Intelligently-controlled oil field gathering and transportation system and method

Abstract

The invention discloses an intelligent control oil field gathering and transportation system and method. The system comprises a data acquisition system used for acquiring oil field production data through sensors installed at different positions of an oil field; the data analysis system is used for carrying out feature extraction on the oil field production data and extracting data feature information; performing statistics and analysis on the oil field production data according to the data feature information, mining a data relationship and a data development trend, and obtaining a data analysis result; predicting oil field future production according to the data analysis result to obtain oil field production prediction data; and the intelligent control system is used for automatically controlling oil field production according to the oil field production data and a predetermined control strategy and optimizing and adjusting the control strategy according to the real-time production data in the production process. Automatic control and optimization of an oil field gathering and transportation system and dynamic adjustment of a control strategy are achieved, manual intervention is reduced, and production efficiency and safety are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oilfield production management, and particularly to an oilfield gathering and transportation system and method with intelligent control. Background Art

[0002] During the oilfield production process, taking necessary safety management measures can improve the safety of oilfield production, reduce the safety risks during oilfield oil production, and better complete the oil production tasks.

[0003] Traditional oilfield production management methods generally adopt manual control and management. Problems in production are discovered through manual inspections, problems in production are improved through manual adjustments, and analysis and decision-making are carried out through manual data analysis. All kinds of control operations during the production process also need to be completed manually. Summary of the Invention

[0004] The inventors of the present application found that the existing oilfield gathering and transportation systems controlled by means such as manual inspections and manual adjustments have the following problems:

[0005] The manual inspection cycle is long and the efficiency is low, and problems cannot be discovered in time;

[0006] Manual adjustment is easily affected by factors such as experience and environment, and there are errors;

[0007] When making manual analysis and decision-making, due to the huge amount of oilfield data, it is difficult to achieve rapid and accurate data analysis and decision-making;

[0008] Manual control is difficult to achieve optimal control of the oilfield gathering and transportation system.

[0009] In short, the oilfield gathering and transportation system controlled by means of manual inspections and manual adjustments has low efficiency and high human resource costs, resulting in great potential safety hazards.

[0010] In view of the above problems, the present invention is proposed to provide an oilfield gathering and transportation system and method with intelligent control that overcomes the above problems or at least partially solves the above problems.

[0011] An embodiment of the present invention provides an oilfield gathering and transportation system with intelligent control, including: a data acquisition system, a data analysis system, and an intelligent control system;

[0012] The data acquisition system is used to collect oilfield production data through sensors installed at different positions in the oilfield;

[0013] A data analysis system is used to extract features from the oilfield production data to obtain data feature information; statistically analyze the oilfield production data based on the data feature information to explore data relationships and data development trends, and obtain data analysis results; predict the future production of the oilfield based on the data analysis results to obtain oilfield production prediction data.

[0014] An intelligent control system is used to automatically control the oilfield production according to the oilfield production data and a pre-determined control strategy, and optimize and adjust the control strategy according to the real-time production data during the production process.

[0015] In some alternative embodiments, the data acquisition system includes:

[0016] Multiple types of sensors are installed at the acquisition positions of oil wells, water wells, and pipelines in the oilfield for oilfield production data at the acquisition positions of oil wells, water wells, and pipelines.

[0017] A data transmission module is used to send the acquired oilfield production data to the data analysis system.

[0018] In some alternative embodiments, the data analysis system includes:

[0019] A data preprocessing module is used to clean, denoise, and correct the acquired oilfield production data, reduce the dimension and extract features from the processed oilfield production data using selected machine learning methods and data mining methods, extract data feature information, and normalize the data features.

[0020] A data analysis module is used to statistically analyze the oilfield production data based on the data feature information using selected statistical and analysis methods to explore data relationships and data development trends, and obtain data analysis results; the data analysis methods include at least one of the following methods: linear regression or logistic regression analysis methods for analyzing data association relationships and influence relationships between data, clustering analysis methods for analyzing data similarities and differences, and time series analysis methods for analyzing data development trends based on time series and predicting future development trends.

[0021] A prediction module is used to predict the future production of the oilfield based on the data analysis results using selected prediction models and simulation methods to obtain oilfield production prediction data.

[0022] In some alternative embodiments, the data analysis system further includes:

[0023] A data storage module, using a distributed database system, is used to store the oilfield production data.

