Oilfield digital twin station operation optimization method and system based on intelligent digitization

Abstract

The invention relates to the technical field of digital oil fields, in particular to an oil field digital twin station operation optimization method and system based on intelligent digitization. The intelligent digital oil field digital twin station operation optimization method comprises an intelligent digital base technology, a multi-parameter optimization technology and production parameter analysis. The intelligent digital base technology comprises an intelligent oil field station digital twin one-key optimization module, an intelligent digital base module and an Internet of Things edge service module. The comprehensive optimization capability is that only specific parameters or equipment can be optimized in the prior art, but the comprehensive optimization can be carried out on multiple aspects of the oil field station yard, including air door setting, production indexes, combustor matching, safety alarm, energy-saving mode and the like; the comprehensive optimization capability can significantly improve the overall efficiency and production benefits of the system, not just optimization of a single parameter.

Description

Technical Field

[0001] The present invention relates to the field of digital oilfield technology, and in particular to an oilfield digital twin station operation optimization method and system based on intelligent digitization. Background Art

[0002] The oilfield digital twin station operation optimization method and system based on the intelligent digital base of the present invention mainly solves the problems of production efficiency, equipment adaptability, safety and energy efficiency optimization. The system optimizes key parameters such as damper settings, production indicators and alarm parameters through real-time data collection and modeling, using machine learning and data mining algorithms to ensure that the operating parameters are reasonable and stable.

[0003] Through real-time analysis of production data, the system can optimize the production process, reduce shutdown times, improve production compliance rates, and control energy consumption and reduce costs in energy-saving mode.

[0004] At the same time, the system checks the compatibility of equipment and processes to avoid reduced efficiency or safety hazards caused by mismatch. In terms of safety, the system monitors the production process in real time through intelligent alarm and fault warning functions, promptly discovers and handles safety hazards, and ensures the continuity of production. In terms of energy efficiency, the system monitors real-time energy consumption to ensure that operations meet energy-saving standards, and provides optimization suggestions to improve overall energy efficiency. Finally, through a comprehensive optimization engine, the system provides users with decision-making support to help achieve efficient, safe, and energy-saving production. Summary of the invention

[0005] The purpose of the present invention is to solve the above-mentioned shortcomings in the prior art and to propose an oilfield digital twin station operation optimization method and system based on intelligent digitization.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] Designing an oilfield digital twin station operation optimization method based on intelligent digitization, wherein the oilfield digital twin station operation optimization method includes intelligent digital base technology, multi-parameter optimization technology and production parameter analysis;

[0008] The intelligent digital base technology includes a one-key optimization module for the digital twin of a smart oilfield station, an intelligent digital base module, and an IoT edge service module;

[0009] The multi-parameter optimization technology includes pump operation parameter optimization, water mixing system parameter optimization, heating system parameter optimization, conveying system parameter optimization, safety and alarm parameter optimization, energy consumption parameter optimization, and production process parameter optimization;

[0010] The production parameter analysis includes the average line current analysis of each pump, the average line voltage analysis of each pump, the total water mixing volume analysis, the water mixing outlet temperature analysis, the water mixing manifold temperature analysis, the water mixing pump outlet manifold pressure analysis, the heating pump inlet and outlet pressure analysis, the water mixing medium specific heat capacity analysis, the water mixing medium density analysis, the external liquid volume and the station liquid volume analysis, the liquid temperature analysis, the water content analysis, the external medium specific heat capacity and density analysis, and the station power consumption analysis.

[0011] In detail, the digital oilfield digital twin station operation optimization system is as follows:

[0012] Step 1: Parameter optimization and setting rationality;

[0013] Step 2: Production efficiency and energy consumption control;

[0014] Step 3: Check the compatibility of equipment and process;

[0015] Step 4: Real-time monitoring and optimization of safety hazards;

[0016] Step 5: Energy efficiency monitoring and compliance inspection;

[0017] Step 6: Comprehensive operation optimization and decision support.

[0018] In detail, based on the results obtained in the first step, through data collection and modeling, sensors and data acquisition equipment in the oil field station are used to obtain key operating parameters in the production process in real time, such as damper settings, production indicators and alarm parameters;

[0019] The data acquired include key indicators such as temperature, pressure, flow and liquid level. The system applies machine learning and data mining algorithms to model and analyze current parameters based on historical data and real-time data, identify optimization space, and automatically adjust parameter settings through simulation.

[0020] In detail, based on the results obtained in the second step, through real-time data analysis combined with a high-frequency data acquisition system, key indicators in the production process, such as liquid production, oil production and energy consumption data, are monitored. Based on historical data and using big data analysis technology, the system identifies inefficient links in the production process and builds a production optimization model.

