Petroleum drilling and production equipment management method and system based on big data analysis

Abstract

The invention discloses a petroleum drilling and production equipment management method and system based on big data analysis, and relates to the technical field of equipment management.The method comprises the steps that operation parameters and environment parameters of equipment are collected in real time by installing various sensors; based on the historical operation data of the equipment, calculating a dynamic baseline value of the operation parameters of the equipment, and setting a normal fluctuation range; the method comprises the following steps: analyzing operation parameters of a plurality of devices in the same operation area, calculating a correlation degree among the devices, and identifying a correlation mode among the devices; when the operation parameters of the equipment exceed the dynamic baseline range and the relevance between the equipment is abnormally changed, judging that the equipment is abnormal, and triggering early warning; and according to an abnormal detection result, the importance of the equipment and the operation condition, evaluating the maintenance priority, and making an optimized maintenance plan. According to the method, by introducing calculation of the dynamic baseline value and recognition of the association mode between the devices, early warning can be given out in time when the parameters are abnormal and the association changes, and the accuracy of anomaly detection is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment management, and in particular to a method and system for managing oil drilling equipment based on big data analysis. Background Art

[0002] As global energy demand continues to grow, the oil drilling industry plays a key role in ensuring energy supply. Traditional equipment management methods mainly rely on regular manual inspections and experience-based fault diagnosis, which has many limitations when dealing with complex and changing drilling environments. In recent years, the introduction of Internet of Things (IoT) technology has made it possible to collect equipment operating parameters in real time through sensors. Further combined with big data analysis technology, massive equipment data can be efficiently processed and deeply mined, thereby achieving accurate monitoring of equipment status and fault prediction.

[0003] However, the existing oil drilling equipment management technology still has many deficiencies. First, many existing technologies rely on static parameter thresholds to determine the equipment status, lack adaptability to dynamic changes in equipment operation, and cannot timely identify deep-seated equipment anomalies. Secondly, existing technologies usually use a single device as a management unit, ignoring the interrelationships between devices and failing to fully utilize the associated data generated by the coordinated operation of multiple devices, which to a certain extent limits the accuracy of fault diagnosis and the timeliness of early warning. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a petroleum drilling equipment management method and system based on big data analysis to solve the problems of relying on static parameter thresholds, ignoring the correlation between equipment and failing to dynamically optimize maintenance plans in existing petroleum drilling equipment management.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: In the first aspect, an embodiment of the present invention provides a method for managing oil drilling equipment based on big data analysis, which includes: collecting the operating parameters and environmental parameters of the equipment in real time by installing multiple sensors; calculating the dynamic baseline value of the equipment operating parameters based on the historical operating data of the equipment, and setting the normal fluctuation range; calculating the degree of correlation between the equipment and identifying the correlation pattern between the equipment by analyzing the operating parameters of multiple equipment in the same operating area; when the equipment operating parameters exceed the dynamic baseline range and the correlation between the equipment changes abnormally, determining that the equipment is abnormal and triggering an early warning; evaluating the maintenance priority and formulating an optimized maintenance plan based on the abnormal detection results, the importance of the equipment and the operating status.

[0007] As a preferred solution of the oil drilling and production equipment management method based on big data analysis described in the present invention, the multiple sensors include temperature sensors, pressure sensors, vibration sensors, flow meters, ambient temperature and humidity sensors, and air pressure sensors; the operating parameters of the equipment include pressure, temperature, vibration and flow; the environmental parameters include ambient temperature, humidity and air pressure.

[0008] As a preferred solution of the oil drilling equipment management method based on big data analysis described in the present invention, the following steps are included: based on the historical operation data of the equipment, the dynamic baseline value of the equipment operation parameter is calculated, and the normal fluctuation range is set. Extract historical operation data of the equipment from the data storage system; Preprocess historical data; According to the equipment operation characteristics, set the size of the sliding window, set the data collection frequency, and calculate the moving average of each parameter and moving standard deviation ; Use the calculated moving average and moving standard deviation , calculate the lower limit and upper limit of each time point, and record the lower limit and upper limit as the normal fluctuation range of the parameter; Every time a new data point Arrived, the sliding window moves forward one data point and removes the earliest data point , and add new data points ; Recalculate the moving average within the current window and moving standard deviation , update the normal fluctuation range; Define weights for each data point , calculate the weighted moving average ,use Alternative moving average ; Define the operation mode detection mechanism to determine whether the mode changes occur by analyzing the operation parameters of the device; When the operating mode of the device changes, the baseline value is adjusted through weighted moving average, and the parameter multiples are dynamically adjusted according to the change of the operating mode. , and recalculate the lower and upper bounds.

[0009] As a preferred solution of the oil drilling equipment management method based on big data analysis described in the present invention, wherein: the operation mode detection mechanism is defined to determine whether a mode change occurs by analyzing the operation parameters of the equipment, including the following steps: Conduct preliminary data analysis on the equipment operating parameters collected in real time to evaluate the importance of each parameter to the identification of the operating mode; Use correlation analysis to check the correlation between parameters and eliminate redundant parameters; Based on importance assessment and redundancy check, determine the key parameter set that will be used for operation mode detection; Based on the key parameter set, the data of different frequencies are synchronized and aligned by interpolation method, the data of each parameter is standardized, the statistical features of time series data are extracted by sliding window technology, and the feature dimension is reduced by principal component analysis; Select Gaussian mixture model GMM, determine the optimal number of clusters K through GMM model selection method, use K-Means algorithm for initial clustering, set the initial mean and covariance of GMM, use expectation maximization algorithm to train GMM, and optimize parameter settings; Use historical data to train the GMM model, and evaluate the fitting effect of the GMM model through cross-validation and statistical indicators; Set the sliding window size for real-time detection; Input the real-time collected equipment operation parameter data into the trained GMM model to calculate the probability of each sample belonging to each operation mode; When the mode of M consecutive data points is different from the previous one and the matching probability exceeds the set threshold, it is judged as a mode change.

