An intelligent monitoring system for oil well production process

Abstract

The application discloses an intelligent monitoring system for oil well production process, and relates to the technical field of oil well production monitoring.The system comprises a data acquisition module, which is used for acquiring multi-type sensor data in the oil well production process; and a data processing module, which is used for receiving the multi-type sensor data acquired by the data acquisition module, performing data cleaning, time sequence alignment and standardization processing on the multi-type sensor data, and constructing a multi-dimensional feature set representing the running state of the oil well from the processed data, the multi-dimensional feature set representing the complex mutual correlation between the multi-type sensor data.The multi-type sensor data is acquired by the data acquisition module, and is cleaned, time sequence aligned, standardized processed and multi-dimensional feature set constructed by the data processing module, so that the complex correlation between the multi-type data is effectively captured, and the problem that the existing system is insufficient in analyzing the mutual influence of multi-data is solved.

Description

Technical Field

[0001] This invention relates to the field of oil well production monitoring technology, specifically to an intelligent monitoring system for the oil well production process. Background Technology

[0002] Oil wells generate various data during production, such as temperature, pressure, and flow rate. This data is collected by sensors and transmitted to computers for analysis. Currently, common monitoring systems rely on manually set alarm thresholds; an alarm is triggered when a certain data point exceeds or falls below a set value. These systems can detect obvious anomalies, such as sudden pressure drops or abnormal temperature increases, but they are slow to react to slowly changing potential problems. With the development of artificial intelligence technology, some new systems are beginning to attempt to automatically analyze data using algorithms, but there is still significant room for improvement in their practical effectiveness.

[0003] The most critical shortcoming of current oil well monitoring systems lies in their inability to predict slowly accumulating minute anomalies in advance. For example, problems such as gradual scaling inside the tubing or slight wear on pumps and valves initially only cause very subtle data changes. Traditional systems either fail to detect these issues entirely or only trigger alarms when the problem becomes severe. This delayed detection leads to many small problems accumulating into major malfunctions, resulting in high maintenance costs and potentially causing production shutdowns. While existing intelligent algorithms can analyze the trends of single types of data, they are insufficient for analyzing the complex interrelationships between multiple data types, making it difficult to detect potential problems from small fluctuations. This issue seriously affects the long-term stable operation of oil wells, necessitating a new method to detect minute anomalies earlier and more accurately. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an intelligent monitoring system for oil well production processes, which solves the problems of existing technologies, such as the inability to identify slowly accumulating minor anomalies in advance and insufficient correlation analysis of multiple types of data.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent monitoring system for oil well production processes, comprising:

[0006] The data acquisition module is used to collect various types of sensor data during the oil well production process;

[0007] The data processing module is used to receive multi-type sensor data collected by the data acquisition module, perform data cleaning, time-series alignment and standardization on the multi-type sensor data, and construct a multi-dimensional feature set representing the operating status of the oil well from the processed data. The multi-dimensional feature set represents the complex interrelationship between the multi-type sensor data.

[0008] The pattern construction module is used to receive the multi-dimensional feature set constructed by the data processing module. Based on the multi-dimensional feature set, a multi-dimensional data pattern model under normal operation of the oil well is constructed through a deep learning model. The multi-dimensional data pattern model represents the temporal evolution law of multi-type sensor data under normal operating conditions.

[0009] The real-time analysis module is used to continuously receive real-time oil well production data, process the real-time oil well production data in real time, generate a real-time feature set, and input the real-time feature set into the multi-dimensional data pattern model constructed by the pattern construction module.

[0010] The anomaly identification module is used to identify minor deviations in real-time oil well production data from the multidimensional data pattern of normal operation based on the deviation between the prediction results of the multidimensional data pattern model on the real-time feature set and the actual observed data. Minor deviations refer to deviations where the magnitude of data change is less than a preset threshold but the duration reaches a preset duration.

[0011] The early warning generation module is used to generate early warning information about potential anomalies in oil wells based on minor deviations identified by the anomaly identification module.

[0012] Furthermore, the data processing module includes:

[0013] The data cleaning unit is used to remove outliers and fill in missing values ​​in data from various types of sensors.

[0014] The time-series alignment unit is used to perform time-series alignment on the cleaned multi-type sensor data.

[0015] The data standardization unit is used to perform feature scaling and standardization processing on aligned multi-type sensor data.

