Oil field operation data aggregation and analysis method based on big data

Abstract

The invention relates to the technical field of oil field operation data analysis and optimization, and discloses an oil field operation data aggregation and analysis method based on big data, which comprises the following steps: S1, data acquisition and aggregation: deploying a plurality of distributed data acquisition nodes in an oil field operation site, the data acquisition module is used for acquiring various types of data from a sensor, monitoring equipment and an external data source in real time, and the data types comprise environment and equipment data of temperature, pressure and flow. Through the big data and deep learning technology, intelligent analysis and real-time prediction of oil field operation data are realized, equipment monitoring and resource scheduling are optimized, the problems of data inconsistency, analysis lagging and prediction inaccuracy are effectively solved, the efficiency of oil field operation and the timeliness of equipment maintenance are improved, the self-adaptive capability of the system is enhanced, and the service life of the system is prolonged. And comprehensive decision support is provided, the failure rate is reduced, and the production efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oilfield operation data analysis and optimization, and specifically provides a method for aggregating and analyzing oilfield operation data based on big data. Background Art

[0002] In oilfield operations, traditional monitoring systems and data acquisition devices are widely used to obtain various types of operation data, such as temperature, pressure, flow rate, etc. However, the data acquisition capabilities of these devices are limited by their technical characteristics. Data is acquired on a single device basis, and there are problems such as inconsistent data formats and incompatible transmissions between different devices. In addition, existing technologies mostly rely on manual experience for oilfield operation scheduling and management, lacking an intelligent system to effectively analyze and predict data, resulting in a large number of potential production risks and equipment failures that cannot be discovered and resolved in a timely manner.

[0003] The existing technology consists of scattered sensors, equipment monitoring systems, and data processing modules. The work of these modules has not achieved sufficient linkage, resulting in a relatively lagged process of oilfield data acquisition, analysis, and decision-making. In terms of data processing, existing technologies mostly use simple statistical methods to process data, but fail to effectively solve problems such as data missing, noise interference, and inconsistent data formats, affecting the accuracy of analysis results. Traditional systems lack the ability of dynamic adjustment and cannot adapt to changes in the operation environment in a timely manner. Therefore, there are significant limitations in equipment status monitoring, trend prediction, and resource allocation. In contrast, the present invention can achieve effective integration, precise analysis, and dynamic prediction of multi-source data by adopting deep learning, big data technology, and intelligent optimization algorithms, thereby greatly improving the operation efficiency and the intelligent level of equipment management, and avoiding problems such as late warning and unreasonable resource allocation in traditional methods. Summary of the Invention

[0004] Aiming at the deficiencies of the existing technology, the present invention provides a method for aggregating and analyzing oilfield operation data based on big data, which solves the problems of lagged data acquisition, analysis, and decision-making, low intelligent level, and inability to effectively process multi-source data and real-time prediction in the existing technology.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A method for aggregating and analyzing oilfield operation data based on big data, comprising the following steps: S1. Data acquisition and aggregation: Deploy multiple distributed data acquisition nodes at the oilfield operation site to collect various types of data from sensors, monitoring devices, and external data sources in real time. The data types include environmental and equipment data such as temperature, pressure, and flow rate. Upload the acquired multi-source data to the central data management platform through a unified API interface, and the interface supports the compatibility of multiple data sources and processes the differences of different types of data sources; S2. Data preprocessing and cleaning: Clean and preprocess the collected data. The steps include removing redundant data, filling missing values, normalizing data, format conversion, and data denoising. Algorithms such as KNN filling method and mean filling method are used to fill missing values. S3. Big data analysis and deep learning model training: Based on the big data processing platform, analyze the cleaned data, and use deep learning algorithms to perform intelligent analysis on the data. The algorithms include convolutional neural network for oilfield monitoring image analysis, recurrent neural network for real-time time series data analysis, and long short-term memory network for long time series data analysis. S4. Anomaly detection and trend prediction: Based on the analysis results, conduct anomaly detection and trend prediction, monitor the equipment status and production parameters in oilfield operations in real time, and output corresponding warning information. Warnings are issued by setting thresholds or based on deviations from historical data. S5. Decision support and optimization: Provide decision support based on the analysis results, automatically generate optimization suggestions, and assist oilfield managers in operation scheduling, equipment maintenance, and resource allocation. The optimization suggestions include equipment overhaul, operation plan adjustment, and resource allocation optimization. S6. System deployment and maintenance: The system automatically adjusts the prediction model according to the feedback data to optimize the accuracy of the analysis results and decision suggestions.

[0006] Preferably, the data collection nodes in S1 support the access of multiple data sources, including but not limited to sensor data, equipment monitoring data, environmental data, and external data sources.

[0007] Preferably, the big data processing platform in S3 adopts a distributed computing framework, including but not limited to Apache Hadoop and Apache Spark. The platform supports batch processing and real-time stream processing, and can dynamically schedule computing resources according to different job requirements. The computing resources include computing nodes, storage nodes, and bandwidth.

