Drill bit wear detection method, device and equipment

Abstract

This specification relates to the technical field of oil and gas drilling construction optimization, and in particular to a wear detection method, device, and equipment. The wear detection method includes: clustering the first wear data of the drill bit; the first wear data includes second wear data and third wear data; the second wear data represents the historical wear data of the drill bit during the drilling of the current well; the third wear data represents the real-time wear data of the drill bit during the drilling of the current well; based on the fourth wear data of the drill bit, feature migration is performed during the clustering process of the first wear data; the fourth wear data represents the normal historical wear data of the drill bit during the drilling of historical wells; based on the loss value from the third wear data to the cluster center of the first wear data and the fourth wear data of the drill bit, the real-time wear level of the drill bit is determined. The embodiments of this specification obtain the wear data of the drill bit, use feature migration to accelerate the clustering of the wear data, and improve the accuracy and real-time performance of wear detection.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of oil and gas drilling construction optimization, and specifically to a wear detection method, device and equipment. Background Art

[0002] Unconventional formations are characterized by dense, hard, and highly abrasive rock, leading to severe drill bit wear during drilling. Complex drill bit conditions, such as bit wear and stick-slip vibration, are common, resulting in low drilling rates, long drilling cycles, and high drilling costs. Complex downhole conditions, such as high temperature and pressure, high rock hardness, high abrasiveness, and high heterogeneity, are inevitable, limiting understanding of drill bit operating conditions and leading to frequent abnormal rock breaking conditions. This is a significant factor hindering improvements in speed and efficiency in complex oil and gas drilling operations, such as deep and unconventional formations. Therefore, accurately identifying downhole drill bit conditions and enabling real-time monitoring of rock breaking are crucial foundations for optimizing and controlling the rock breaking process in oil and gas drilling. Drill bit wear directly impacts the ROP, thus impacting the overall drilling project schedule and cost. However, downhole drill bit wear is a hidden process, making it impossible to monitor downhole conditions on-site. Downhole conditions can only be indirectly understood through changes in surface parameters, making drill bit status monitoring challenging.

[0003] Existing drill bit wear detection methods primarily include traditional drill bit condition monitoring methods and drill bit wear prediction methods based on machine learning models. Traditional drill bit condition monitoring methods typically rely on comprehensive analysis of laboratory tests and field data to determine the mapping relationship between the changing trends of parameters such as mechanical penetration rate and mechanical specific energy and drill bit condition. The basic idea of ​​these methods is to analyze various parameters collected during the drilling process to infer the rock breaking state and wear status of the drill bit. Machine learning algorithms establish data-driven models to enable real-time monitoring and prediction of drill bit wear status. Compared with traditional physical model-based methods, machine learning technology can significantly improve the accuracy and real-time nature of monitoring, thereby providing more reliable decision support for drilling operations. The drill bit wear prediction method based on machine learning models first collects and stores data generated during the drilling process. Then, through feature extraction, a suitable training dataset is constructed. Next, machine learning algorithms (such as support vector machines, random forests, decision trees, and logistic regression) are applied to the data to train the data, generating a model capable of identifying and predicting drill bit wear status. Finally, the model is applied to actual drilling operations through a real-time monitoring system to achieve dynamic assessment of drill bit wear.

[0004] Traditional drill bit status monitoring methods use the mechanical penetration rate (ROP) to determine the working status of downhole drill bits. However, ROP is affected by multiple factors, making it difficult to determine whether a decrease in ROP is due to a decrease in the drill bit's own cutting performance. Furthermore, there is no point-to-point mapping between ROP and drill bit wear (i.e., a low ROP does not necessarily mean high drill bit wear, and a high ROP does not necessarily mean low drill bit wear). This results in an incomplete assessment of the drill bit's overall performance, reducing the accuracy and reliability of monitoring. Furthermore, in actual drilling operations, the response speed of drill bit wear status prediction methods based on machine learning models often fails to meet the needs of real-time monitoring. Furthermore, drill bit wear labels are difficult to obtain, and drill bit wear status can only be determined when the drill bit is out of the wellbore. This leaves room for improvement in the accuracy and real-time performance of existing drill bit wear status prediction methods based on machine learning models. Therefore, overcoming the problems of incomplete evaluation factors, difficulty in obtaining drill bit wear labels, poor real-time performance, and low accuracy in existing methods is a key issue that needs to be addressed urgently. Proposing a drill bit wear detection method with comprehensive evaluation factors, strong real-time performance, and high accuracy is a key issue that needs to be addressed urgently. Summary of the Invention

[0005] The purpose of the embodiments of this specification is to provide a wear detection method, device and equipment to overcome the problems existing in the existing methods, such as incomplete evaluation factors, difficulty in obtaining drill wear labels, poor real-time performance and low accuracy, and to propose a drill wear detection method with comprehensive evaluation factors, strong real-time performance and high accuracy.

[0006] On the one hand, an embodiment of the present specification proposes a wear detection method, which includes: clustering first wear data of the drill bit; the first wear data includes second wear data and third wear data; the second wear data represents the historical wear data of the drill bit during the drilling of the current well; the third wear data represents the real-time wear data of the drill bit during the drilling of the current well; based on the fourth wear data of the drill bit, feature migration is performed in the clustering process of the first wear data; the fourth wear data represents the normal historical wear data of the drill bit during the drilling of historical wells; based on the loss value from the third wear data to the cluster center of the first wear data and the fourth wear data of the drill bit, the real-time wear level of the drill bit is determined.