[0016] Furthermore, the data processing module also includes:

[0017] The feature calculation unit is used to calculate the statistical characteristics of multiple types of sensor data within a preset time window. The statistical characteristics include mean and variance.

[0018] The correlation feature generation unit is used to generate cross features that characterize the complex interrelationships between data from multiple types of sensors based on statistical features. The cross features are obtained by calculating the covariance or mutual information between the data.

[0019] Furthermore, the pattern construction module includes:

[0020] The model training unit is used to input multi-dimensional feature sets into the deep learning model for training. The deep learning model includes a long short-term memory network or a gated recurrent unit network.

[0021] The model optimization unit is used to optimize the parameters of the deep learning model based on training performance metrics.

[0022] Furthermore, the anomaly detection module includes:

[0023] The prediction output acquisition unit is used to input the real-time feature set into the pattern model and obtain the prediction output.

[0024] The deviation calculation unit is used to calculate the deviation between the predicted output and the real-time feature set;

[0025] The minute deviation judgment unit is used to determine whether the deviation meets the minute deviation condition.

[0026] Furthermore, the early warning generation module includes:

[0027] The early warning information encapsulation unit is used to encapsulate early warning information, including the warning time, anomaly type, and affected parameters;

[0028] The early warning sending unit is used to send early warning information to the terminal of the designated operator or the remote control center.

[0029] Furthermore, the pattern construction module also includes:

[0030] The model update unit is used to receive new oil well operation data and labeled abnormal event data, and to perform incremental training or periodic retraining on the deep learning model.

[0031] The performance evaluation unit is used to evaluate the accuracy and recall of the updated deep learning model.

[0032] Furthermore, it also includes:

[0033] The historical data storage module is used to store historical production data of oil wells, multi-dimensional feature sets, and historical minor deviation records;

[0034] The visualization module is used to generate trend charts of oil well operating status or timeline charts of abnormal events based on historical data.

[0035] Furthermore, the anomaly detection module also includes:

[0036] The anomaly tracing unit is used to trace the manifestation of minor deviations in the original data and locate relevant physical parameters;

[0037] The confidence assessment unit is used to evaluate the confidence level of minor biases, which is based on their persistence and similarity to historical patterns.

[0038] Furthermore, the system also includes:

[0039] The system management module is used to manage the operation of the data acquisition module, data processing module, pattern building module, real-time analysis module, anomaly identification module, and early warning generation module.

[0040] The user interaction interface is used to provide system configuration, data query, real-time data display, and early warning information display.

[0041] Beneficial effects

[0042] This invention acquires multi-type sensor data through a data acquisition module, and then cleans, aligns, and standardizes the data through a data processing module to construct a multi-dimensional feature set. This effectively captures the complex correlations between multiple data types, solving the problem of insufficient analysis of the mutual influence of multiple data in existing systems. The pattern construction module builds a multi-dimensional data pattern model of normal operation based on a deep learning model. Combined with a real-time analysis module and an anomaly identification module, it can accurately identify small deviations in data changes that are less than a preset threshold but last for a preset duration, overcoming the deficiency of traditional systems in predicting slowly accumulating minor anomalies in advance. The early warning generation module generates and sends early warnings containing key information in a timely manner, preventing small problems from accumulating into major failures, reducing maintenance costs and the risk of production stoppage. At the same time, the system's adaptability and practicality are improved through modules such as model updates and historical data storage, ensuring the long-term stable operation of oil wells. Attached Figure Description

[0043] Figure 1 This is a system structure diagram of the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] Please see Figure 1 This invention provides an intelligent monitoring system for oil well production processes, comprising:

[0046] The data acquisition module is used to collect various types of sensor data during the oil well production process;

[0047] The data processing module is used to receive multi-type sensor data collected by the data acquisition module, perform data cleaning, time-series alignment and standardization on the multi-type sensor data, and construct a multi-dimensional feature set representing the operating status of the oil well from the processed data. The multi-dimensional feature set represents the complex interrelationship between the multi-type sensor data.

[0048] The pattern construction module is used to receive the multi-dimensional feature set constructed by the data processing module. Based on the multi-dimensional feature set, a multi-dimensional data pattern model under normal operation of the oil well is constructed through a deep learning model. The multi-dimensional data pattern model represents the temporal evolution law of multi-type sensor data under normal operating conditions.

