Method and device for identifying grouting depth

Abstract

This invention relates to the field of oil and gas exploration and development, and discloses a method for identifying grouting depth. The method includes: acquiring measured values ​​of the target drilling depth and the target hook load, where each measured value of the target drilling depth corresponds to a measured value of the target hook load; using a preset number of target samples as the length of a sliding window; calculating the mean value of each measured value of the target hook load within the sliding window; obtaining the mean value of the target hook load within the sliding window at different positions; performing linear fitting using the measured values ​​of the target drilling depth and the mean value of the target hook load to obtain a depth-hook load function; calculating the measured value of the target drilling depth corresponding to the peak point of the target hook load in the depth-hook load function; identifying the peak point of the target hook load through the curve between the target drilling depth and the target hook load; and determining the measured value of the target drilling depth corresponding to the peak point as the grouting depth.

Description

Technical Field

[0001] This invention relates to the field of oil and gas exploration and development technology, and in particular to a method and apparatus for identifying grouting depth. Background Technology

[0002] As oil and gas exploration and development enters complex areas such as deep and ultra-deep formations, offshore areas, and unconventional drilling, deep wells, directional wells, and horizontal wells have become the main means of increasing reserves and production. Risk monitoring and prevention during the drilling process of complex well structures have become major challenges in drilling engineering. Sticking risk is one of the main downhole risks faced in drilling engineering, potentially leading to stuck pipe, drill string falling into the well, and increased drilling costs. Real-time monitoring of sticking risk during drilling is essential for preventing downhole accidents.

[0003] Currently, the primary method for real-time monitoring of friction during drilling is the "broom chart," which dynamically identifies friction calibration points in the logging data, such as during lifting, lowering, and idling. These calibration points are then plotted on a standardized chart calculated based on the tubing string mechanical model. The location of the calibration point indicates the magnitude of the friction coefficient at the corresponding depth; a higher friction coefficient indicates greater axial resistance. With the development of digital and intelligent drilling technologies, the "broom chart" for friction analysis is gradually becoming the standard method for monitoring friction during drilling and casing installation.

[0004] However, due to the influence of the downhole check valve, the inside of the drill string or casing is hollowed out, requiring correction of the theoretical calculation values ​​of the friction analysis chart. The grouting depth needs to be calculated and determined by technicians, resulting in low work efficiency. Summary of the Invention

[0005] The purpose of this invention is to provide at least one method and apparatus for identifying grouting depth, which can at least solve the technical problem that grouting depth needs to be calculated by technicians, and at least improve work efficiency.

[0006] To address the aforementioned technical problems, at least one embodiment of this application provides a method for identifying grouting depth, comprising: acquiring measured values ​​of a target drilling depth and measured values ​​of a target hook load; wherein each measured value of the target drilling depth corresponds to a measured value of the target hook load; and the corresponding measured value of the target drilling depth and the measured value of the target hook load constitute a target sample;

[0007] Using the preset number of target samples as the length of the sliding window, the sliding window is slid along the direction of increasing / decreasing the measured value of the target drilling depth, and the average value of each measured value of the target hook load within the sliding window is calculated to obtain the average value of the target hook load within the sliding window at different positions.

[0008] A depth-hook load function is obtained by linearly fitting the measured values ​​of the target drilling depth and the mean value of the target hook load. The depth-hook load function is used to quantify the relationship between the target drilling depth and the target hook load.

[0009] The grouting depth is obtained by calculating the measured value of the target drilling depth corresponding to the peak point of the target hook load in the depth hook load function.

[0010] At least one embodiment of this application also provides a grouting depth identification device, comprising:

[0011] The target sample acquisition module is used to acquire each measured value of the target drilling depth and each measured value of the target hook load; wherein, each measured value of the target drilling depth corresponds to a measured value of the target hook load; the corresponding measured value of the target drilling depth and the measured value of the target hook load constitute a target sample;

[0012] The window sliding calculation module is used to slide the sliding window along the direction of increasing / decreasing the measured value of the target drilling depth with a preset number of target samples as the length of the sliding window, calculate the average value of each measured value of the target hook load within the sliding window, and obtain the average value of the target hook load within the sliding window at different positions.

[0013] The depth hook load relationship module is used to perform linear fitting using the measured values ​​of the target drilling depth and the mean value of the target hook load to obtain the depth hook load function. The depth hook load function is used to quantify the relationship between the target drilling depth and the target hook load.

[0014] The peak depth identification module is used to calculate the measured value of the target drilling depth corresponding to the peak point of the target hook load in the depth hook load function, and obtain the grouting depth.

[0015] At least one embodiment of this application also provides an electronic device, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described method for identifying grouting depth.

[0016] At least one embodiment of this application also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method for identifying grouting depth.