[0021] In detail, based on the data obtained in the third step, by building an equipment parameter library that covers detailed parameters such as burners and heating furnaces, including power, combustion mode and pressure mode, the system automatically determines the compatibility of equipment and processes by comparing real-time data with the equipment parameter library and applying a matching algorithm.

[0022] In detail, based on what is obtained in the fourth step, the key sensor data in the production process is monitored in real time through the safety monitoring function, and compared with the system's built-in safety rule library to identify potential safety hazards.

[0023] In detail, based on the results of the fifth step and in accordance with relevant national standards, an energy efficiency monitoring module is designed to collect and analyze the system's energy consumption data in real time, and compare the data with preset energy efficiency standards to determine the system's energy-saving status. The system automatically generates an energy efficiency compliance report to display the compliance status of various energy efficiency indicators.

[0024] In detail, based on the results of the sixth step, a comprehensive optimization engine is constructed, which integrates parameter optimization module, production efficiency optimization module, equipment adaptability inspection module, safety monitoring and energy efficiency inspection module. Through the output of the optimization engine, the system designs a user-friendly decision support interface to display optimization suggestions, operating status and prediction results, helping users make scientific decisions in the production process.

[0025] The design scheme proposed by the present invention has the following beneficial effects during application:

[0026] 1. Comprehensive optimization capability: The existing technology can usually only optimize specific parameters or equipment, while the present invention can comprehensively optimize multiple aspects of the oil field station, including damper setting, production indicators, burner matching, safety alarm, energy-saving mode, etc. This comprehensive optimization capability can significantly improve the overall efficiency and production benefits of the system, rather than just optimizing a single parameter.

[0027] 2. Highly intelligent: The present invention adopts advanced algorithms and models, and can perform real-time data analysis, predictive maintenance and intelligent decision support. Compared with the manual or semi-automatic processing methods of the prior art, the present invention significantly improves the automation and intelligence level of the system, and can optimize the production process more efficiently and accurately.

[0028] 3. Integrated multi-dimensional safety monitoring: The present invention not only focuses on production optimization, but also has a complete safety monitoring function, which can perform real-time fault warning and safety hazard investigation. Compared with the prior art, the multi-dimensional monitoring and warning function of the present invention can detect potential risks earlier and prevent accidents, thereby greatly improving the safety and reliability of the system.

[0029] 4. Combination of optimization and energy saving: The present invention takes special consideration of energy saving goals during the optimization process, especially in the operation of high-energy-consuming equipment such as heating furnaces. It can reduce energy consumption by intelligently allocating resources. Compared with traditional technologies, the present invention combines energy saving with optimization, which not only reduces operating costs, but also significantly improves environmental benefits.

[0030] 5. High degree of user customization and ease of use: The present invention allows users to customize and optimize according to specific process requirements and production goals, and provides an intuitive user interface and operating experience. Compared with the existing technology, the high degree of customization and ease of use of the present invention enable the system to better adapt to the needs of different users and improve the user experience.

[0031] 6. Efficient real-time response and data processing: The present invention has powerful data processing and real-time response capabilities, can quickly process large amounts of real-time data and provide optimization suggestions instantly. Compared with the existing technology, this real-time and high efficiency significantly reduces decision-making time and improves production and maintenance efficiency.

[0032] 7. Convenience of one-key operation: The one-key optimization function provided by the present invention makes complex optimization operations simple and easy. Users only need one key to complete the optimization task of the entire station, which reduces the complexity of the operation and reduces the errors and workload of manual operation. This is a convenient function that is often lacking in the prior art.

[0033] 8. Comprehensive standardization support: The present invention is designed strictly in accordance with industry standards and specifications to ensure that all operations and parameters in the optimization process meet the latest safety, energy efficiency and production standards. Compared with some existing non-standardized systems, the standardized design of the present invention ensures the compliance and reliability of the system and enhances the stability and sustainability of the system in practical applications.

[0034] 9. Efficient reporting and visualization functions: The present invention provides detailed optimization reports and visualization display functions, allowing users to intuitively understand the optimization status and potential problems of the system. This function makes data interpretation more intuitive and optimization suggestions more transparent, which helps users make subsequent decisions and improvements, and has more advantages than existing technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 This is a technical architecture diagram of the intelligent digital base of the present invention;

[0036] Figure 2 It is a multi-parameter optimization scheme diagram of the present invention;

[0037] Figure 3 It is a production parameter analysis diagram of the present invention;

[0038] Figure 4 It is a detailed technical scheme diagram of the present invention. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0040] Reference Figure 1-Figure 4 , an oilfield digital twin station operation optimization method based on intelligent digitization, which includes intelligent digital base technology, multi-parameter optimization technology and production parameter analysis;