[0008] Preferably, the deep learning algorithms in S3 are used for intelligent analysis of oilfield operation data, including: Convolutional neural network, used for image recognition of oilfield monitoring images to detect the status of oilfield equipment and abnormal working conditions; Recurrent neural network, used for analyzing real-time time series data to extract time-dependent features in the data; Long short-term memory network, used for analyzing long time series data to accurately predict trend changes in oilfield operations.

[0009] Preferably, in S4, the anomaly detection module combines the threshold algorithm and the statistical model based on the features output by the deep learning model to identify equipment failures and abnormal working conditions in oilfield operations in real time, and sends warning messages to the operators in a timely manner through the warning mechanism. The thresholds include the standard deviation threshold and the deviation threshold.

[0010] Preferably, in S4, the trend prediction module analyzes the change trends of key indicators in oilfield operations based on the long short-term memory network, provides short-term and long-term production forecasts, and optimizes the operation plan according to the prediction results. The optimization includes adjusting equipment operation parameters and adjusting the production process.

[0011] Preferably, in S2, the data cleaning and preprocessing include data denoising, missing value filling, standardization processing, data format conversion, and redundant data elimination. The denoising methods include filter denoising and deep learning-based denoising methods.

[0012] Preferably, in S6, the system is deployed on the cloud computing platform, and real-time data processing is performed through edge computing nodes. The edge computing nodes include a computing unit, a data storage unit, and a real-time processing module.

[0013] Preferably, in S5, the decision support module can automatically generate optimization suggestions according to the prediction results and the anomaly detection output, and dynamically adjust the operation scheduling, equipment maintenance, and resource allocation strategies through intelligent decision algorithms. The decision algorithms include reinforcement learning, decision trees, and genetic algorithms.

[0014] Preferably, in S1, the data transmission uses a low-latency and high-performance data transmission protocol, including but not limited to the MQTT and WebSocket protocols. The protocols support reliable transmission and real-time requirements.

[0015] The present invention provides a method for aggregating and analyzing oilfield operation data based on big data, having the following beneficial effects: 1. The present invention adopts a method for analyzing oilfield operation data based on big data technology and deep learning algorithms, achieving the technical effects of accurately predicting the equipment status and production trends in oilfield operations and anomaly detection. Compared with the solutions that rely on manual experience or traditional methods in the prior art, the present invention can greatly improve the accuracy of early warnings through intelligent data analysis and processing, identify potential problems in advance, and avoid the problem of slow response to sudden failures and abnormal situations in traditional methods.

[0016] 2. Through the multi-source data aggregation and cleaning technology, the present invention ensures the unity and high quality of various types of data during oilfield operations. The system can seamlessly process data of different types and sources, thus solving the problems of inconsistent data formats, data loss, or excessive noise in traditional methods. Through this technology, oilfield managers can obtain more accurate and complete data support, providing a solid foundation for subsequent analysis and decision-making.

[0017] 3. The present invention utilizes reinforcement learning and adaptive optimization technologies, enabling the system to self-learn and optimize decision-making suggestions. It achieves the effect of automatically adjusting the operation plan and equipment maintenance strategy in a changing oilfield operation environment. Compared with the static models in the prior art that cannot quickly adapt to changes, the present invention can dynamically adjust according to real-time data and historical feedback, ensuring that each optimization is more accurate and improving the adaptability and intelligence level of the system during long-term operation.

[0018] 4. The present invention combines deep learning and ensemble learning technologies to provide high-precision trend prediction and optimization suggestions, achieving the effect of jointly predicting multiple key operation parameters in oilfield operations and providing a comprehensive optimization plan. Compared with the prior art solutions that only perform single-parameter prediction, the present invention can more comprehensively optimize resource allocation and operation scheduling by comprehensively analyzing multiple variables, avoiding the problem of low overall efficiency caused by local optimization in traditional methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flowchart of the method steps of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0021] Please refer to the appended Figure 1 , an embodiment of the present invention provides a method for aggregating and analyzing oilfield operation data based on big data, including the following steps: S1. Data collection and aggregation. Deploy multiple distributed data collection nodes at the oilfield operation site to collect various types of data from sensors, monitoring devices, and external data sources in real time. The data types include environmental and equipment data such as temperature, pressure, and flow rate. The collected multi-source data is uploaded to the central data management platform through a unified API interface, and the interface supports the compatibility of multiple data sources and processes the differences of different types of data sources; In this embodiment, data acquisition nodes are deployed at various key positions in the oilfield, responsible for real-time acquisition of equipment status data and environmental data at the oilfield site. Each data acquisition node consists of multiple hardware systems, including sensors, data acquisition modules, gateways, and storage modules, etc. The sensors can collect various types of data such as temperature, pressure, flow rate, vibration, and noise. After being processed by the data acquisition module, the data is sent to the gateway through the communication network for preliminary aggregation. Subsequently, the data is uploaded to the central data management platform through a unified API interface for subsequent analysis and processing.