[0007] On the other hand, a wear detection device includes: a clustering module for clustering first wear data of a drill bit; the first wear data includes second wear data and third wear data; the second wear data represents historical wear data of the drill bit during the drilling of a current well; the third wear data represents real-time wear data of the drill bit during the drilling of the current well; a migration module for performing feature migration in the clustering process of the first wear data based on the fourth wear data of the drill bit; the fourth wear data represents normal historical wear data of the drill bit during the drilling of historical wells; a determination module for determining the real-time wear level of the drill bit based on the loss value from the third wear data to the cluster center of the first wear data and the fourth wear data of the drill bit.

[0008] In another aspect, a computer device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the wear detection method.

[0009] As can be seen from the technical solutions provided by the embodiments of this specification, the embodiments of this specification can cluster the first wear data of the drill bit; the first wear data includes second wear data and third wear data; the second wear data represents the historical wear data of the drill bit during the drilling of the current well; the third wear data represents the real-time wear data of the drill bit during the drilling of the current well; based on the fourth wear data of the drill bit, feature migration is performed during the clustering process of the first wear data; the fourth wear data represents the normal historical wear data of the drill bit during the drilling of previous wells; the real-time wear level of the drill bit is determined based on the loss value from the third wear data to the cluster center of the first wear data and the fourth wear data of the drill bit. Compared with existing methods, the embodiments of this specification can obtain drill bit wear data and cluster the drill bit wear data to comprehensively assess the wear status of the drill bit. In addition, the use of feature migration can accelerate the clustering process of drill bit wear data, improving the accuracy and real-time performance of drill bit wear detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art.

[0011] Figure 1 is a flow chart of the wear detection method provided in an embodiment of this specification;

[0012] Figure 2 This is the overall logic flow chart of the wear detection method provided in the embodiments of this specification;

[0013] Figure 3 This is a schematic diagram showing that the wear process is unknown when the drill bit is not pulled out during drilling operations provided in the embodiments of this specification;

[0014] Figure 4 is a schematic diagram of the change in drill bit wear level provided in the embodiments of this specification;

[0015] Figure 5 This is a flowchart of processing normal historical wear data and abnormal historical wear data provided by the embodiments of this specification;

[0016] Figure 6 This is a schematic diagram of the structure of the wear detection device provided in the embodiment of this specification;

[0017] Figure 7 It is a schematic diagram of the structural composition of the computer device provided in the embodiment of this specification. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of this specification to clearly and completely describe the technical solutions in the embodiments of this specification. Obviously, the embodiments described are only part of the embodiments of this specification, not all of the embodiments. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this specification.

[0019] Figure 1 Flowchart of the wear detection method. Figure 2 The overall logic flow chart of the wear detection method is shown in Figure 2. Figure 1 and Figure 2 , when implemented specifically, the method includes the following steps:

[0020] S101: Clustering the first wear data of the drill bit; the first wear data includes second wear data and third wear data; the second wear data represents the historical wear data of the drill bit during the drilling of the current well; the third wear data represents the real-time wear data of the drill bit during the drilling of the current well.

[0021] In some embodiments, drilling parameters of the drill bit can be obtained; based on correlation analysis, wear parameters in the drilling parameters can be screened; the wear parameters can be encoded using a pre-trained encoder to generate encoding parameters; based on the encoding parameters, fourth wear data of the drill bit can be obtained.

[0022] Correlation analysis allows for comprehensive and effective selection of wear parameters strongly correlated with drill bit wear, improving data accuracy and reliability and providing a comprehensive, high-quality data foundation for subsequent analysis. Furthermore, encoding wear parameters using a pre-trained encoder reduces data dimensionality, facilitating real-time wear detection.

[0023] Mud logging and well logging data from historical wells can be obtained. Mud logging data is obtained by observing and recording materials (such as cuttings, drilling fluid, and gas) returned from the wellbore during the drilling process, as well as information such as drilling parameters. Well logging data is typically collected after the wellbore is drilled. The data is transmitted to the surface via logging instruments for recording and analysis. Mud logging and well logging data contain many drilling parameters related to the drill bit. Data preprocessing can be performed on the acquired mud logging and well logging data from historical wells, including outlier detection and removal, data completion, and interpolation. Sliding filter algorithms can also be used to reduce noise in the data. Correlation analysis can be used to calculate the correlation coefficient between different drilling parameters and drill bit wear. Based on this correlation coefficient, all drilling parameters can be screened to identify the wear parameter most closely associated with drill bit wear. Correlation analysis can be based on statistical methods such as the Pearson correlation coefficient, the Spearman correlation coefficient, and the coefficient of determination. For example, the Pearson correlation coefficient can be used to calculate the correlation between each drilling parameter and drill bit wear. The absolute value of the calculated correlation coefficient can be used to determine the strength of the correlation between each drilling parameter and drill bit wear. A correlation strength threshold can be set, such as 0.6. Among all drilling parameters, those with a correlation strength greater than or equal to the correlation strength threshold are selected as wear parameters.

[0024] In some embodiments, wear parameters screened using the Pearson correlation coefficient may include at least: drill bit size, cutter diameter, number of blades, number of waterholes, weight on bit (WOB), rotational speed, torque, displacement, and pump pressure. Drill bit size is highly correlated with drill bit type and application. Cutter diameter and number of blades significantly impact the drill bit's average penetration rate and rock-breaking efficiency. The number of waterholes is related to the drill bit's cooling and chip removal capabilities. During drilling, waterholes help deliver drilling fluid to the drill bit to cool it and remove cuttings. WOB refers to the pressure exerted on the drill bit by the weight of a portion of the drill string as it is lowered. The magnitude of WOB significantly impacts drilling efficiency and drill bit life. Rotational speed refers to the speed at which the drill bit rotates during drilling; its speed affects drilling efficiency and drill bit wear. Torque refers to the moment of force generated by the drill bit during rotation; its magnitude reflects the load capacity of the drill bit during drilling. Displacement refers to the flow rate of drilling fluid during the drilling process, and its size affects the fluid's cooling, cleaning, and cuttings-carrying abilities. Pump pressure refers to the pressure generated when drilling fluid is pumped to the drill bit during drilling, and its size affects both the fluid's delivery and the drill bit's cooling.