[0041] Intelligent digital base technology includes the one-click optimization module of the digital twin of the smart oilfield station, the intelligent digital base module, and the IoT edge service module;

[0042] according to Figure 1 Recorded:

[0043] 1. One-click optimization module of digital twin of smart oilfield station:

[0044] One-click optimization: This is the core function. It analyzes various operating data of oilfield stations through the system's intelligent algorithm and provides three types of optimization;

[0045] Production optimization: This function analyzes production equipment and process flow, identifies bottlenecks that affect production capacity, and recommends adjusting production parameters to improve production efficiency;

[0046] Safety optimization: By real-time monitoring of equipment and environmental status, potential safety hazards are identified and targeted optimization suggestions are provided to ensure the safety and stability of production;

[0047] Energy saving optimization: By analyzing energy consumption data, identifying high-energy-consuming equipment or processes, and recommending adjustments or optimization of equipment operation, reducing energy consumption and improving energy utilization efficiency.

[0048] 2. Intelligent digital base module:

[0049] Category management includes the following functions:

[0050] Asset categories: Users can classify assets, such as equipment categories, material categories, etc., to facilitate the management of a large number of assets;

[0051] Category attribute table: This function allows users to define relevant attributes for each asset category, such as equipment model, manufacturer, service life, etc.

[0052] Unit of measurement: define standard units of measurement for each type of asset to ensure data consistency and accuracy;

[0053] Category attribute search: By entering specific attributes, you can quickly find the asset categories that meet the conditions and improve management efficiency;

[0054] Production management includes the following functions:

[0055] Grouping: Divide the production process into different groups. Users can group production lines according to production processes or areas to monitor and optimize production activities of different groups;

[0056] Asset management includes the following functions:

[0057] Assets: The system allows users to manage all assets in the oilfield station, and record the type, status, location and other information of each asset in detail;

[0058] Asset attribute retrieval: Users can quickly find specific assets by entering asset attributes (such as device ID, model, etc.), facilitating asset tracking and management.

[0059] 3. IoT edge service module:

[0060] Device management includes the following functions:

[0061] Device group list: Users can group devices according to function or location, making it easier to centrally view and manage the operating status of a certain type of device;

[0062] Device list: lists all devices connected to the IoT platform, including basic information such as device name, type, and current operating status;

[0063] Device details: After clicking on a device, the system will display detailed information about the device, including model, manufacturer, installation date, maintenance records, etc.

[0064] Device properties: Users can view and edit device properties, such as the device's sensor type, communication protocol, connection method, etc.

[0065] Operation status: monitor the operation status of the equipment in real time and provide key status information, such as equipment online status, data transmission status, operation failures, etc.

[0066] The edge instance includes the following features:

[0067] Device adapter: This function provides adaptation between devices and systems, ensuring that various types of devices can be seamlessly connected to the IoT platform through adapters to achieve real-time data transmission and monitoring.

[0068] Multi-parameter optimization technology includes pump operation parameter optimization, water mixing system parameter optimization, heating system parameter optimization, conveying system parameter optimization, safety and alarm parameter optimization, energy consumption parameter optimization, and production process parameter optimization;

[0069] according to Figure 2 Recorded:

[0070] 1. Optimization of pump operating parameters:

[0071] Goal: Reduce energy consumption and extend equipment life by optimizing pump operating frequency and start-stop logic;

[0072] Strategy: Operation frequency optimization plan: Real-time monitoring of the pump's operating frequency, dynamic adjustment of the frequency based on flow demand, avoidance of increased energy consumption due to excessively high or low frequency, use of variable frequency control technology, digital twin simulation of the operating effects at different frequencies, and selection of the optimal frequency;

[0073] Start-stop logic optimization solution: set reasonable start-stop thresholds, automatically judge based on real-time demand, reduce frequent start-stop, combine historical data analysis, optimize start-stop logic, and reduce mechanical wear and energy consumption;

[0074] Current and voltage optimization: monitors current and voltage, detects overload or underload conditions, and dynamically adjusts electrical parameters based on pump load to ensure stable operation.

[0075] 2. Optimization of water mixing system parameters:

[0076] Objective: Optimize water blending amount and temperature, improve crude oil processing efficiency, and reduce resource waste;

[0077] Strategy:

[0078] Water addition optimization plan: adjust the water addition amount based on real-time moisture content data, and determine the optimal water addition ratio through digital twin simulation of the treatment effects of different water addition amounts;

[0079] Water mixing temperature optimization plan: monitor the water mixing temperature, adjust the temperature in real time according to the process requirements, simulate the treatment effect at different temperatures, and optimize the water mixing temperature setting;

[0080] Water injection pump pressure optimization solution: real-time monitoring of the pump outlet pressure, dynamic adjustment of the pressure setting, and optimization of the pump pressure setting point by simulating the effects under different pressures.