[0022] In a possible implementation, each data acquisition node supports the access of multiple data sources according to specific circumstances. For example, some nodes may be mainly responsible for the acquisition of environmental data such as temperature and air pressure, while other nodes may be responsible for the monitoring of equipment operating status such as flow meters and pressure sensors. Different data sources may use different protocols and formats during the acquisition process, and the acquisition nodes must be compatible to handle different types of data.

[0023] Specifically, in the oilfield site, data acquisition nodes generally use hardware platforms with sensor interfaces, which can interact with sensors through various communication protocols. For example, the MODBUS protocol is used to collect data from pressure sensors, and the CAN protocol is used for data transmission with device monitoring systems. In addition, the nodes can also use wireless communication technologies (such as Wi-Fi, Zigbee, LoRa, etc.) to send data to the central data platform to ensure that data transmission is not affected by the on-site environment.

[0024] In another embodiment, the data acquisition nodes can work in cooperation with edge computing devices. The edge computing devices can perform preliminary processing and filtering on the collected data, reduce the data transmission volume, and optimize the data transmission efficiency. Specifically, the edge computing devices can perform basic processing operations on the data according to preset algorithms, such as data cleaning, denoising, and format conversion. Only the processed data will be transmitted to the central platform for further analysis and decision support. The introduction of edge computing devices further improves the response speed of the system and avoids latency problems during data transmission.

[0025] To ensure the real-time and reliability of data, a low-latency and high-performance data transmission protocol is adopted in this embodiment. Generally, the data transmission between the acquisition nodes and the central data management platform uses efficient transmission protocols such as MQTT and WebSocket. These protocols can support the efficient transmission of large-scale data and have relatively low transmission latency. In particular, the MQTT protocol is widely used in Internet of Things devices, can support reliable message transmission, and can still ensure the reliability of data even in the case of unstable networks.

[0026] During the implementation process, the data collected by sensors is usually unstructured or semi-structured and needs to be uniformly standardized. In one implementation, for different types of sensor data, we designed a unified interface standard to ensure that regardless of the data source, they can be effectively converted into structured data and uploaded to the platform for subsequent analysis. This standardized interface not only improves the efficiency of data collection but also ensures the compatibility and scalability of the system.

[0027] During the data transmission process, corresponding encryption measures need to be adopted for different types of data to ensure data security. For example, for the key equipment monitoring data at the oilfield site, data transmission can be encrypted through TLS (Transport Layer Security Protocol) to prevent data leakage or tampering during transmission. For large-scale equipment monitoring data, the system should have high-throughput transmission capabilities to ensure good stability while transmitting large volumes of data.

[0028] During the data aggregation process, the integrity and timeliness of the data are crucial. To ensure data accuracy, the system can perform quality checks on the collected data and eliminate invalid or abnormal data. Specifically, the system can use rule-based verification algorithms to detect whether the data collected by sensors meets the preset standards or reasonable ranges. If the data does not meet the standards, the system will automatically mark the data as invalid and filter it.

[0029] As an option, redundant sensors can be added at the oilfield operation site to improve the reliability of data collection. When a certain sensor fails, the system will automatically switch to the backup sensor to continue data collection, thereby reducing the monitoring blind spots caused by equipment failures. Redundant sensors not only improve the reliability of data collection but also enhance the accuracy of data to a certain extent.

[0030] Generally speaking, through the above methods, the multi-source data collection and aggregation solution provided by the present invention can effectively solve the problems of collecting and processing multi-type and multi-source data in oilfield operations, ensuring the real-time, reliable, and secure nature of the data. The data collection module provides a basis for subsequent data cleaning, analysis, and decision-making, and also lays a solid foundation for the intelligent management of the system.

[0031] S2. Data preprocessing and cleaning, cleaning and preprocessing the aggregated data, the steps including removing redundant data, filling in missing values, standardizing data, format conversion, and data denoising, using algorithms such as KNN filling method and mean filling method to fill in missing values; In this embodiment, data cleaning is carried out first. This process mainly removes invalid data, abnormal data, and duplicate data. For the data collected by each type of sensor, the system checks its validity through preset rules. For example, if the temperature data exceeds the reasonable working range, the system will automatically mark it as abnormal data and eliminate it. In addition, for the situation of data loss or inconsistency caused by sensor failures, the system will also automatically identify it to avoid interfering with the overall data analysis.

[0032] In some embodiments, the system can adopt a statistics-based method to identify abnormal data. For example, the Z-value or standard deviation method is used to detect abnormal points in the data. If the Z-value of a certain data point is greater than the preset threshold, this data point will be regarded as abnormal and eliminated.

[0033] Next, for the processing of missing data, the system adopts several common filling methods. Generally, the system will select an appropriate filling strategy according to the context data where the missing data is located. In one possible implementation, the KNN (K-Nearest Neighbor) filling method is adopted, that is, by finding the K data points adjacent to the missing data and inferring the missing value based on the values of these data points. This method can effectively avoid the deviation that may be brought by simple mean filling.