[0017] The grouting depth identification method provided in this application acquires measured values ​​of the target drilling depth and the target hook load. Each measured value of the target drilling depth corresponds to a measured value of the target hook load. The measured value of the target hook load reflects the weight borne by the drilling rig at the corresponding measured target drilling depth. The corresponding measured value of the target drilling depth and the measured value of the target hook load constitute a target sample, and there are multiple target samples. The length of the sliding window is set to a preset number of target samples. The preset number is used to determine the number of target samples to be processed in a sliding window. The sliding window slides along the direction of increasing / decreasing of the measured value of the target drilling depth. Each time the sliding window slides once, the target samples within the sliding window are processed, and the average value of each measured value of the target hook load within the sliding window is calculated to obtain the average value of the target hook load within the sliding window at different positions. A depth-load function is obtained by linearly fitting the measured values ​​of the target drilling depth and the mean value of the target hook load. This function quantifies the relationship between the target drilling depth and the target hook load, and can be used to plot a curve between them. The measured value of the target drilling depth corresponding to the peak point of the target hook load in the depth-load function is calculated. The peak point of the target hook load can be determined by calculating the maximum point of the depth-load function, which is the location where grouting occurs. Alternatively, the peak point of the target hook load can be identified by the curve between the target drilling depth and the target hook load, and the measured value of the target drilling depth corresponding to the peak point can be determined as the grouting depth. Therefore, there is no need for technicians to calibrate the grouting depth, improving work efficiency. Attached Figure Description

[0018] One or more embodiments are illustrated by way of example with reference to the accompanying drawings, and these illustrative descriptions do not constitute a limitation on the embodiments.

[0019] Figure 1 This is a flowchart of a method for identifying grouting depth provided in one embodiment of this application;

[0020] Figure 2 This is a flowchart of a method for grout identification and friction calibration during drilling, provided in one embodiment of this application;

[0021] Figure 3 This is a schematic diagram of a well grouting point identification provided in one embodiment of this application;

[0022] Figure 4 This is a schematic diagram of the principle of identifying a grouting point in a well, provided in one embodiment of this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the various embodiments of this application will be described in detail below with reference to the accompanying drawings. However, those skilled in the art will understand that many technical details have been provided in the various embodiments of this application to help readers better understand this application. However, the technical solutions claimed in this application can be implemented even without these technical details and various changes and modifications based on the following embodiments. The division of the various embodiments below is for the convenience of description and should not constitute any limitation on the specific implementation of this application. The various embodiments can be combined with and referenced by each other without contradiction.

[0024] To facilitate understanding of the embodiments of this application, relevant content regarding grouting identification and friction calibration will be introduced first.

[0025] As oil and gas exploration and development enters complex areas such as deep and ultra-deep formations, offshore areas, and unconventional drilling, deep wells, directional wells, and horizontal wells have become the main means of increasing reserves and production. Risk monitoring and prevention during the drilling process of complex well structures have become major challenges in drilling engineering. Sticking risk is one of the main downhole risks faced in drilling engineering, potentially leading to stuck pipe, drill string falling into the well, and increased drilling costs. Real-time monitoring of sticking risk during drilling is essential for preventing downhole accidents.

[0026] Currently, the main method for real-time monitoring of friction during drilling is the "broom chart," which dynamically identifies friction calibration points (lifting, lowering, and idling) in the logging data and plots these points on a standardized chart calculated based on the tubing string mechanical model. The location of the calibration point indicates the friction coefficient at the corresponding depth; a higher friction coefficient indicates greater axial resistance. With the development of digital and intelligent drilling technologies, the "broom chart" for friction analysis is gradually becoming the standard method for monitoring while drilling and casing. However, during the running-in or running-out of casing, due to the influence of the downhole check valve, the inside of the drill string or casing is emptied, requiring correction of the theoretical calculation values ​​on the friction analysis chart. The grouting depth needs to be manually entered and calibrated by technicians, resulting in low efficiency and large errors, severely restricting the automation level of friction analysis.

[0027] To address the technical problem of requiring manual input and calibration of grouting depth by technicians, this invention proposes a grouting depth identification method. The implementation details of the grouting depth identification method in this embodiment are described below. The following implementation details are provided for ease of understanding and are not essential for implementing this solution.

[0028] Example 1:

[0029] The grouting depth identification method of this embodiment can be applied to electronic devices with communication, computing, and data storage capabilities. Its specific process includes:

[0030] Step 110: Obtain the measured values ​​of the target drilling depth and the target hook load; wherein, each measured value of the target drilling depth corresponds to a measured value of the target hook load; the corresponding measured value of the target drilling depth and the measured value of the target hook load constitute a target sample.

[0031] Specifically, the target drilling depth is the depth of the drill bit, and each measured value of the target drilling depth corresponds to a measured value of the target hook load. The target hook load is the weight borne by the drilling rig, which refers to the load on the hook during drilling. When the drill bit moves to a certain depth, the measured values ​​of the target drilling depth and the target hook load at that time are collected as a target sample for evaluating that depth.