[0081] 3. Heating system parameter optimization:

[0082] Objective: Optimize furnace temperature and burner power, improve heating efficiency, and reduce emissions and energy consumption;

[0083] Strategy:

[0084] Heating furnace temperature optimization solution: Dynamically adjust the heating furnace temperature set point based on crude oil flow and quality, simulate the impact of different temperatures on heating effects, and determine the optimal setting;

[0085] Burner power optimization solution: real-time monitoring of power output, dynamic adjustment based on heating demand, simulation of effects under different powers, and selection of the best setting;

[0086] Fuel gas volume optimization plan: monitor the relationship between fuel gas volume and heating furnace temperature, adjust the fuel supply in real time, simulate the impact of different gas volumes on efficiency, and optimize fuel supply.

[0087] 4. Optimization of conveying system parameters:

[0088] Objective: Ensure the stability of the liquid volume delivered and optimize pipeline pressure to reduce energy consumption.

[0089] Strategy:

[0090] Optimization plan for external infusion volume: monitor the liquid volume in real time, dynamically adjust the delivery volume based on downstream demand, and optimize the delivery strategy through simulation analysis of the effects under different liquid volumes.

[0091] Delivery pressure optimization scheme: monitor pipeline pressure, dynamically adjust pressure set point, simulate delivery efficiency under different pressures, and select the best control strategy.

[0092] 5. Security and alarm parameter optimization:

[0093] Goal: Ensure safe operation, optimize alarm response, and avoid false alarms.

[0094] Strategy:

[0095] Safety valve setting value optimization solution: Based on real-time pressure data, dynamically adjust the safety valve setting value, and optimize the safety valve parameters by simulating the safety effects under different settings.

[0096] Alarm threshold optimization plan: Combine historical data and real-time monitoring to adjust the alarm threshold, simulate the alarm effect under different thresholds, optimize the alarm settings, and ensure timely response to potential dangers.

[0097] Energy consumption parameter optimization

[0098] Goal: Reduce energy waste and ensure energy efficiency meets standards.

[0099] Strategy:

[0100] Energy consumption monitoring and allocation optimization plan: real-time monitoring of energy consumption data of each device, optimization of energy allocation, simulation analysis of energy consumption under different strategies, and selection of the best solution.

[0101] Energy-saving operation mode optimization plan: Dynamically switch equipment operation modes (such as low power mode and high efficiency mode) according to production needs, and optimize energy-saving strategies by simulating the effects under different modes.

[0102] Optimization of production process parameters

[0103] Goal: Ensure optimal operation of production processes and improve product quality and production efficiency.

[0104] Strategy:

[0105] Process temperature and pressure optimization solution: monitor process temperature and pressure, adjust set points in real time according to production needs, simulate production effects under different combinations, and optimize process parameters.

[0106] Material flow and mixing ratio optimization plan: Real-time monitoring of material flow and mixing ratio, dynamic adjustment of parameters, simulation testing of the effects under different flow rates and ratios, and selection of the optimal material configuration plan.

[0107] The production parameter analysis includes the average line current analysis of each pump, the average line voltage analysis of each pump, the total water mixing volume analysis, the water mixing outlet temperature analysis, the water mixing manifold temperature analysis, the water mixing pump outlet manifold pressure analysis, the heating pump inlet and outlet pressure analysis, the water mixing medium specific heat capacity analysis, the water mixing medium density analysis, the external liquid volume and the station liquid volume analysis, the liquid temperature analysis, the water content analysis, the external medium specific heat capacity and density analysis, and the station power consumption analysis.

[0108] according to Figure 3 Recorded:

[0109] 1. Analysis of average line current of each pump

[0110] Objective: Ensure that each pump operates within a reasonable current range, avoid overload or underload, extend equipment life and reduce downtime.

[0111] Solution: Monitor the average line current of each pump in real time, and optimize the operating parameters through time series analysis and comparison with historical data.

[0112] Data collection: Current sensors are installed to monitor the average line current of the pump in real time, and the data is stored in the digital twin system.

[0113] Data analysis: Analyze the pattern of current fluctuations, set the optimal current range and set the warning threshold.

[0114] Optimization measures: When the current exceeds the upper and lower limits, the system automatically adjusts the pump's operating parameters or issues a maintenance alarm and generates a current analysis report regularly.

[0115] 2. Analysis of average line voltage of each pump

[0116] Goal: Keep the pump running within a stable voltage range to avoid reduced efficiency or equipment damage due to voltage fluctuations.

[0117] Solution: Monitor voltage changes and optimize voltage control strategies to ensure that equipment operates at the optimal voltage.

