Semiconductor chip device for well drilling and workover internet-of-things control

Abstract

The invention relates to the technical field of internet-of-things control, in particular to a semiconductor chip device for internet-of-things control of well drilling and workover, which comprises an impact signal analysis module, a hysteresis characteristic calculation module, a drilling ratio calculation module, a state deviation judgment module and a drilling state identification module. According to the method, by comparing the pressure gradient fluctuation ranges of the previous and later moments, accurately extracting the shock wave front edge position, analyzing the time sequence lag characteristics of the previous and later moments and combining the peak pressure comparison value, separation of a main impact component and a reflection component is more accurate, and the ratio of torque data to axial load data is calculated; the dynamic characteristics of the load in the drilling process can be effectively revealed, the maximum offset amplitude is calculated and combined with the time change trend, stability changes can be recognized, misjudgment is reduced, the state classification standard is set according to the change interval of the ratio offset, comprehensive judgment is carried out in combination with the axial load change condition, fine recognition of the drilling state can be enhanced, and the drilling efficiency is improved. And the potential anomaly sensing capability in the drilling process is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things control, and in particular to a semiconductor chip device for Internet of Things control of well drilling and workover. Background Art

[0002] The field of IoT control technology encompasses automated control and management based on wireless communications, sensor technology, embedded devices, and cloud computing. Its core focus is on leveraging communication networks to interconnect devices, with data collection, remote monitoring, and intelligent control as key objectives. IoT control devices typically consist of a terminal device, a communication module, a data processing unit, and a control actuator. The terminal device collects environmental or device status data, the communication module transmits data, the data processing unit analyzes and makes decisions, and the control actuator executes specific actions. These devices are widely used in a variety of fields, including industrial automation, intelligent manufacturing, smart cities, and energy management, enhancing device intelligence and operational efficiency through information exchange and remote control.

[0003] The semiconductor chip device for IoT control in drilling and well repair refers to a specialized semiconductor chip used for data transmission, environmental monitoring, and remote control during drilling and well repair operations. This chip device addresses technical requirements such as downhole operating condition data collection, drill tool status monitoring, and control signal transmission. It utilizes an integrated multi-channel data acquisition circuit to acquire key parameters such as pressure, temperature, and vibration. This is filtered and decoded using an embedded data processing unit, and remotely transmitted using low-power wireless communication circuitry. Furthermore, the device employs an adaptive power management unit to optimize energy consumption by dynamically adjusting power supply strategies, thereby meeting the requirements for long-term stable operation in complex downhole environments.

[0004] In existing IoT-connected control processes for drilling and well maintenance, the identification of pressure fluctuations during impact signal analysis relies on a fixed threshold, making it difficult to adapt to pressure variations in diverse media environments. This results in a lag in the separation of impact signals, impacting the accuracy of identifying the main impact component and the reflected component. Regarding drilling status monitoring, existing solutions typically rely on a single physical quantity for judgment, failing to fully incorporate the temporal dynamics of multiple parameters and failing to fully capture the complexity of drilling conditions. Single-parameter identification methods are susceptible to localized anomalies, potentially leading to misjudgment of drilling status. For example, relying solely on torque fluctuations can misinterpret short-term disturbances as drill bit anomalies while ignoring the changing trend of axial load, making it impossible to accurately distinguish between drill tool obstruction and normal resistance fluctuations. Furthermore, existing technologies lack the ability to accurately calculate the trend of contrast value deviations, resulting in insensitive identification of drilling status changes. This makes it difficult to promptly detect stability deviations or sudden changes in rock formations, leading to a lag in the detection of abnormal conditions, impacting the safety and efficiency of drilling tool operations. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a semiconductor chip device for IoT control of well drilling and workover.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A semiconductor chip device for IoT control of well drilling and workover comprises: The shock signal analysis module obtains the time series data of the shock wave signal, calculates the pressure gradient change rate at adjacent moments, compares the fluctuation range of the previous and next moments, calculates the time interval between the shock wave front position and the adjacent shock signal feature points, and obtains the shock wave front lag data; The hysteresis characteristic calculation module calculates the instantaneous phase jump point of the shock signal based on the shock wave leading edge hysteresis data, analyzes the front and rear timing hysteresis characteristics of the shock signal, calculates the peak pressure contrast value and separates the main shock component and the reflected component of the shock signal to obtain the shock wave hysteresis characteristic data; The drilling ratio calculation module calculates the ratio of torque to axial load at adjacent data points based on the shock wave hysteresis characteristic data, analyzes the torque fluctuation range, calculates the stability of the axial load, and plots a ratio curve that changes with time to obtain drilling torque-axial load ratio sequence data; The state offset determination module sets a ratio reference value based on the drilling torque-axial load ratio sequence data, calculates the maximum offset amplitude of the ratio sequence relative to the ratio reference value, analyzes the time variation trend of the ratio offset, and obtains the drilling ratio offset trend analysis result.

[0007] As a further solution of the present invention, the shock wave front lag data includes the extreme point of the pressure gradient change rate, the time coordinate of the shock wave front position, and the time interval of adjacent shock signal characteristic points; the shock wave lag characteristic data includes the instantaneous phase jump point, the peak pressure comparison value, the main shock component separation threshold, and the reflection component separation threshold; the drilling torque-axial load ratio sequence data includes the torque fluctuation range, the axial load stability parameter, and the ratio time change curve; the drilling ratio offset trend analysis results include the ratio baseline value, the maximum offset amplitude, and the offset time change trend analysis results.