[0024] In some alternative embodiments, the data storage module adopts the distributed storage architecture of Hadoop, and this storage architecture can process the oilfield production data as follows:

[0025] Data segmentation: Segment the big data in the oilfield production data into multiple small files according to certain segmentation rules;

[0026] Data backup: Back up the data on multiple nodes;

[0027] Data recovery: Implement data recovery by using the data block replication mechanism provided by Hadoop.

[0028] In some alternative embodiments, the intelligent control system includes:

[0029] A data processing module, which is used to process and clean the oilfield production data, including data screening, denoising, and completion operations;

[0030] A control strategy module, which is used to formulate control strategies for oilfield production; the control strategy includes PID control, which controls the specified parameters of the oilfield production data to make the oilfield production data meet the preset requirements; the specified parameters include at least one of error, deviation, and integral; and optimize and adjust the control strategy according to the real-time production data during the production process;

[0031] An intelligent decision-making module, which is used to train an intelligent decision-making model using the selected deep learning algorithm based on the pre-determined control strategy with the oilfield production data, and make intelligent decisions and controls on the oilfield production process based on the trained intelligent decision-making model.

[0032] In some alternative embodiments, the intelligent control system further includes:

[0033] An alarm and early warning module, which is used to give early warning prompts and provide reference solutions after abnormal situations occur in the production environment;

[0034] A remote monitoring module, which is used to send the oilfield production data, production prediction data, control strategy, and automatic control data to relevant terminal devices for display, and obtain the oilfield production data and production control information input by production management personnel through the terminal devices.

[0035] In some alternative embodiments, the data processing module includes:

[0036] A data screening sub-module, which is used to screen the data that meets the conditions from the oilfield production data according to the set screening conditions;

[0037] A data denoising sub-module, which is used to remove the noise in the oilfield production data by using the wavelet denoising method;

[0038] The data completion submodule is used to complete the oilfield production data using the interpolation method.

[0039] In some optional embodiments, an intelligently controlled oilfield gathering and transportation method is implemented using any of the intelligently controlled oilfield gathering and transportation systems described above.

[0040] The embodiment of the present invention further provides an intelligently controlled oilfield gathering and transportation method, which is characterized by comprising:

[0041] Collect oilfield production data through sensors installed at different locations in the oilfield;

[0042] Extracting features from the oilfield production data to obtain data feature information; performing statistics and analysis on the oilfield production data based on the data feature information, mining data relationships and data development trends, and obtaining data analysis results; predicting future production of the oilfield based on the data analysis results to obtain oilfield production prediction data;

[0043] According to the oilfield production data and the predetermined control strategy, the oilfield production is automatically controlled, and the control strategy is optimized and adjusted according to the real-time production data during the production process.

[0044] An embodiment of the present invention further provides a computer storage medium, characterized in that the computer storage medium stores computer executable instructions, and when the computer executable instructions are executed by a processor, the intelligently controlled oilfield gathering and transportation method described in any one of claims 9-10 is implemented.

[0045] An embodiment of the present invention also provides a terminal device, characterized in that it includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the intelligently controlled oilfield gathering and transportation method described in any one of claims 9-10 is implemented.

[0046] The beneficial effects of the above technical solution provided by the embodiment of the present invention include at least:

[0047] The oilfield gathering and transportation system provided by the embodiments of the present invention extracts and analyzes the characteristics of the oilfield production data collected by the data acquisition system through the data analysis system, makes predictions on the future production of the oilfield based on the analysis results, helps production management personnel make decisions, and improves production efficiency and benefits; realizes the automatic control of oilfield production based on the oilfield production data through the intelligent control system, adapts to the changes in the production environment, conducts automatic control, reduces manual intervention, and improves the robustness and adaptability of the system; can also adjust and optimize the control strategy in a timely manner according to the dynamic changes in production, adapt to the changes in the production environment, and improve the robustness and adaptability of the system. In addition, a visual interface is provided in the system, and production management personnel can view the production data and analysis reports in real time through the interface, make production decisions and adjust the control strategy, and there is also a permission management mechanism in the system, which realizes the control and management of the system functions according to the permission levels of different users, ensuring the safe and stable operation of the system.

[0048] In summary, the present invention realizes the automatic control and optimization of the oilfield gathering and transportation system, realizes comprehensive production management and collaborative operation, reduces manual intervention, reduces costs, and improves production efficiency and safety.