[0025] After the wear parameters are determined, a pre-trained encoder can be used to encode the wear parameters. The encoder can be a machine learning model such as RNN, LSTM or GRU. For example, a pre-trained GRU can be used as an encoder. First, the input wear parameter sequence can be converted into a wear vector sequence using the encoder's embedding, and then the wear vector sequence is fed into the encoding layer. The encoder can be used to process the wear vector sequence one by one, updating its hidden state each time it is processed, and the final hidden state is used as the encoding parameter representation of the entire wear vector sequence. Based on the normal logging data and normal logging data of historical wells, normal wear parameter data of multiple historical wells at different locations can be obtained. Use the pre-trained encoder to encode the normal wear parameter data of each historical well at each time point to obtain the encoding parameter representation corresponding to each historical well at each time point. Based on the encoding parameter representation of all positions of all historical wells, the fourth wear data for characterizing the normal wear of historical wells can be obtained.

[0026] In some embodiments, based on the internal and external tooth wear ratings of the drill bit corresponding to the fourth wear data, the number of cluster centers of the fourth wear data can be determined; based on the number of cluster centers of the fourth wear data, the fourth wear data can be clustered; based on the cluster centers of the fourth wear data, a wear level label of the fourth wear data can be generated; the cluster centers of the fourth wear data can be recorded to generate a historical cluster center set.

[0027] By generating wear grade labels for normal historical wear data, the number of drill wear labels is expanded, addressing the difficulty in obtaining these labels. This provides sufficient training sample data for wear detection, thereby helping to improve the accuracy of drill wear detection. Furthermore, by recording the cluster centers of the fourth wear data to generate a set of historical cluster centers, this provides a data foundation for feature migration.

[0028] Reference Figure 3 and Figure 4The drill bit wear grade cannot be determined during live drilling (i.e., before the drill bit exits the wellbore). However, the drill bit wear grade can be determined after the drill bit exits the wellbore using a drill bit wear rating method. An example of a drill bit wear rating method is the IADC drill bit wear grading standard for internal and external cutter wear. The IADC drill bit wear grading standard uses specific coding and grading to describe the drill bit's cutting structure, wear characteristics, location, bearing / seal condition, gage wear, and other related information. The internal cutter teeth of a drill bit represent the inner two-thirds of the drill bit radius in a fixed-cutter drill bit. During drilling, the internal cutter teeth primarily perform rock crushing. The external cutter teeth represent the outer one-third of the drill bit radius in a fixed-cutter drill bit. They also contribute to rock crushing during drilling, but their wear characteristics typically differ from those of the internal cutter teeth. The IADC drill bit wear grading standard for internal and external cutter wear uses a linear scale from 0 to 8 to measure and define the wear condition of the cutters at various locations on the drill bit surface. Higher values ​​indicate greater wear on the cutters. 0 means no wear on the cutting teeth, 4 means 50% wear on the cutting teeth, and 8 means the cutting teeth are completely worn out with no remaining wear. The IADC drill wear grading standard method for internal and external tooth wear calculates the average wear level of each tooth in the internal and external tooth areas to obtain the average wear level of each area, thereby determining the wear level of the internal and external teeth.

[0029] Therefore, for the fourth wear data related to multiple historical wells, the internal and external tooth wear IADC drill bit wear grading standard method can be used to obtain the internal tooth wear level and external tooth wear level of the corresponding drill bit after the drilling of each historical well. The internal tooth wear level and the external tooth wear level of the drill bit can be fused to obtain a wear level that can characterize the overall wear condition of the drill bit. The fusion can be a simple arithmetic average of the internal tooth wear level and the external tooth wear level, or a more complex weighted superposition. For example, the arithmetic average of the internal tooth wear level and the external tooth wear level of the drill bit can be taken to generate a wear level that characterizes the overall wear condition of the drill bit. For another example, different weight factors can be assigned to the internal tooth wear level and the external tooth wear level of the drill bit according to the actual role of the internal teeth and external teeth of the drill bit in the drilling process, and then the wear level that can characterize the overall wear condition of the drill bit can be calculated.

[0030] Reference Figure 5After determining the wear level of the drill bit corresponding to each historical well, the maximum wear level can be used as the number of cluster centers corresponding to the fourth wear data. The fourth wear data can be clustered based on the number of cluster centers corresponding to the fourth wear data. For example, the K-Means clustering algorithm can be used to cluster the fourth wear data based on the number of cluster centers corresponding to the fourth wear data. K fourth wear data points can be randomly selected as initial cluster centers. For each data point in the fourth wear data, the distance from each cluster center is calculated and assigned to the cluster center with the closest distance. Next, the cluster center of each cluster is recalculated. The new cluster center is the mean of all fourth wear data points within that cluster. Multiple rounds of iterations are performed until the cluster center no longer changes. Each cluster center of the final fourth wear data can represent a wear level. Therefore, for each fourth wear data point, the wear level represented by the cluster center closest to it can be used as its wear level label. For each cluster center of the final fourth wear data, the position of each cluster center can also be recorded to generate a set of historical cluster centers.

[0031] In some embodiments, based on the coding parameters, the fifth wear data of the drill bit can be obtained; the fifth wear data represents the abnormal historical wear data of the drill bit during the historical well drilling process; the abnormal working conditions corresponding to the fifth wear data can be determined; based on the fifth wear data and the fourth wear data, the confidence interval of the wear parameter can be determined.