[0008] As a further solution of the present invention, the impact signal analysis module includes: The pressure gradient calculation submodule collects the time series data of the shock wave signal, calculates the pressure change value at adjacent moments, performs normalization processing based on the time difference, calculates the pressure gradient change rate, stores all time points, and obtains the pressure gradient change rate sequence data; The local extreme value screening submodule screens local maximum points based on the pressure gradient change rate sequence data by comparing the pressure gradient change rates at adjacent moments, and eliminates data points whose difference between the previous and next points is less than the extreme value threshold to obtain a set of valid local extreme value points; The shock wave front edge positioning submodule analyzes the pressure fluctuation range before and after the effective local extreme point set and calculates the change trend of the local gradient peak point using the formula: ; Calculate shock wave front position data , the time interval between the shock wave front position and the adjacent shock signal characteristic points is counted to obtain the shock wave front lag data, where, Represents the pressure gradient value of the i-th effective local extreme point, Represents the weight value of the corresponding extreme point, Represents the average gradient value of the effective local extreme point, Represents the number of extreme points.

[0009] As a further solution of the present invention, the hysteresis characteristic calculation module includes: The instantaneous phase jump calculation submodule calculates the time series of the shock signal based on the shock wave leading edge lag data, extracts the instantaneous phase value of the time series signal, determines the jump point according to the phase continuous change characteristics, calculates the phase change rate, and uses the formula: ; Calculating phase jump strength , and screen the significant jump points to generate instantaneous phase jump feature data, where represents the instantaneous phase change rate, represents the peak pressure, represents the reference pressure, Representative The phase value at a moment, represents the time interval, Represents the number of moments; The hysteresis timing feature analysis submodule extracts the hysteresis time before and after the jump point based on the instantaneous phase jump feature data, calculates the hysteresis rate between the before and after time series, and performs statistical analysis on the hysteresis features to generate a set of impulse signal hysteresis features; The main shock component separation submodule sets the initial separation threshold according to the maximum pressure value based on the shock signal hysteresis feature set, performs dynamic adjustment, calculates the peak pressure contrast value, and uses a dual-threshold mechanism to separate the main shock component and the reflected component to generate shock wave hysteresis feature data.

[0010] As a further solution of the present invention, the drilling ratio calculation module includes: The data acquisition submodule collects torque data and axial load data during the drilling process based on the shock wave hysteresis characteristic data, extracts torque data points and axial load data points at adjacent moments, calculates the time difference between adjacent data points, and generates torque-axial load time series data; The ratio calculation submodule is based on the torque-axial load time series data and uses the formula: ; Calculate the torque-axial load ratio at each moment , and obtain the torque-axial load ratio series data, where Representative Torque data at each moment, Representative Axial load data at a certain moment, represents the mean value of torque data, Represents the absolute deviation of torque and axial load, represents the square root correction factor of the axial load, represents the total number of data points; The ratio analysis submodule analyzes the fluctuation range of the ratio based on the torque-axial load ratio sequence data, extracts the periodic change characteristics, calculates the stability of the axial load, analyzes whether there are abnormal sudden increases or decreases, draws a ratio curve that changes with time, and obtains the torque-axial load ratio change trend analysis results.

[0011] As a further solution of the present invention, the state deviation determination module includes: The ratio reference calculation submodule extracts the ratio sequence data under a stable operation state based on the drilling torque-axial load ratio sequence data, calculates the mean of the ratio sequence and outputs it as a ratio reference value; The offset amplitude calculation submodule calculates the deviation between the ratio at each moment in the ratio sequence and the ratio reference value based on the ratio reference value, obtains the absolute value of the deviation, and uses the formula: ; Calculate the maximum deviation of the ratio , establish the ratio maximum offset amplitude data, where, represents the i-th ratio in the ratio sequence, represents the ratio benchmark value; The ratio offset trend analysis submodule analyzes the change of the maximum ratio offset amplitude in the time series based on the ratio maximum offset amplitude data, calculates the change rate of the maximum ratio offset amplitude, determines the ratio offset change trend, and obtains the ratio offset trend analysis results.

[0012] As a further solution of the present invention, the device further includes a drilling status identification module; The drilling status identification module sets a status classification standard based on the variation range of the ratio offset according to the analysis results of the drilling ratio offset trend, determines whether the ratio offset is within a stable range, performs a comprehensive classification judgment based on the variation of the axial load, analyzes the short-term sudden increase of the ratio offset, and screens the offset status according to the variation trend of the drilling rate to obtain the drilling status classification result; The drilling status classification results specifically refer to ratio deviation stable state records, ratio deviation abnormal state records, ratio sudden increase state records, axial load sudden change state records, and drilling rate deviation state records.

[0013] As a further solution of the present invention, the drilling status identification module includes: The ratio offset calculation submodule obtains the drilling ratio offset based on the drilling ratio offset trend analysis result, calculates the ratio offset change interval, and sets the state classification standard according to the change interval, using the formula: ; Calculate the ratio offset change , and judge whether it is in a stable range, and obtain the trend characteristic data of the ratio offset, where Representative Drilling ratio, represents the mean of the ratio offset, represents the total number of data points; The axial load analysis submodule analyzes the axial load change based on the ratio offset trend characteristic data and the axial load data, calculates the load fluctuation range, determines whether it exceeds the threshold, and screens abnormal conditions based on the ratio offset change to obtain the axial load change classification result; The drilling rate screening submodule obtains drilling rate data based on the axial load change classification result, analyzes the drilling rate change trend, screens the offset state, comprehensively judges the drilling state, and obtains the drilling state classification result.