[0010] As a preferred solution of the oil drilling equipment management method based on big data analysis described in the present invention, the following steps are included: by analyzing the operating parameters of multiple devices in the same operating area, calculating the degree of association between the devices, and identifying the association pattern between the devices, Based on the acquired operating parameters of each device, the Pearson correlation coefficient is used to calculate the correlation coefficient between each set of device operating parameters; Construct a correlation matrix, where the rows and columns represent the operating parameters of different devices, and the values ​​in the matrix represent the correlation coefficients of the corresponding parameter pairs; Classify the correlation coefficients and identify highly correlated device pairs; Based on historical data, calculate the correlation coefficients between devices, count their distribution, and determine the normal range of the correlation coefficients between each group of devices through statistical analysis; The device whose current parameters deviate from the baseline is identified through the set dynamic baseline range, the correlation coefficient between the device and other devices in the same operating area is calculated, and it is determined whether the correlation coefficient is within the normal range. If it exceeds the normal range, it is recorded as an associated abnormality.

[0011] As a preferred solution of the oil drilling equipment management method based on big data analysis described in the present invention, when the equipment operating parameters exceed the dynamic baseline range and the correlation between the equipment changes abnormally, the equipment is judged to be abnormal and an early warning is triggered, including the following steps: Obtain the latest equipment operating parameter data of the equipment in real time, obtain the lower limit and upper limit of the current time point t, and determine whether the equipment operating parameters exceed the normal fluctuation range; If it is determined that the parameter exceeds the limit, the event is recorded; Obtain a list of all equipment that is judged to be exceeding the standard, and for each equipment whose parameters exceed the standard, calculate its correlation coefficient with other equipment in the same operating area; For each pair of devices, determine whether the correlation coefficient deviates from the historical average correlation coefficient beyond the set tolerance range, and record all device pairs that are determined to be abnormally correlated; Obtain all device pairing information that is determined to be abnormal from parameter exceeding limits and associated anomalies; The device is judged to be abnormal only when the device parameters exceed the standard and the correlation abnormalities with other devices are met at the same time; After determining that the equipment is abnormal, early warning information is triggered through multiple channels. Maintenance personnel obtain the early warning information and respond to relevant information to handle the fault; The warning information includes device ID, abnormal parameters, current values, historical baseline values, a list of abnormal associated devices and a timestamp.

[0012] As a preferred solution of the oil drilling equipment management method based on big data analysis described in the present invention, wherein: according to the abnormal detection results, the importance and operating status of the equipment, the maintenance priority is evaluated and an optimized maintenance plan is formulated, including the following steps: Identify abnormality severity, equipment criticality, and operational downtime as risk assessment indicators; Normalize the data of each indicator; The multi-index weighted scoring method is used to combine different risk factors according to weights to obtain a comprehensive risk score for the equipment; Based on the calculated risk scores, the devices are divided into three risk levels: high, medium, and low; If the equipment is classified as high risk, immediately arrange maintenance tasks, increase monitoring frequency, track the equipment operating status in real time, and shorten the equipment maintenance cycle based on the risk score; For medium-risk equipment, regular equipment maintenance is carried out according to the scheduled maintenance plan. The maintenance cycle is dynamically adjusted according to the equipment operating status and risk score. The maintenance strategy is adjusted based on real-time monitoring and risk score. For low-risk equipment, maintenance is carried out according to the established maintenance plan, and the maintenance cycle of the equipment is extended based on the risk score.

[0013] In the second aspect, the present invention provides an oil drilling equipment management system based on big data analysis, including a data acquisition module, which is responsible for collecting the operating parameters and environmental parameters of the equipment in real time by installing multiple sensors; a dynamic baseline setting module, which is responsible for calculating the dynamic baseline value of the equipment operating parameters based on the historical operating data of the equipment, and setting the normal fluctuation range; an equipment association analysis module, which is responsible for calculating the degree of association between the equipment and identifying the association pattern between the equipment by analyzing the operating parameters of multiple equipment in the same operating area; an anomaly detection and early warning module, which is responsible for determining that the equipment is abnormal and triggering an early warning when the equipment operating parameters exceed the dynamic baseline range and the association between the equipment changes abnormally; a maintenance strategy optimization module, which is responsible for evaluating the maintenance priority and formulating an optimized maintenance plan based on the anomaly detection results, the importance of the equipment and the operating status.

[0014] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program is executed by the processor, any step of the oil drilling equipment management method based on big data analysis as described in the first aspect of the present invention is implemented.

[0015] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the oil drilling and production equipment management method based on big data analysis as described in the first aspect of the present invention is implemented.

[0016] The beneficial effects of the present invention are as follows: by introducing the calculation of dynamic baseline values ​​and the identification of correlation patterns between devices, the fluctuation of equipment operating parameters can be accurately monitored, and early warnings can be issued in a timely manner when parameters are abnormal and correlations change. This method not only improves the accuracy of abnormality detection, but also significantly improves the intelligence level and maintenance efficiency of equipment management by optimizing maintenance priorities and formulating scientific maintenance plans, providing effective technical support for equipment management in the oil drilling industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0018] Figure 1 This is a flow chart of the oil drilling equipment management method based on big data analysis in Example 1.