[0032] Step 120: Using the preset number of target samples as the length of the sliding window, slide the sliding window along the direction of increasing / decreasing the measured value of the target drilling depth, calculate the average value of each measured value of the target hook load within the sliding window, and obtain the average value of the target hook load within the sliding window at different positions.

[0033] Specifically, the preset quantity is the length of a sliding window, which represents the amount of data that needs to be processed in a single sliding window, for example, 20. Therefore, the sliding window needs to frame 20 measured values ​​at target drilling depths for calculation. To study the relationship between the target hook load and the target drilling depth, the measured values ​​of the target drilling depth are sorted in ascending order for each target sample. Then, the sliding window slides along the direction of increasing / decreasing of the measured values ​​of the target drilling depth, which can be done with a step size of 1. After each slide, the mean value of each measured value of the target hook load within the sliding window is calculated. This mean value can be updated to the first or last measured value of the target drilling depth within the sliding window, thus updating the target drilling depth and obtaining the mean value of the target hook load at different positions of the sliding window.

[0034] Step 130: Linear fitting is performed using the measured values ​​of the target drilling depth and the mean value of the target hook load to obtain the depth hook load function. The depth hook load function is used to quantify the relationship between the target drilling depth and the target hook load.

[0035] Specifically, in order to quantify the relationship between the target drilling depth and the target hook load, a linear fit is performed using the measured values ​​of the target drilling depth and the mean value of the target hook load to obtain the functional relationship between the target drilling depth and the target hook load, namely the depth hook load function.

[0036] Step 140: Calculate the measured value of the target drilling depth corresponding to the peak point of the target hook load in the depth hook load function to obtain the grouting depth.

[0037] In practical implementation, to identify the depth, the depth-hook function can be plotted as a curve, and the peak point in the curve can be detected and taken as the grouting depth. Alternatively, the depth-hook function can be differentiated, and the maximum point can be determined based on the derivative. The target drilling depth corresponding to the maximum point is the grouting depth.

[0038] The grouting depth identification method in this embodiment obtains the measured values ​​of the target drilling depth and the target hook load. Each measured value of the target drilling depth corresponds to a measured value of the target hook load. The measured value of the target hook load reflects the weight borne by the drilling rig at the corresponding target drilling depth. The corresponding measured value of the target drilling depth and the measured value of the target hook load constitute a target sample, and there are multiple target samples. The length of the sliding window is set to a preset number of target samples. The preset number is used to determine the number of target samples to be processed in a sliding window. The sliding window slides along the direction of increasing / decreasing of the measured value of the target drilling depth. Each time the sliding window slides once, the target samples within the sliding window are processed, and the average value of each measured value of the target hook load within the sliding window is calculated to obtain the average value of the target hook load within the sliding window at different positions. A depth-load function is obtained by linearly fitting the measured values ​​of the target drilling depth and the mean value of the target hook load. This function quantifies the relationship between the target drilling depth and the target hook load, and can be used to plot a curve between them. The measured value of the target drilling depth corresponding to the peak point of the target hook load in the depth-load function is calculated. The peak point of the target hook load can be determined by calculating the maximum point of the depth-load function, which is the location where grouting occurs. Alternatively, the peak point of the target hook load can be identified by the curve between the target drilling depth and the target hook load, and the measured value of the target drilling depth corresponding to the peak point can be determined as the grouting depth. Therefore, there is no need for technicians to calibrate the grouting depth, improving work efficiency.

[0039] In one embodiment, the step of obtaining the measured values ​​of the target drilling depth and the measured values ​​of the target hook load includes:

[0040] Measured values ​​of drill bit depth and drilling hook load were collected under different working conditions and different friction calibration points; wherein each measured value of drill bit depth corresponds to a measured value of drilling hook load.

[0041] By selecting the measured values ​​of the drill bit depth and the drill hook load that meet the preset working conditions and / or preset friction calibration points, the measured values ​​of the target drilling depth and the target hook load are obtained.

[0042] In this embodiment, the working conditions include drilling and casing installation, and the friction calibration points include lifting, lowering, and idling. During the drilling process, the downhole check valve has a significant impact, and the drill string or casing is in a hollowed-out state, resulting in a large deviation in the target hook load. To improve the analysis accuracy of the drilling process, the preset working condition is drilling, and the preset friction calibration point is lowering. The measured values ​​of the drill bit depth and the measured values ​​of the drilling hook load that meet the preset working condition and / or preset friction calibration point are selected as target samples. The grouting depth is analyzed based on the selected target samples.