[0034] In some other embodiments, the system adopts the mean filling method to process relatively simple missing data. Specifically, the system will fill the missing value according to the mean or median of the same data set, especially suitable for the situation where the proportion of missing values is not high. This method is simple and efficient, and can ensure the accuracy of data filling in most cases.

[0035] After the data cleaning is completed, the next stage is entered: data standardization. In oilfield operations, the data dimensions and units collected by various sensors vary greatly. For example, the pressure and temperature of oil wells may be expressed in different units, which requires unifying the data from different sources into a standardized form to ensure that they can be analyzed uniformly. The system uses a standardization algorithm to process the data and converts all data into a dimensionless form. Specifically, for each type of data, the system standardizes it through the following formula: ; where X is the original data, is the mean of this data category, is the standard deviation of this data category. After standardization, the mean of the data is 0 and the standard deviation is 1, ensuring the comparability and consistency of data with different dimensions.

[0036] In addition, to ensure data unity and compatibility, the system also performs data format conversion. In oilfield operations, the collected data may not only include numerical data, but also involve images, audio, or other unstructured data. For such unstructured data, the system will adopt corresponding data processing methods. For example, for equipment video surveillance data, the system will use image processing technology to convert it into structured data for subsequent analysis. This process not only improves the data processing efficiency but also ensures the seamless connection of different types of data.

[0037] In some embodiments, the system also supports the processing of time series data. In oilfield operations, data often has obvious time characteristics, and there is a strong time dependence between data points. To make full use of these time features, the system will preprocess the data through time series analysis methods to remove periodic fluctuations and seasonal variations in order to extract the most representative trend information. Specifically, the system can apply the moving average method or the exponential smoothing method to smooth the time series data, remove short-term fluctuations, and improve the predictability of the data.

[0038] As an option, the system can also perform data denoising. Due to environmental factors in the oilfield site, the collected signals may contain different degrees of noise, which affects subsequent analysis and prediction. At this time, the system will use a filtering algorithm to denoise the data. Specifically, a low-pass filter or a high-pass filter is used to remove high-frequency noise and retain low-frequency signals. In addition, deep learning-based denoising methods can also be applied to process some complex noise data to improve the denoising effect.

[0039] Generally speaking, the task of the data cleaning and preprocessing stage is to provide clean and standardized data for subsequent data analysis. In this embodiment, the various methods adopted are combined with each other to ensure the quality and usability of the data. This process provides a solid foundation for subsequent data modeling, analysis, and decision support, ensuring the smooth operation of subsequent modules.

[0040] Through this series of processing steps, the present invention can convert the raw data in oilfield operations into a high-quality data set, further providing a reliable data basis for tasks such as the training of deep learning algorithms, data analysis, and anomaly detection.

[0041] S3. Big data analysis and deep learning model training. Based on the big data processing platform, analyze the cleaned data, and use deep learning algorithms to perform intelligent analysis on the data. The algorithms include convolutional neural networks for oilfield monitoring image analysis, recurrent neural networks for real-time time series data analysis, and long short-term memory networks for long time series data analysis. In this embodiment, for different types of data, the system uses deep learning algorithms for analysis. According to the characteristics of different data in oilfield operations, the system will select appropriate algorithms for analysis to improve accuracy and efficiency.

[0042] For oilfield monitoring video or image data, the system uses a Convolutional Neural Network (CNN). CNN is a classic method for processing image data and can automatically extract spatial features in images. In some embodiments, the system uses CNN for condition monitoring and fault diagnosis of oilfield equipment. Specifically, the system can extract features of the operating state of the equipment from the images of the oilfield equipment and determine whether there are faults or abnormal conditions of the equipment through a trained CNN model. Usually, CNN can identify problems such as pipeline leaks and equipment looseness in oilfield monitoring images. These analysis results can provide timely warnings for equipment maintenance in the oilfield.

[0043] In some other embodiments, for the processing of time series data, the system uses a Recurrent Neural Network (RNN). RNN is particularly suitable for processing data with strong time correlation, such as pressure, temperature, and flow rate data collected by various sensors in the oilfield. These data have significant time dependence. RNN can capture long-term and short-term dependencies in the time series through recursive connections and then predict future trends.

[0044] For example, assume that the system needs to predict the production pressure of an oil well in the oilfield. RNN can analyze the pressure data over a past period of time, learn its change pattern, and then predict the future pressure value. As an option, RNN can be combined with a Long Short-Term Memory network (LSTM) to further enhance the processing ability for long time series data. In some embodiments, LSTM can effectively avoid the common problem of gradient vanishing in traditional RNN when dealing with long-term dependencies, thereby improving the prediction accuracy of the model.

[0045] The system is not limited to the prediction of time series data and can also use deep learning for anomaly detection. Generally, the system will combine RNN or LSTM for the detection of outliers. By training the model to identify normal and abnormal behaviors during oilfield operations, the system can monitor the operating state of the equipment in real time. Once an anomaly occurs, the system will immediately issue an alarm and propose possible causes of the fault and solutions. The main goal of this anomaly detection is to promptly detect equipment failures and avoid further expansion of losses.