[0019] Figure 2 This is a flowchart of equipment abnormality determination and triggering warning in Example 1. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0021] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0022] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0023] Example 1, reference Figure 1 and Figure 2 , which is the first embodiment of the present invention, provides a method for managing oil drilling equipment based on big data analysis, comprising the following steps: S1. By installing a variety of sensors, the operating parameters and environmental parameters of the equipment can be collected in real time.

[0024] Specifically, select a high-precision pressure sensor with a pressure range that meets the needs of the equipment, such as the PTP series pressure sensor, which has high sensitivity and linearity. The temperature sensor uses an industrial-grade thermocouple or RTD (resistance temperature detector), such as PT100, to ensure the accuracy of temperature measurement. The vibration sensor uses an accelerometer (such as a three-axis accelerometer) for vibration monitoring to ensure that the multi-directional vibration data of the equipment can be captured. Select an electromagnetic flowmeter or a vortex flowmeter to ensure the stability and accuracy of flow measurement. Use a high-precision temperature and humidity sensor, such as DHT22, to ensure the reliability of environmental monitoring data. Select an accurate air pressure sensor, such as BMP280, to monitor changes in atmospheric pressure. According to the equipment structure and operation characteristics, sensors are deployed at key parts of the equipment, such as pressure sensors installed at the inlet and outlet of the pressure vessel, vibration sensors installed at the motor bearings, and temperature and humidity sensors deployed in different areas around the equipment to ensure that the environmental conditions are fully reflected and blind spots are avoided; Collect equipment operating parameters such as pressure, temperature, vibration, and flow, as well as environmental parameters that affect equipment operation, such as ambient temperature and humidity; ensure that the collected data meets the accuracy requirements of equipment monitoring, such as the pressure sensor error does not exceed ±1%, and the temperature sensor error does not exceed ±0.5°C; set the collection frequency of different parameters according to equipment characteristics. For example, vibration data may require a higher collection frequency (such as 10 times per second), while ambient temperature and humidity can be collected once a minute; Select edge computing devices with high computing power and low power consumption, such as industrial-grade edge gateways based on ARM architecture (such as NVIDIA Jetson series) for near-real-time data processing and preprocessing, and install edge computing nodes near the equipment to reduce data transmission delays and improve response speed; deploy data acquisition software (such as Node-RED, Ignition SCADA) on edge computing nodes, configure the reading interface and data format of each sensor to ensure accurate data reading, and configure the software to support multiple communication protocols (such as Modbus, OPC UA) to ensure compatibility with different types of sensors; Use a moving average filter to smooth the data, use the Z-score method to detect and process abnormal data, convert the data collected by different sensors into a unified format (such as JSON, CSV) to ensure the convenience of subsequent data storage and analysis, standardize timestamps, and ensure the temporal synchronization of each source data.

[0025] S2. Based on the historical operation data of the equipment, calculate the dynamic baseline value of the equipment operation parameters and set the normal fluctuation range, including the following steps: S2.1. Extract the historical operation data of the equipment from the data storage system, and determine the time range as the past six months to cover the long-term operation characteristics and operating condition changes of the equipment. The extracted data includes equipment operation parameters (pressure, temperature, vibration, flow, etc.) and timestamps; Perform missing value processing, outlier detection and processing, and data standardization on historical data. Specifically, for continuous time series data, identify the location of missing values ​​in the data. For each missing value, use two adjacent known data points for linear interpolation; calculate the Z-score of each data point to identify and process extreme outliers. For data points determined to be outliers, use the mean of the adjacent data points for replacement; for each device operating parameter, calculate its mean and standard deviation, and standardize all data points to ensure that each parameter is analyzed at the same scale.

[0026] S2.2. According to the operation characteristics of the equipment, the size of the sliding window is set. Generally, the data of the past hour is selected for calculation. The data collection frequency is set to once per minute to ensure that the number of data points in each window is 60, covering the data of the past hour. For each sliding window, the average value of all data points in the window is calculated to obtain , when the sliding window moves forward, the new ; For each sliding window, calculate all data points in the window and the moving average The difference between the two, squared, averaged, and squared, we get ; Use the calculated moving average and moving standard deviation , calculate the lower and upper limits of each time point, the expression is,

[0027] in, and The time The lower and upper limits of =2 means the normal fluctuation range is 2 standard deviations above and below the mean; record the lower limit and upper limit as the normal fluctuation range of the parameter.

[0028] S2.3. Whenever a new data point Arrived, the sliding window moves forward one data point and removes the earliest data point , and add new data points ; Recalculate the moving average within the current window and moving standard deviation , update the normal fluctuation range; Define weights for each data point , so that the most recent data point has the highest weight, and calculate the weighted moving average , the expression is,

[0029] in, is the weight coefficient, which usually gives higher weight to recent data. For at time point The original data value at is the current time point, represents the sum of the products of each data point and its weight, represents the sum of all weights, ensuring The calculation results are on the correct scale; The definition is as follows:

[0030] Represents the latest data point The weight is the highest, and the weights of the previous data points decrease in sequence; among them, From 1 to The sum of the integer sequence is used to normalize the weight coefficients to ensure that the sum of the weights is 1. Index variables, indicating the relative position of data points within the sliding window, is an index variable used to represent each item in the summation operation. The value range is from 1 to , that is, k=1,2,3,…, ; use Alternative moving average , to enhance the response speed to new data; Define an operation mode detection mechanism to determine whether a mode change occurs by analyzing the operation parameters of the equipment; when the operation mode of the equipment changes (for example, load increases, ambient temperature changes significantly), adjust the baseline value through weighted moving average to ensure that the baseline can reflect the new operation status in a timely manner, and dynamically adjust the parameter multiples according to the change of the operation mode To adapt to the fluctuation characteristics of the equipment, and recalculate the lower and upper limits, the expression is,

[0031] in, Dynamically adjust according to the current operating status of the device. The specific strategies are as follows: When the equipment is running stably, =2, that is, the normal fluctuation range is 2 standard deviations above and below the mean. When the equipment is running at high load, the value increases. Value (such as =2.5), relax the fluctuation range to adapt to larger fluctuations, and reduce Value (such as =1.5), improving the sensitivity of anomaly detection.

