Intelligent management system for oil production and method thereof

Abstract

The application discloses a kind of petroleum production intelligent management system and method thereof, it is related to petroleum technical field, the application includes multi-source production data association acquisition and processing module, energy consumption and environmental protection green analysis module, decision execution and effect feedback module.Identify production anomaly and timely early warning, effectively reduce equipment failure, production fluctuation risk, guarantee production whole-process continuous stable operation, reduce unplanned shutdown loss, improve production operation stability and abnormal prevention and control capability;Rely on environmental protection green index dynamic calibration model, integrate multidimensional environmental protection monitoring data, strengthen environmental protection compliance and green production level, effectively reduce energy consumption per unit output, improve energy utilization efficiency;Combined with strategy effect prediction model, early prediction control effect, optimization control strategy, automatically match adaptive scheme for different control levels, reduce manual intervention, improve management efficiency.

Description

Technical Field

[0001] This invention relates to the field of petroleum technology, specifically to an intelligent management system and method for petroleum production. Background Technology

[0002] With the rapid iteration of petroleum technology, the petroleum industry, as a crucial pillar of energy supply, encompasses multiple key scenarios in its production process, including wellhead extraction, pipeline transportation, gathering and processing, and dynamic reservoir control. This involves complex processes, diverse equipment types, and dynamically changing on-site conditions. Currently, most oilfield production management still relies heavily on a decentralized, experience-based control model. Various production parameters, equipment operation data, environmental monitoring data, and energy consumption data are collected and stored independently, lacking a unified spatiotemporal correlation and integration mechanism. This results in significant fragmentation of multi-source data. Therefore, it is necessary to analyze an intelligent management system and its methods for petroleum production.

[0003] Existing technologies, such as the invention application patent with publication number CN116662896A, disclose an intelligent management system and method for oil production. This system first acquires the wellhead pressure, oil well production, and drainage volume values ​​for each day of the past month. Then, based on deep learning, it extracts the temporal correlation dynamic feature distribution information between oil well production and wellhead pressure, as well as the temporal correlation dynamic feature distribution information between oil well production and drainage volume, to determine the development level of the oil well. This allows for understanding the development level of the oil well, assessing the stability of its production capacity and potential problems, and formulating corresponding development strategies and adjusting production parameters to maintain stable oil well production and optimize extraction efficiency.

[0004] While existing technologies can meet basic requirements for intelligent management systems and methods for oil production, they also present some potential defects and challenges, specifically in the following aspects: First, existing technologies involve independent data collection and fragmented storage of data from wellheads, pipelines, gathering and transportation stations, and reservoirs, lacking a unified time synchronization and spatial correlation calibration mechanism. Energy consumption control levels are mostly analyzed independently, failing to form a multi-dimensional comprehensive evaluation system. Second, existing technologies rely heavily on manual experience-based decision-making for oil production regulation, lacking a quantitative index-based hierarchical control mechanism and differentiated hierarchical governance for production operations. Third, existing technologies often employ reactive, post-event rectification strategies, lacking the ability to predict regulation effects in advance. This affects the analysis of operational, environmental, and energy consumption index trends after parameter adjustments, reducing strategy adaptability. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent management system and method for petroleum production, which solves the problems existing in the background art.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides an intelligent management system for petroleum production, including a multi-source production data association acquisition and processing module, an energy consumption and environmental protection green analysis module, and a decision execution and effect feedback module.

[0007] Multi-source production data association acquisition and processing module: Collects production condition parameters and equipment health parameters throughout the entire oil production process, establishes multi-source spatiotemporal correlation data, and constructs a dual-index dynamic evaluation model to evaluate the comprehensive operating status value throughout the entire oil production process, determine whether any abnormal conditions have occurred in the entire oil production process, issue early warnings if abnormal conditions occur, and perform green analysis if no abnormal conditions occur.

[0008] Energy Consumption and Environmental Green Analysis Module: Through gas sensors and video monitoring systems, environmental data is collected, a dynamic calibration model for the environmental green index is constructed, the environmental compliance value of the entire oil production process is evaluated, and it is determined whether the environmental protection of the entire oil production process is compliant. If compliant, it combines real-time monitoring of electricity, water, and gas energy consumption to form an energy consumption dataset. Through an energy consumption-output linkage algorithm, the energy consumption compliance value of the entire oil production process is analyzed.