[0049] Other features and advantages of the present invention will be described in the following specification, and part of them will become obvious from the specification, or be understood by implementing the present invention. The objectives and other advantages of the present invention can be realized and obtained through the structures specifically pointed out in the written specification, claims, and drawings.

[0050] The technical solutions of the present invention will be further described in detail below through the drawings and embodiments. Description of the Drawings

[0051] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention, and do not constitute a limitation to the present invention. In the drawings:

[0052] Figure 1 It is a schematic diagram of the overall structure of the intelligent control oilfield gathering and transportation system in the embodiments of the present invention;

[0053] Figure 2 It is a schematic diagram of the structure of the data acquisition system in the embodiments of the present invention;

[0054] Figure 3 It is a schematic diagram of the structure of the data analysis system in the embodiments of the present invention;

[0055] Figure 4 It is a schematic diagram of the structure of the intelligent control system in the embodiments of the present invention;

[0056] Figure 5This is a flowchart of the intelligent control method for oilfield gathering and transportation in the embodiments of the present invention. Detailed implementation manners

[0057] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0058] To solve the problems of low efficiency, high cost, and great potential safety hazards in the traditional oilfield production management method in the prior art, the embodiments of the present invention provide an intelligent control oilfield gathering and transportation system and method, which can quickly and accurately obtain data and make analysis and decisions, realizing the automatic control and optimization of the oilfield gathering and transportation system.

[0059] The embodiments of the present invention provide an intelligent control oilfield gathering and transportation system and method, and its overall structural schematic diagram is as Figure 1 shown. The oilfield gathering and transportation system 1 includes: a data acquisition system 11, a data analysis system 12, and an intelligent control system 13;

[0060] The data acquisition system 11 is used to collect oilfield production data through sensors installed at different positions in the oilfield;

[0061] The data analysis system 12 is used to extract feature information from the oilfield production data, extract data feature information; statistically analyze and analyze the oilfield production data according to the data feature information, mine data relationships and data development trends, and obtain data analysis results; predict the future production of the oilfield according to the data analysis results to obtain oilfield production prediction data;

[0062] The intelligent control system 13 is used to automatically control the oilfield production according to the oilfield production data and a pre-determined control strategy, and optimize and adjust the control strategy according to the real-time production data during the production process.

[0063] In some optional embodiments, the data acquisition system monitors various parameters during the production process through on-site installed sensors and other devices, realizing the real-time monitoring and control of the production process. The sensors may include temperature sensors, pressure sensors, flow sensors, oil quality sensors, etc., which are specifically determined according to the actual situation.

[0064] The above data acquisition system, its structure is as Figure 2 shown, and includes: various types of sensors 111 and a data transmission module 112.

[0065] Multiple types of sensors 111 are installed at the acquisition positions of oil wells, water wells, and pipelines in the oilfield for collecting oilfield production data at the acquisition positions of oil wells, water wells, and pipelines. The sensors can collect various parameters during the oilfield production process, including oil well production, water injection volume of water wells, pipeline pressure, temperature, pressure, flow rate, oil product quality, etc.

[0066] The data transmission module 112 is used to send the collected oilfield production data to the data analysis system. The oilfield production data can be transmitted to the data storage module, the data analysis and processing system, and the intelligent control system through wired or wireless networks.

[0067] Optionally, the above data acquisition system 11 may further include a data storage module 113 for storing the collected data in a database for subsequent processing and analysis.

[0068] In some alternative embodiments, the data analysis system 12 collects, processes, and analyzes sensor data and other production data to provide decision-making support and reference for production management personnel, and realizes the optimization and management of the production process.

[0069] The above data analysis system 12 has a structure as Figure 3 shown, including: a data preprocessing module 121, a data analysis module 122, and a prediction module 123.

[0070] The data preprocessing module 121 is used to clean, denoise, and correct the collected oilfield production data, and uses selected machine learning methods and data mining methods to reduce the dimension and extract features of the processed oilfield production data, extract data feature information, and perform normalization processing on the data features.