[0049] The real-time analysis module is used to continuously receive real-time oil well production data, process the real-time oil well production data in real time, generate a real-time feature set, and input the real-time feature set into the multi-dimensional data pattern model constructed by the pattern construction module.

[0050] The anomaly identification module is used to identify minor deviations in real-time oil well production data from the multidimensional data pattern of normal operation based on the deviation between the prediction results of the multidimensional data pattern model on the real-time feature set and the actual observed data. Minor deviations refer to deviations where the magnitude of data change is less than a preset threshold but the duration reaches a preset duration.

[0051] The early warning generation module is used to generate early warning information about potential anomalies in oil wells based on minor deviations identified by the anomaly identification module.

[0052] Specifically, the data acquisition module is deployed at key locations such as oil wellheads and oil pipelines, and includes temperature sensors, pressure sensors, flow sensors, and vibration sensors. For example, the temperature sensor uses a thermocouple sensor with an accuracy of ±0.5℃, acquiring data every 10 seconds; the pressure sensor uses a strain gauge sensor with a range of 0-10MPa, acquiring data every 5 seconds. These sensors transmit the acquired data, including temperature, pressure, flow rate, and vibration, to the data processing module via a wireless transmission module.

[0053] The data processing module first processes the received data. During data cleaning, the 3σ rule is used to remove outliers; that is, data values ​​deviating from the mean by more than three times the standard deviation are considered outliers and removed. For missing values, if the missing time interval is short, linear interpolation is used to fill them. For example, if three points of traffic data are missing within a certain time period, the slope is calculated using adjacent valid data to fill the missing points. Time alignment uses the system's unified timestamp as a benchmark, adjusting for time differences caused by acquisition delays from different sensors to ensure that various types of data at the same time correspond. Standardization uses Z-score standardization, with the formula: Where x represents the original data, μ represents the mean of the data type, and σ represents the standard deviation of the data type, this processing transforms data of different magnitudes to the same scale. When constructing a multi-dimensional feature set, it not only includes the features of individual sensor data, but also calculates the correlation features between different types of data, such as calculating the cross product of temperature and pressure, the ratio of flow rate to vibration, etc., to characterize the complex interrelationships between multiple types of sensor data.

[0054] After receiving a multi-dimensional feature set, the pattern construction module uses a Long Short-Term Memory (LSTM) network to construct a multi-dimensional data pattern model. This model consists of an input layer, three hidden layers, and an output layer. The number of neurons in the input layer matches the dimension of the multi-dimensional feature set. The hidden layers contain 128, 64, and 32 neurons respectively, and the number of neurons in the output layer matches the dimension of the input features. During training, the multi-dimensional feature set under normal operating conditions is divided into a training set and a validation set in a 7:3 ratio. The Adam optimizer is used with a learning rate of 0.001 and 100 iterations. Through training, the model is able to accurately represent the temporal evolution patterns of various types of sensor data under normal operating conditions.

[0055] The real-time analysis module continuously receives real-time oil well production data through the data receiving interface, performs real-time processing in the same way as the data processing module (cleaning, time-series alignment, and standardization), generates a real-time feature set, and inputs it into the multidimensional data pattern model in real time.

[0056] In the anomaly detection module, the preset threshold is set to 5%, and the preset duration is set to 24 hours. After the multidimensional data pattern model predicts the real-time feature set, it calculates the deviation between the prediction result and the actual observed data. The deviation calculation formula is as follows: Where y pred For the prediction result, y obs This data is based on actual observations. Deviations less than 5% but lasting for 24 hours are considered minor deviations.

[0057] After identifying minor deviations, the early warning generation module generates early warning information that includes the time of the anomaly, the possible types of anomalies (such as oil pipe scaling, pump and valve wear, etc.), and the affected parameters (such as flow rate, pressure, etc.), and sends it to the display screen in the monitoring center and the mobile terminals of relevant personnel via a wired network.

[0058] This embodiment can effectively solve the problem that traditional systems cannot predict small anomalies that accumulate slowly in advance. Through multi-dimensional analysis and deep learning models, it can detect anomalies in the early stages of problems such as oil pipe scaling and slight wear of pumps and valves, and issue early warnings.

[0059] In this embodiment, the data processing module includes:

[0060] The data cleaning unit is used to remove outliers and fill in missing values ​​in data from various types of sensors.

[0061] The time-series alignment unit is used to perform time-series alignment on the cleaned multi-type sensor data.