[0046] In the data analysis phase, in addition to processing image and time series data, the system also uses deep learning to model other complex data types. Specifically, the system constructs a Fully Connected Neural Network (FCNN) to model structured data. With the maintenance records of oilfield equipment, the geographical information of the oilfield, and environmental factors as inputs, the system can predict key indicators such as resource requirements and production capacity in oilfield operations.

[0047] To ensure the accuracy and reliability of the model, in this embodiment, the Backpropagation algorithm in deep learning is used for model training. The Backpropagation algorithm calculates the gradient of the loss function and gradually adjusts the network weights, thereby making the prediction results closer to the true values. Specifically, the system will optimize the network parameters using the gradient descent method based on the collected dataset and labeled data, so that the error of the model gradually decreases, and finally an accurate and robust prediction model is obtained.

[0048] Generally, the training process of the model is iterative. In each round of training, the system will learn the model through a part of the known data and then verify the performance of the model through another part of the data. By continuously adjusting the parameters, the system can self-optimize in the changing oilfield operation environment to ensure that the model has strong generalization ability.

[0049] In another embodiment, to further improve the accuracy and generalization ability of the model, the system can also adopt the method of ensemble learning. Specifically, the system can combine multiple different types of deep learning models, such as CNN, RNN, and FCNN, and combine the results of multiple models through a weighted average or voting mechanism to obtain a more accurate prediction result.

[0050] For example, when predicting the production pressure of an oilfield, the system can make independent predictions through CNN, RNN, and FCNN respectively, and then obtain the final result through weighted average. Ensemble learning can effectively reduce the error of a single model, thereby improving the stability and reliability of the system.

[0051] For deep learning algorithms, the system also supports self-adjustment and optimization of the model. Specifically, when the system collects new operation data, it can update the existing model in real time through online learning. Through this self-learning mechanism, the system can gradually adapt to the changes in the oilfield operation environment and improve the accuracy and real-time performance of the prediction.

[0052] In this embodiment, during the data analysis and modeling process, the system adopts a method that combines advanced deep learning algorithms with traditional machine learning methods. It can not only accurately analyze the real-time data in oilfield operations but also provide decision-making support for oilfield production scheduling and equipment maintenance. The trained deep learning model can effectively help oilfield operators improve production efficiency, reduce equipment failure rates, and enhance resource utilization rates.

[0053] In summary, data analysis and modeling are the core parts of the technical solution of the present invention. Through the application of deep learning and machine learning technologies, the present invention can provide accurate data analysis results and give optimization suggestions based on the analysis results, helping oilfield operators make better decisions and improving the overall operation efficiency of the oilfield.

[0054] S4. Anomaly detection and trend prediction: Based on the analysis results, perform anomaly detection and trend prediction, monitor the equipment status and production parameters in oilfield operations in real time, and output corresponding early warning information, with early warning carried out by setting thresholds or based on the deviation from historical data. In this embodiment, anomaly detection first relies on the deep learning models constructed in the previous steps, especially convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs). Through the features extracted by these models, the system can accurately identify the possible abnormal situations that may occur during oilfield operations.

[0055] Specifically, the system will, based on the trained deep learning model and in combination with the set thresholds, detect the operating status of oilfield equipment in real time. For example, assume that there is a sudden temperature fluctuation or pressure anomaly in the equipment of a certain oil well in the oilfield. The system will use the LSTM model to capture this change trend. By analyzing the difference between historical data and real-time data, the system will automatically determine whether the fluctuation exceeds the predetermined safe range, thus triggering an anomaly warning.

[0056] In some embodiments, anomaly detection also combines rule-based algorithms, such as the standard deviation algorithm, Z-value algorithm, etc. Specifically, the system will calculate the historical mean and standard deviation of the key parameters in oilfield operations. If the deviation of a certain data point exceeds the set threshold, the system will mark it as abnormal, reminding the operator to pay attention to this problem. Through this method, the system can quickly identify the sudden anomalies caused by sensor failures or environmental changes.

[0057] For trend prediction, in this embodiment, an LSTM model is adopted to predict time series data. The LSTM model can effectively capture the long-term dependencies in the data and then accurately predict future trends. Specifically, the system will use the LSTM model to analyze the historical data of key operation parameters such as pressure, temperature, and flow rate in the oilfield, identify the long-term change trends therein, and then predict the changes of these parameters in the next few hours or days.

[0058] For example, in the daily operation of an oilfield, the pressure value usually shows a certain fluctuation pattern. The system uses LSTM to learn the data of the past few days and then predicts the trend of pressure change in the next 24 hours. If the system detects that the pressure is about to exceed the set safety range, it will issue an early warning in advance to remind the management to make adjustments or check the equipment.