[0043] In one embodiment, the step of sliding the sliding window along the direction of increasing / decreasing the measured value of the target drilling depth, calculating the average value of each measured value of the target hook load within the sliding window, and obtaining the average value of the target hook load within the sliding window at different positions includes:

[0044] Data is filled using the measured values ​​of the target drilling depth to construct equally spaced measured values ​​of the target drilling depth;

[0045] By using linear interpolation of the measured values ​​of the target hook load, the measured values ​​of the target hook load under each equally spaced measured value at the target drilling depth are calculated.

[0046] The sliding window is slid along the direction of increasing / decreasing the measured values ​​distributed at equal intervals at the target drilling depth. The average value of the measured value of the target hook load under each measured value distributed at equal intervals at the target drilling depth is calculated, and the average value of the target hook load in the sliding window at different positions is obtained.

[0047] In this embodiment, during data analysis, the measured values ​​of the target drilling depth at unequal intervals can introduce noise or interference. By determining the intervals and starting points of the equal intervals, the required measured values ​​for the unequal distribution of the target drilling depth are determined. When the required measured value exists in the target sample, the mean of the measured value of the target drilling depth and the corresponding measured value of the target hook load is retained. When the required measured value does not exist in the target sample, the nearest existing measured values ​​on both sides of the target hook load corresponding to the required measured value are selected for linear interpolation to calculate the measured value of the intermediate target hook load at the required measured value for the target drilling depth, thereby improving the accuracy and continuity of the data. Maintaining consistent intervals between data points contributes to the consistency of data analysis. When analyzing indicators such as hook load data that vary with depth, unequally spaced data points are easier to compare and analyze.

[0048] In one embodiment, before the step of calculating the average of each measured value of the target hook load within the sliding window to obtain the average value of the target hook load within the sliding window at different positions, the method includes:

[0049] Outlier detection is performed on the measured value of the target hook load to obtain the measured value of the target hook load after outlier removal;

[0050] The step of calculating the average of the measured values ​​of the target hook load within the sliding window, and obtaining the average value of the target hook load within the sliding window at different positions, includes:

[0051] Calculate the mean value of each measured value of the target hook load within the sliding window after removing outliers, and obtain the mean value of the target hook load within the sliding window at different positions.

[0052] In this embodiment, outlier detection is used to detect outliers that deviate from the data center. This can be achieved using methods such as the standard deviation method or the interquartile range method. Before calculating the mean of the target hook load, outlier detection is performed on the measured value of the target hook load. For example, the standard deviation method is used to detect outliers that are outside the mean plus three standard deviations and outside the mean minus three standard deviations. These outliers are then removed, resulting in the measured value of the target hook load after outlier removal. Finally, the mean of the target hook load after outlier removal is calculated using a sliding window.

[0053] In one embodiment, the step of detecting outliers in the measured value of the target hook load and obtaining the measured value of the target hook load after removing outliers includes:

[0054] Using a preset number of target samples as the length of the sliding window, the sliding window is slid along the direction of increasing / decreasing the measured value of the target drilling depth, and outlier values ​​of the measured value of the target hook load within the sliding window are detected to obtain the hook load outlier value within the sliding window;

[0055] The outlier values ​​of the target hook load are filtered out from the measured values ​​of the target hook load within the sliding window to obtain the measured values ​​of the target hook load after filtering out the outliers.

[0056] In this embodiment, to improve the accuracy of calculating the mean target hook load, outlier detection is performed within a sliding window. Each time the sliding window moves to the next position according to a pre-defined step size, a preset number of target samples within the sliding window are detected for outliers. These outliers are then deleted, and the mean target hook load within the sliding window is calculated again. Since the measured values ​​of the target hook load defined by the sliding window at different positions are different, outliers at different depths can be flexibly detected, improving the accuracy of subsequent calculations of the mean target hook load.

[0057] In one embodiment, the method further includes:

[0058] Data cleaning is performed on the target hook load and the target drilling depth to obtain the cleaned measured values ​​of the target hook load and the cleaned measured values ​​of the target drilling depth.

[0059] The step of sliding the sliding window along the direction of increasing / decreasing the measured value of the target drilling depth, using a preset number of target samples as the length of the sliding window, calculating the average value of each measured value of the target hook load within the sliding window, and obtaining the average value of the target hook load within the sliding window at different positions, includes:

[0060] Using the preset number of target samples as the length of the sliding window, the sliding window is slid along the direction of increasing / decreasing the measured value of the target drilling depth after cleaning. The average value of each measured value of the target hook load after cleaning within the sliding window is calculated to obtain the average value of the target hook load within the sliding window at different positions.

[0061] In this embodiment, before calculating the mean value of the target hook load, the target samples are first cleaned. Target samples with missing valid values ​​in the target hook load and target drilling depth are detected and deleted, resulting in the cleaned measured values ​​of the target hook load and target drilling depth. Based on this, an arithmetic sequence is constructed from the cleaned measured values ​​of the target hook load using a sliding window. Then, the mean value of each measured value of the cleaned target hook load corresponding to the arithmetic distribution of the target hook load is calculated to obtain the mean value of the target hook load.