[0062] The data standardization unit is used to perform feature scaling and standardization processing on aligned multi-type sensor data.

[0063] Specifically, when handling outliers, the data cleaning unit identifies and removes temperature data if a temperature value suddenly exceeds three times the historical normal range at a given moment, and this value shows no reasonable trend compared to data from before and after it. For missing values, if the amount of missing data accounts for less than 5% of the total data volume for that time period, the average of the preceding and following data is used for filling. For example, if 10 pressure data points are missing within a one-hour period, the average of the 10 valid data points before and after that hour is used to fill the missing data. If the missing percentage is large, it is marked as a missing data segment and will be avoided during subsequent pattern construction.

[0064] The timing alignment unit first obtains the timestamp of each sensor's data. Then, using the system's standard time axis as a reference, it calculates the deviation between each sensor's data timestamp and the standard time axis, and adjusts the data to the standard time point through interpolation or resampling. For example, if the timestamp of a pressure sensor's data is 2 seconds later than the standard time, the data of that sensor is shifted forward by 2 seconds to align with the other data.

[0065] The data standardization unit uses the min-max standardization method to process the aligned data, and the formula is as follows: Where x represents the original data, x min x is the minimum value of this type of data. max This represents the maximum value of this type of data, and the processed data range is between [0,1].

[0066] Through the processing of each unit in the data processing module, the accuracy, consistency, and standardization of the data can be ensured, providing a reliable data foundation for subsequent pattern building and anomaly identification.

[0067] In this embodiment, the data processing module further includes:

[0068] The feature calculation unit is used to calculate the statistical characteristics of multiple types of sensor data within a preset time window. The statistical characteristics include mean and variance.

[0069] The correlation feature generation unit is used to generate cross features that characterize the complex interrelationships between data from multiple types of sensors based on statistical features. The cross features are obtained by calculating the covariance or mutual information between the data.

[0070] Specifically, the feature calculation unit sets a preset time window of 1 hour, calculating the statistical characteristics of multiple types of sensor data within this window once per hour. For example, for traffic flow data, it calculates the average traffic flow over 1 hour. Where n is the number of traffic data points in 1 hour, x i For the i-th flow data point; calculate the variance.

[0071] Based on the aforementioned statistical features, the correlation feature generation unit calculates the covariance and mutual information between different types of data. The formula for calculating covariance is as follows: Where X and Y are two different types of data, μ X μ Y These are their respective means. The formula for calculating mutual information is: Where p(x,y) is the joint probability distribution of X and Y, and p(x) and p(y) are the marginal probability distributions of X and Y, respectively. For example, the covariance and mutual information of temperature and pressure data can be calculated as the cross-features between them.

[0072] By generating these statistical and cross-features, we can more comprehensively reflect the operating status of oil wells, capture the intrinsic relationships between multiple types of data, and improve the accuracy of subsequent model construction.

[0073] In this embodiment, the pattern construction module includes:

[0074] The model training unit is used to input multi-dimensional feature sets into the deep learning model for training. The deep learning model includes a long short-term memory network or a gated recurrent unit network.

[0075] The model optimization unit is used to optimize the parameters of the deep learning model based on training performance metrics.

[0076] Specifically, the model training unit uses a Gated Recurrent Unit (GRU) network as the deep learning model. The input layer of this model receives a multi-dimensional feature set and contains two hidden layers with 64 and 32 neurons respectively. The output layer outputs predicted features with the same dimension as the input features. During training, the multi-dimensional feature set is divided into consecutive sequence segments in chronological order. Each sequence segment contains data from the past 24 hours as input and data from the next hour as the output label. Mean squared error is used as the loss function, as shown in the formula: Where m is the sample size, y i For actual labels, These are the model's predicted values.

[0077] The model optimization unit uses the validation set accuracy during training as the primary performance metric. When the validation set accuracy no longer improves, the learning rate is adjusted, gradually decreasing from the initial 0.001 to 0.0001. Simultaneously, the number of neurons in the hidden layer is adjusted, and the optimal neuron configuration is selected by comparing the model's performance with different numbers of neurons. For example, when the hidden layer has 64 and 32 neurons, the validation set accuracy is 92%, while adjusting to 128 and 64 neurons improves the accuracy to 95%, so the latter configuration is chosen.

[0078] Through model training and optimization, the constructed multidimensional data pattern model can more accurately fit the data patterns under normal operating conditions of oil wells, thereby improving the prediction accuracy of real-time data.