[0059] As an option, the system can also optimize trend prediction by combining reinforcement learning algorithms. In this implementation, the system gradually optimizes the prediction model through self-adjusting learning to improve the prediction accuracy. Specifically, after each prediction is completed, the system will compare the actual result with the prediction result to generate feedback signals. These feedback signals will be used to further optimize the parameters of the LSTM model so that the model can better adapt to the changing environment in oilfield operations.

[0060] Generally, various data in oilfield operations may be disturbed by different factors, such as equipment failures and external environmental changes. These factors may cause sudden changes in the operating state of the equipment and even have unforeseen impacts. Therefore, when making predictions, the system not only relies on historical data but also combines real-time data and external environmental information to improve the accuracy of predictions.

[0061] In a possible implementation, the system is not limited to predicting a single operation parameter but can also achieve joint prediction of multiple parameters. For example, the system can simultaneously predict the change trends of multiple key indicators such as oil production, pressure, and temperature in the oilfield. These joint predictions can provide a more comprehensive decision-making basis for oilfield production scheduling.

[0062] It is worth mentioning that trend prediction does not solely rely on deep learning models. In some embodiments, the system also combines traditional statistical methods such as time series analysis and exponential smoothing method. These methods can effectively extract seasonal fluctuations and periodic changes in the data and further improve the accuracy of prediction results.

[0063] Generally speaking, anomaly detection and trend prediction are key aspects in the entire oilfield data analysis system. By combining deep learning algorithms with traditional statistical methods, the present invention can accurately detect various anomalies in oilfield operations and scientifically predict future production trends. Through this technology, oilfield managers can make preparations in advance, avoid potential risks, and thus optimize the overall production efficiency of the oilfield.

[0064] Ultimately, anomaly detection and trend prediction not only provide strong safety guarantees for oilfield operations but also offer important bases for decision-making in aspects such as production scheduling and equipment maintenance.

[0065] S5. Decision support and optimization, providing decision support based on analysis results, automatically generating optimization suggestions, and assisting oilfield managers in operation scheduling, equipment maintenance, and resource allocation. The optimization suggestions include equipment overhaul, operation plan adjustment, and resource allocation optimization. In this embodiment, the core of decision support is to combine the results of anomaly detection and trend prediction with the actual requirements of oilfield operations. The system automatically generates optimization suggestions based on the current operating status and future prediction trends. For example, when the system detects an anomaly in a certain device or predicts that the future production pressure will exceed the safety threshold, the system will automatically generate a reminder message and suggest equipment overhaul or adjustment of operation strategies. Specifically, the system will provide a series of optimization operation plans based on data analysis, such as the arrangement of equipment overhaul time, the adjustment of production plans, and the reallocation of resources.

[0066] In some embodiments, the system generates personalized optimization suggestions based on machine learning algorithms. For example, using reinforcement learning algorithms, the system can gradually learn how to optimize resource allocation and operation arrangements in different operating environments based on oilfield historical data and real-time feedback. Whenever the system implements an optimization measure, it will feedback the results to the decision-making module to further improve the accuracy of the optimization suggestions.

[0067] Specifically, the system first comprehensively considers various data such as the failure modes of equipment, operation loads, and environmental factors, and generates optimization plans based on prediction models. For example, when the output of a certain oil well is about to decline, the system will predict the possible types of equipment failures based on historical data and combine the environmental conditions at the operation site to give recommended overhaul strategies. These optimization suggestions can not only reduce the probability of equipment failures but also improve production efficiency.

[0068] As an option, the system also supports the coordinated optimization of multiple parameters. In some embodiments, the system not only optimizes the operation of a single device, but also can synchronously adjust the resource allocation of multiple operation links. For example, assuming that the oil production in the oilfield fluctuates, the system can simultaneously adjust the operation parameters of multiple oil wells to optimize the overall production efficiency. Through this coordinated optimization, the system can more comprehensively improve the efficiency of oilfield operations.

[0069] In another implementation, the system uses a data-based optimization model to schedule various operations. By establishing a mathematical model of oilfield operations, the system can calculate the optimal production scheduling plan in real time. For example, by analyzing real-time data and predictive data, the system generates the optimal operation sequence and equipment usage plan, thereby improving the utilization rate of oilfield resources and reducing waste caused by equipment idleness or overloading.

[0070] The optimization suggestions generated by the system are not limited to equipment maintenance. Generally, the system will also optimize resource allocation based on predicted production trends. For example, when the system predicts that the oil production in the oilfield will increase in the next period of time, the system will suggest allocating more resources in advance or adjusting the production plan to ensure the maximization of production.

[0071] To ensure the effectiveness of the suggestions, the system also adopts a feedback mechanism. After each optimization plan is executed, the system will evaluate according to the actual operation results and adjust the optimization strategy according to the evaluation results. This feedback mechanism ensures the dynamic adjustment and continuous improvement of the optimization plan. In this way, the system can gradually adapt to the changing situations in oilfield operations and continuously improve the accuracy of optimization suggestions.