[0071] The data preprocessing module 121 can perform preprocessing operations such as cleaning, denoising, and correction on the collected raw data, and extract valuable features and information. The data preprocessing module 121 can use technologies such as machine learning and data mining to extract features and reduce the dimension of the data, reduce the data dimension and complexity, and reduce the calculation amount and storage space. The data mining and machine learning algorithms used by the data preprocessing module include, but are not limited to, principal component analysis (PCA), minimum-maximum normalization (Min-Max Scaling) algorithm, and independent component analysis (ICA) and other algorithms. The redundant features in the data set are extracted by principal component analysis (PCA), the feature values in the data set are normalized by algorithms such as minimum-maximum normalization (Min-Max Scaling), and the noise components in the data set are separated by algorithms such as independent component analysis (ICA), thereby improving the quality of the data.

[0072] The data analysis module 122 is used to perform statistics and analysis on the oilfield production data according to the selected statistical and analysis methods based on the data characteristic information, mine the data relationships and data development trends, and obtain the data analysis results. The data analysis methods include at least one of the following methods: linear regression or logistic regression analysis methods for analyzing data correlation relationships and influence relationships between data, clustering analysis methods for analyzing data similarities and differences, and time series analysis methods for analyzing data development trends based on time series and predicting future development trends.

[0073] The data analysis module 122 can analyze and mine the preprocessed data to discover the relationships and patterns between the data. The data analysis module 122 can use a variety of statistical and analysis methods for data analysis and mining, including but not limited to regression analysis, clustering analysis, time series analysis, etc. Through the analysis of the data, the correlation and trend between the data can be discovered, providing a basis for subsequent prediction and decision-making. For example: The relationship between the production of each oil well and the influence of various parameters on the production can be obtained by using regression analysis; Clustering analysis is used to study the similarities and differences in the production data of different oil wells, and the oil wells with similar characteristics are divided into the same group, thereby identifying the differences between different oil wells, providing a basis for formulating targeted oilfield development strategies; Time series analysis is used to master the characteristics of time series data such as trends, periodicity, and seasonality, for studying the change trends and periodicity of oil well production and predicting future production. Through time series analysis, the change trends and periodicity of future production can be predicted, helping decision-makers formulate reasonable adjustment strategies. Based on the data analysis results, the correlation and trend between the data can be discovered, facilitating the analysis and processing of abnormal situations and optimization plans in the production process, and providing a basis for subsequent prediction and decision-making.

[0074] The implementation method of the data analysis module can adopt a variety of statistical and analysis algorithms, including methods such as linear regression, logistic regression, decision tree, support vector machine, etc., for analyzing and modeling different data requirements.

[0075] The prediction module 123 is used to predict the future production of the oilfield according to the data analysis results, using the selected prediction model and simulation method to obtain the oilfield production prediction data.

[0076] The prediction module 123 can predict and simulate the future production data, providing predictions and optimization suggestions for the production process. The prediction module can adopt the prediction and simulation methods of time series models. Through the learning and simulation of historical data, it predicts the future production data and trends, providing a basis for the optimization and decision-making of the production process. The implementation method of the prediction module is to use the ARIMA algorithm to predict and simulate the data.

[0077] Optionally, the above data analysis system 12 further includes:

[0078] A data storage module 124, which adopts a distributed database system and is used to store oilfield production data.

[0079] The data storage module 124 can store various parameter data collected during the production process, including temperature, pressure, flow rate, oil product quality, etc. The data storage module adopts a highly reliable distributed database system, supports real-time data collection and storage, and also supports offline data import and batch processing. The implementation method of the data storage module is to adopt distributed storage technology to distribute and store data on multiple nodes to ensure the security and reliability of the data.

[0080] The data storage module 124 adopts a distributed storage architecture based on Hadoop, and this storage architecture can process the oilfield production data as follows:

[0081] Data splitting: Split the large data in the oilfield production data into multiple small files according to certain splitting rules; the splitting rules can be set as needed, and the size of each file can also be selected. For example, the size of each small file is generally 64MB or 128MB to avoid the load pressure on the system caused by a single large file.

[0082] Data backup: Back up data on multiple nodes; in a distributed storage system, by backing up data on multiple nodes, the security and reliability of the data can be guaranteed. The HDFS (Hadoop Distributed File System) provided by Hadoop can be used to implement data backup.

[0083] Data recovery: Adopt the data block replication mechanism provided by Hadoop to implement data recovery. That is, each data block is defaultly backed up to multiple nodes. When a node fails, the system can automatically switch to the backup node to read data, thus ensuring the reliability of the data.