[0079] In this embodiment, the anomaly detection module includes:

[0080] The prediction output acquisition unit is used to input the real-time feature set into the pattern model and obtain the prediction output.

[0081] The deviation calculation unit is used to calculate the deviation between the predicted output and the real-time feature set;

[0082] The minute deviation judgment unit is used to determine whether the deviation meets the minute deviation condition.

[0083] Specifically, the prediction output acquisition unit organizes the real-time feature set according to the input format required by the pattern model. For example, it combines the real-time feature sets every 5 minutes into an input sequence and inputs it into the multidimensional data pattern model. The model outputs the corresponding prediction feature set as the prediction output.

[0084] The bias calculation unit calculates the absolute and relative biases between the predicted output and the real-time feature set for each feature dimension. The formula for the absolute bias is d. abs =|y pred -y obs The relative deviation formula is: Where y pred To predict the output, y obs This is real-time feature set data.

[0085] In the minor deviation judgment unit, the preset change threshold is 3%, and the preset duration is 12 hours. Timing begins when the relative deviation is less than 3%, and if the duration reaches 12 hours, it is determined to meet the minor deviation condition. For example, if the relative deviation of a certain feature remains at 2.5% for 12 hours, it is judged as a minor deviation.

[0086] This anomaly detection module can accurately identify anomalies that have small but persistent changes, avoiding the omission of such anomalies by traditional systems.

[0087] In this embodiment, the early warning generation module includes:

[0088] The early warning information encapsulation unit is used to encapsulate early warning information, including the warning time, anomaly type, and affected parameters;

[0089] The early warning sending unit is used to send early warning information to the terminal of the designated operator or the remote control center.

[0090] Specifically, when generating early warning information, the warning time is accurate to the second. The anomaly type is determined based on the characteristics corresponding to minor deviations and historical failure cases. For example, when there is a minor deviation in the correlation between flow rate and pressure, it is determined that it may be due to oil pipe scaling. The affected parameters clearly list the sensor data types related to the anomaly, such as flow rate and pressure. The encapsulated information is organized in JSON format.

[0091] The early warning sending unit transmits early warning information to the designated operator's mobile app via a wireless network, and simultaneously to the monitoring system at the remote control center via an industrial Ethernet network. The warning is displayed as a red pop-up on the monitoring system interface, accompanied by an audible alert. For example, when a minor deviation such as slight wear of a pump or valve is detected, information including the specific warning time, the type of "pump / valve wear" anomaly, and the affected "vibration and pressure" parameters will be sent out.

[0092] This early warning generation module can promptly transmit abnormal information to relevant personnel, enabling them to take timely measures to address the issue.

[0093] In this embodiment, the pattern construction module further includes:

[0094] The model update unit is used to receive new oil well operation data and labeled abnormal event data, and to perform incremental training or periodic retraining on the deep learning model.

[0095] The performance evaluation unit is used to evaluate the accuracy and recall of the updated deep learning model.

[0096] Specifically, the model update unit is designed to undergo periodic retraining every three months. Incremental training is also performed when new well operation data is received and reaches 20% of the original training data. During incremental training, some low-level parameters of the model are frozen, and only the top-level parameters are adjusted. Periodic retraining uses all new data and historical data to retrain the model. Anomaly event data is labeled by technicians based on actual maintenance records and inspection reports, serving as negative samples for model training.

[0097] The performance evaluation unit uses accuracy and recall as evaluation metrics. The accuracy formula is: The recall formula is Where TP represents the number of correctly identified anomalous samples, FP represents the number of normal samples incorrectly identified as anomalous, and FN represents the number of unidentified anomalous samples. When the updated model achieves both accuracy and recall above 90%, it is deemed qualified and put into use.

[0098] Through model updates and evaluations, the model can adapt to changes in the operating conditions of oil wells and maintain good performance.

[0099] In this embodiment, it also includes:

[0100] The historical data storage module is used to store historical production data of oil wells, multi-dimensional feature sets, and historical minor deviation records; the visualization module is used to generate trend charts of oil well operation status or timeline charts of abnormal events based on historical data.

[0101] Specifically, the historical data storage module uses a distributed database for data storage, storing historical oil well production data in time-series partitions, with each six-month period forming a partition. Multi-dimensional feature sets and historical minor deviation records are associated with their corresponding timestamps for easy querying and tracing. For example, historical temperature data and corresponding feature sets from January to June 2024 can be queried.