[0072] In some embodiments, the decision support module can also provide a graphical user interface to help oilfield managers more intuitively understand the optimization suggestions. Specifically, the system will display the optimization suggestions to the managers in the form of charts, curves, etc., enabling them to quickly understand the current operation situation and make corresponding adjustments.

[0073] The optimization suggestions in this embodiment are not static, but are dynamically generated based on real-time data and historical feedback. This adaptive optimization process ensures that the system can always provide the best decision support for oilfield operations.

[0074] Generally speaking, decision support and optimization suggestions are an indispensable part of the present invention. Through real-time data analysis and prediction, the system can provide scientific and accurate optimization suggestions for oilfield managers, thereby improving the production efficiency of the oilfield and the reliability of equipment. With the help of this technology, the management of oilfield operations will become more efficient and intelligent.

[0075] S6. System Deployment and Maintenance. The system automatically adjusts the prediction model based on the feedback data to optimize the accuracy of the analysis results and decision-making suggestions; In this embodiment, the system first relies on the previously generated optimization suggestions and decision support results to collect the actual feedback data after implementing these suggestions. The system evaluates the effectiveness of the optimization suggestions by continuously comparing the actual results with the expected results. For example, when the system suggests maintenance or adjustment of the operation plan for a certain device, the system will track the operating status of the device after implementation to see if it effectively reduces the failure frequency or improves the production efficiency. Through this feedback, the system can gradually understand the implementation effect of the optimization suggestions.

[0076] In some embodiments, the system uses a reinforcement learning algorithm for self-optimization. In the reinforcement learning framework, the system is regarded as an agent that can adjust its behavior strategy according to the feedback of the environment. Specifically, the system evaluates the execution results of each optimization decision and feeds the reward signal back into the learning model, thereby continuously adjusting the decision-making strategy. This process enables the system to dynamically adjust the optimization suggestions according to the changes in the oilfield operation environment to ensure the best performance during long-term operation.

[0077] For example, in oilfield operations, due to factors such as equipment aging and external environment changes, the effectiveness of the optimization suggestions initially given by the system gradually decreases. Through reinforcement learning, the system can "remember" the historical feedback information and make more accurate optimization suggestions for future similar situations. The system will gradually improve its adaptability to different environmental changes by adjusting the reward function and policy parameters, avoiding a one-size-fits-all optimization scheme.

[0078] Specifically, the process of reinforcement learning involves the exploration and exploitation of the agent in the state space. When the system receives the data feedback from the oilfield operation, it will select an action according to the current state and observe the result of this action. After the result is fed back to the system, the system optimizes the decision-making process by updating its policy. For example, when the maintenance strategy of the equipment does not achieve the expected effect, the system will adjust the strategy and increase the exploration of this strategy until a better solution is found.

[0079] In another implementation, the system combines an adaptive filtering algorithm to optimize its parameter update process. Generally, the data in oilfield operations will have certain fluctuations, and the variation laws of different types of data are different. To address this challenge, the system will automatically adjust the filtering parameters according to the trend and fluctuation of the historical data, remove the influence of abnormal fluctuations, and ensure that the system can accurately predict the long-term change trend. This adaptive ability enables the system to not only cope with short-term changes but also adapt to long-term operation condition adjustments.

[0080] As an option, the system can also further enhance its learning ability through a self-supervised learning mechanism. In this implementation, the system learns through pre-set unlabeled data, thereby reducing the dependence on manually labeled data. Specifically, the system analyzes the potential patterns existing in oilfield operations, such as the correlation between well pressure and temperature, and automatically discovers new features and patterns to improve the generalization ability of the model.

[0081] In this embodiment, the system continuously self-optimizes through online learning. When the system obtains new data, it automatically updates the parameters of the existing model. This method ensures that the system can gradually improve and enhance accuracy over time. For example, the seasonal changes in oilfield operations may cause periodic fluctuations in some production parameters, and the system can capture these change patterns through online learning and automatically adjust the prediction model to improve the accuracy of future predictions.

[0082] For example, in a certain oilfield, the temperature fluctuates greatly between summer and winter, resulting in changes in the workload of oilfield equipment. The system adjusts the prediction model through self-learning to adapt to these periodic changes. Through continuous learning and updating, the system can provide adaptive optimization suggestions in different seasons, reduce equipment failure rates, and ensure production efficiency.

[0083] In some embodiments, the system not only supports the self-learning of a single model but also can further enhance adaptability through model integration. For example, the system can integrate multiple machine learning models (such as deep neural networks, support vector machines, etc.) and fuse the output results of each model through weighted averaging. This integration method can reduce the bias of a single model and improve the accuracy and stability of the system.

[0084] In addition, the self-learning process of the system is also closely related to data quality. To ensure the effectiveness of self-learning, the system evaluates the data quality during each update and filters according to the data quality. For example, the system will filter out abnormal data or noise data to ensure that the updated model has good generalization ability.