[0118] Data Collection: Use voltage sensors to monitor the average line voltage of the pump in real time to ensure data continuity and accuracy.

[0119] Data analysis: Through trend analysis, the relationship between voltage fluctuation and pump operating efficiency can be identified and a reasonable voltage fluctuation range can be set.

[0120] Optimization measures: The system automatically adjusts the operating status of the pump or switches to the backup power supply when the voltage is abnormal, and provides voltage fluctuation protection suggestions.

[0121] 3. Analysis of total water content

[0122] Objective: To optimize the control of water blending and improve crude oil processing efficiency and oil-water separation effect.

[0123] Solution: Monitor water addition, analyze its impact on oil-water separation, and automatically adjust water addition to meet real-time needs.

[0124] Data collection: Use flow meters to monitor water content in real time and upload it to the system for storage and analysis.

[0125] Data analysis: Analyze the relationship between water content and treatment efficiency, and predict the impact of water content based on historical data.

[0126] Optimization measures: Automatically adjust the water dosage according to real-time demand and provide optimization suggestions to improve separation efficiency.

[0127] 4. Analysis of water outlet temperature

[0128] Objective: To control the water mixing outlet temperature and ensure the stability and efficiency of the water mixing process.

[0129] Solution: Use temperature sensors to monitor the outlet temperature of the water blending system and analyze the impact of temperature on process results.

[0130] Data collection: Install temperature sensors to monitor the water outlet temperature in real time, and upload the data to the system.

[0131] Data analysis: Determine the optimal water mixing temperature range through regression analysis and identify the relationship between temperature change and oil-water separation effect.

[0132] Optimization measures: Automatically adjust the working status of the heating equipment according to the temperature data, issue an early warning when an abnormality occurs, and provide process adjustment suggestions.

[0133] 5. Temperature analysis of water mixing manifold

[0134] Objective: To reduce heat loss and ensure the stability of water mixing temperature.

[0135] Solution: Monitor the temperature of the water header, evaluate heat loss and optimize heating system parameters.

[0136] Data collection: Monitor the temperature changes of the header through the temperature sensor and upload the data in real time.

[0137] Data analysis: Compare the water injection manifold and outlet temperatures, evaluate heat loss, and establish a temperature control model.

[0138] Optimization measures: Automatically adjust the heating system to reduce heat energy waste and provide adjustment suggestions when the temperature fluctuates greatly.

[0139] 6. Analysis of the pressure of the outlet manifold of the water mixing pump

[0140] Objective: To maintain a stable pressure at the outlet manifold of the water injection pump to ensure process continuity and efficiency.

[0141] Solution: Monitor the changes in manifold pressure, analyze the impact of pressure on water mixing effects, and automatically optimize the operating parameters of the pump.

[0142] Data collection: Install pressure sensors to monitor outlet pressure in real time and upload to the system for analysis.

[0143] Data analysis: Analyze the relationship between pressure fluctuations and water addition process, and identify critical pressure ranges.

[0144] Optimization measures: When the pressure is abnormal, the system automatically adjusts the pump operating parameters or adjusts the power of the heating equipment to ensure stable pressure.

[0145] 7. Analysis of inlet and outlet pressure of heating pump

[0146] Objective: Optimize the operating efficiency of heating pumps and improve system stability and energy utilization.

[0147] Solution: Monitor the inlet and outlet pressure difference, analyze the pump efficiency, and optimize the pump operating speed and flow rate.

[0148] Data collection: Use pressure sensors to monitor the inlet and outlet pressures of the heating pump and record data changes.

[0149] Data analysis: Analyze the impact of the inlet and outlet pressure difference on pump efficiency and energy consumption, and determine the optimal working pressure range.

[0150] Optimization measures: Automatically adjust the running speed of the pump to keep the pressure difference within the optimal range, issue prompts and recommend maintenance when abnormalities occur.

[0151] 8. Analysis of specific heat capacity of water-doped media

[0152] Objective: To improve heating and water mixing efficiency by optimizing the heat capacity of the water mixing medium.

[0153] Solution: Monitor specific heat capacity data, analyze its impact on energy consumption, and optimize heating power output.

[0154] Data collection: Real-time monitoring of the specific heat capacity changes of the water-mixed medium, recording relevant data and uploading them to the system.

[0155] Data analysis: Analyze the water mixing efficiency under different specific heat capacities and find the optimal medium and temperature control strategy.

[0156] Optimization measures: The system automatically adjusts the heating power based on real-time specific heat capacity data and provides optimization suggestions to improve energy efficiency.

[0157] 9. Density analysis of water-mixed media

[0158] Objective: To ensure that the density of the water-doped medium is within the optimal range and optimize the oil-water separation and transportation process.