[0102] The visualization module uses a chart library to generate various visualization charts. The oil well operation status trend chart uses time as the horizontal axis and sensor data values ​​or characteristic values ​​as the vertical axis, with different types of data represented by curves of different colors. The anomaly event timeline chart marks the time points and durations of historical minor deviations on the timeline and labels the corresponding anomaly types. For example, it can generate a flow data trend chart and an anomaly event timeline chart for the past year, intuitively displaying flow changes and anomaly occurrences.

[0103] The historical data storage and visualization module facilitates the analysis of oil well operation history and anomalies by staff, providing a reference for oil well maintenance and management.

[0104] In this embodiment, the anomaly identification module further includes: an anomaly tracing unit, used to trace the performance of minor deviations in the original data and locate relevant physical parameters; and a confidence evaluation unit, used to evaluate the confidence of minor deviations, the confidence being based on their persistence and similarity to historical patterns.

[0105] Specifically, the anomaly tracing unit uses reverse mapping to trace the features corresponding to minute deviations back to the original sensor data, analyzing the specific manifestation of the deviation in the original data. For example, a minute deviation of a certain cross-feature can be traced back to a slow rise in the original temperature data over a specific time period. Simultaneously, it locates the physical parameters related to the deviation, such as the temperature of the oil well produced fluid.

[0106] In the confidence assessment unit, persistence accounts for 60% of the weight, and historical pattern similarity accounts for 40%. The persistence score is calculated based on the duration of the minor deviation; the longer the duration, the higher the score. Historical pattern similarity is obtained by calculating the similarity between the current minor deviation pattern and previously confirmed anomalous patterns; the higher the similarity, the higher the score. The confidence formula is C = 0.6 × S. c +0.4×S s S c For sustained scoring, Ss This represents the similarity score to historical patterns, with a confidence level ranging from 0 to 100. A higher score indicates higher confidence. For example, if a small deviation persists for a long time and has a high similarity to historical oil pipe scaling patterns, the confidence score would be 85.

[0107] The supplementary unit of this anomaly identification module can help staff understand the root cause and credibility of anomalies, thereby improving the accuracy of anomaly judgment.

[0108] In this embodiment, the system further includes: a system management module for managing the operation of the data acquisition module, data processing module, pattern construction module, real-time analysis module, anomaly identification module, and early warning generation module; and a user interaction interface for providing system configuration, data query, real-time data display, and early warning information display.

[0109] Specifically, the system management module monitors and schedules the operation of each module through a central controller. When a module malfunctions, it automatically issues an alarm and attempts to restart the module. Simultaneously, it records the operation logs of each module, including startup time, amount of data processed, and fault information. For example, if communication with a sensor in the data acquisition module is interrupted, the system management module promptly issues an alarm and records the fault time and sensor number.

[0110] The user interface is a web page. After logging in with an account and password, staff can configure the system, such as setting the data collection frequency and adjusting the anomaly detection threshold. The data query function supports querying historical data by time, data type, and other criteria. The real-time data display area shows the data from each sensor in real time in the form of dashboards and graphs. The early warning information display area lists all early warning information in chronological order, and details can be viewed. For example, staff can adjust the preset duration of anomaly detection from 24 hours to 18 hours through the interface.

[0111] In summary, this invention acquires multi-type sensor data through a data acquisition module, and then cleans, aligns, and standardizes the data through a data processing module to construct a multi-dimensional feature set. This effectively captures the complex correlations between multiple data types, solving the problem of insufficient analysis of the mutual influence of multiple data in existing systems. The pattern construction module builds a multi-dimensional data pattern model of normal operation based on a deep learning model. Combined with a real-time analysis module and anomaly identification module, it can accurately identify small deviations in data change that are less than a preset threshold but last for a preset duration, overcoming the deficiency of traditional systems in predicting slowly accumulating minor anomalies in advance. The early warning generation module generates and sends early warnings containing key information in a timely manner, preventing small problems from accumulating into major failures, reducing maintenance costs and the risk of production stoppage. At the same time, the system's adaptability and practicality are improved through modules such as model updates and historical data storage, ensuring the long-term stable operation of oil wells.

[0112] The system management module and user interface improve the system's manageability and ease of use, making it easier for staff to operate and maintain the system.