[0014] Compared with the prior art, the advantages and positive effects of the present invention are: The present invention acquires time series data of shock wave signals, calculates the pressure gradient change rate at adjacent moments, selects local maxima, and compares the fluctuation ranges of preceding and following moments. This allows accurate extraction of the shock wave front position, reducing the impact of interference signals on data analysis. The instantaneous phase jump point of the shock signal is calculated, and the time series hysteresis characteristics are analyzed. Combined with the peak pressure contrast value, the separation of the main shock component and the reflected component is more accurate, avoiding errors caused by setting a fixed threshold. Torque data and axial load data during drilling are collected, their ratio is calculated, and the time series variation trend of the ratio is combined to effectively reveal the dynamic characteristics of the load during drilling, making drilling condition monitoring more hierarchical. A ratio baseline value is set, and the maximum deviation amplitude of the ratio sequence relative to the baseline value is calculated. Combined with the time variation trend, stability changes can be identified, reducing misjudgments caused by short-term fluctuations. State classification criteria are set based on the variation range of the ratio deviation, and a comprehensive judgment is made based on the axial load variation. This enhances the refined identification of drilling conditions. By combining the drilling rate variation trend to select the deviation state, the identification of conditions such as drill bit wear and rock formation mutation is more clear, and the ability to perceive potential anomalies during drilling is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 is a flow chart of the device of the present invention; Figure 2 This is a flow chart of the impact signal analysis module of the present invention; Figure 3 This is a flow chart of the hysteresis characteristic calculation module of the present invention; Figure 4 This is a flow chart of the drilling ratio calculation module of the present invention; Figure 5 This is a flow chart of the state deviation determination module of the present invention; Figure 6 This is a flow chart of the drilling status identification module of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0018] See also Figure 1 A semiconductor chip device for IoT control of well drilling and workover includes: The shock signal analysis module obtains the time series data of the shock wave signal, calculates the pressure gradient change rate at adjacent moments, selects the local maximum and compares the fluctuation range of the previous and next moments, extracts the shock wave front position, calculates the time interval between the shock wave front position and the adjacent shock signal feature points, and obtains the shock wave front lag data; The hysteresis characteristic calculation module calculates the instantaneous phase jump point of the shock signal based on the shock wave leading edge hysteresis data, analyzes the front and back timing hysteresis characteristics of the shock signal, calculates the peak pressure contrast value, sets the initial separation threshold and adjusts it according to the maximum pressure value, and uses a dual-threshold dynamic adjustment mechanism to separate the main shock component and the reflected component of the shock signal to obtain the shock wave hysteresis characteristic data; The drilling ratio calculation module collects torque and axial load data during the drilling process based on the shock wave hysteresis characteristic data, selects data points at adjacent moments to calculate the torque-to-axial load ratio, analyzes the torque fluctuation range, extracts periodic variation characteristics, calculates the stability of the axial load, analyzes whether there are abnormal increases or decreases, and plots a ratio curve that changes over time to obtain drilling torque-axial load ratio sequence data. The state deviation judgment module sets the ratio reference value under the stable operation state of drilling and workover based on the drilling torque-axial load ratio sequence data, calculates the maximum deviation amplitude of the ratio sequence relative to the ratio reference value, analyzes the time variation trend of the ratio deviation, and obtains the drilling ratio deviation trend analysis results; The drilling status identification module analyzes the drilling ratio offset trend and sets the status classification criteria based on the variation range of the ratio offset. It determines whether the ratio offset is within a stable range and makes a comprehensive classification judgment based on the axial load variation. It analyzes the short-term sudden increase of the ratio offset and selects the offset status according to the drilling rate variation trend to obtain the drilling status classification result.

[0019] The shock wave front lag data includes the extreme point of the pressure gradient change rate, the time coordinate of the shock wave front position, and the time interval between adjacent shock signal characteristic points; the shock wave lag characteristic data includes the instantaneous phase jump point, the peak pressure comparison value, the main shock component separation threshold, and the reflection component separation threshold; the drilling torque-axial load ratio sequence data includes the torque fluctuation range, the axial load stability parameter, and the ratio time change curve; the drilling ratio offset trend analysis results include the ratio baseline value, the maximum offset amplitude, and the offset time change trend analysis results; the drilling status classification results specifically refer to the ratio offset stable state record, the ratio offset abnormal state record, the ratio sudden increase state record, the axial load mutation state record, and the drilling rate offset state record.

[0020] See also Figure 2 , the impact signal analysis module includes: The pressure gradient calculation submodule collects the time series data of the shock wave signal, calculates the pressure change values at adjacent moments, performs normalization processing based on the time difference, calculates the pressure gradient change rate, stores all time points, and obtains the pressure gradient change rate sequence data; Based on the time series data of the shock wave signal, the time point of the shock signal is first collected. The corresponding pressure value , stored as a sequence And to ensure data integrity, if there are missing data points, linear interpolation is used to supplement them. For example, when Missing and MPa, MPa, the calculation is MPa, after completing data preprocessing, calculate the pressure change value of the pressure values at adjacent moments , and normalize the time interval , and thus calculate the pressure gradient change rate , the calculation results are stored to form a pressure gradient change rate sequence, as shown in Table 1.1: Table 1.1 Pressure gradient change rate calculation table As shown in Table 1.1, the calculated pressure gradient change rate sequence data are used for subsequent analysis.