[0159] Solution: Real-time monitoring of medium density, analysis of its impact on separation effect, and automatic adjustment of water content or temperature.

[0160] Data collection: The density sensor monitors the medium density in real time and records the change data.

[0161] Data analysis: Analyze the relationship between medium density and oil-water separation efficiency to determine the optimal density range.

[0162] Optimization measures: Adjust the water content and temperature according to the density data, and provide adjustment suggestions when the density is abnormal.

[0163] 10. Analysis of external infusion volume and inflow volume

[0164] Objective: To optimize the matching of the external and inlet liquid volumes, ensure the balance of transportation, and improve system stability.

[0165] Solution: Monitor the amount of liquid delivered and inflowing, analyze the delivery balance, and automatically adjust the pump operation strategy.

[0166] Data collection: Use flow meters to monitor changes in the amount of liquid delivered and inflowing in real time and record historical data.

[0167] Data analysis: Analyze the impact of liquid volume changes on delivery efficiency and evaluate the balance of the system.

[0168] Optimization measures: The system automatically adjusts the delivery strategy based on real-time liquid volume data to ensure liquid volume balance and issues adjustment suggestions when abnormalities occur.

[0169] 11. Liquid temperature analysis (each stage)

[0170] Objective: To optimize liquid temperature control and improve thermal energy efficiency and production process stability.

[0171] Solution: Monitor liquid temperature at each stage, analyze the impact of temperature on process efficiency, and automatically adjust temperature control equipment.

[0172] Data collection: Install temperature sensors at each production stage (such as incoming liquid, external transmission, preheating furnace, etc.) to collect data in real time.

[0173] Data analysis: Determine the optimal temperature range for each stage through regression analysis and find out the key temperature points that affect efficiency.

[0174] Optimization measures: Automatically adjust the operating status of temperature equipment according to real-time temperature data, and provide inspection and adjustment suggestions when the temperature is abnormal.

[0175] 12. Water content analysis (external infusion and inlet fluid)

[0176] Objective: Optimize water content control and improve oil-water separation efficiency and crude oil quality.

[0177] Solution: Monitor the water content of external infusion and inlet liquid, optimize the water mixing and separation process, and ensure that the water content is within the optimal range.

[0178] Data collection: Use moisture content sensors to monitor moisture content in real time and store data.

[0179] Data analysis: Analyze the impact of moisture content on separation effect and establish a moisture content optimization model.

[0180] Optimization measures: The system automatically adjusts process parameters based on real-time data and provides optimization suggestions in case of abnormalities.

[0181] 13. Analysis of specific heat capacity and density of external transmission medium

[0182] Objective: To optimize the heat capacity and density of the external transmission medium and improve the transmission efficiency and stability.

[0183] Solution: Monitor the specific heat capacity and density of the external medium, analyze its impact on the transportation efficiency, and automatically adjust the transportation process.

[0184] Data collection: Use sensors to monitor the specific heat capacity and density of the external medium in real time and record historical data.

[0185] Data analysis: Analyze the impact of heat capacity and density on transportation efficiency and establish an energy efficiency model.

[0186] Optimization measures: The system automatically adjusts the heat input or medium composition based on the data, and provides adjustment suggestions when abnormalities occur.

[0187] 14. Analysis of station fuel gas consumption and fuel-based lower calorific value

[0188] Objective: Optimize fuel gas volume and calorific value control, reduce energy consumption, and improve system energy efficiency.

[0189] Solution: Monitor fuel gas volume and calorific value, analyze its impact on system energy efficiency, and automatically adjust fuel consumption.

[0190] Data collection: Use flow meters and calorific value sensors to monitor fuel gas volume and low calorific value in real time.

[0191] Data analysis: Establish energy efficiency model and analyze the impact of the combination of fuel gas volume and calorific value on system energy efficiency.

[0192] Optimization measures: The system automatically adjusts fuel consumption and heat output based on real-time data, and provides optimization suggestions when abnormalities occur.

[0193] 15. Station power consumption analysis

[0194] Objective: Optimize electricity consumption, reduce production costs and improve energy efficiency.

[0195] Solution: Monitor the power consumption of each device, analyze the power usage of the device, and optimize the power distribution strategy.

[0196] Data collection: Use power monitoring equipment to monitor the power consumption of each device in real time and record historical data.

[0197] Data analysis: Analyze the relationship between power consumption and production efficiency, and identify key equipment and process links for optimization.

[0198] Optimization measures: The system automatically adjusts the operating status of equipment based on real-time power data, and provides optimization suggestions for power combinations and operating strategies in the event of an abnormality.