[0062] In one embodiment, the method further includes:

[0063] Based on the grouting depth and the average value of the target hook load at the grouting depth, mark the obstruction point on the friction analysis chart and set the early warning information at the obstruction point.

[0064] In this embodiment, to facilitate the identification of locations where grouting is likely to occur by workers, the grouting depth is searched in the friction analysis diagram, and the average value of the standard target hook load at that location is used to configure grouting warning information.

[0065] In one embodiment, measured values ​​of drill bit depth and drilling hook load are collected under different working conditions and different friction calibration points; wherein, each measured value of drill bit depth corresponds to a measured value of drilling hook load.

[0066] Data cleaning is performed on the measured values ​​of drill bit depth and drill hook load to obtain the cleaned measured values ​​of drill hook load and drill bit depth.

[0067] By selecting the measured values ​​of the drill bit depth and the drill hook load that meet the preset working conditions and / or preset friction calibration points, the measured values ​​of the target drilling depth and the measured values ​​of the target hook load are obtained.

[0068] Data is filled using the measured values ​​of the target drilling depth to construct equally spaced measured values ​​of the target drilling depth;

[0069] By using linear interpolation of the measured values ​​of the target hook load, the measured values ​​of the target hook load under each equally spaced measured value at the target drilling depth are calculated.

[0070] Using a preset number of target samples as the length of the sliding window, the sliding window is slid along the direction of increasing / decreasing the measured value of the target drilling depth, and outlier values ​​of the measured value of the target hook load within the sliding window are detected to obtain the hook load outlier value within the sliding window;

[0071] The outlier values ​​of the target hook load are screened out from the measured values ​​of the target hook load within the sliding window to obtain the measured values ​​of the target hook load after removing the outliers.

[0072] The sliding window is slid along the direction of increasing / decreasing the measured values ​​distributed at equal intervals at the target drilling depth. The average value of the target hook load at each measured value distributed at equal intervals at the target drilling depth is calculated, thus obtaining the average value of the target hook load within the sliding window at different positions.

[0073] In this embodiment, measured values ​​of drill bit depth and drilling hook load under different working conditions and different friction calibration points are collected. The collected measured values ​​of drill bit depth and drilling hook load are cleaned to obtain cleaned measured values ​​of drilling hook load and drill bit depth. From the cleaned measured values ​​of drilling hook load and drill bit depth, measured values ​​of drill bit depth and drilling hook load that meet preset working conditions and / or preset friction calibration points are selected to obtain measured values ​​of the target drilling depth and the target hook load. Equally spaced measured values ​​of the target drilling depth are constructed. Then, linear interpolation is performed on the measured values ​​of the target hook load to calculate the measured value of the target hook load under the equally spaced measured values ​​of the target drilling depth. Then, outlier detection is performed on the measured values ​​of the target hook load under the equally spaced distribution at the target drilling depth. Outlier values ​​of the hook load are screened out from the measured values ​​of the target hook load within the sliding window to obtain the measured values ​​of the target hook load after outlier removal. The sliding window is then slid along the direction of increasing / decreasing the measured values ​​of the equally spaced distribution at the target drilling depth, and the mean value of the measured values ​​of the target hook load under each measured value of the equally spaced distribution at the target drilling depth is calculated.

[0074] Example 2:

[0075] The grouting depth identification method of this embodiment can be applied to electronic devices with communication, computing, and data storage capabilities. The specific process is as follows: Figure 2 As shown, it includes:

[0076] (1) Collect data from drilled sections and perform data preprocessing.

[0077] ①Acquire logging-while-drilling data, including depth and corresponding hook load data;

[0078] ② Select data on the drill bit's descent status during the drilling process;

[0079] ③ Reconstruct the arithmetic sequence of depth data.

[0080] In this embodiment, ① logging data while drilling is acquired, including the depth of the drill bit and the corresponding hook load data; the hook load data is the weight borne by the drilling rig, which refers to the hook load during drilling;

[0081] ② Select data on the drill bit's descent status during the drilling process;

[0082] As shown in Table 1, well logging information for the drilled section was obtained, including depth, hook load, operating conditions, and friction calibration points. The data was preprocessed, including data cleaning: missing values ​​were removed from the original dataset, and extracted using the following criteria: drilling progress and bit lowering data were extracted for operating condition classification.

[0083] Drilling conditions during running-in: Condition = Running-in;

[0084] Drill bit lowering process: Friction calibration point = lowering; Select "drilling" for working condition, and select "lowering" for friction calibration point.

[0085] Table 1. Partial dataset of a well used to identify grouting points during drilling.

[0086]

[0087] Obtain the drill bit depth and hook load data under continuous drill bit lowering conditions during a certain drilling run of the well.

