories as a function of depth. The polar coordinates of the signal intensity after preliminary clustering and noise reduction are shown below. Figure 6 As shown, Figure 6 The image shows a polar map of signal strength obtained after preliminary clustering and noise reduction at a specific depth (83 meters), displaying the orientation (theta: 330 degrees) and distance (r: 7 meters) of a strong signal cluster (red circle).

[0097] Then, based on the azimuth sequence, outliers among the signal points in the target cluster are identified. The changing trend of the azimuth sequence is analyzed. Since adjacent wells are physical pipelines, their spatial trajectories are usually continuous and smooth; therefore, the change of their azimuth with depth should be gradual, without drastic jumps. Based on this physical characteristic, a preset outlier detection algorithm (such as threshold judgment based on the rate of angle change) is used to scan the sequence, identifying signal points with abrupt changes in azimuth values ​​or those that deviate significantly from the overall trend, and marking them as outliers. These outliers are usually caused by residual background noise, abnormally strong reflections at the casing coupling, or signal aliasing from multiple wells.

[0098] Next, based on the 3D spatial coordinates of the signal points (excluding outliers) in the target cluster, the 3D spatial coordinates of the adjacent wells are obtained. The signal points marked as outliers in the previous steps are removed from the target cluster, retaining only the signal points that conform to the trajectory continuity pattern. Using these filtered, high-quality signal points, the statistical characteristics of their spatial distribution are calculated, such as calculating the centroid coordinates of these signal points or fitting the centerline of their spatial curve. This calculation result is the refined 3D spatial coordinates of the adjacent wells. By removing outliers, the calculation results can be effectively prevented from being skewed by individual anomalies, thereby improving the robustness of the positioning. The effect of the secondary filtering step is as follows: Figure 7 As shown in the figure, the horizontal axis represents depth, and the vertical axis represents the signal azimuth. The original azimuth sequence (cyan curve) may exhibit drastic fluctuations due to local noise or signal volatility. Considering the physical stiffness of the drill string and the continuity of the wellbore trajectory, the signal azimuth is unlikely to change drastically and irregularly over a short distance. In this embodiment, a clustering or filtering algorithm (such as one-dimensional DBSCAN or moving average filtering) can be used again to process the azimuth sequence, effectively identifying and eliminating outliers (false signals) that deviate from the main trend. After secondary filtering, a smoother, more stable final azimuth trajectory that conforms to physical constraints is obtained (yellow curve).

[0099] Finally, the location of the adjacent well is determined based on its three-dimensional spatial coordinates. Based on the calculated three-dimensional spatial coordinates of the adjacent well, combined with the coordinate information of the current well, the specific location of the adjacent well in the underground space is finally determined. This location information can be converted into distance and azimuth relative to the current wellbore and output to the drilling guidance system in real time to guide drilling operations and ensure drilling safety.

[0100] This embodiment identifies and removes outliers through azimuth sequence analysis, eliminating coordinate deviations caused by instrument jitter and formation clutter. The adjacent well positions calculated based on the purified effective signal points are closer to the actual trajectory. The azimuth sequence is determined based on the physical continuity of the adjacent well trajectories, which conforms to actual engineering laws, making the identification of outliers more scientific and the positioning results more stable. It connects to field applications: the output relative position information can be directly connected to the drilling anti-collision early warning system to guide field operations and reduce the risk of cluster well collision accidents.

[0101] This application embodiment achieves a high degree of matching between adjacent well signals and the physical search kernel by constructing a three-dimensional signal data volume and combining it with an adaptive ellipsoidal search range based on well logging physical parameters, thereby significantly improving the accuracy of signal acquisition. Before clustering, the connected regions are finely screened using dual constraints of signal strength and density, which greatly eliminates background noise and reduces the computational load of subsequent clustering. At the same time, by filtering based on geometric features of vertical extension length and horizontal deviation, and removing outliers based on azimuth sequence, non-target interference such as formation reflection is effectively eliminated, ultimately achieving high-precision, high-efficiency, and high-robust identification of adjacent well locations in complex downhole environments.