[0032] By analyzing abnormal wear processes and carrying out corresponding marking and processing, a foundation is laid for timely identification of abnormal conditions of the drill bit under abnormal working conditions during real-time wear detection, which in turn helps to enhance the safety of actual drilling operations.

[0033] After the wear parameters are determined, a pre-trained encoder can be used to encode the wear parameters. The encoder can be a machine learning model such as RNN, LSTM or GRU. For example, a pre-trained GRU can be used as an encoder. First, the input wear parameter sequence can be converted into a wear vector sequence using the encoder's embedding, and then the wear vector sequence is fed into the encoding layer. The encoder can be used to process the wear vector sequence one by one, updating its hidden state each time it is processed, and the final hidden state is used as the encoding parameter representation of the entire wear vector sequence. Based on the abnormal logging data and abnormal logging data of historical wells, abnormal wear parameter data of multiple historical wells at different locations can be obtained. Use the pre-trained encoder to encode the abnormal wear parameter data of each historical well at each time point to obtain the encoding parameter representation corresponding to each historical well at each time point. Based on the encoding parameter representation of all positions of all historical wells, the fifth wear data for characterizing the abnormal wear of historical wells can be obtained.

[0034] For the fifth wear data associated with multiple historical wells, the internal and external wear grades of the drill bit corresponding to each historical well after drilling can be obtained using the internal and external wear grade (IADC) drill bit wear grading standard. The internal and external wear grades of the drill bit can be fused to obtain a wear grade that represents the overall wear of the drill bit. This fusion can be a simple arithmetic average of the internal and external wear grades or a more complex weighted superposition. For example, the arithmetic average of the internal and external wear grades can be taken to generate a wear grade that represents the overall wear of the drill bit. For another example, different weighting factors can be assigned to the internal and external wear grades of the drill bit based on their actual role in the drilling process, thereby calculating a wear grade that represents the overall wear of the drill bit. After determining the wear grade of the drill bit corresponding to each historical well, the maximum wear grade can be used as the number of cluster centers corresponding to the fifth wear data. Based on the number of cluster centers corresponding to the fifth wear data, the fifth wear data can be clustered using the K-Means clustering algorithm. K fifth wear data points can be randomly selected as the initial cluster centers. For each data point in the fifth wear data, its distance from each cluster center is calculated and assigned to the cluster center with the closest distance. Then, for each cluster, its cluster center is recalculated. The new cluster center is the mean of all fourth wear data points in the cluster. After multiple rounds of iterations, until the cluster center no longer changes. For each cluster center of the fifth wear data finally obtained, the distance loss value between different cluster centers is calculated. Based on the clustering results of the fourth wear data, a set of historical cluster centers can be obtained. The distance loss value between each cluster center in the set of historical cluster centers can be calculated. It can be found that the distance loss value between each cluster center of the fifth wear data is much greater than the distance loss value between each cluster center of the fourth wear data. This is because some wear parameters related to the fifth wear data have mutated. Based on the abnormal logging data and abnormal logging data of historical wells, the abnormal working conditions corresponding to each fifth wear data can be determined. Abnormal working conditions may include but are not limited to abnormal wear conditions such as broken teeth, broken teeth, mud balls, etc. Reference Figure 5Based on the wear parameter variation range of the fourth wear data and the wear parameter variation range of the fifth wear data, a digital signal analysis method can be used to identify the fluctuation range of the wear parameter variation corresponding to each abnormal operating condition and the confidence interval of the wear parameter variation under normal wear conditions. The digital signal analysis method can include short-time Fourier transform, wavelet transform, HHT transform, etc. For example, wavelet transform analysis can be used to identify the fluctuation range of the wear parameter variation corresponding to each abnormal operating condition and the confidence interval of the wear parameter variation under normal wear conditions. Specifically, the wear parameter data corresponding to the plurality of fourth wear data and the plurality of fifth wear data can be decomposed using wavelet transform to obtain sub-wear signals of different frequencies. By comparing the characteristics of adjacent sub-wear signals (such as energy, amplitude, frequency, etc.), the location of the mutation point can be detected. Interpolation or other methods can be used to precisely locate the mutation point. At the mutation point, relevant parameters such as amplitude, frequency, and phase are extracted. Based on the extracted parameters, the variation range of the mutation parameter is determined, and then the fluctuation range of the wear parameter variation corresponding to each abnormal operating condition and the confidence interval of the wear parameter variation under normal wear conditions can be determined.

[0035] In some embodiments, the first wear data may be clustered for different numbers of cluster centers; and based on the historical cluster center set of the fourth wear data, cluster centers of the first wear data with different numbers of cluster centers may be selected.

[0036] By optimizing the random selection of cluster centers to a method based on a set of historical cluster centers, the clustering iteration speed of the first wear data is accelerated, which helps to improve the real-time response capability of wear detection.

[0037] The IADC drill wear classification standard for internal and external tooth wear uses a linear scale from 0 to 8 to measure and define the wear state of the cutting teeth on various parts of the drill bit surface. Therefore, the number of cluster centers for the first wear data can be set from 1 to 9, and the first wear data can be clustered under nine different numbers of cluster centers. During clustering of the first wear data, cluster centers can be selected from a set of historical cluster centers, thereby accelerating the convergence of the clustering algorithm. For example, based on the number of cluster centers corresponding to the first wear data, the K-Means clustering algorithm can be used to cluster the first wear data. K first wear data points can be randomly selected. For each selected first wear data point, the cluster center with the smallest distance from it can be selected from the set of historical cluster centers as the initial cluster center for clustering the first wear data. For each data point in the first wear data, its distance to each cluster center is calculated and assigned to the cluster center with the closest distance. Next, the cluster center for each cluster is recalculated. The new cluster center is the mean of all first wear data points within that cluster. After multiple rounds of iterations, the cluster center no longer changes.