[0088] In drilling data, depth is typically a continuous variable, but the actual collected data may not be uniformly distributed. By converting depth data into an arithmetic progression sequence, interpolation methods can be used to fill in missing data points, improving data accuracy and continuity. Furthermore, maintaining consistent spacing between data points contributes to the consistency of data analysis. When analyzing depth-dependent parameters such as hook load data, equally spaced data points are easier to compare and analyze. During data analysis, unevenly spaced data points can introduce noise or interference; converting the data into an arithmetic progression sequence reduces this impact, making the analysis results more accurate and reliable.

[0089] In this embodiment, continuous functions are interpolated onto discrete data. Based on existing well data, an arithmetic depth sequence with a depth interval of 1 is established, and new data points are estimated using interpolation. Reconstructing the arithmetic depth sequence is crucial because depth is typically a continuous variable in drilling data, but the actual collected data may not be uniformly distributed. By converting the depth data into an arithmetic depth sequence, interpolation methods can be used to fill in missing data points, improving data accuracy and continuity. Maintaining consistent intervals between data points also contributes to the consistency of data analysis. When analyzing indicators such as hook load data that vary with depth, equally spaced data points are easier to compare and analyze. During data analysis, unequally spaced data points may introduce noise or interference; converting the data into an arithmetic depth sequence reduces this impact, making the analysis results more accurate and reliable.

[0090] The depth arithmetic sequence and interpolated supplementary data points are processed in two steps. First, new arithmetic depth data is generated. Then, new hook load data is generated using interpolation, forming a new dataset. Only two columns of data are used for subsequent sliding window calculations. The missing hook load data in the arithmetic depth is estimated using linear interpolation. The depth data is ensured to be an arithmetic sequence for the next sliding window calculation, guaranteeing that the depth length of each window is consistent.

[0091] (2) Outlier detection

[0092] ① Create a sliding window with a data length of 20;

[0093] ② Use outlier detection to filter out data points in each window that are not within 3 standard deviations of the mean shift;

[0094] (3) Identify grouting depth

[0095] ① Calculate the average value of the hook load data in each window and form an average curve by sliding; using the number of data points as the scale, each window contains 20 data points, and the calculation is only performed on the hook load data, taking the average value of the hook load data in each window;

[0096] ②The grouting depth is determined by finding the peak value of the curve.

[0097] See Figure 3 and Figure 4 , Figure 4The horizontal axis represents depth in meters, and the vertical axis represents hook load data in kN. The upper curve shows the hook load increasing with depth, and the lower curve shows the average value curve for each window calculated by the sliding window. Black dots represent identified grouting points. During drilling, the hook load data increases uniformly with the drill bit depth. During grouting, the hook load data increases in a stepwise manner, showing a peak value on the average curve. The identified peak value can be automatically located as the grouting depth. By calculating the average hook load data within each window, an average curve is formed. Figure 3 The black line segment in the graph represents the hook load data visualization, which also serves as a friction analysis chart. On this curve, look for prominent peaks; the appearance of these peaks typically indicates the occurrence of grouting operations. By automatically identifying these peaks, we can accurately pinpoint the grouting depth, which is crucial for monitoring key events during drilling.

[0098] (4) Draw a friction analysis chart and identify the risk of encountering resistance.

[0099] ①Based on the identified grouting depth and hook load variation information, further functions can be developed to draw friction analysis charts.

[0100] ② Utilize the friction analysis chart to analyze potential resistance during drilling, thereby identifying potential obstruction risks. Mark possible obstruction points or areas on the friction analysis chart. These points typically correspond to locations with high friction, requiring extra attention or intervention to avoid serious drilling problems. Automatic calibration will be reflected at the location of image breaks. Figure 4 Add warning markers and information prompts to the depth position of the mid-peak value, marking the points of obstruction encountered. The algorithm can automatically detect points in the data where friction increases and provide prompts and warnings.

[0101] In this embodiment, the method and system for automatically identifying grouting depth and calibrating friction during drilling automatically identify grouting depth based on real-time logging data and automatically calibrate the friction analysis "broom diagram" chart, realizing automatic friction monitoring during drilling and casing running, and effectively improving the automation and intelligence level of drilling risk monitoring.

[0102] In this embodiment, to address the difficulties in calibrating drilling friction during oil drilling and the low degree of automation, this invention proposes an automatic grouting identification method during drilling. First, the grouting depth is automatically identified based on real-time logging data. Then, a friction analysis chart is drawn by combining the grouting depth with a three-dimensional tubing mechanical model. Finally, the analysis chart is used to perform real-time analysis of the risk of obstruction during drilling or casing installation.