[0113] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0114] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent monitoring system for oil well production processes, characterized in that, The method comprises the following steps: a data acquisition module is configured to collect multi-type sensor data in the oil well production process; a data processing module is configured to receive the multi-type sensor data collected by the data acquisition module, perform data cleaning, time series alignment and standardization processing on the multi-type sensor data, and construct a multi-dimensional feature set representing the running state of the oil well from the processed data, the multi-dimensional feature set representing the complex mutual correlation between the multi-type sensor data; the data processing module further comprises: a feature calculation unit configured to calculate statistical features of the multi-type sensor data within a preset time window, the statistical features including mean and variance; an associated feature generation unit configured to generate cross features representing the complex mutual correlation between the multi-type sensor data based on the statistical features, the cross features being obtained by calculating the covariance or mutual information between the data; a pattern construction module configured to receive the multi-dimensional feature set constructed by the data processing module, and construct a multi-dimensional data pattern model under the normal running state of the oil well based on the multi-dimensional feature set by using a deep learning model, the multi-dimensional data pattern model representing the time series evolution rule of the multi-type sensor data under the normal working condition; the pattern construction module further comprises: a model updating unit configured to receive new oil well running data and labeled abnormal event data, and perform incremental training or periodic retraining on the deep learning model; a performance evaluation unit configured to evaluate the accuracy and recall rate of the updated deep learning model; a real-time analysis module configured to continuously receive real-time oil well production data, perform real-time processing on the real-time oil well production data, generate a real-time feature set, and input the real-time feature set into the multi-dimensional data pattern model constructed by the pattern construction module; an anomaly identification module configured to identify a slight deviation in the real-time oil well production data from the normal running state multi-dimensional data pattern according to the deviation between the prediction result of the multi-dimensional data pattern model on the real-time feature set and the actual observation data, the slight deviation being a deviation with a data change amplitude less than a preset threshold but a duration reaching a preset time length; the anomaly identification module further comprises: an anomaly tracing unit configured to trace the manifestation of the slight deviation in the original data and locate the related physical parameters; a confidence evaluation unit configured to evaluate the confidence of the slight deviation, the confidence being based on the persistence and historical pattern similarity of the slight deviation; an early warning generation module configured to generate early warning information of potential anomalies in the oil well according to the slight deviation identified by the anomaly identification module.

2. The intelligent monitoring system for oil well production processes according to claim 1, characterized in that, The data processing module comprises: a data cleaning unit configured to perform outlier rejection and missing value filling on the multi-type sensor data; a time series alignment unit configured to perform time series alignment on the cleaned multi-type sensor data; a data standardization unit configured to perform feature scaling and standardization processing on the aligned multi-type sensor data.

3. The intelligent monitoring system for oil well production processes according to claim 1, characterized in that, The pattern construction module comprises: a model training unit configured to input the multi-dimensional feature set into the deep learning model for training, the deep learning model including a long short-term memory network or a gated recurrent unit network; a model optimization unit configured to optimize the parameters of the deep learning model according to the training performance indicators.

4. The intelligent monitoring system for oil well production processes according to claim 1, characterized in that, The anomaly identification module comprises: The prediction output acquisition unit is configured to input the real-time feature set into the pattern model to acquire a prediction output; The deviation calculation unit is configured to calculate a deviation between the prediction output and the real-time feature set; The slight deviation judgment unit is configured to judge whether the deviation meets a slight deviation condition.

5. The intelligent monitoring system for oil well production processes according to claim 1, characterized in that, The early warning generation module comprises: The early warning information packaging unit is configured to package early warning information, and the information comprises a warning time, an abnormal type and an affected parameter; The early warning sending unit is configured to send the early warning information to a designated operator terminal or a remote control center.

6. The intelligent monitoring system for oil well production processes according to claim 1, characterized in that, The system further comprises: The historical data storage module is configured to store oil well historical production data, multi-dimensional feature sets and historical slight deviation records; The visualization module is configured to generate an oil well operation state trend graph or an abnormal event time axis graph according to the historical data.

7. The intelligent monitoring system for oil well production processes according to claim 1, characterized in that, The system further comprises: The system management module is configured to manage the operation of the data acquisition module, the data processing module, the pattern construction module, the real-time analysis module, the abnormality identification module and the early warning generation module; The user interaction interface is configured to provide system configuration, data query, real-time data display and early warning information display.

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