[0009] The decision execution and effect feedback module integrates the comprehensive operating status value, environmental compliance value, and energy consumption compliance value of the entire oil production process through multi-dimensional weighted fusion to construct a comprehensive green and intelligent management and control index for the entire oil production process. Based on the comprehensive green and intelligent management and control index, oil production operations are graded and evaluated, and classified into excellent, qualified, and management and control levels that need optimization. At the same time, a strategy effect prediction model is embedded to predict the changing trend of each index after the control strategy is implemented, and then automatically matches the corresponding control strategy for different management and control levels.

[0010] Furthermore, the specific analysis method for collecting production condition parameters and equipment health parameters throughout the entire oil production process and establishing multi-source spatiotemporal correlation data is as follows: Based on the collection of the entire oil production process, multi-source real-time equipment data from wellheads, pipelines, gathering and transportation stations, and reservoirs are collected and divided into four categories according to production scenario rules: wellhead production scenario, pipeline transportation scenario, gathering and transportation processing scenario, and reservoir dynamic scenario. This yields various scenarios throughout the entire oil production process. The production rule matching method is used to establish data linkage within scenarios and data correlation between equipment. Data correlation processing is completed to obtain multi-source spatiotemporal correlation data. The multi-source spatiotemporal correlation data includes: oil pressure, casing pressure, production volume, water content, current, load, and equipment vibration, temperature, current fluctuation, and lubricating oil quality values ​​for each scenario throughout the entire oil production process.

[0011] Furthermore, the specific analysis method for evaluating the comprehensive operating status value of the entire oil production process is as follows: Based on the obtained values ​​of oil pressure, casing pressure, fluid production, water content, current, load, equipment vibration, temperature, current fluctuation, and lubricating oil quality for each time period in each scenario of the entire oil production process, and combined with the reference operating matrix data stored in the database, the offset of the operating matrix data for each time period in each scenario of the entire oil production process is obtained through numerical fitting processing. Then, the comprehensive operating status value of the entire oil production process is analyzed, and the following dual-exponential dynamic evaluation model is used. The processing yields comprehensive operational status values ​​for the entire oil production process. , among which, among which The first in the entire oil production process The first scenario The offset of the running matrix data for each time period. , These are the left and right endpoints of the allowed offset interval for the running matrix data defined in the database. This represents the runtime impact factor corresponding to a unit offset of the runtime matrix data defined in the database. Number each scene. The times 1, 2, 3, and 4 correspond to the wellhead production scenario, pipeline transportation scenario, gathering and transportation processing scenario, and reservoir dynamic scenario, respectively. This is represented by the number of each time period. , It can be any integer greater than 2.

[0012] Furthermore, the specific analysis method for determining whether an abnormal situation occurs in the entire oil production process is as follows: Based on the obtained comprehensive operating status value of the entire oil production process, the comprehensive operating status value of the entire oil production process is compared with the comprehensive operating status reference value of the entire oil production process stored in the database. If the comprehensive operating status value of the entire oil production process is less than the comprehensive operating status reference value, it indicates that an abnormal situation has occurred. If the comprehensive operating status value of the entire oil production process is greater than or equal to the comprehensive operating status reference value, it indicates that there is no abnormal situation.

[0013] Furthermore, the specific analysis method for assessing the environmental compliance value of the entire oil production process is as follows: Based on the dynamic calibration model of the environmental green index, the latest local environmental compliance standards and industry environmental regulations stored in the database are synchronized to extract key environmental assessment indicators. These key environmental assessment indicators include hazardous gas concentration indicators, leakage early warning indicators, illegal operation indicators, sewage discharge indicators, and solid waste disposal indicators. The hazardous gas concentration data collected by gas sensors, the leakage, open flame, and illegal operation identification data collected by the video monitoring system, and the real-time monitoring data of sewage discharge and solid waste disposal in the entire oil production process are standardized and normalized according to preset standards to obtain standardized values ​​for each key environmental assessment indicator. Based on the environmental impact weight of each key environmental assessment indicator, a weighted summation algorithm is used to calculate the basic environmental green value of the entire oil production process. Through the dynamic calibration model of the environmental green index, combined with the adjustment coefficients of various scenarios in the entire oil production process and the latest environmental policies, the basic environmental green value is dynamically calibrated to finally obtain the environmental compliance value of the entire oil production process.