[0021] The local extreme value screening submodule is based on the pressure gradient change rate sequence data. By comparing the pressure gradient change rates at adjacent moments, it screens the local maximum points and eliminates the data points whose difference between the previous and the next points is less than the extreme value threshold to obtain the valid local extreme value point set. Set the threshold based on the pressure gradient change rate sequence data To screen out the local extreme points with prominent gradient changes and define the local extreme points Requirements: and or and , filter the set of local extreme points to , set the extreme value fluctuation range threshold MPa / ms. The threshold is set based on the typical fluctuation range of the pressure change rate of the shock wave in different gas media. Usually, based on the shock tube experimental data, the pressure gradient change rate in the air medium is mainly concentrated between 0.10-0.25MPa / ms. When the medium is denser (such as carbon dioxide), the gradient change can reach more than 0.30MPa / ms. Therefore, 0.15MPa / ms is used as the benchmark value for fluctuation screening, which can effectively remove background noise points while retaining the main shock wave feature points and eliminating extreme points with fluctuations less than the threshold, such as MPa / ms, its absolute value is greater than , retain this point, and MPa / ms is lower than is eliminated, and finally the effective local extreme point is obtained .

[0022] The shock wave front positioning submodule analyzes the pressure fluctuation range before and after the effective local extreme point set and calculates the change trend of the local gradient peak point using the formula: ; Calculate shock wave front position data , the time interval between the shock wave front position and the adjacent shock signal characteristic points is counted to obtain the shock wave front lag data, where, Represents the pressure gradient value of the i-th effective local extreme point, Represents the weight value of the corresponding extreme point, Represents the average gradient value of the effective local extreme point, Represents the number of extreme points; Combined with the effective local extreme point set, calculate the pressure fluctuation range before and after The weight is used to calculate the front edge position of the shock wave, where MPa / ms, MPa / ms, set its weight , The weight setting is based on the influence of the extreme point on the shock wave front. Usually, in the process of shock wave signal propagation, the early gradient change has a greater impact on the shape of the shock wave, while the subsequent gradient change is more affected by the attenuation effect. According to the typical data analysis of the shock tube experiment, the pressure change in the early shock wave formation stage contributes about 60%-70%, while the contribution of the subsequent attenuation stage is about 30%-40%. Therefore, in this example, the early extreme point is given a higher weight of 0.6, and the subsequent extreme points Given a weight of 0.4, the calculation is: ; ; ; The calculated shock wave front position data is MPa, and calculate the time interval between it and the adjacent impact signal feature points The shock wave front lag data is 1.5ms, which shows that the shock wave front lags at 0.25MPa, with a time delay of 1.5ms, providing a basis for the subsequent shock signal feature analysis.

[0023] See also Figure 3 , the hysteresis characteristic calculation module includes: The instantaneous phase jump calculation submodule calculates the time series of the shock signal based on the shock wave leading edge lag data, extracts the instantaneous phase value of the time series signal, determines the jump point according to the phase continuous change characteristics, calculates the phase change rate, and uses the formula: ; Calculating phase jump strength , and screen the significant jump points to generate instantaneous phase jump feature data, where represents the instantaneous phase change rate, represents the peak pressure, represents the reference pressure, Representative The phase value at a moment, represents the time interval, Represents the number of moments; Based on the semiconductor chip device used for well drilling and workover IoT control, the shock wave front lag data of the chip under different working conditions is first collected. The time interval is set to 1μs, and the instantaneous pressure value collected by the pressure sensor inside the chip is recorded. To construct a time series, extract the phase information of the signal, and use Fourier transform to analyze the signal phase distribution. The specific operation is to obtain the instantaneous phase at different time points by performing short-time Fourier transform (STFT) on the vibration signal inside the chip. , and then calculate the phase change rate in the time series , the phase change rate between discrete data is calculated by numerical differentiation, and then the mutation point is identified. For a certain time point Calculate the phase change rate .

[0024] in, is the instantaneous peak pressure recorded by the sensor inside the chip, As the reference pressure, in the drilling and workover environment, it is usually set is the static pressure at the wellhead, assuming it is 101.3 kPa. Calculate at a certain moment When the phase change rate is , take 10 time points before and after, μs. Calculation is performed using actual data, assuming that the peak pressure at a certain moment kPa, then , and then calculate the phase change rate, if rad / μs, and the summation term is calculated to be 0.6, then . Set the threshold The threshold is set based on the distribution range of the instantaneous phase change rate of the chip control signal under normal working conditions. During the drilling and repair process, due to the influence of high temperature and high pressure environment, the chip may be subjected to unexpected impact, causing the instantaneous phase change to exceed the normal range. By analyzing the signal data under multiple drilling operations, it is found that the instantaneous phase change rate of the chip is mostly in the normal operating state. ~ When subjected to abnormal shock, it usually exceeds , so set To further verify the applicability of this threshold, we constructed a probability density distribution curve of the instantaneous phase change rate by statistically analyzing the instantaneous phase change data at different drilling depths, and calculated the cumulative distribution function (CDF) of this value in the experimental data. The corresponding probability quantile , that is, this threshold can effectively filter out 90% of the normal chip control signals and only retain the mutation signals for analysis, avoiding misjudgment and missed judgment. It is determined to be an abnormal signal point, and finally the signal abnormal time series that meets the conditions are screened out to generate instantaneous phase jump feature data.

[0025] Table 2.1 Calculation results of instantaneous phase change rate As shown in Table 2.1, some time points meet Condition, so it is determined to be an instantaneous phase jump point.