[0038] S102: performing feature migration in the clustering process of the first wear data based on fourth wear data of the drill bit; the fourth wear data represents normal historical wear data of the drill bit during historical well drilling.

[0039] In some embodiments, the fourth wear data of the drill bit can be clustered; the first wear data clustering loss and the fourth wear data clustering loss can be obtained; based on minimizing the difference between the first wear data clustering loss and the fourth wear data clustering loss, the feature distribution in the fourth wear data clustering process can be learned in the process of the first wear data clustering.

[0040] By introducing the feature migration method, the learning speed in the first wear data clustering process can be accelerated, the training time can be reduced, and the real-time response capability of wear detection can be improved. In turn, the drill bit wear can be monitored in real time, ensuring timely identification of the wear level during the drilling process, avoiding equipment damage and operation interruptions caused by wear.

[0041] The number of cluster centers for the first wear data can be set from 1 to 9, and the first wear data can be clustered with nine different numbers of cluster centers. Each clustering step can also be performed on the fourth wear data. The fourth wear data has a corresponding wear level label, but the first wear data does not. The clustering loss for the first wear data and the clustering loss for the fourth wear data can be obtained. The difference between these two clustering losses can be minimized using a transfer learning model, thereby learning the feature distribution of the fourth wear data during clustering during the first wear data. The transfer learning model can be an MMD, TCA, DAN, or other algorithm.

[0042] For example, MMD can be used to learn the feature distribution of the fourth wear data clustering process during the first wear data clustering process. The clustering loss of the first wear data and the clustering loss of the fourth wear data can be obtained, and the difference between the clustering losses of the first and fourth wear data can be minimized based on the following formula. This allows the feature distribution of the fourth wear data clustering process to be learned during the first wear data clustering process.

[0043]

[0044] Among them, D s D represents the source domain with wear level labels, i.e., the fourth wear data; t is a target domain without a wear level label, i.e., the first wear data; Represents the kernel function of MMD; and Represent the clustering loss of the fourth wear data and the clustering loss of the first wear data respectively; α1 and α2 represent the adjustable weight parameters of the clustering loss of the fourth wear data and the adjustable weight parameters of the clustering loss of the first wear data respectively. The clustering process is a two-stage training process. In the first stage, α1 can be set to be greater than α2 until Convergence; the second stage is to gradually decay α1 to 0 until convergence Through the two-stage feature migration clustering, the feature distribution in the fourth wear data clustering process can be learned in the first wear data clustering process.

[0045] S103: Determine a real-time wear level of the drill bit based on the loss value from the third wear data to the cluster center of the first wear data and the fourth wear data of the drill bit.

[0046] In some embodiments, the loss value from the third wear data to the cluster center of the first wear data can be calculated; based on the loss value corresponding to the third wear data under different numbers of cluster centers, the wear change of the third wear data can be calculated.

[0047] By analyzing the relationship between cluster centers and tertiary wear data, the wear detection method is more interpretable, enabling users to understand the model's decision logic and increasing confidence in field applications. Furthermore, by calculating the wear variation corresponding to tertiary wear data, a data foundation is established for implementing appropriate countermeasures based on normal and abnormal wear conditions.

[0048] The number of cluster centers for the first wear data can be set to 1-9, and the first wear data can be clustered under 9 different numbers of cluster centers. The clustering results of the first wear data under 9 different numbers of cluster centers can be obtained. For each case, the loss value between the third wear data and each cluster center of the first wear data is calculated, and the minimum loss value is taken. The corresponding loss values ​​in the 9 cases can be averaged, or the minimum value can be taken, or other methods can be used to calculate the loss value of the fused third wear data, which will not be repeated here. The loss value of the third wear data recorded at the previous time point can be obtained, and the difference between the loss value of the third wear data at the current time point and the loss value of the third wear data at the previous time point is used as the wear change.

[0049] In some embodiments, if the wear change is less than a preset wear change threshold, the number of cluster centers of the first wear data can be determined based on the loss value corresponding to the third wear data under different numbers of cluster centers and the fourth wear data; based on the number of cluster centers of the first wear data, the real-time wear level of the drill bit can be determined.

[0050] When the wear change is less than a preset wear change threshold, the accurate real-time wear level of the drill bit can be obtained based on the number of cluster centers of the first wear data, thereby realizing real-time monitoring of the drill bit wear condition.

[0051] Based on the clustering results of the fourth wear data, a wear change threshold can be pre-set. Specifically, during the clustering process of the fourth wear data, the difference between the corresponding loss values ​​of the fourth wear data at adjacent time points can be calculated. The maximum value of the difference values ​​obtained from multiple calculations can be taken, and a magnification factor greater than 1 can be set. The product of the maximum value of the difference values ​​obtained from multiple calculations and the magnification factor is used as the wear change threshold. If the wear change of the third wear data is less than the preset wear change threshold, the third wear data at this time can be considered normal wear data. Similarly, based on the clustering results of the fourth wear data, a set of historical cluster centers can be obtained. Each cluster center in the set of historical cluster centers can represent a wear level. Based on the fourth wear data corresponding to each cluster center in the set of historical cluster centers, the loss value fluctuation range corresponding to each wear level can be determined. For the clustering results of the first wear data under nine different numbers of cluster centers, for each case, the loss value between the third wear data and each cluster center of the first wear data is calculated, and the minimum loss value is taken. The loss value fluctuation range corresponding to each wear level for each number of cluster centers is obtained and compared with the minimum loss value corresponding to the third wear data for each number of cluster centers. The number of cluster centers whose minimum loss value corresponding to the third wear data best matches the loss value fluctuation range corresponding to the wear level is determined. This number of cluster centers is used as the number of cluster centers for the first wear data. Under normal wear conditions, the wear level of a drill bit increases approximately linearly with drilling depth. Therefore, the cluster centers of the current first wear data can represent the wear levels from the lowest wear level to the current third wear data. The cluster center corresponding to the third wear data at the current time point corresponds to the current maximum wear level. In other words, the number of cluster centers for the first wear data is the real-time wear level corresponding to the third wear data at the current time point. For example, based on the IADC drill bit wear grading standard method for internal and external tooth wear, if the number of cluster centers for the current first wear data is 5, then the cluster centers of the first wear data can represent the 0th wear level, the 1st wear level, the 2nd wear level, the 3rd wear level, and the 4th wear level, respectively. The real-time wear level corresponding to the third wear data is the number of cluster centers of the current first wear data, that is, the real-time wear level corresponding to the third wear data is the fourth wear level.