[0032] S3. Define an operation mode detection mechanism to determine whether a mode change occurs by analyzing the operation parameters of the device, including the following steps: S3.1. Determine the equipment operating status parameters used for operating mode detection, including pressure, temperature, vibration and flow rate. These parameters can fully reflect the operating status and working condition changes of the equipment. Perform preliminary data analysis on the equipment operating parameters collected in real time, evaluate the importance of each equipment operating parameter to the operating mode identification, and give priority to the equipment operating parameters with greater influence; Specifically, the preprocessed equipment operation parameter data is used to train the random forest classifier, the target variable is the equipment operation mode, and the feature importance score is extracted, which is expressed as,

[0033] in, is the number of decision trees in the random forest, For the Importance of tree features; The mutual information value between each feature and the target variable is calculated, which reflects the information contribution of each equipment operating parameter in the operation mode identification; the random forest feature importance and mutual information score are normalized, and the comprehensive feature importance score is obtained by combining the weighted average; According to the comprehensive feature importance score, all equipment operating parameters are sorted in descending order to determine the importance order of each equipment operating parameter; A threshold is set to filter out equipment operating parameters that have a greater impact on operating mode identification. The threshold can be determined based on the cumulative importance contribution ratio. For example, parameters with a cumulative importance contribution of 80% are selected as core equipment operating parameters. According to the cumulative contribution ratio, the top k parameters are selected as the core equipment operating parameters that have a greater impact on the operating mode identification.

[0034] Use correlation analysis to check the correlation between equipment operating parameters, eliminate redundant equipment operating parameters, and retain equipment operating parameters with independent information; based on importance assessment and redundancy check, determine the final set of equipment operating parameters used for operating mode detection.

[0035] S3.2. Based on the set of equipment operating parameters, the data of different frequencies are synchronized and aligned through interpolation method to ensure that the data of all equipment operating parameters have the same timestamp. The data of each equipment operating parameter is standardized to eliminate the dimension effect so that the operating parameters of different equipment can be analyzed at the same scale. The sliding window technology is used to extract the statistical features of the time series data, such as mean, standard deviation, maximum value, minimum value, kurtosis and skewness, etc. The principal component analysis is used to reduce the feature dimension, retain the main information and reduce the computational complexity.

[0036] S3.3, select Gaussian mixture model GMM, determine the optimal number of clusters K through GMM model selection method (such as Akaike information criterion AIC, Bayesian information criterion BIC), use K-Means algorithm for initial clustering, set the initial mean and covariance of GMM; use expectation maximization algorithm (EM) to train GMM and optimize parameter settings; Use historical data to train the GMM model, evaluate the fitting effect of the GMM model through cross-validation and statistical indicators (such as log likelihood, AIC, BIC), and select a suitable K value to ensure that the GMM model avoids overfitting while ensuring goodness of fit; set the sliding window size for real-time detection, which is usually consistent with the window size in the baseline setting (such as the past 1 hour); The real-time collected equipment operating parameter data is input into the trained GMM model to calculate the probability of each sample belonging to each operating mode. When the mode to which M consecutive data points belong is different from the previous one and the probability of conformity exceeds the set threshold (such as 80%), it is judged as a mode change.

[0037] S4, by analyzing the operating parameters of multiple devices in the same operating area, calculating the degree of association between the devices, and identifying the association pattern between the devices, including the following steps, S4.1. Based on the acquired operating parameters of each device, use the Pearson correlation coefficient to calculate the correlation coefficient between each set of device operating parameters. For example, calculate the pressure correlation coefficient between device A and device B, the temperature correlation coefficient between device A and device C, and so on. The calculation formula is:

[0038] in, For parameters and The Pearson correlation coefficient between and Respectively Devices in the time window and equipment Parameter value, and Equipment and equipment The average value of the parameter, is the number of data points in the time window (e.g. 60 times of data); Construct a correlation matrix, where the rows and columns represent the operating parameters of different devices, and each value in the matrix represents the correlation coefficient between corresponding parameter pairs. Specifically, Based on the calculated Pearson correlation coefficient, a correlation matrix is ​​constructed. The rows and columns of the matrix represent the operating parameters of different devices. Each value in the matrix represents the correlation coefficient between corresponding parameter pairs; The following is a specific example of a correlation matrix, showing the Pearson correlation coefficients between the operating parameters of four devices (device A, device B, device C, device D): Table 1 Correlation matrix Parameter equipment Equipment A pressure (Pa) Device A_Temperature C9 Equipment B_vibration (m / s) Equipment C_Flow rate (L / min) Equipment D ambient temperature (G Equipment D humidity (%) Equipment D_Air pressure (hPa) Equipment A_Pressure (Pa) 1.00 0.60 0.40 0.25 0.15 0.10 0.05 Equipment A_Temperature(G 0.60 1.00 0.55 0.35 0.20 0.12 0.08 Equipment B_Vibration (m / s²) 0.40 0.55 1.00 0.45 0.10 0.60 0.15 Equipment C_Flow rate (L / min) 0.25 0.35 0.45 1.00 0.05 0.20 0.10 Equipment D ambient temperature (G) 0.15 0.20 0.10 0.05 1.00 0.25 0.30 Equipment D_Humidity (%) 0.10 0.12 0.60 0.20 0.25 1.00 0.40 Equipment D_Air pressure (hPa) 0.05 0.08 0.15 0.10 0.30 0.40 1.00 Matrix Description: Autocorrelation: The values ​​on the main diagonal of the matrix are all 1.00, indicating perfect correlation of each parameter with itself.