[0084] Data storage modules can be set in both the data acquisition system 11 and the data analysis system 12, or one of them can be set with a data storage module.

[0085] In some optional embodiments, the intelligent control system, by adopting advanced control theories and methods and combining with the actual situation in the oilfield gathering and transportation process, realizes the automatic control and optimization of each link such as oil production, transportation, and storage. By carrying out intelligent control and optimization on each link in the production process, the production efficiency is improved and the cost is reduced.

[0086] The above intelligent control system 13 has a structure as Figure 4As shown in the figure, it includes: a data processing module 131, a control strategy module 132, and an intelligent decision-making module 133.

[0087] The data processing module 131 is used to process and clean the oilfield production data, including data screening, denoising, and completion operations;

[0088] The control strategy module 132 is used to formulate control strategies for oilfield production; the control strategies include PID control, which controls the specified parameters of the oilfield production data to make the oilfield production data meet the preset requirements; the specified parameters include at least one of error, deviation, and integral; and optimize and adjust the control strategy according to the real-time production data during the production process;

[0089] The intelligent decision-making module 133 is used to train an intelligent decision-making model using a selected deep learning algorithm based on the pre-determined control strategy with the oilfield production data, and make intelligent decisions and controls on the oilfield production process based on the trained intelligent decision-making model.

[0090] Optionally, the data processing module 131 includes:

[0091] The data screening sub-module is used to screen the qualified data from the oilfield production data according to the set screening conditions; the required data can be screened according to conditions such as data type and data quality. The screening conditions can also include a specified time period, a specified category, or a combination of multiple conditions. For example, select the data within a specified time period, or only select a certain category of data, or select a certain data type of data within a specified time period.

[0092] The data denoising sub-module is used to remove the noise in the oilfield production data by using the wavelet denoising method; improve the accuracy and reliability of the data.

[0093] The data completion sub-module is used to complete the oilfield production data by using the interpolation method. For the situation of missing data, the interpolation method can be used to complete the data to ensure the integrity and accuracy of the data.

[0094] The control strategy module 132 is the core part of the entire intelligent control system, responsible for integrating and managing various control algorithms and models, and controlling and optimizing each link in the production process. The control strategy module 132 can adopt the PID control method to control the error, deviation, and integral, so that the system can quickly and stably reach the set value. It includes oil well production control, water injection volume control of water wells, pipeline pressure control, etc.

[0095] Based on the collected data and control strategies, the intelligent decision-making module 133 adopts the intelligent control algorithm Deep Q Network (DQN) of deep reinforcement learning, combines physical models and data models, and uses historical data for model training to achieve the automatic control and optimization of the oilfield gathering and transportation system. During the production process, the intelligent decision-making module can dynamically adjust control strategies to adapt to changes in the production environment. The embedded control algorithms and models of the intelligent decision-making module 133 adopt advanced control theories and methods, and combine the actual situation in the oilfield gathering and transportation process for intelligent control and optimization. Specifically, methods such as PID control, model predictive control, and optimization algorithms can be used to achieve the automatic control and optimization of various links such as oil production, transportation, and storage.

[0096] In some alternative embodiments, the above intelligent control system further includes:

[0097] An alarm and early warning module 134, which is used to give an early warning prompt in a timely manner and provide corresponding reference solutions after abnormal situations occur in the production environment;

[0098] A remote monitoring module 135, which is used to send oilfield production data, production prediction data, control strategies, and automatic control data to relevant terminal devices for display, and obtain the oilfield production data and production control information input by production management personnel through the terminal devices. The remote monitoring module 135 can achieve remote monitoring and control of the oilfield gathering and transportation system through network connection, facilitating production management personnel to grasp the production status in real time.

[0099] The above oilfield gathering and transportation system is an oilfield gathering and transportation system based on intelligent control. Through intelligent control and data analysis technologies, it can quickly and accurately obtain data and conduct analysis and decision-making, realizing the automatic control and optimization of the oilfield gathering and transportation system, improving production efficiency, reducing production costs, and being applicable to oilfield production management of various scales. The oilfield gathering and transportation system has the following advantages:

[0100] 1. It realizes the automatic control and optimization of the oilfield gathering and transportation system, reduces manual intervention, and improves production efficiency and safety.

[0101] 2. Adopting intelligent control strategies and decision-making modules, it can dynamically adjust control strategies to adapt to changes in the production environment, improving the robustness and adaptability of the system.