[0199] according to Figure 4 Recorded:

[0200] It should be further explained that the digital oilfield digital twin station operation optimization system is as follows:

[0201] Step 1: Parameter optimization and setting rationality;

[0202] Step 2: Production efficiency and energy consumption control;

[0203] Step 3: Check the compatibility of equipment and process;

[0204] Step 4: Real-time monitoring and optimization of safety hazards;

[0205] Step 5: Energy efficiency monitoring and compliance inspection;

[0206] Step 6: Comprehensive operation optimization and decision support.

[0207] It should be further explained that, based on the results obtained in the first step, through data collection and modeling, sensors and data acquisition equipment in the oil field station are used to obtain key operating parameters in the production process in real time, such as damper settings, production indicators and alarm parameters;

[0208] The data acquired include key indicators such as temperature, pressure, flow and liquid level. The system applies machine learning and data mining algorithms to model and analyze current parameters based on historical data and real-time data, identify optimization space, and automatically adjust parameter settings through simulation to ensure their rationality and stability under different working conditions, thereby avoiding errors caused by manual adjustments and improving production efficiency and safety.

[0209] It should be further explained that, based on the results obtained in the second step, through real-time data analysis, combined with a high-frequency data acquisition system, key indicators in the production process, such as liquid production, oil production and energy consumption data, are monitored. Based on historical data, the system uses big data analysis technology to identify inefficient links in the production process and build a production optimization model. This model is based on multivariate regression analysis and optimization algorithms to simulate the impact of different production strategies on system efficiency and energy consumption. By comparing the efficiency and energy consumption under each strategy, the system selects the optimal solution to implement, optimize the production process, reduce the number of shutdowns, and improve production compliance. In addition, the system combines the production optimization model to formulate energy-saving operation strategies, adjust the operation modes of heating furnaces and pumps during low-load periods, reduce energy consumption, and maintain necessary production capacity.

[0210] It should be further explained that, based on the third step, by building an equipment parameter library, covering detailed parameters such as burners and heating furnaces, including power, combustion mode and pressure mode, the system automatically determines the compatibility of equipment and process by comparing real-time data with the equipment parameter library and applying matching algorithms. The system evaluates the rationality of the equipment combination based on the matching results to ensure the best coordination between the equipment. If equipment mismatch is detected, the system automatically issues an early warning and provides adjustment suggestions or alternative solutions to ensure the safety and efficiency of equipment operation.

[0211] It should be further explained that, based on the data obtained in the fourth step, the key sensor data in the production process is monitored in real time through the safety monitoring function, and compared with the system's built-in safety rule library to discover potential safety hazards. The system has designed a multi-level intelligent alarm system, set the optimal alarm value, and trigger corresponding alarms for different levels of abnormal conditions in order to respond to potential dangers in a timely manner. In addition, the system uses data analysis and predictive algorithms to detect abnormal signs of equipment in advance and provide preventive maintenance suggestions to reduce the occurrence of equipment failures and ensure the continuity and safety of the production process.

[0212] It should be further explained that, based on the results of the fifth step and in accordance with relevant national standards, an energy efficiency monitoring module is designed to collect and analyze the system's energy consumption data in real time, and compare the data with the preset energy efficiency standards to determine the system's energy-saving status. The system automatically generates an energy efficiency compliance report to display the compliance status of various energy efficiency indicators. If non-compliance is detected, the system will provide optimization suggestions and record the changes in energy efficiency before and after optimization to ensure that the system always runs in an energy-saving state.

[0213] It should be further explained that, based on the results of the sixth step, a comprehensive optimization engine is constructed, which integrates parameter optimization module, production efficiency optimization module, equipment adaptability inspection module, safety monitoring and energy efficiency inspection module. The system designs a user-friendly decision support interface through the output of the optimization engine to display optimization suggestions, operating status and prediction results, helping users to make scientific decisions in the production process. The system also has a feedback and learning mechanism to compare the optimization results with the actual operating conditions, and continuously adjust the optimization model through feedback learning to improve the optimization accuracy and effect, ensuring that the expected optimization effect can be achieved under different working conditions.

[0214] Core content: Data collection and analysis: Through real-time data collection and historical data analysis, we build optimization models and conduct predictive analysis. This is the basis for ensuring optimization and decision-making. The accuracy and timeliness of data directly affect the optimization effect of the system.

[0215] Optimization algorithms and models: Use machine learning, data mining, multivariate regression analysis and other algorithms to perform parameter optimization, production efficiency optimization and energy efficiency inspection. The effectiveness of these algorithms and models directly affects the optimization effect and decision support capabilities of the system.

[0216] Equipment and process compatibility check: The compatibility check algorithm based on multi-dimensional feature matching ensures the best combination of equipment and process. The compatibility of equipment and process affects the overall efficiency and safety of the system and is the key to system optimization.