[0102] Please see Figure 8 This application also provides an array acoustic remote detection signal processing device 800 for drilling collision prevention, which can implement the above-mentioned array acoustic remote detection signal processing method for drilling collision prevention. The device includes: The acquisition module 10 is used to acquire logging physical parameters through logging equipment during the logging process and to transmit detection acoustic waves to the target area, which refers to the area where the well to be exploited is located. Acquisition module 20 is used to acquire multiple arrayed acoustic waves obtained after the probe acoustic waves are reflected or scattered by the stratum interface in the target area; The construction module 30 is used to construct a three-dimensional signal data volume in a preset three-dimensional spatial coordinate system based on the multiple array acoustic waves. The three-dimensional signal data volume includes the spatial coordinates of each signal point in the array acoustic waves in the three-dimensional spatial coordinate system and the acoustic wave signal intensity corresponding to the signal point. The calculation module 40 is used to determine the search range based on the well logging physical parameters; The traversal module 50 is used to traverse the signal points in the three-dimensional signal data volume. For each signal point, at least one cluster is determined based on the number of all signal points within the search range centered on the signal point. The filtering module 60 is used to select clusters whose shape satisfies a preset shape from the signal points in the at least one cluster as target clusters; The determination module 70 is used to determine the location of adjacent wells based on the three-dimensional spatial coordinates of the signal points in the target cluster.

[0103] In some embodiments, the arrayed acoustic waves are two-dimensional arrayed acoustic waves, and each arrayed acoustic wave corresponds to a different acquisition depth; the construction module 30 may include: The stacking submodule is used to stack multiple array acoustic waves in depth order to obtain the three-dimensional signal data volume. The three-dimensional spatial coordinate system uses the X-axis and Y-axis to represent the detection range on the horizontal plane and the Z-axis to represent the depth.

[0104] In some embodiments, the logging physical parameters include the logging speed of the logging equipment, the data sampling rate of the sound waves, the sound wave frequency, and the detected formation acoustic characteristics; the calculation module 40 may include: The first calculation submodule is used to determine the maximum longitudinal length based on the product of the logging speed and the data sampling rate, wherein the maximum longitudinal length is the maximum search range in depth; The second calculation submodule is used to calculate the sound wave wavelength based on the sound wave frequency and the acoustic characteristics of the strata. The third calculation submodule is used to determine the maximum horizontal length based on the sound wave wavelength and a preset coefficient, and to determine the search range on the horizontal plane based on the maximum horizontal length. A submodule is constructed to build the search range based on the maximum vertical length and the maximum horizontal length.

[0105] In some implementations, the search range is ellipsoidal, the maximum longitudinal length is the longitudinal semi-axis length of the ellipsoid, and the maximum transverse length is the transverse semi-axis length of the ellipsoid.

[0106] In some implementations, the traversal module 50 may include: The comparison submodule is used to select signal points in the three-dimensional signal data volume whose signal strength is greater than the signal strength threshold as points in the initial signal point set; The first acquisition submodule is used to acquire multiple connected regions formed by signal points in the initial signal point set; The filtering submodule is used to select signal points in sub-regions within each connected region that meet preset conditions as candidate signal points. The traversal submodule is used to traverse each of the candidate signal points, and for each candidate signal point, to determine at least one cluster based on the number of all signal points within the search range centered on the candidate signal point.

[0107] In some implementations, the preset conditions include: The signal strength of the signal points in the sub-region is within a preset strength range; The density of signal points in the sub-region is greater than the preset density.

[0108] In some implementations, traversing submodules may include: The processing unit is configured to perform the following processing for any of the candidate signal points: The first acquisition unit is used to acquire a first number of all signal points within a search range centered on the candidate signal point; A marking unit is used to mark the candidate signal points as core signal points if the first quantity is greater than a preset threshold. Clustering unit, used to take all signal points within the search range centered on the core signal point as the first signal point in the initial cluster; The second acquisition unit is used to acquire, for each of the first signal points in the initial cluster, a second number of all signal points within a search range centered on the first signal point; The traversal unit is configured to, if the second number is greater than the preset threshold, add all signal points within the search range centered on the first signal point as first signal points to the initial cluster, jump to the step of obtaining the second number of all signal points within the search range centered on the first signal point for each first signal point in the initial cluster, until all first signal points in all the initial clusters have been traversed to obtain a cluster.