[0052] In some embodiments, if the wear change is greater than or equal to a preset wear change threshold, a pre-trained decoder can be used to decode the third wear data to generate wear parameter data; based on the wear parameter data and the confidence interval of the wear parameter, the abnormal operating condition corresponding to the third wear data can be determined.

[0053] When the wear change is greater than or equal to the preset wear change threshold, the abnormal operating condition corresponding to the third wear data can be determined based on the wear parameter data and the confidence interval of the wear parameter, and then corresponding countermeasures for the abnormal operating condition can be taken, which helps to ensure the safety of actual drilling operations.

[0054] Based on the clustering results of the fourth wear data, a wear change threshold can be pre-set. Specifically, during the clustering process of the fourth wear data, the difference between the corresponding loss values ​​of the fourth wear data at adjacent time points can be calculated. The maximum value of these calculated differences can be taken, and a magnification factor greater than 1 can be set. The product of the maximum value and the magnification factor is used as the wear change threshold. If the wear change of the third wear data is greater than or equal to the preset wear change threshold, the third wear data can be considered abnormal wear data. A pre-trained decoder can be used to decode the third wear data. The decoder can be a machine learning model such as an RNN, LSTM, or GRU. For example, a pre-trained GRU can be used as the decoder. The third wear data can be directly used as the output of the decoder, which decodes the third wear data and generates wear parameter data. Based on the wear parameter variation range of the fourth wear data and the wear parameter variation range of the fifth wear data, the fluctuation range of the wear parameter variation corresponding to each abnormal operating condition and the confidence interval of the wear parameter variation under normal wear conditions are determined. Each wear parameter in the wear parameter data corresponding to the third wear data can be matched with the fluctuation range of the wear parameter change corresponding to each abnormal working condition and the confidence interval of the wear parameter change under normal wear conditions, so as to determine the abnormal working condition corresponding to the third wear data.

[0055] In some embodiments, a true wear level label of the first wear data after it leaves the wellbore may be obtained; based on the true wear level label, a clustering result of the first wear data may be optimized.

[0056] By obtaining the actual wear level label of the drill bit after leaving the well, the data can be corrected and the clustering results can be updated in real time, further improving the accuracy and reliability of the wear detection method.

[0057] After the drilling work of the current well is completed, the internal and external tooth wear IADC drill bit wear grading standard method can be used to obtain the internal and external tooth wear level of the drill bit after each current well is exited. The internal and external tooth wear levels of the drill bit can be fused to obtain a wear level that can characterize the overall wear condition of the drill bit. The fusion can be a simple arithmetic average of the internal and external tooth wear levels, or a more complex weighted superposition. For example, the arithmetic average of the internal and external tooth wear levels of the drill bit can be taken to generate a wear level that characterizes the overall wear condition of the drill bit. For another example, different weight factors can be assigned to the internal and external tooth wear levels of the drill bit according to the actual role of the internal and external teeth of the drill bit in the drilling process, and then the real wear level label that can characterize the overall wear condition of the drill bit can be calculated.

[0058] After determining the true wear level label of the current well drill bit, the true wear level label of the current well drill bit can be used as the number of cluster centers of the first wear data. According to the number of cluster centers corresponding to the first wear data, the first wear data can be clustered. For example, according to the number of cluster centers corresponding to the first wear data, the first wear data can be clustered using the K-Means clustering algorithm. K first wear data points can be randomly selected as the initial cluster centers. For each data point in the first wear data, its distance from each cluster center is calculated and assigned to the cluster center with the closest distance. Then, for each cluster, its cluster center is recalculated. The new cluster center is the mean of all first wear data points in the cluster. After multiple rounds of iterations, until the cluster center no longer changes. For each cluster center of the first wear data finally obtained, each cluster center can represent a wear level. Therefore, for each first wear data point, the wear level represented by the cluster center closest to it can be used as its wear level label. For each cluster center of the first wear data finally obtained, the position of each cluster center can also be recorded, the historical cluster center set can be updated, and then the confidence interval of the wear parameter change under normal wear conditions can be updated, which will not be repeated here.

[0059] Based on the above wear detection method, this specification also proposes an embodiment of a wear detection device. Figure 6 As shown, the evaluation device may specifically include the following modules:

[0060] Clustering module 601 can be used to cluster first wear data of the drill bit; the first wear data includes second wear data and third wear data; the second wear data represents historical wear data of the drill bit during the drilling of the current well; the third wear data represents real-time wear data of the drill bit during the drilling of the current well;

[0061] A migration module 602 may be configured to perform feature migration in the clustering process of the first wear data based on fourth wear data of the drill bit, wherein the fourth wear data represents normal historical wear data of the drill bit during the drilling process of the historical well;

[0062] The determination module 603 may be configured to determine a real-time wear level of the drill bit based on a loss value from the third wear data to a cluster center of the first wear data and fourth wear data of the drill bit.