[0103] In this embodiment, to address the difficulties in calibrating drill bit friction and the low level of automation during drilling operations in the oil drilling field, this invention proposes an automatic grouting identification method during drilling. This method first utilizes real-time logging data and an algorithm to automatically identify the grouting depth using the average peak value of the hook load within a sliding window, providing accurate data support for subsequent work. Secondly, by combining the grouting depth and a three-dimensional tubing mechanical model, a detailed friction analysis chart is generated, effectively assessing the friction status during drilling. Finally, using the analysis chart, the goal of real-time analysis of potential resistance risks during drilling or casing installation is achieved, providing strong support for risk management and decision-making. This innovative method improves the automation level of drilling operations and provides more accurate and reliable data and information support for subsequent drilling operations.

[0104] In this embodiment, automatic grouting point identification offers multiple advantages over manual identification, particularly in terms of efficiency and accuracy in drilling engineering: the automatic identification system can rapidly process large amounts of data, completing the analysis and peak identification of data within each window in a short time, greatly improving work efficiency while eliminating the influence of subjective human factors, thereby improving the accuracy and consistency of identification. The automatic identification system can operate continuously, ensuring data monitoring and identification 24 / 7. In actual well applications, automatic grouting point identification can more accurately pinpoint the grouting depth. Compared to the potential errors of manual identification, algorithmic automatic identification can reduce the positioning error to 0.15 meters, effectively improving the automation and intelligence level of drilling risk monitoring.

[0105] In this embodiment, a method and system for automatically identifying grouting depth and calibrating friction during the drilling process based on time series analysis technology are described. By using real-time logging data, the grouting depth is automatically identified, and the "broom diagram" in friction analysis is automatically calibrated, enabling automatic friction monitoring during drilling and casing running. This method significantly reduces errors in grouting point identification and improves the automation and intelligence level of drilling risk monitoring, providing more efficient technical support for risk control during the drilling process.

[0106] Example 3:

[0107] Another embodiment of this application relates to a grouting depth identification device. The implementation details of the grouting depth identification device of this embodiment are described in detail below. The following implementation details are provided for ease of understanding and are not necessary for implementing this solution. The schematic diagram of the grouting depth identification device of this embodiment includes a target sample acquisition module, a window sliding calculation module, a depth hook relationship module, and a peak depth identification module.

[0108] The target sample acquisition module is used to acquire each measured value of the target drilling depth and each measured value of the target hook load; wherein, each measured value of the target drilling depth corresponds to a measured value of the target hook load; the corresponding measured value of the target drilling depth and the measured value of the target hook load constitute a target sample;

[0109] The window sliding calculation module is used to slide the sliding window along the direction of increasing / decreasing the measured value of the target drilling depth with a preset number of target samples as the length of the sliding window, calculate the average value of each measured value of the target hook load within the sliding window, and obtain the average value of the target hook load within the sliding window at different positions.

[0110] The depth hook load relationship module is used to perform linear fitting using the measured values ​​of the target drilling depth and the mean value of the target hook load to obtain the depth hook load function. The depth hook load function is used to quantify the relationship between the target drilling depth and the target hook load.

[0111] The peak depth identification module is used to calculate the measured value of the target drilling depth corresponding to the peak point of the target hook load in the depth hook load function, and obtain the grouting depth.

[0112] It is worth mentioning that all modules involved in this embodiment are logical modules. In practical applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. Furthermore, to highlight the innovative aspects of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed in this application; however, this does not mean that other units are absent in this embodiment.

[0113] Example 4:

[0114] Another embodiment of this application relates to an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the grouting depth identification method in the above embodiments.

[0115] The memory and processor are connected via a bus, which can include any number of interconnecting buses and bridges, connecting various circuits of one or more processors and memories. The bus can also connect various other circuits, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and will not be described further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be a single element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices over a transmission medium. Data processed by the processor is transmitted over the wireless medium via an antenna, which further receives data and transmits it to the processor.

[0116] The processor manages the bus and general processing, and also provides various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory is used to store data used by the processor during operation.

[0117] Example 5:

[0118] Another embodiment of this application relates to a computer-readable storage medium storing a computer program. When executed by a processor, the computer program implements the method embodiments described above.

[0119] That is, those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0120] Those skilled in the art will understand that the above embodiments are specific embodiments for implementing this application, and in practical applications, various changes can be made to them in form and detail without departing from the spirit and scope of this application.

Claims

1. A method for identifying grouting depth, characterized in that, include: Obtain each measured value of the target drilling depth and each measured value of the target hook load; wherein, each measured value of the target drilling depth corresponds to a measured value of the target hook load; the corresponding measured value of the target drilling depth and the measured value of the target hook load constitute a target sample; Using the preset number of target samples as the length of the sliding window, the sliding window is slid along the direction of increasing / decreasing the measured value of the target drilling depth, and the average value of each measured value of the target hook load within the sliding window is calculated to obtain the average value of the target hook load within the sliding window at different positions. A depth-hook load function is obtained by linearly fitting the measured values ​​of the target drilling depth and the mean value of the target hook load. The depth-hook load function is used to quantify the relationship between the target drilling depth and the target hook load. The grouting depth is obtained by calculating the measured value of the target drilling depth corresponding to the peak point of the target hook load in the depth hook load function.