[0039] Equipment A Pressure and Equipment A Temperature (0.60): Indicates that there is a moderate positive correlation between pressure and temperature, meaning that the temperature tends to increase when the pressure of equipment A increases.

[0040] Equipment A pressure and equipment B vibration (0.40): indicates that there is a weak positive correlation between equipment A pressure and equipment B vibration, indicating that there is a certain association between the two.

[0041] Equipment B vibration and equipment C flow rate (0.35): Indicates that there is a moderate positive correlation between the vibration of equipment B and the flow rate of equipment C.

[0042] Device D ambient temperature and other parameters (0.05): indicates that there is almost no linear correlation between device D ambient temperature and other device parameters; The correlation coefficients are classified to identify highly correlated device pairs, specifically, | ∣=1 means completely positive or negative correlation, ∣ ∣>0.8 is highly correlated, 0.5<∣ ∣≤0.8 is moderately correlated, ∣ ∣≤0.5 indicates low correlation.

[0043] S4.2. Based on the historical data of the past six months, calculate the correlation coefficient between each device, calculate the mean and standard deviation of the correlation coefficient, and count its distribution. Through statistical analysis, determine the normal range of the correlation coefficient between each group of devices, for example, the correlation coefficient Between 0.8 and 1 is considered normal; The device whose current parameters deviate from the baseline is identified through the set dynamic baseline, and the correlation coefficient between the device and other devices in the same operating area is calculated. It is then determined whether the correlation coefficient is within the normal range. If it is exceeded, it is recorded as a correlation abnormality. The correlation abnormality determination conditions are:

[0044] in, For time The real-time correlation coefficient of is the historical average correlation coefficient, is the set tolerance range, such as 0.1.

[0045] S5. When the equipment operation parameters exceed the dynamic baseline range and the correlation between the equipment changes abnormally, the equipment is judged to be abnormal and an early warning is triggered, including the following steps: S5.1. Obtain the latest equipment operating parameter data of the equipment in real time, obtain the lower limit and upper limit of the current time point t, and determine whether the equipment operating parameters exceed the normal fluctuation range; If it is determined that the parameter exceeds the limit, the event is recorded; Obtain a list of all equipment that is judged to be exceeding the standard, and for each equipment whose parameters exceed the standard, calculate its correlation coefficient with other equipment in the same operating area; For each pair of devices , to determine whether its correlation coefficient deviates from the historical average correlation coefficient Exceeding the set tolerance range , and record all device pairs that are judged to be abnormally associated; Obtain all device pairing information that is determined to be abnormal from parameter exceeding limits and associated anomalies; Only when the device parameters exceed the standard and the associated abnormalities of other devices are met at the same time, the device is judged to be abnormal; S5.2. After determining that the equipment is abnormal, early warning information is triggered through multiple channels. Maintenance personnel obtain early warning information and respond to relevant information to handle the fault; Specifically, the warning information includes: the device ID is the device identification where the abnormality occurred, the abnormal parameter is the specific parameter that exceeds the standard (such as pressure), and the current value is the actual measured value of the current parameter , the historical baseline value is the corresponding time point and , the abnormal associated device list is other devices with abnormal association with the device, and the timestamp is the specific time when the abnormality occurred; Furthermore, emergency warning information is sent to the maintenance personnel's mobile phone through the SMS gateway, and emails containing detailed warning information are sent to the maintenance personnel's work mailbox. Real-time warning notifications are pushed through the company's internal mobile application, supporting instant viewing and operation, highlighting abnormal devices on the visual interface of the monitoring system, and providing detailed information and operation instructions; Integrate the above transmission methods to ensure that warning information is sent through all configured channels.

[0046] S6. Evaluate maintenance priorities and develop an optimized maintenance plan based on the abnormal detection results, the importance of the equipment, and the operating status, including the following steps: S6.1. Determine the severity of abnormalities, equipment criticality, and operational downtime as risk assessment indicators; Specifically, the severity of the abnormality is evaluated based on the magnitude and duration of the deviation of the equipment operating parameters from the baseline (e.g., the greater the deviation and the longer the duration, the higher the score); the criticality of the equipment is scored based on the importance of the equipment in the production process (e.g., critical equipment is scored high and non-critical equipment is scored low); the downtime is scored based on the downtime caused by the equipment failure (e.g., the longer the downtime, the higher the score); Normalize the data of each indicator to ensure that the scores of different indicators are within the same range; A multi-index weighted scoring method is used to combine different risk factors according to weights to obtain a comprehensive risk score for the equipment.

[0047] S6.2. Based on the calculated risk scores, the equipment is divided into three risk levels: high, medium and low, and corresponding maintenance strategies are formulated to ensure the rational allocation and efficient use of maintenance resources; Specifically, according to the risk score, the equipment is assigned to high, medium and low risk levels.