[0063] In some embodiments, the clustering module 601 can be specifically used to obtain drilling parameters of the drill bit; based on Pearson correlation detection, filter the wear parameters in the drilling parameters; use a pre-trained encoder to encode the wear parameters to generate encoding parameters; based on the encoding parameters, obtain the fourth wear data of the drill bit.

[0064] In some embodiments, the above-mentioned clustering module 601 can also be specifically used to determine the number of cluster centers of the fourth wear data based on the internal and external tooth wear ratings of the drill bit corresponding to the fourth wear data; cluster the fourth wear data based on the number of cluster centers of the fourth wear data; generate a wear level label for the fourth wear data based on the fourth wear data and the cluster centers of the fourth wear data; record the cluster centers of the fourth wear data to generate a historical cluster center set.

[0065] In some embodiments, the above-mentioned clustering module 601 can also be specifically used to obtain the fifth wear data of the drill bit based on the encoding parameters; the fifth wear data represents the abnormal historical wear data of the drill bit during the historical well drilling process; determine the abnormal working conditions corresponding to the fifth wear data; and determine the confidence interval of the wear parameter based on the abnormal working conditions corresponding to the fifth wear data.

[0066] In some embodiments, the clustering module 601 can be used to cluster the first wear data according to different numbers of cluster centers; based on the historical cluster center set of the fourth wear data, the cluster centers of the first wear data with different numbers of cluster centers are selected.

[0067] In some embodiments, the above-mentioned migration module 602 can be specifically used to cluster the fourth wear data of the drill bit; obtain the first wear data clustering loss and the fourth wear data clustering loss; based on minimizing the difference between the first wear data clustering loss and the fourth wear data clustering loss, learn the feature distribution in the fourth wear data clustering process during the first wear data clustering process.

[0068] In some embodiments, the above-mentioned determination module 603 can be specifically used to calculate the loss value from the third wear data to the cluster center of the first wear data; calculate the wear change of the third wear data based on the loss value corresponding to the third wear data under different numbers of cluster centers; if the wear change is less than the preset wear change threshold, determine the number of cluster centers of the first wear data based on the loss value corresponding to the third wear data under different numbers of cluster centers and the fourth wear data; determine the real-time wear level of the drill bit based on the number of cluster centers of the first wear data; if the wear change is greater than or equal to the preset wear change threshold, use a pre-trained decoder to decode the third wear data to generate wear parameter data; based on the wear parameter data and the confidence interval of the wear parameter, determine the abnormal working condition corresponding to the third wear data.

[0069] In some embodiments, the determination module 603 may be further configured to obtain a true wear level label of the first wear data after it leaves the wellbore; and optimize the clustering result of the first wear data based on the true wear level label.

[0070] It should be noted that the units, devices or modules described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. For the convenience of description, the above devices are described in terms of functions and are divided into various modules and described separately. Of course, when implementing this specification, the functions of each module can be implemented in the same or multiple software and / or hardware, or the module that implements the same function can be implemented by a combination of multiple sub-modules or sub-units. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0071] As can be seen from the above, the wear detection device provided in the embodiments of this specification can obtain drill bit wear data and cluster the drill bit wear data to comprehensively assess the drill bit wear state. In addition, the use of feature transfer can accelerate the clustering process of drill bit wear data, improving the accuracy and real-time performance of drill bit wear detection.

[0072] An embodiment of the present specification also provides a computer device for a wear detection method, including a processor and a memory for storing processor executable instructions. When the processor is specifically implemented, it can perform the following steps according to the instructions: clustering the first wear data of the drill bit; the first wear data includes second wear data and third wear data; the second wear data represents the historical wear data of the drill bit during the drilling of the current well; the third wear data represents the real-time wear data of the drill bit during the drilling of the current well; based on the fourth wear data of the drill bit, feature migration is performed in the clustering process of the first wear data; the fourth wear data represents the normal historical wear data of the drill bit during the drilling of historical wells; based on the loss value from the third wear data to the cluster center of the first wear data and the fourth wear data of the drill bit, the real-time wear level of the drill bit is determined.

[0073] In order to complete the above instructions more accurately, refer to Figure 7 As shown, the embodiment of this specification also provides another specific electronic device, wherein the electronic device includes a network communication port 701, a processor 702 and a memory 703, and the above structures are connected through internal cables so that each structure can perform specific data interaction.

[0074] The processor 702 can be specifically used to cluster the first wear data of the drill bit; the first wear data includes second wear data and third wear data; the second wear data represents the historical wear data of the drill bit during the drilling of the current well; the third wear data represents the real-time wear data of the drill bit during the drilling of the current well; based on the fourth wear data of the drill bit, feature migration is performed in the clustering process of the first wear data; the fourth wear data represents the normal historical wear data of the drill bit during the drilling of historical wells; based on the loss value from the third wear data to the cluster center of the first wear data and the fourth wear data of the drill bit, the real-time wear level of the drill bit is determined.

[0075] The memory 703 may be specifically used to store corresponding instruction programs.

[0076] In this embodiment, the network communication port 701 can be a virtual port that is bound to different communication protocols, thereby being capable of sending or receiving different data. For example, the network communication port can be a port responsible for web data communication, a port responsible for FTP data communication, or a port responsible for email data communication. Furthermore, the network communication port can also be a physical communication interface or communication chip. For example, it can be a wireless mobile network communication chip, such as GSM or CDMA; it can also be a Wi-Fi chip; or it can be a Bluetooth chip.

[0077] In this embodiment, the processor 702 may be implemented in any suitable manner. For example, the processor may take the form of a microprocessor or a processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. This specification is not intended to limit this.