[0102] 3. Adopting a data analysis module and a prediction module, it can process and analyze the collected data, provide data reports and predictive analysis, help production management personnel make decisions, and improve production efficiency and benefits.

[0103] The above may also include other production management systems, such as personnel management systems, equipment management systems, financial management systems, etc. These systems are interfaced with the intelligent control system and the data analysis system to achieve comprehensive production management and collaborative operations.

[0104] In the implementation process of the present invention, corresponding software and hardware systems need to be designed and developed, and the systems need to be tested and verified to ensure their stability and reliability. The specific implementation plans and technical details can be adjusted and modified according to the actual situation, but it does not affect the core technical solutions and ideas of the present invention.

[0105] The above-mentioned oilfield gathering and transportation system provided by the embodiments of the present invention, in which the intelligent control system and the data analysis system can be interfaced with other oilfield production management systems to achieve comprehensive production management and collaborative operations, improve production efficiency and reduce costs. At the same time, a visual interface and an alarm mechanism are provided in the system. Production management personnel can view production data and analysis reports in real time through the interface, make production decisions and adjust control strategies, and monitor and alarm abnormal situations in the production process in real time to remind production management personnel to take timely measures to avoid production accidents. In addition, a permission management mechanism is also provided in the system to control and manage the system functions according to the permission levels of different users to ensure the safe and stable operation of the system.

[0106] Implementation Case 1

[0107] Based on the above-mentioned intelligent control oilfield gathering and transportation system, it is applied to an oilfield, and the oil well production efficiency has been improved, achieving good results. During the oil well production process, the following operations are carried out:

[0108] 1) Through the sensors installed at different positions in the oilfield in the data acquisition system, multiple parameter data such as the oil production volume, water injection volume, and oil temperature of the oil well are collected in real time;

[0109] 2) Through the data analysis system, the collected data is processed and cleaned, and the data related to the oil well production efficiency is screened out; through the analysis of the data, it is found that there is a certain correlation between the oil production volume and the water injection volume, that is, to a certain extent, increasing the water injection volume can improve the oil production volume; while for the oil temperature, its relationship with the oil production volume and the water injection volume is not significant;

[0110] 3) Through the intelligent control system, based on the above data relationship between the oil production volume and the water injection volume, a control strategy is formulated, and the water injection volume is dynamically adjusted through the intelligent control system to improve the oil production efficiency.

[0111] Based on the above operations, it can be found that before the implementation of intelligent control, the average daily oil production volume of this oil well was 15 tons, the average water injection volume was 400m 3 , and the average daily water production volume was 600m3 After implementing intelligent control, the average water injection volume increased to 450 m 3 , the average daily oil production increased to 18 tons, and at the same time, the average daily water production decreased to 500 m 3 . It can be seen from the comparison data that the intelligent control oilfield gathering and transportation system has improved the production efficiency of oil wells.

[0112] Implementation Case 2

[0113] For a gathering and transportation system of a certain oilfield, the intelligent control system in the present invention was used. During the process of oil well production, the following operations were carried out:

[0114] 1) Data such as oil well production, water well production, water pressure, oil pressure, water level, etc. were collected through various sensors and instruments in the data acquisition system;

[0115] 2) Through the data analysis system, preprocessing such as cleaning, denoising, and calibration was performed on the collected original oilfield production data, and valuable features and information were extracted;

[0116] 3) Through the intelligent control system, in the control strategy module, the PID control algorithm and neural network model were used to formulate corresponding control strategies; in the intelligent decision-making module, the intelligent control algorithm DeepQNetwork (DQN) based on deep reinforcement learning was used to optimize and adjust the preset control strategies, realizing the adaptive control and optimization of the system.

[0117] Before implementing the intelligent control system, the gathering and transportation system of this oilfield had frequent shutdowns and failures, and the production efficiency was low. The average daily production was only 140 tons. After implementing the intelligent control system, the system can automatically monitor and diagnose problems, and optimize and adjust according to the preset control strategies, reducing the shutdowns and failures of the system and improving the production efficiency. After one year of implementation, the average daily output of this oilfield increased from 140 tons to 210 tons, and the production efficiency increased by 50%.