[0048] If the equipment is classified as high risk, immediately arrange maintenance tasks to reduce the risk of equipment failure, increase monitoring frequency, track the equipment operating status in real time, identify potential problems in a timely manner, and appropriately shorten the equipment maintenance cycle based on the risk score to ensure that the equipment is in the best operating condition; For medium-risk equipment, regular equipment maintenance is carried out according to the scheduled maintenance plan to prevent potential failures. The maintenance cycle is dynamically adjusted according to the equipment operating status and risk score to improve the utilization efficiency of maintenance resources. Based on real-time monitoring and risk assessment results, the maintenance strategy is adjusted in a timely manner to ensure the healthy operation of the equipment. For low-risk equipment, perform maintenance according to the established maintenance plan to keep the equipment in good operating condition. According to the risk score, appropriately extend the equipment maintenance cycle, reduce the maintenance frequency, optimize the allocation of maintenance resources, and perform maintenance only when necessary to reduce unnecessary maintenance operations and costs; Generate a corresponding maintenance plan based on the risk level of the equipment, clarify the maintenance tasks, time and person in charge, and assign the maintenance tasks to relevant maintenance personnel to ensure timely execution and tracking of the tasks.

[0049] This embodiment also provides a petroleum drilling equipment management system based on big data analysis, including: a data acquisition module, which is responsible for collecting the operating parameters and environmental parameters of the equipment in real time by installing multiple sensors; a dynamic baseline setting module, which is responsible for calculating the dynamic baseline value of the equipment operating parameters based on the historical operating data of the equipment, and setting the normal fluctuation range; an equipment association analysis module, which is responsible for calculating the degree of association between the equipment and identifying the association pattern between the equipment by analyzing the operating parameters of multiple equipment in the same operating area; an anomaly detection and early warning module, which is responsible for determining that the equipment is abnormal and triggering an early warning when the equipment operating parameters exceed the dynamic baseline range and the association between the equipment changes abnormally; a maintenance strategy optimization module, which is responsible for evaluating the maintenance priority and formulating an optimized maintenance plan based on the anomaly detection results, the importance of the equipment and the operating status.

[0050] This embodiment also provides a computer device, which is suitable for the case of a method for managing oil drilling and production equipment based on big data analysis, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the method for managing oil drilling and production equipment based on big data analysis proposed in the above embodiment.

[0051] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0052] The present embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for managing oil drilling equipment based on big data analysis proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0053] In summary, the present invention obtains multi-dimensional and real-time equipment data by installing multiple sensors to collect the operating parameters and environmental parameters of the equipment in real time, realizes comprehensive data coverage and high-precision monitoring in equipment management, and significantly improves the real-time and accuracy of equipment management; extracts the historical operating data of the equipment, calculates the dynamic baseline value of the equipment operating parameters, and sets the normal fluctuation range, thereby ensuring the real-time update and adaptability of the baseline value, and can more accurately identify abnormal fluctuations in equipment operation, reducing false positives and false negatives; constructs a correlation matrix using the Pearson correlation coefficient, and uses the Gaussian mixture model (GMM) and K-Mea ns algorithm is used for cluster analysis to identify the association patterns between devices, thus realizing dynamic monitoring and collaborative management of the mutual influence between devices, enhancing the accuracy and comprehensiveness of fault diagnosis, and facilitating early warning of potential linkage faults. By integrating the dynamic baseline deviation of equipment operating parameters and the abnormal changes in the correlation between devices, it is possible to accurately determine equipment abnormalities and issue early warning information in a timely manner, which not only improves the accuracy and timeliness of abnormality identification, but also ensures that maintenance personnel can respond quickly and handle faults through multi-channel early warning information transmission, thereby ensuring the continuity of the production process and the safe operation of equipment, and reducing the risk of production stagnation and maintenance costs.

[0054] Example 2, referring to Table 1, is the second example of the present invention. To further verify the technical solution of the present invention, experimental simulation data of a petroleum drilling equipment management method based on big data analysis is provided.

[0055] In order to verify the effectiveness and advantages of the present invention, the following specific embodiments are designed. The experiment selected four key equipment (equipment A, equipment B, equipment C, equipment D) at a certain oil drilling site as the test objects. These devices are equipped with temperature sensors, pressure sensors, vibration sensors, flow meters, environmental temperature and humidity sensors, and air pressure sensors to collect various equipment operating parameters and environmental parameters in real time. During the experiment, the equipment operation data is monitored and analyzed simultaneously by the existing static parameter threshold management method and the method based on big data analysis of the present invention to ensure the integrity and comparability of the data.

[0056] First, by installing a variety of sensors, the operating parameters (including pressure, temperature, vibration, flow) and environmental parameters (including ambient temperature, humidity, and air pressure) of the equipment are collected in real time. The data collection frequency is set to once per minute to ensure the high timeliness and accuracy of the data. Subsequently, based on the historical operating data of the equipment in the past six months, the method of the present invention is used to calculate the dynamic baseline value of the equipment operating parameters and set the normal fluctuation range. During the data processing process, missing value processing, outlier detection and processing, and data standardization processing are performed to eliminate data noise and interference and improve data quality.

[0057] After the dynamic baseline value is calculated, the operating parameters of multiple devices in the same operating area are analyzed to calculate the degree of correlation between the devices and identify the correlation mode between the devices. The specific steps include using the Pearson correlation coefficient to build a correlation matrix, applying the Gaussian mixture model (GMM) and K-Means algorithm for cluster analysis, so as to identify the mutual influence relationship and collaborative operation mode between the devices. This step helps to discover potential linkage faults and improve the accuracy and timeliness of fault diagnosis.

[0058] When the equipment operating parameters exceed the dynamic baseline range and the correlation between the equipment changes abnormally, the method of the present invention can accurately determine the equipment abnormality and trigger an early warning, ensuring that maintenance personnel can quickly obtain key information and respond. In addition, based on the abnormal detection results, the importance and operating status of the equipment, the maintenance priority is evaluated and an optimized maintenance plan is formulated. The formulation of the maintenance plan is based on a multi-index weighted scoring method, which comprehensively considers the severity of the equipment abnormality, the importance score in the production process, and the historical operation downtime data, so as to achieve the rational allocation and efficient use of maintenance resources.