[0109] In some implementations, the filtering module 60 may include: The fourth calculation submodule is used to calculate at least one of the clusters to obtain the vertical extension length and horizontal deviation of each cluster. The vertical extension length is the coordinate range of all signal points in the cluster in the depth direction, and the horizontal deviation is the standard deviation or maximum distribution radius of the coordinates of all signal points in the cluster on the horizontal plane. The first determining submodule is used to identify clusters whose vertical extension length is greater than a first threshold and whose horizontal deviation is less than a second threshold as the target clusters.

[0110] In some implementations, the determining module 70 may include: The second acquisition submodule is used to acquire the azimuth sequence of signal points in the target cluster as a function of depth; The second determining submodule is used to determine outliers among the signal points in the target cluster based on the azimuth sequence. The screening submodule is used to obtain the three-dimensional spatial coordinates of the adjacent wells based on the three-dimensional spatial coordinates of the signal points in the target cluster other than the outliers. The third determining submodule is used to determine the location of the adjacent well based on its three-dimensional spatial coordinates.

[0111] The specific implementation of the array acoustic remote detection signal processing device for drilling collision prevention is basically the same as the specific implementation of the array acoustic remote detection signal processing method for drilling collision prevention described above, and will not be repeated here.

[0112] This application also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described array acoustic long-range detection signal processing method for drilling collision prevention. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0113] Please see Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes: The processor 901 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, 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 this application. The memory 902 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 902 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 902 and is called and executed by the processor 901 to execute the array acoustic remote detection signal processing method for drilling collision prevention according to the embodiments of this application. The input / output interface 903 is used to implement information input and output; The communication interface 904 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, Ethernet cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 905 transmits information between various components of the device (e.g., processor 901, memory 902, input / output interface 903, and communication interface 904); The processor 901, memory 902, input / output interface 903, and communication interface 904 are connected to each other within the device via bus 905.

[0114] This application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described array acoustic remote detection signal processing method for drilling collision prevention.

[0115] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0116] The array acoustic remote detection signal processing method, device, electronic equipment, and storage medium for drilling collision prevention provided in this application embodiment acquires logging physical parameters during the logging process and transmits detection acoustic waves to the target area where the well to be exploited is located. It obtains array acoustic waves reflected or scattered by the formation interface and constructs a three-dimensional signal data volume containing the spatial coordinates of signal points and the intensity of acoustic wave signals in a preset three-dimensional spatial coordinate system. Based on the logging physical parameters, a search range is determined, and the signal points in the three-dimensional signal data volume are traversed. At least one cluster is formed based on the number of signal points within the search range centered on each signal point. Target clusters with shapes that meet preset requirements are selected, and finally, the location of adjacent wells is determined based on the three-dimensional spatial coordinates of the signal points in the target clusters. By dynamically adjusting the search range by combining physical parameters and using three-dimensional spatial geometric features for clustering and selection, it can adaptively match the physical characteristics of the signals, effectively filter out background noise, and accurately identify continuous adjacent well signals. This achieves high-precision and robust detection of adjacent well locations in complex noise environments, solving the problem of drilling collision prevention.

[0117] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0118] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0119] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0120] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0121] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0122] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0123] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0124] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0125] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0126] If the integrated unit is implemented as 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 this application, in essence, 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. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0127] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for processing array acoustic long-range detection signals for drilling collision prevention, characterized in that, The method includes: During the logging process, logging physical parameters are collected using logging equipment, and sound waves are emitted into the target area, which refers to the area where the well to be exploited is located. Multiple arrayed acoustic waves are obtained after the probed acoustic waves are reflected or scattered by the geological interface within the target area; Based on the multiple arrayed acoustic waves, a three-dimensional signal data volume is constructed in a preset three-dimensional spatial coordinate system. The three-dimensional signal data volume includes the spatial coordinates of each signal point in the arrayed acoustic waves in the three-dimensional spatial coordinate system and the acoustic wave signal intensity corresponding to the signal point. The search range is determined based on the well logging physical parameters. The signal points in the three-dimensional signal data volume are traversed. For each signal point, at least one cluster is determined based on the number of all signal points within the search range centered on the signal point. Clusters whose shape satisfies a preset shape and whose signal points in at least one cluster are designated as target clusters; The location of adjacent wells is determined based on the three-dimensional spatial coordinates of the signal points in the target cluster.