[0085] Through this series of self-learning and adaptive optimization processes, the present invention can ensure that in the long-term operation, the system can adapt to the changes in the oilfield operation environment, continuously provide accurate predictions and optimization suggestions. The result of each optimization will promote the further development of the system, improve the efficiency of oilfield operations and the reliability of equipment. This mechanism not only enhances the intelligent level of oilfield management but also provides solutions for new challenges that may arise in the future.

[0086] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for aggregating and analyzing oilfield operation data based on big data, characterized in that, It includes the following steps: S1. Data collection and aggregation: Deploy multiple distributed data collection nodes at the oilfield operation site to collect various types of data from sensors, monitoring devices, and external data sources in real time. The data types include environmental and equipment data such as temperature, pressure, and flow rate. The collected multi-source data is uploaded to the central data management platform through a unified API interface, and the interface supports the compatibility of multiple data sources and handles the differences of different types of data sources; S2. Data preprocessing and cleaning: Clean and preprocess the aggregated data. The steps include removing redundant data, filling in missing values, standardizing data, format conversion, and data denoising. Algorithms such as KNN filling method and mean filling method are used to fill in missing values; S3. Big data analysis and deep learning model training: Based on the big data processing platform, analyze the cleaned data, and use deep learning algorithms to perform intelligent analysis on the data. The algorithms include convolutional neural network for oilfield monitoring image analysis, recurrent neural network for real-time time series data analysis, and long short-term memory network for long time series data analysis; S4. Anomaly detection and trend prediction: Based on the analysis results, conduct anomaly detection and trend prediction, monitor the equipment status and production parameters in oilfield operations in real time, and output corresponding early warning information. Warnings are issued by setting thresholds or based on deviations from historical data; S5. Decision support and optimization: Provide decision support based on the analysis results, automatically generate optimization suggestions, and assist oilfield managers in operation scheduling, equipment maintenance, and resource allocation. The optimization suggestions include equipment overhaul, adjustment of operation plans, and optimization of resource allocation; S6. System deployment and maintenance: The system automatically adjusts the prediction model according to the feedback data to optimize the accuracy of the analysis results and decision-making suggestions.

2. The method for aggregating and analyzing oilfield operation data based on big data according to claim 1, characterized in that, In S1, the data collection nodes support the access of multiple data sources, including but not limited to sensor data, equipment monitoring data, environmental data, and external data sources.

3. A method for aggregating and analyzing oilfield operation data based on big data according to claim 1, characterized in that, In S3, the big data processing platform adopts a distributed computing framework, including but not limited to Apache Hadoop and Apache Spark. The platform supports batch processing and real-time stream processing, and can dynamically schedule computing resources according to different job requirements. The computing resources include computing nodes, storage nodes, and bandwidth.

4. A method for aggregating and analyzing oilfield operation data based on big data according to claim 1, characterized in that, In S3, the deep learning algorithms are used for intelligent analysis of oilfield operation data, including: Convolutional neural network, used for image recognition of oilfield monitoring images to detect the status and abnormal conditions of oilfield equipment; Recurrent neural network, used for analyzing real-time time series data to extract time-dependent features in the data; Long short-term memory network, used for analyzing long time series data to accurately predict trend changes in oilfield operations.

5. A method for aggregating and analyzing oilfield operation data based on big data according to claim 1, characterized in that, In S4, the anomaly detection module, based on the features output by the deep learning model, combines the threshold algorithm and the statistical model to identify equipment failures and abnormal conditions in oilfield operations in real time, and sends early warning information to the operators in a timely manner through the early warning mechanism. The thresholds include standard deviation thresholds and deviation thresholds.

6. A method for aggregating and analyzing oilfield operation data based on big data according to claim 1, characterized in that, In S4, the trend prediction module analyzes the changing trends of key indicators in oilfield operations based on long short-term memory networks, provides short-term and long-term production forecasts, and optimizes the operation plan according to the prediction results. The optimization includes adjusting equipment operation parameters and adjusting the production process.

7. A method for aggregating and analyzing oilfield operation data based on big data according to claim 1, characterized in that, In S2, data cleaning and preprocessing include data denoising, missing value filling, standardization processing, data format conversion, and redundant data elimination. The denoising methods include filter denoising and deep learning-based denoising methods.

8. A method for aggregating and analyzing oilfield operation data based on big data according to claim 1, characterized in that, In S6, the system is deployed on a cloud computing platform and real-time data processing is performed through edge computing nodes. The edge computing nodes include a computing unit, a data storage unit, and a real-time processing module.

9. A method for aggregating and analyzing oilfield operation data based on big data according to claim 1, characterized in that In S5, the decision support module can automatically generate optimization suggestions based on the prediction results and anomaly detection outputs, and dynamically adjust operation scheduling, equipment maintenance, and resource allocation strategies through intelligent decision algorithms. The decision algorithms include reinforcement learning, decision trees, and genetic algorithms.

10. A method for aggregating and analyzing oilfield operation data based on big data according to claim 1, characterized in that, In S1, low-latency and high-performance data transmission protocols are used for data transmission, including but not limited to MQTT and WebSocket protocols. The protocols support reliable transmission and real-time requirements.

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