[0059] During the test, by comparing the existing static parameter threshold management method and the big data analysis management method of the present invention, data on anomaly detection rate, false alarm rate, maintenance response time and maintenance cost were collected. The test results show that the method of the present invention is superior to the existing methods in all indicators, showing significant advantages and innovations.

[0060] The details are shown in Table 2 below: Table 2 Experimental data table parameter Device A_Existing Method Device A_Method of the Invention Device B_Existing Method Device B_Method of the Invention Device C_Existing Methods Device C_Method of the Invention Device D_Existing Methods Device D_Method of the Invention Anomaly detection rate (%) 75 95 70 90 80 93 65 88 False alarm rate (%) 15 5 20 4 18 6 25 3 Maintenance response time (hours) 48 24 50 22 45 20 60 18 Maintenance cost (ten thousand yuan) 10 7 12 8 11 7.5 15 9 Equipment downtime (hours / month) 30 10 35 8 25 7 40 6 It can be clearly seen from the above experimental data table that the present invention is significantly superior to the traditional existing static parameter threshold management method in multiple key performance indicators. First, in terms of abnormality detection rate, the detection rate of device A increased from 75% to 95%, device B increased from 70% to 90%, device C increased from 80% to 93%, and device D increased from 65% to 88%. This shows that the present invention has higher accuracy and sensitivity in identifying equipment anomalies, can detect potential problems earlier, and reduce the impact of equipment failures on production.

[0061] Secondly, in terms of false alarm rate, the false alarm rate of device A dropped from 15% to 5%, that of device B dropped from 20% to 4%, that of device C dropped from 18% to 6%, and that of device D dropped from 25% to 3%. The reduction in false alarm rate not only reduces unnecessary maintenance operations and saves maintenance resources, but also increases the trust of maintenance personnel in early warning information.

[0062] Under the existing method, the maintenance response time of device A is 48 hours, device B is 50 hours, device C is 45 hours, and device D is 60 hours. After adopting the method of the present invention, the response time is significantly shortened to 24 hours, 22 hours, 20 hours, and 18 hours. This means that when an abnormality occurs in the equipment, the maintenance personnel can take measures more quickly, reduce the risk of fault expansion, and ensure the continuity of the production process and the normal operation of the equipment.

[0063] In terms of maintenance costs, the maintenance costs of equipment A dropped from 100,000 yuan to 70,000 yuan, equipment B dropped from 120,000 yuan to 80,000 yuan, equipment C dropped from 110,000 yuan to 75,000 yuan, and equipment D dropped from 150,000 yuan to 90,000 yuan. This is mainly due to the reduction of false alarm rate and shortened maintenance response time, which reduced unnecessary maintenance operations and equipment downtime.

[0064] Finally, from the data of equipment downtime, under the existing method, the downtime of equipment A is 30 hours / month, equipment B is 35 hours / month, equipment C is 25 hours / month, and equipment D is 40 hours / month. However, the downtime of the method of the present invention is reduced to 10 hours / month, 8 hours / month, 7 hours / month, and 6 hours / month, respectively. This result shows that the method of the present invention can effectively prevent the occurrence of major equipment failures and improve the availability and production efficiency of the equipment.

[0065] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for managing oil drilling equipment based on big data analysis, characterized in that: include, By installing a variety of sensors, the operating parameters and environmental parameters of the equipment can be collected in real time; Based on the historical operation data of the equipment, calculate the dynamic baseline value of the equipment operation parameters and set the normal fluctuation range; By analyzing the operating parameters of multiple devices in the same operating area, the degree of correlation between devices is calculated and the correlation pattern between devices is identified; When the equipment operating parameters exceed the dynamic baseline range and the correlation between equipment changes abnormally, the equipment is judged to be abnormal and an early warning is triggered; Evaluate maintenance priorities and develop optimized maintenance plans based on anomaly detection results, equipment importance, and operating conditions.

2. The oil drilling equipment management method based on big data analysis according to claim 1, characterized in that: The multiple sensors include temperature sensors, pressure sensors, vibration sensors, flow meters, ambient temperature and humidity sensors, and air pressure sensors; The operating parameters of the equipment, including pressure, temperature, vibration and flow; The environmental parameters include ambient temperature, humidity and air pressure.

3. The oil drilling equipment management method based on big data analysis as claimed in claim 2, characterized in that: Based on the historical operation data of the equipment, the dynamic baseline value of the equipment operation parameters is calculated and the normal fluctuation range is set, including the following steps: Extract historical operation data of the equipment from the data storage system; Preprocess historical data; According to the equipment operation characteristics, set the size of the sliding window, set the data collection frequency, and calculate the moving average of each parameter and moving standard deviation ; Use the calculated moving average and moving standard deviation , calculate the lower limit and upper limit of each time point, and record the lower limit and upper limit as the normal fluctuation range of the parameter; Every time a new data point Arrived, the sliding window moves forward one data point and removes the earliest data point , and add new data points ; Recalculate the moving average within the current window and moving standard deviation , update the normal fluctuation range; Define weights for each data point , calculate the weighted moving average ,use Alternative moving average ; Define the operation mode detection mechanism to determine whether the mode changes occur by analyzing the operation parameters of the device; When the operating mode of the device changes, the baseline value is adjusted through weighted moving average, and the parameter multiples are dynamically adjusted according to the change of the operating mode. , and recalculate the lower and upper bounds.