[0026] The hysteresis timing feature analysis submodule extracts the hysteresis time before and after the jump point based on the instantaneous phase jump feature data, calculates the hysteresis rate between the before and after time series, and performs statistical analysis on the hysteresis features to generate a set of hysteresis features of the impulse signal; Extract the jump point time based on the instantaneous phase jump characteristics , calculate the lag time of chip signal under different well depth environments , count the lag time of all jump points and set the time interval μs segment analysis, counting the number of jump points in each time period, and calculating the average hysteresis time ,in is the number of jump points, and the mean is used to measure the signal transmission stability of the chip during drilling operations. For example, in practical applications, μs, μs, μs, the lag time differences are μs and μs, number of sample points , calculated The average value of all hysteresis times is calculated to generate shock signal hysteresis characteristic data, which is used for chip signal control and abnormal signal troubleshooting.

[0027] Table 2.2 Lag time calculation results Refer to Table 2.2 to calculate the lag time at different time points and obtain the distribution of the lag time.

[0028] The main shock component separation submodule sets the initial separation threshold according to the maximum pressure value based on the shock signal hysteresis feature set, performs dynamic adjustment, calculates the peak pressure contrast value, and uses a dual-threshold mechanism to separate the main shock component and the reflected component to generate shock wave hysteresis feature data; Based on the impact signal hysteresis characteristic data, the initial separation threshold is set according to the maximum pressure value measured by the chip, and the maximum pressure is set. is 180kPa, and the initial separation threshold is set to kPa, filter all impact signals above the threshold as the main impact component, and the signals below the threshold as the reflection component, and then adjust according to the dynamic changes of the peak pressure to set the adjustment factor The adjustment factor is set based on the pressure environment of the chip and the dynamic impact pressure variation range. During the drilling and well repair process, the pressure of the chip working environment will be affected by factors such as drilling fluid flow and downhole tool vibration. Therefore, it is necessary to adapt to pressure fluctuations under different working conditions. By analyzing the chip signal response characteristics under different well depths and pressure levels, it is found that the maximum pressure variation range measured inside the chip under the drilling fluid circulation state is generally no more than , so set As a dynamic adjustment ratio, the threshold can flexibly adapt to pressure fluctuations while avoiding misjudgment caused by excessive adjustment. The range of the adjustment factor is usually affected by the drilling depth, drilling pressure, and drilling fluid density. For example, at high drilling pressure ( kPa), Appropriate increase (such as ), while in low pressure environment ( kPa), then Can be appropriately reduced (such as ). In the specific calculation process, set the current peak pressure kPa, the new threshold is calculated as follows: ; ; After updating, perform secondary screening and calculate peak pressure comparison value ,like is considered as the main impact component, and vice versa is the reflection component. , and then regarded as the main shock component, finally completing the classification of chip signals and generating shock wave hysteresis characteristic data, which is used to monitor the abnormal response of chip signals during drilling and workover, and improve the reliability of downhole equipment signal processing.

[0029] See also Figure 4 , the drilling ratio calculation module includes: The data acquisition submodule collects torque data and axial load data during the drilling process based on the shock wave hysteresis characteristic data, extracts torque data points and axial load data points at adjacent moments, calculates the time difference between adjacent data points, and generates torque-axial load time series data; During the drilling process, the torque data and axial load data are monitored in real time. High-precision sensors are used to record data every second to obtain time series data. The torque data points and axial load data points at adjacent moments are extracted and stored in the database. The time intervals between adjacent moments are calculated to establish a complete time series data. For example, during a drilling operation, the torque data monitored is Nm, axial load data is kN, and the time interval is 1s, then the time series data matrix is constructed as follows: Table 3.1 Torque-axial load time series data As shown in Table 3.1, the torque-axial load time series data has been established and will be used for ratio calculations later.

[0030] The ratio calculation submodule is based on the torque-axial load time series data and uses the formula: ; Calculate the torque-axial load ratio at each moment , and obtain the torque-axial load ratio series data, where Representative Torque data at each moment, Representative Axial load data at a certain moment, represents the mean value of torque data, Represents the absolute deviation of torque and axial load, represents the square root correction factor of the axial load, represents the total number of data points; Based on the torque-axial load time series data, the torque-axial load ratio at each moment is calculated using the formula.

[0031] Using the data in Table 3.1, calculate: ; Calculate each item: ; ; ; The final calculation is: ; ; The results show that the currently calculated torque-axial load ratio series data has a high value and can be used for subsequent ratio fluctuation analysis.

[0032] The ratio analysis submodule analyzes the fluctuation range of the ratio based on the torque-axial load ratio sequence data, extracts the periodic variation characteristics, calculates the stability of the axial load, analyzes whether there are abnormal sudden increases or decreases, plots the ratio curve over time, and obtains the torque-axial load ratio variation trend analysis results; Based on the torque-axial load ratio sequence data, the fluctuation range of the ratio is calculated, the periodic change characteristics are extracted, the stability of the axial load is calculated, and whether there are abnormal sudden increases or decreases is analyzed. The ratio curve that changes with time is drawn. For example, in the calculation, if the ratio data of the first four time points are , the fluctuation range is calculated as follows: ; Calculate the ratio stability based on the fluctuation range: ; Table 3.2 Torque-axial load ratio analysis data As shown in Table 3.2, the data fluctuation range is small and the stability coefficient If the value is lower than 0.005, it means the ratio is relatively stable. The value is set based on the axial load variation and torque response characteristics during drilling. According to drilling industry standards, the stability of the axial load can be measured by the ratio change rate. To measure, when When it is lower than 0.005, it indicates that the fluctuation range of the torque-axial load ratio is within an acceptable range, that is, the drilling parameters are relatively stable, without large impacts or violent fluctuations. Usually, during normal drilling, the axial load fluctuation range is about 3% to 7% of the total load, and the corresponding torque fluctuation range is about 1.5% to 4%. The value is usually between 0.001 and 0.0045. If it exceeds 0.005, it may indicate that the drill bit is blocked, the bit pressure changes drastically, or the occurrence of complex downhole conditions.