[0118] Based on the same inventive concept, the embodiment of the present invention also provides an intelligent control method for an oilfield gathering and transportation, which is implemented by using the above-mentioned intelligent control oilfield gathering and transportation system. The flow of this method is as Figure 5 shown, including:

[0119] S101: Collect oilfield production data through sensors installed at different positions in the oilfield;

[0120] S102: Extract features from the oilfield production data to obtain data feature information; perform statistics and analysis on the oilfield production data based on the data feature information to mine data relationships and data development trends, and obtain a data analysis result; predict the future production of the oilfield based on the data analysis result to obtain oilfield production prediction data.

[0121] S103: Automatically control the oilfield production according to the oilfield production data and a pre-determined control strategy, and optimize and adjust the control strategy according to the real-time production data during the production process.

[0122] Regarding the method in the above embodiments, the relevant content has been described in detail in the system and each module of the system, and will not be elaborated here.

[0123] The embodiment of the present invention also provides a computer storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, the intelligent control method for oilfield gathering and transportation described above is implemented.

[0124] The embodiment of the present invention also provides a control device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the intelligent control method for oilfield gathering and transportation described above is implemented.

[0125] Unless otherwise specifically stated, terms such as processing, calculating, computing, determining, displaying, etc. can refer to the actions and / or processes of one or more processing or computing systems, or similar devices, which operate on and transform data represented as physical (such as electronic) quantities in the registers or memories of the processing system into other data represented as physical quantities in the memories, registers, or other such information storage, transmission, or display devices of the processing system. Information and signals can be represented using any of a variety of different technologies and methods. For example, the data, instructions, commands, information, signals, bits, symbols, and chips mentioned throughout the above description can be represented by voltages, currents, electromagnetic waves, magnetic fields or particles, optical fields or particles, or any combination thereof.

[0126] It should be understood that the specific order or hierarchy of steps in the disclosed process is an example of an exemplary method. Based on design preferences, it should be understood that the specific order or hierarchy of steps in the process can be rearranged without departing from the protection scope of the present disclosure. The appended method claims present the elements of the various steps in an exemplary order and are not intended to be limited to the specific order or hierarchy.

[0127] In the foregoing detailed description, various features are combined in a single embodiment to simplify the disclosure. This method of disclosure should not be interpreted as reflecting an intention that the embodiments of the claimed subject matter require more features than are expressly recited in each claim. On the contrary, as reflected in the appended claims, the invention lies in less than all of the features of a single disclosed embodiment. Accordingly, the appended claims are hereby expressly incorporated into the detailed description, with each claim standing on its own as a separate preferred embodiment of the invention.

[0128] Those skilled in the art should also understand that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments herein can be implemented as electronic hardware, computer software, or combinations thereof. To clearly illustrate the interchangeability of hardware and software, the various illustrative components, blocks, modules, circuits, and steps have been generally described in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as departing from the scope of the present disclosure.

[0129] The steps of a method or algorithm described in connection with the embodiments herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination thereof. The software modules may be located in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium well known in the art. An exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may be located in an ASIC. The ASIC may be located in a user terminal. Of course, the processor and the storage medium may also exist as discrete components in a user terminal.

[0130] For a software implementation, the techniques described in this application can be implemented using modules (e.g., procedures, functions, etc.) that perform the functions described herein. These software codes can be stored in a memory unit and executed by a processor. The memory unit may be implemented within the processor or outside the processor, in the latter case, it is coupled to the processor in a communication manner by various means, which are well known in the art.

[0131] The foregoing description includes examples of one or more embodiments. Of course, it is not possible to describe all possible combinations of components or methods for the purpose of describing the above embodiments, but those of ordinary skill in the art should recognize that the various embodiments can be further combined and arranged. Accordingly, the embodiments described herein are intended to embrace all such changes, modifications, and variations that fall within the scope of the appended claims. In addition, with respect to the term "comprising" as used in the specification or claims, the word is inclusive in a manner similar to the term "including" as interpreted when used as a transitional word in a claim. Further, any use of the term "or" in the specification or claims is to be meant "non-exclusive or".

Claims

1. An oilfield gathering and transportation system with intelligent control, characterized in that, Including: A data acquisition system, a data analysis system, and an intelligent control system; The data acquisition system is used to collect oilfield production data through sensors installed at different positions in the oilfield; The data analysis system is used to extract features from the oilfield production data to extract data feature information; Statistically analyze and mine the data relationships and data development trends of the oilfield production data based on the data feature information to obtain data analysis results; predict the future production of the oilfield based on the data analysis results to obtain oilfield production prediction data; The intelligent control system is used to automatically control the oilfield production according to the oilfield production data and a pre-determined control strategy, and optimize and adjust the control strategy according to the real-time production data during the production process.