4. The oil drilling equipment management method based on big data analysis as claimed in claim 3 is characterized by: The operation mode detection mechanism is defined to determine whether a mode change occurs by analyzing the operation parameters of the device, including the following steps: Conduct preliminary data analysis on the equipment operating parameters collected in real time to evaluate the importance of each parameter to the identification of the operating mode; Use correlation analysis to check the correlation between parameters and eliminate redundant parameters; Based on importance assessment and redundancy check, determine the key parameter set that will be used for operation mode detection; Based on the key parameter set, the data of different frequencies are synchronized and aligned by interpolation method, the data of each parameter is standardized, the statistical features of time series data are extracted by sliding window technology, and the feature dimension is reduced by principal component analysis; Select Gaussian mixture model GMM, determine the optimal number of clusters K through GMM model selection method, use K-Means algorithm for initial clustering, set the initial mean and covariance of GMM, use expectation maximization algorithm to train GMM, and optimize parameter settings; Use historical data to train the GMM model, and evaluate the fitting effect of the GMM model through cross-validation and statistical indicators; Set the sliding window size for real-time detection; Input the real-time collected equipment operation parameter data into the trained GMM model to calculate the probability of each sample belonging to each operation mode; When the mode of M consecutive data points is different from the previous one and the matching probability exceeds the set threshold, it is judged as a mode change.

5. The oil drilling equipment management method based on big data analysis according to claim 4 is characterized in that: By analyzing the operating parameters of multiple devices in the same operating area, calculating the degree of association between devices, and identifying the association pattern between devices, the following steps are included: Based on the acquired operating parameters of each device, the Pearson correlation coefficient is used to calculate the correlation coefficient between each set of device operating parameters; Construct a correlation matrix, where the rows and columns represent the operating parameters of different devices, and the values ​​in the matrix represent the correlation coefficients of the corresponding parameter pairs; Classify the correlation coefficients and identify highly correlated device pairs; Based on historical data, calculate the correlation coefficients between devices, count their distribution, and determine the normal range of the correlation coefficients between each group of devices through statistical analysis; The device whose current parameters deviate from the baseline is identified through the set dynamic baseline range, the correlation coefficient between the device and other devices in the same operating area is calculated, and it is determined whether the correlation coefficient is within the normal range. If it exceeds the normal range, it is recorded as an associated abnormality.

6. The oil drilling equipment management method based on big data analysis according to claim 5 is characterized in that: When the equipment operating parameters exceed the dynamic baseline range and the correlation between equipment changes abnormally, the equipment is judged to be abnormal and an early warning is triggered, including the following steps: Obtain the latest equipment operating parameter data of the equipment in real time, obtain the lower limit and upper limit of the current time point t, and determine whether the equipment operating parameters exceed the normal fluctuation range; If it is determined that the parameter exceeds the limit, the event is recorded; Obtain a list of all equipment that is judged to be exceeding the standard, and for each equipment whose parameters exceed the standard, calculate its correlation coefficient with other equipment in the same operating area; For each pair of devices, determine whether the correlation coefficient deviates from the historical average correlation coefficient beyond the set tolerance range, and record all device pairs that are determined to be abnormally correlated; Obtain all device pairing information that is determined to be abnormal from parameter exceeding limits and associated anomalies; Only when the device parameters exceed the standard and the associated abnormalities of other devices are met at the same time, the device is judged to be abnormal; After determining that the equipment is abnormal, early warning information is triggered through multiple channels. Maintenance personnel obtain the early warning information and respond to relevant information to handle the fault; The warning information includes device ID, abnormal parameters, current values, historical baseline values, a list of abnormal associated devices and a timestamp.

7. The oil drilling equipment management method based on big data analysis according to claim 6 is characterized in that: According to the abnormal detection results, the importance and operating status of the equipment, the maintenance priority is evaluated and an optimized maintenance plan is formulated, including the following steps: Identify abnormality severity, equipment criticality, and operational downtime as risk assessment indicators; Normalize the data of each indicator; The multi-index weighted scoring method is used to combine different risk factors according to weights to obtain a comprehensive risk score for the equipment; Based on the calculated comprehensive risk score, the equipment is divided into three risk levels: high, medium, and low; If the equipment is classified as high risk, immediately arrange maintenance tasks, increase monitoring frequency, track the equipment operating status in real time, and shorten the equipment maintenance cycle based on the risk score; For medium-risk equipment, regular equipment maintenance is carried out according to the scheduled maintenance plan. The maintenance cycle is dynamically adjusted according to the equipment operating status and risk score. The maintenance strategy is adjusted based on real-time monitoring and risk score. For low-risk equipment, maintenance is carried out according to the established maintenance plan, and the maintenance cycle of the equipment is extended based on the risk score.

8. A petroleum drilling equipment management system based on big data analysis, based on the petroleum drilling equipment management method based on big data analysis according to any one of claims 1 to 7, characterized in that: include, The data acquisition module is responsible for collecting the operating parameters and environmental parameters of the equipment in real time by installing a variety of sensors; The dynamic baseline setting module is responsible for calculating the dynamic baseline value of the equipment operating parameters based on the historical operating data of the equipment and setting the normal fluctuation range; The equipment association analysis module is responsible for calculating the degree of association between equipment and identifying the association pattern between equipment by analyzing the operating parameters of multiple equipment in the same operation area; The anomaly detection and warning module is responsible for determining that the equipment is abnormal and triggering an alarm when the equipment operating parameters exceed the dynamic baseline range and the correlation between the equipment changes abnormally; The maintenance strategy optimization module is responsible for evaluating maintenance priorities and formulating optimized maintenance plans based on anomaly detection results, equipment importance, and operating conditions.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the oil drilling equipment management method based on big data analysis described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the oil drilling equipment management method based on big data analysis described in any one of claims 1 to 7 are implemented.

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