[0033] If the ratio at a certain point in time suddenly increases to 150,000 or drops to 130,000, further analysis is needed to determine whether there are abnormal drilling conditions, such as drill bit sticking or changes in mud density. This will ultimately lead to a trend analysis of the torque-to-axial load ratio.

[0034] See also Figure 5 , the state deviation determination module includes: The ratio benchmark calculation submodule extracts the ratio sequence data under the stable operation state based on the drilling torque-axial load ratio sequence data, calculates the mean of the ratio sequence and outputs it as the ratio benchmark value; Based on the drilling torque-axial load ratio sequence data, it is necessary to first collect and preliminarily organize the sequence, set a time window, such as a 10-minute rolling window, extract the ratio data within the time period, and store it in an array. The data points in this array Represents the ratio per minute. Next, we need to calculate the mean value within the time window. The mean calculation adopts the arithmetic mean method, that is: ; in, is the total number of data points in the window. Assume that the data in the current window are as follows (unit: dimensionless ratio): Calculate the mean: ; The mean It serves as the ratio reference value and is used in the subsequent calculation of the ratio offset amplitude, as shown in Table 4.1.

[0035] Table 4.1 Ratio benchmark calculation table As shown in Table 4.1, the ratio reference value is obtained, which is used to judge the normal operating status of the ratio during drilling.

[0036] The offset amplitude calculation submodule calculates the deviation between the ratio at each moment in the ratio sequence and the ratio reference value based on the ratio reference value, obtains the absolute value of the deviation, and uses the formula: ; Calculate the maximum deviation of the ratio , establish the ratio maximum offset amplitude data, where, represents the i-th ratio in the ratio sequence, represents the ratio benchmark value; According to the ratio benchmark value , calculate the ratio of each moment in the ratio sequence and The deviation between the two values is calculated using the absolute value method. The formula for calculating the deviation of the above ratio data is as follows: Calculate the maximum deviation value: The results show that the maximum deviation of the ratio in the current drilling process is 0.023, as shown in Table 2.

[0037] Table 4.2 Ratio offset calculation table As shown in Table 4.2, the maximum deviation amplitude data of the ratio is established for subsequent trend analysis.

[0038] The ratio offset trend analysis submodule analyzes the change of the maximum ratio offset amplitude in the time series based on the maximum ratio offset amplitude data, calculates the change rate of the maximum ratio offset amplitude, determines the ratio offset change trend, and obtains the ratio offset trend analysis results; Based on the maximum deviation amplitude of the ratio , analyze its changes in time series, for which it is necessary to calculate the rate of change of the ratio offset amplitude , the rate of change is calculated as follows: ; in, is the maximum offset in the previous time window. Assuming that the maximum offset in the previous time window is 0.020, the maximum offset in the current window is 0.023, and the time window is 10 minutes, the rate of change is calculated as follows: ; The rate of change indicates that the ratio deviation increases by 0.0003 per minute. If this value increases for a long time, it may indicate that the drilling state is unstable, as shown in Table 4.3.

[0039] Table 4.3 Ratio deviation trend analysis table As shown in Table 4.3, the ratio deviation trend analysis results are obtained to determine the stability of the drilling process.

[0040] See also Figure 6 , the drilling status recognition module includes: The ratio offset calculation submodule obtains the drilling ratio offset based on the drilling ratio offset trend analysis results, calculates the ratio offset change interval, and sets the status classification standard according to the change interval using the formula: ; Calculate the ratio offset change , and judge whether it is in a stable range, and obtain the trend characteristic data of the ratio offset, where Representative Drilling ratio, represents the mean of the ratio offset, represents the total number of data points; Based on the results of the drilling ratio deviation trend analysis, the drilling ratio deviation is first obtained. The drilling ratio refers to the change in a certain parameter (such as the ratio of torque to bit weight) during the drilling process. The specific data can be collected through the drilling monitoring system, as shown in Table 5.1.

[0041] Table 5.1 Drilling ratio monitoring data As shown in Table 5.1, the drilling ratio data changes over time. After extracting the ratio, its change trend is calculated. First, the mean of the ratio offset is calculated. , substituting the data in Table 5.1, we get ; Then calculate the ratio offset change .

[0042] Compute the mean shift of the first term: ; ; Compute the variance correction term for the second term: ; ; ; Finally, the ratio offset change is calculated: ; The preset stability range (within 1.5) is determined based on the ratio fluctuation range during the historical stable operation of the drilling equipment. This range is calculated by the standard deviation of the ratio under stable drilling conditions. Generally speaking, in the test data of 1000 seconds of continuous drilling, the maximum standard deviation is about 1.5. Exceeding this range is usually accompanied by drilling anomalies, such as drill bit wear or formation hardening. Therefore, the stability range is set to within 1.5 to ensure that the drilling state is in normal operation. Ratio offset change If it is greater than 1.5, it is judged that the drilling ratio fluctuates greatly and is in an unstable state, and the trend characteristics of the ratio offset are obtained.

[0043] The axial load analysis submodule analyzes axial load changes based on the ratio offset trend characteristic data and axial load data, calculates the load fluctuation range, determines whether it exceeds the threshold, and screens abnormal conditions based on the ratio offset change to obtain axial load change classification results. Based on the trend characteristics of the ratio offset, the axial load data is called to obtain the axial load at different time points during the drilling process, as shown in Table 5.2.