[0078] In this embodiment, the memory 703 includes volatile memory and non-volatile memory. The memory 703 can include multiple levels. In digital systems, anything that can store binary data can be considered a memory. In integrated circuits, a circuit with a storage function that does not have a physical form is also called a memory, such as RAM and FIFO. In systems, a physical storage device is also called a memory, such as a memory stick or TF card.

[0079] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0080] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0081] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0082] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0083] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A drill bit wear detection method, characterized in that: include: obtaining fourth wear data of the drill bit; The fourth wear data represents normal historical wear data of the drill bit during historical well drilling; Clustering the fourth wear data; Record the cluster center of the fourth wear data to generate a historical cluster center set; Clustering the first wear data of the drill bit; the first wear data includes the second wear data and the third wear data; the second wear data represents the historical wear data of the drill bit during the drilling of the current well; The third wear data represents real-time wear data of the drill bit during the current well drilling process; wherein clustering the first wear data of the drill bit includes: clustering the first wear data for different numbers of cluster centers; and selecting cluster centers of the first wear data for different numbers of cluster centers based on a set of historical cluster centers of the fourth wear data; Based on the fourth wear data of the drill bit, feature migration is performed in the clustering process of the first wear data; Based on the loss value from the third wear data to the cluster center of the first wear data and the fourth wear data of the drill bit, the real-time wear level of the drill bit is determined, which includes: calculating the loss value from the third wear data to the cluster center of the first wear data; calculating the wear change of the third wear data based on the loss value corresponding to the third wear data under different numbers of cluster centers; if the wear change is less than a preset wear change threshold, determining the number of cluster centers of the first wear data based on the loss value corresponding to the third wear data under different numbers of cluster centers and the fourth wear data; determining the real-time wear level of the drill bit based on the number of cluster centers of the first wear data; if the wear change is greater than or equal to the preset wear change threshold, using a pre-trained decoder to decode the third wear data to generate wear parameter data; and determining the abnormal working condition corresponding to the third wear data based on the wear parameter data and the confidence interval of the wear parameter.

2. The method according to claim 1, characterized in that The obtaining of fourth wear data of the drill bit includes: Obtain drilling parameters of the drill bit; screening wear parameters from the drilling parameters based on Pearson correlation detection; encoding the wear parameters using a pre-trained encoder to generate encoding parameters; Based on the encoding parameters, fourth wear data of the drill bit is acquired.

3. The method according to claim 2, characterized in that said clustering the fourth wear data; Record the cluster center of the fourth wear data and generate a historical cluster center set, including: determining the number of cluster centers of the fourth wear data based on the wear ratings of the internal and external teeth of the drill bit corresponding to the fourth wear data; clustering the fourth wear data based on the number of cluster centers of the fourth wear data; generating a wear level label for the fourth wear data based on the fourth wear data and a cluster center of the fourth wear data; The cluster center of the fourth wear data is recorded to generate a historical cluster center set.

4. The method according to claim 2, characterized in that The method further comprises: Based on the coding parameters, fifth wear data of the drill bit is acquired; the fifth wear data represents abnormal historical wear data of the drill bit during the historical well drilling process; determining an abnormal operating condition corresponding to the fifth wear data; A confidence interval of the wear parameter is determined based on the abnormal operating condition corresponding to the fifth wear data.

5. The method according to claim 1, wherein The feature migration based on the fourth wear data of the drill bit in the clustering process of the first wear data includes: Clustering the fourth wear data of the drill bit; Obtaining the first wear data clustering loss and the fourth wear data clustering loss; Based on minimization of the difference between the first wear data clustering loss and the fourth wear data clustering loss, feature distribution in the fourth wear data clustering process is learned in the first wear data clustering process.

6. The method according to claim 1, characterized in that The method further comprises: Obtain the actual wear level label after the first wear data is taken out of the well; Based on the true wear level labels, a clustering result of the first wear data is optimized.

7. A drill bit wear detection device, characterized in that: The device comprises: A clustering module is configured to cluster first wear data of a drill bit; the first wear data includes second wear data and third wear data; the second wear data represents historical wear data of the drill bit during drilling of a current well; and the third wear data represents real-time wear data of the drill bit during drilling of the current well; wherein clustering the first wear data of the drill bit comprises: clustering the first wear data for different numbers of cluster centers; and selecting cluster centers of the first wear data for different numbers of cluster centers based on a set of historical cluster centers of the fourth wear data; a migration module, configured to perform feature migration in the clustering process of the first wear data based on fourth wear data of the drill bit, wherein the fourth wear data represents normal historical wear data of the drill bit during the drilling process of the historical well; a determination module for determining a real-time wear level of the drill bit based on a loss value from the third wear data to a cluster center of the first wear data and fourth wear data of the drill bit, the determination module comprising: calculating a loss value from the third wear data to a cluster center of the first wear data; calculating a wear variation of the third wear data based on the loss values ​​corresponding to the third wear data under different numbers of cluster centers; if the wear variation is less than a preset wear variation threshold, determining the number of cluster centers of the first wear data based on the loss values ​​corresponding to the third wear data under different numbers of cluster centers and the fourth wear data; determining the real-time wear level of the drill bit based on the number of cluster centers of the first wear data; if the wear variation is greater than or equal to the preset wear variation threshold, decoding the third wear data using a pre-trained decoder to generate wear parameter data; and determining an abnormal operating condition corresponding to the third wear data based on the wear parameter data and a confidence interval of the wear parameter; The drill bit wear detection device is also used to obtain fourth wear data of the drill bit; the fourth wear data represents normal historical wear data of the drill bit during historical well drilling; cluster the fourth wear data; record the cluster center of the fourth wear data, and generate a historical cluster center set.

8. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the method according to any one of claims 1 to 6 is implemented.

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