2. The system according to claim 1, characterized in that The data acquisition system includes: Multiple types of sensors installed at the acquisition positions of oil wells, water wells, and pipelines in the oilfield for oilfield production data at the acquisition positions of oil wells, water wells, and pipelines; A data transmission module for sending the collected oilfield production data to the data analysis system.

3. The system according to claim 1, wherein The data analysis system includes: A data preprocessing module for cleaning, denoising, and correcting the collected oilfield production data, reducing the dimension and extracting features from the processed oilfield production data using selected machine learning methods and data mining methods to extract data feature information, and normalizing the data features; A data analysis module for statistically analyzing and mining the data relationships and data development trends of the oilfield production data based on the data feature information using selected statistical and analysis methods to obtain data analysis results; the data analysis methods include at least one of the following methods: linear regression or logistic regression analysis methods for analyzing data correlation relationships and influence relationships between data, clustering analysis methods for analyzing data similarities and differences, and time series analysis methods for analyzing data development trends based on time series and predicting future development trends; A prediction module for predicting the future production of the oilfield to obtain oilfield production prediction data using selected prediction models and simulation methods based on the data analysis results.

4. The system according to claim 3, wherein The data analysis system further includes: A data storage module using a distributed database system for storing the oilfield production data.

5. The system according to claim 4, wherein The data storage module adopts the distributed storage architecture of Hadoop, and this storage architecture can process the oilfield production data as follows: Data segmentation: Segmenting the large data in the oilfield production data into multiple small files according to certain segmentation rules; Data backup: Backing up the data on multiple nodes; Data recovery: Implementing data recovery using the data block replication mechanism provided by Hadoop.

6. The system according to claim 1, characterized in that, The intelligent control system includes: A data processing module for processing and cleaning the oilfield production data, including data screening, denoising, and completion operations; A control strategy module for formulating control strategies for oilfield production; the control strategy includes PID control to control specified parameters of oilfield production data so that the oilfield production data meets preset requirements; the specified parameters include at least one of error, deviation, and integral; and optimizing and adjusting the control strategy according to real-time production data during the production process; An intelligent decision-making module for training an intelligent decision-making model using a selected deep learning algorithm based on the pre-determined control strategy and the oilfield production data, and making intelligent decisions and controls for the oilfield production process based on the trained intelligent decision-making model.

7. The system according to claim 6, wherein The intelligent control system further includes: An alarm and warning module for giving early warning prompts and providing reference solutions after abnormal situations occur in the production environment; A remote monitoring module for sending oilfield production data, production prediction data, control strategies, and automatic control data to relevant terminal devices for display, and obtaining oilfield production data and production control information input by production management personnel through the terminal devices.

8. The system according to claim 10, wherein, The data processing module includes: A data screening sub-module for screening qualified data from the oilfield production data according to the set screening conditions; A data denoising sub-module for removing noise in the oilfield production data by using the wavelet denoising method; A data completion sub-module for completing the oilfield production data by using the interpolation method.

9. An intelligent control method for oilfield gathering and transportation, characterized in that, It is implemented by using the oilfield gathering and transportation system with intelligent control according to any one of claims 1-8.

10. An intelligent control method for oilfield gathering and transportation, characterized in that, Including: Collecting oilfield production data through sensors installed at different positions in the oilfield; Performing feature extraction on the oilfield production data to extract data feature information; Statistically analyzing the oilfield production data according to the data feature information, mining data relationships and data development trends to obtain data analysis results; predicting future oilfield production according to the data analysis results to obtain oilfield production prediction data; Automatically controlling the oilfield production according to the oilfield production data and the pre-determined control strategy, and optimizing and adjusting the control strategy according to real-time production data during the production process.

11. A computer storage medium, characterized in that, The computer storage medium stores computer-executable instructions, and when the computer-executable instructions are executed by a processor, the intelligent control method for oilfield gathering and transportation according to any one of claims 9-10 is implemented.

12. A terminal device, characterized in that, Including: A memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, the intelligent control method for oilfield gathering and transportation according to any one of claims 9-10 is implemented.

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