[0044] Table 5.2 Axial load monitoring data To calculate the fluctuation range of the axial load, use the difference between the maximum and minimum values: ; The stability threshold is 35kN. This is based on the fact that during normal drilling, the drill string structure is affected by changes in formation strength and mechanical vibration of the drill tool, and the maximum fluctuation range of the axial load usually does not exceed 35kN. This value is obtained by statistically analyzing the axial load fluctuation data of the drilling rig when drilling under different formation conditions. In soft formations (such as mudstone), the fluctuation range is usually less than 25kN, while in hard formations (such as sandstone), the fluctuation range can reach about 30kN. The maximum safe value of 35kN is taken as the stability threshold. kN is less than the threshold, it is judged to be in a stable state, and the abnormal state is screened by combining the ratio offset change. , does not meet the stability standard, and the axial load fluctuation range is less than the threshold, so the state is classified as "abnormal" state, and the axial load change classification result is obtained.

[0045] The axial load analysis submodule analyzes axial load changes based on the ratio offset trend characteristic data and axial load data, calculates the load fluctuation range, determines whether it exceeds the threshold, and screens abnormal conditions based on the ratio offset change to obtain axial load change classification results. Based on the trend characteristics of the ratio offset, the axial load data is called to obtain the axial load at different time points during the drilling process, as shown in Table 5.2.

[0046] Table 5.2 Axial load monitoring data To calculate the fluctuation range of the axial load, use the difference between the maximum and minimum values: ; The stability threshold is 35kN. This is based on the fact that during normal drilling, the drill string structure is affected by changes in formation strength and mechanical vibration of the drill tool, and the maximum fluctuation range of the axial load usually does not exceed 35kN. This value is obtained by statistically analyzing the axial load fluctuation data of the drilling rig when drilling under different formation conditions. In soft formations (such as mudstone), the fluctuation range is usually less than 25kN, while in hard formations (such as sandstone), the fluctuation range can reach about 30kN. The maximum safe value of 35kN is taken as the stability threshold. kN is less than the threshold, it is judged to be in a stable state, and the abnormal state is screened by combining the ratio offset change. , does not meet the stability standard, and the axial load fluctuation range is less than the threshold, so the state is classified as "abnormal" state, and the axial load change classification result is obtained.

[0047] The drilling rate screening submodule obtains drilling rate data based on the axial load change classification results, analyzes the drilling rate change trend, screens the offset status, comprehensively judges the drilling status, and obtains the drilling status classification results; Based on the classification results of axial load changes, the drilling rate data is obtained and the drilling rate at different time points is calculated, as shown in Table 5.3: Table 5.3 Drilling rate data Analyze the drilling rate change trend and calculate the average drilling rate: ; Determine whether the drilling rate is stable and calculate the maximum fluctuation: ; The drilling rate stable fluctuation range threshold is 0.02m / s. This is based on the fact that during drilling, the drilling rate is affected by the drill bit speed, bit pressure, and formation properties, and usually fluctuates between 0.015m / s and 0.02m / s. Based on long-term drilling data, the rate fluctuation range in soft formations (such as mudstone) is less than 0.015m / s, while in hard formations (such as sandstone), it can reach 0.02m / s. Therefore, 0.02m / s is used as the stability determination threshold. m / s is less than the threshold, and the state is determined by combining the classification results of the axial load change, and the final drilling state classification result is "stable drilling".

[0048] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. A semiconductor chip device for IoT control of well drilling and workover, characterized in that: The device comprises: The shock signal analysis module obtains the time series data of the shock wave signal, calculates the pressure gradient change rate at adjacent moments, compares the fluctuation range of the previous and next moments, calculates the time interval between the shock wave front position and the adjacent shock signal feature points, and obtains the shock wave front lag data; The hysteresis characteristic calculation module calculates the instantaneous phase jump point of the shock signal based on the shock wave leading edge hysteresis data, analyzes the front and rear timing hysteresis characteristics of the shock signal, calculates the peak pressure contrast value and separates the main shock component and the reflected component of the shock signal to obtain the shock wave hysteresis characteristic data; The drilling ratio calculation module calculates the ratio of torque to axial load at adjacent data points based on the shock wave hysteresis characteristic data, analyzes the fluctuation range of the torque, calculates the stability of the axial load, and plots a ratio curve that changes with time to obtain drilling torque-axial load ratio sequence data; The state offset determination module sets a ratio reference value based on the drilling torque-axial load ratio sequence data, calculates the maximum offset amplitude of the ratio sequence relative to the ratio reference value, analyzes the time variation trend of the ratio offset, and obtains the drilling ratio offset trend analysis result.

2. The semiconductor chip device for IoT control of well drilling and workover according to claim 1, characterized in that: The shock wave front lag data includes the extreme point of the pressure gradient change rate, the time coordinate of the shock wave front position, and the time interval of adjacent shock signal characteristic points; the shock wave lag characteristic data includes the instantaneous phase jump point, the peak pressure comparison value, the main shock component separation threshold, and the reflection component separation threshold; the drilling torque-axial load ratio sequence data includes the torque fluctuation range, the axial load stability parameter, and the ratio time change curve; the drilling ratio offset trend analysis results include the ratio baseline value, the maximum offset amplitude, and the offset time change trend analysis results.