2. The method according to claim 1, characterized in that, The array acoustic wave is a two-dimensional array acoustic wave, and each array acoustic wave corresponds to a different acquisition depth; The step of constructing a three-dimensional signal data volume in a preset three-dimensional spatial coordinate system based on multiple arrayed acoustic waves includes: Multiple array acoustic waves are stacked in depth order to obtain the three-dimensional signal data volume. The three-dimensional spatial coordinate system uses the X-axis and Y-axis to represent the detection range on the horizontal plane and the Z-axis to represent the depth.

3. The method according to claim 1, characterized in that, The logging physical parameters include the logging speed of the logging equipment, the data sampling rate of the sound waves, the sound wave frequency, and the detected formation acoustic characteristics. Determining the search range based on the well logging physical parameters includes: The maximum longitudinal length is determined by multiplying the logging rate by the data sampling rate, where the maximum longitudinal length is the maximum search range in depth. The wavelength of the sound wave is calculated based on the sound wave frequency and the acoustic characteristics of the strata. Based on the sound wave wavelength and a preset coefficient, the maximum horizontal length is determined, and the search range on the horizontal plane is determined based on the maximum horizontal length. The search range is constructed based on the maximum vertical length and the maximum horizontal length.

4. The method according to claim 3, characterized in that, The search range is ellipsoidal, the maximum longitudinal length is the longitudinal semi-axis length of the ellipsoid, and the maximum transverse length is the transverse semi-axis length of the ellipsoid.

5. The method according to claim 1, characterized in that, The step of traversing the signal points in the three-dimensional signal data volume, and for each signal point, determining at least one cluster based on the number of all signal points within a search range centered on the signal point, includes: Signal points in the three-dimensional signal data volume whose signal strength is greater than the signal strength threshold are taken as points in the initial signal point set; Obtain multiple connected regions formed by the signal points in the initial signal point set; For each connected region, signal points in sub-regions within the connected region that meet preset conditions are selected as candidate signal points; For each candidate signal point, traverse the search and determine at least one cluster based on the number of all signal points within the search range centered on the candidate signal point.

6. The method according to claim 5, characterized in that, The preset conditions include: The signal strength of the signal points in the sub-region is within a preset strength range; The density of signal points in the sub-region is greater than the preset density.

7. The method according to claim 5, characterized in that, The step of traversing each candidate signal point, and for each candidate signal point, determining at least one cluster based on the number of all signal points within a search range centered on the candidate signal point, includes: For any of the candidate signal points, the following processing is performed: Obtain the first number of all signal points within the search range centered on the candidate signal point; If the first number is greater than a preset threshold, then the candidate signal points are marked as core signal points; All signal points within the search range centered on the core signal point are used as the first signal point in the initial cluster. For each of the first signal points in the initial cluster, obtain a second number of all signal points within the search range centered on the first signal point; If the second number is greater than the preset threshold, then all signal points within the search range centered on the first signal point are added to the initial cluster as first signal points. Then, the process jumps to the step of obtaining the second number of all signal points within the search range centered on the first signal point for each first signal point in the initial cluster, until all first signal points in all the initial clusters are traversed, and a cluster is obtained.

8. The method according to claim 1, characterized in that, The step of selecting a target cluster as the cluster whose shape, formed by the signal points in the at least one cluster, satisfies a preset shape includes: Calculate the vertical extension length and horizontal deviation of each cluster for at least one of the clusters, where the vertical extension length is the coordinate range of all signal points in the cluster in the depth direction, and the horizontal deviation is the standard deviation or maximum distribution radius of the coordinates of all signal points in the cluster on the horizontal plane. Clusters whose vertical extension length is greater than a first threshold and whose horizontal deviation is less than a second threshold are designated as the target clusters.

9. The method according to claim 1, characterized in that, Determining the location of adjacent wells based on the three-dimensional spatial coordinates of signal points in the target cluster includes: Obtain the azimuth sequence of signal points in the target cluster as a function of depth; Based on the azimuth sequence, outliers among the signal points in the target cluster are determined; The three-dimensional spatial coordinates of the adjacent well are obtained based on the three-dimensional spatial coordinates of the signal points in the target cluster, excluding the outlier points. The location of the adjacent well is determined based on its three-dimensional spatial coordinates.

10. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, it implements the array acoustic remote detection signal processing method for drilling collision prevention as described in any one of claims 1 to 9.

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