2. The identification method according to claim 1, characterized in that, The steps for obtaining the measured values ​​of the target drilling depth and the measured values ​​of the target hook load include: Collect measured values ​​of drill bit depth and drilling hook load under different working conditions and different friction calibration points; wherein, each measured value of drill bit depth corresponds to a measured value of drilling hook load; By selecting the measured values ​​of the drill bit depth and the drill hook load that meet the preset working conditions and / or preset friction calibration points, the measured values ​​of the target drilling depth and the target hook load are obtained.

3. The identification method according to claim 1, characterized in that, The step of sliding the sliding window along the direction of increasing / decreasing the measured value of the target drilling depth, calculating the average value of each measured value of the target hook load within the sliding window, and obtaining the average value of the target hook load within the sliding window at different positions includes: Data is filled using the measured values ​​of the target drilling depth to construct equally spaced measured values ​​of the target drilling depth; By using linear interpolation of the measured values ​​of the target hook load, the measured values ​​of the target hook load under each equally spaced measured value at the target drilling depth are calculated. The sliding window is slid along the direction of increasing / decreasing the measured values ​​distributed at equal intervals at the target drilling depth. The average value of the measured value of the target hook load under each measured value distributed at equal intervals at the target drilling depth is calculated, and the average value of the target hook load in the sliding window at different positions is obtained.

4. The identification method according to claim 1, characterized in that, Before the step of calculating the average of each measured value of the target hook load within the sliding window to obtain the average value of the target hook load within the sliding window at different positions, the following steps are included: Outlier detection is performed on the measured value of the target hook load to obtain the measured value of the target hook load after outlier removal; The step of calculating the average of the measured values ​​of the target hook load within the sliding window, and obtaining the average value of the target hook load within the sliding window at different positions, includes: Calculate the mean value of each measured value of the target hook load within the sliding window after removing outliers, and obtain the mean value of the target hook load within the sliding window at different positions.

5. The identification method according to claim 4, characterized in that, The step of detecting outliers in the measured value of the target hook load and obtaining the measured value of the target hook load after removing outliers includes: Using a preset number of target samples as the length of the sliding window, the sliding window is slid along the direction of increasing / decreasing the measured value of the target drilling depth, and outlier values ​​of the measured value of the target hook load within the sliding window are detected to obtain the hook load outlier value within the sliding window; The outlier values ​​of the target hook load are filtered out from the measured values ​​of the target hook load within the sliding window to obtain the measured values ​​of the target hook load after filtering out the outliers.

6. The identification method according to any one of claims 1 to 5, characterized in that, The method further includes: Based on the grouting depth and the average value of the target hook load at the grouting depth, mark the obstruction point on the friction analysis chart and set the early warning information at the obstruction point.

7. The identification method according to any one of claims 1 to 5, characterized in that, The method further includes: Data cleaning is performed on the target hook load and the target drilling depth to obtain the cleaned measured values ​​of the target hook load and the cleaned measured values ​​of the target drilling depth. The step of sliding the sliding window along the direction of increasing / decreasing the measured value of the target drilling depth, using a preset number of target samples as the length of the sliding window, calculating the average value of each measured value of the target hook load within the sliding window, and obtaining the average value of the target hook load within the sliding window at different positions, includes: Using the preset number of target samples as the length of the sliding window, the sliding window is slid along the direction of increasing / decreasing the measured value of the target drilling depth after cleaning. The average value of each measured value of the target hook load after cleaning within the sliding window is calculated to obtain the average value of the target hook load within the sliding window at different positions.

8. A device for identifying grouting depth, characterized in that, include: The target sample acquisition module is used to acquire each measured value of the target drilling depth and each measured value of the target hook load; wherein, each measured value of the target drilling depth corresponds to a measured value of the target hook load; the corresponding measured value of the target drilling depth and the measured value of the target hook load constitute a target sample; The window sliding calculation module is used to slide the sliding window along the direction of increasing / decreasing the measured value of the target drilling depth with a preset number of target samples as the length of the sliding window, calculate the average value of each measured value of the target hook load within the sliding window, and obtain the average value of the target hook load within the sliding window at different positions. The depth hook load relationship module is used to perform linear fitting using the measured values ​​of the target drilling depth and the mean value of the target hook load to obtain the depth hook load function. The depth hook load function is used to quantify the relationship between the target drilling depth and the target hook load. The peak depth identification module is used to calculate the measured value of the target drilling depth corresponding to the peak point of the target hook load in the depth hook load function, and obtain the grouting depth.

9. An electronic device, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the grouting depth identification method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for identifying the grouting depth as described in any one of claims 1 to 7.

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