3. The semiconductor chip device for IoT control of well drilling and workover according to claim 1, characterized in that: The impact signal analysis module includes: The pressure gradient calculation submodule collects the time series data of the shock wave signal, calculates the pressure change values at adjacent moments, performs normalization processing based on the time difference, calculates the pressure gradient change rate, stores all time points, and obtains the pressure gradient change rate sequence data; The local extreme value screening submodule screens local maximum points based on the pressure gradient change rate sequence data by comparing the pressure gradient change rates at adjacent moments, and eliminates data points whose difference between the previous and next points is less than the extreme value threshold to obtain a set of valid local extreme value points; The shock wave front edge positioning submodule analyzes the pressure fluctuation range before and after the effective local extreme point set and calculates the change trend of the local gradient peak point using the formula: ； Calculate shock wave front position data , the time interval between the shock wave front position and the adjacent shock signal characteristic points is counted to obtain the shock wave front lag data, where, Represents the pressure gradient value of the i-th effective local extreme point, Represents the weight value of the corresponding extreme point, Represents the average gradient value of the effective local extreme point, Represents the number of extreme points.

4. The semiconductor chip device for IoT control of well drilling and workover according to claim 1, characterized in that: The hysteresis characteristic calculation module includes: The instantaneous phase jump calculation submodule calculates the time series of the shock signal based on the shock wave leading edge lag data, extracts the instantaneous phase value of the time series signal, determines the jump point according to the phase continuous change characteristics, calculates the phase change rate, and uses the formula: ； Calculating phase jump strength , and screen the significant jump points to generate instantaneous phase jump feature data, where represents the instantaneous phase change rate, represents the peak pressure, represents the reference pressure, Representative The phase value at a moment, represents the time interval, Represents the number of moments; The hysteresis timing feature analysis submodule extracts the hysteresis time before and after the jump point based on the instantaneous phase jump feature data, calculates the hysteresis rate between the before and after time series, and performs statistical analysis on the hysteresis features to generate a set of impulse signal hysteresis features; The main shock component separation submodule sets the initial separation threshold according to the maximum pressure value based on the shock signal hysteresis feature set, performs dynamic adjustment, calculates the peak pressure contrast value, and uses a dual-threshold mechanism to separate the main shock component and the reflected component to generate shock wave hysteresis feature data.

5. The semiconductor chip device for IoT control of well drilling and workover according to claim 1, characterized in that: The drilling ratio calculation module includes: The data acquisition submodule collects torque data and axial load data during the drilling process based on the shock wave hysteresis characteristic data, extracts torque data points and axial load data points at adjacent moments, calculates the time difference between adjacent data points, and generates torque-axial load time series data; The ratio calculation submodule is based on the torque-axial load time series data and uses the formula: ； Calculate the torque-axial load ratio at each moment , and obtain the torque-axial load ratio series data, where Representative Torque data at each moment, Representative Axial load data at a certain moment, represents the mean value of torque data, Represents the absolute deviation of torque and axial load, represents the square root correction factor of the axial load, represents the total number of data points; The ratio analysis submodule analyzes the fluctuation range of the ratio based on the torque-axial load ratio sequence data, extracts the periodic change characteristics, calculates the stability of the axial load, analyzes whether there are abnormal sudden increases or decreases, draws a ratio curve that changes with time, and obtains the torque-axial load ratio change trend analysis results.

6. The semiconductor chip device for IoT control of well drilling and workover according to claim 1, characterized in that: The state deviation determination module includes: The ratio reference calculation submodule extracts the ratio sequence data under a stable operation state based on the drilling torque-axial load ratio sequence data, calculates the mean of the ratio sequence and outputs it as a ratio reference value; The offset amplitude calculation submodule calculates the deviation between the ratio at each moment in the ratio sequence and the ratio reference value based on the ratio reference value, obtains the absolute value of the deviation, and uses the formula: ； Calculate the maximum deviation of the ratio , establish the ratio maximum offset amplitude data, where, represents the i-th ratio in the ratio sequence, represents the ratio benchmark value; The ratio offset trend analysis submodule analyzes the change of the maximum ratio offset amplitude in the time series based on the ratio maximum offset amplitude data, calculates the change rate of the maximum ratio offset amplitude, determines the ratio offset change trend, and obtains the ratio offset trend analysis results.

7. The semiconductor chip device for IoT control of well drilling and workover according to claim 1, characterized in that: The device also includes a drilling status identification module; The drilling status identification module sets a status classification standard based on the variation range of the ratio offset according to the analysis results of the drilling ratio offset trend, determines whether the ratio offset is within a stable range, performs a comprehensive classification judgment based on the variation of the axial load, analyzes the short-term sudden increase of the ratio offset, and screens the offset status according to the variation trend of the drilling rate to obtain the drilling status classification result; The drilling status classification results specifically refer to ratio deviation stable state records, ratio deviation abnormal state records, ratio sudden increase state records, axial load sudden change state records, and drilling rate deviation state records.

8. The semiconductor chip device for IoT control of well drilling and workover according to claim 7, characterized in that: The drilling status identification module includes: The ratio offset calculation submodule obtains the drilling ratio offset based on the drilling ratio offset trend analysis result, calculates the ratio offset change interval, and sets the state classification standard according to the change interval, using the formula: ； Calculate the ratio offset change , and judge whether it is in a stable range, and obtain the trend characteristic data of the ratio offset, where Representative Drilling ratio, represents the mean of the ratio offset, represents the total number of data points; The axial load analysis submodule analyzes the axial load change based on the ratio offset trend characteristic data and the axial load data, calculates the load fluctuation range, determines whether it exceeds the threshold, and screens abnormal conditions based on the ratio offset change to obtain the axial load change classification result; The drilling rate screening submodule obtains drilling rate data based on the axial load change classification result, analyzes the drilling rate change trend, screens the offset state, comprehensively judges the drilling state, and obtains the drilling state classification result.

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