[0217] Safety monitoring and fault warning: Real-time monitoring of safety hazards, reducing the occurrence of safety accidents through intelligent alarm systems and predictive maintenance, improving the safety and reliability of the system, and ensuring the continuity of the production process are the core elements of the system's success.

[0218] Comprehensive optimization engine and decision support system: Integrates multiple optimization algorithms, provides decision support, optimizes production processes, energy consumption management and safety management. The engine is the core of realizing the one-click optimization function of the system and directly affects the overall performance of the system.

[0219] User interface and decision support functions: user interface design methods, decision support display methods and interaction logic. The interface and decision support functions directly affect the user's operational convenience and decision accuracy, and are an important guarantee for the system's user experience.

[0220] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. The oilfield digital twin station operation optimization method based on intelligent digitization is characterized by: The intelligent digital oilfield digital twin station operation optimization method includes intelligent digital base technology, multi-parameter optimization technology and production parameter analysis; The intelligent digital base technology includes a one-key optimization module for the digital twin of a smart oilfield station, an intelligent digital base module, and an IoT edge service module; The multi-parameter optimization technology includes pump operation parameter optimization, water mixing system parameter optimization, heating system parameter optimization, conveying system parameter optimization, safety and alarm parameter optimization, energy consumption parameter optimization, and production process parameter optimization; The production parameter analysis includes the average line current analysis of each pump, the average line voltage analysis of each pump, the total water mixing volume analysis, the water mixing outlet temperature analysis, the water mixing manifold temperature analysis, the water mixing pump outlet manifold pressure analysis, the heating pump inlet and outlet pressure analysis, the water mixing medium specific heat capacity analysis, the water mixing medium density analysis, the external liquid volume and the station liquid volume analysis, the liquid temperature analysis, the water content analysis, the external medium specific heat capacity and density analysis, and the station power consumption analysis.

2. The oilfield digital twin station operation optimization method and system based on intelligent digitization according to claim 1 is characterized by: The oilfield digital twin station operation optimization system capable of being digitized is as follows: Step 1: Parameter optimization and setting rationality; Step 2: Production efficiency and energy consumption control; Step 3: Check the compatibility of equipment and process; Step 4: Real-time monitoring and optimization of safety hazards; Step 5: Energy efficiency monitoring and compliance inspection; Step 6: Comprehensive operation optimization and decision support.

3. The oilfield digital twin station operation optimization method and system based on intelligent digitization according to claim 2 is characterized by: Based on the results obtained in the first step, through data collection and modeling, using sensors and data acquisition equipment in the oil field station, key operating parameters in the production process, such as damper settings, production indicators and alarm parameters, are obtained in real time; The data acquired include key indicators such as temperature, pressure, flow and liquid level. The system applies machine learning and data mining algorithms to model and analyze current parameters based on historical data and real-time data, identify optimization space, and automatically adjust parameter settings through simulation.

4. The oilfield digital twin station operation optimization method and system based on intelligent digitization according to claim 2 is characterized by: Based on the results obtained in the second step, real-time data analysis is combined with a high-frequency data acquisition system to monitor key indicators in the production process, such as liquid production, oil production and energy consumption data. The system uses big data analysis technology based on historical data to identify inefficient links in the production process and build a production optimization model.

5. The oilfield digital twin station operation optimization method and system based on intelligent digitization according to claim 2 is characterized by: Based on the results obtained in the third step, by building an equipment parameter library that covers detailed parameters such as burners and heating furnaces, including power, combustion mode and pressure mode, the system automatically determines the compatibility of equipment and processes by comparing real-time data with the equipment parameter library and applying a matching algorithm.

6. The oilfield digital twin station operation optimization method and system based on intelligent digitization according to claim 2 is characterized by: Based on the data obtained in the fourth step, the key sensor data in the production process is monitored in real time through the safety monitoring function, and compared with the system's built-in safety rule library to identify potential safety hazards.

7. The oilfield digital twin station operation optimization method and system based on intelligent digitization according to claim 2 is characterized by: Based on the results of the fifth step and in accordance with relevant national standards, an energy efficiency monitoring module is designed to collect and analyze the system's energy consumption data in real time, and compare the data with the preset energy efficiency standards to determine the system's energy-saving status. The system automatically generates an energy efficiency compliance report to display the compliance status of various energy efficiency indicators.

8. The oilfield digital twin station operation optimization method and system based on intelligent digitization according to claim 2 is characterized by: Based on the results of the sixth step, a comprehensive optimization engine is constructed, integrating parameter optimization module, production efficiency optimization module, equipment adaptability inspection module, safety monitoring and energy efficiency inspection module. The system designs a user-friendly decision support interface through the output of the optimization engine to display optimization suggestions, operating status and prediction results, helping users make scientific decisions in the production process.

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