[0014] Furthermore, the specific analysis method for determining whether environmental protection compliance exists throughout the entire oil production process is as follows: Based on the obtained environmental compliance value throughout the entire oil production process, the environmental compliance value throughout the entire oil production process is compared with the environmental green reference value throughout the entire oil production process stored in the database. If the environmental compliance value throughout the entire oil production process is greater than or equal to the environmental green reference value, it indicates that the entire oil production process is environmentally compliant; if the environmental compliance value throughout the entire oil production process is less than the environmental green reference value, it indicates that it is not compliant.

[0015] Furthermore, the specific analysis method for the energy consumption compliance value in the entire oil production process is as follows: Combining real-time monitoring of electricity, water, and gas energy consumption, real-time data on electricity consumption, water resource consumption, and gas consumption in various scenarios throughout the entire oil production process are collected synchronously. After removing invalid data, a complete energy consumption dataset is formed. Using an energy consumption-production linkage algorithm, real-time liquid production and oil production data from each scenario are introduced to establish a linkage model between energy consumption and production. Abnormal energy consumption deviations caused by production fluctuations are eliminated. The energy consumption dataset is calibrated, and key energy consumption assessment indicators are extracted. These indicators are then standardized according to preset standards to obtain standardized values ​​for each key energy consumption assessment indicator. Based on the energy-saving impact weights of each key energy consumption assessment indicator, a weighted summation algorithm is used to calculate the energy consumption compliance value in the entire oil production process.

[0016] Furthermore, the method for classifying and evaluating petroleum production operations based on the comprehensive green and intelligent management and control index, dividing them into excellent, qualified, and management and control levels requiring optimization, is as follows: the comprehensive operating status value, environmental compliance value, and energy consumption compliance value in the entire petroleum production process are weighted and integrated in multiple dimensions. Based on the actual needs of petroleum production, the integration weights of the comprehensive operating status value, environmental compliance value, and energy consumption compliance value are adaptively allocated according to the management and control priority standards stored in the database. The comprehensive green and intelligent management and control index in the entire petroleum production process is calculated through a weighted summation algorithm.

[0017] The comprehensive green and intelligent management index of the entire oil production process is compared with the management level classification thresholds stored in the database. When the comprehensive green and intelligent management index of the entire oil production process is less than the minimum value, it is classified as a management level that needs optimization. When the comprehensive green and intelligent management index of the entire oil production process is greater than or equal to the minimum value and less than or equal to the maximum value, it is classified as a qualified management level. When the comprehensive green and intelligent management index of the entire oil production process is greater than the maximum value, it is classified as an excellent management level.

[0018] Furthermore, the specific analysis method for predicting the changing trends of various indices after regulation and then automatically matching corresponding regulation strategies for different control levels is as follows: Based on the strategy effect prediction model, the comprehensive operating status value, environmental compliance value, and energy consumption compliance value of the current entire oil production process are input, along with the preset regulation strategy parameters for the corresponding control level. Combined with historical regulation data and production scenario matching data stored in the database, a regression analysis algorithm is used to predict the change range and final value of the comprehensive operating status value, environmental compliance value, and energy consumption compliance value after regulation, and to determine whether the regulation strategy can make each index reach the preset reasonable range. If the prediction result meets expectations, the corresponding regulation strategy is executed; if the prediction result does not meet expectations, the regulation strategy parameters are adjusted, and the prediction is repeated until it meets expectations before execution.

[0019] Automatic matching control strategies for different control levels: Superior control level: Maintain the current production parameters, equipment operation mode and energy distribution plan of the pumping unit, pump, valve and compressor equipment, perform routine intelligent inspections, collect data of each index every preset period, conduct dynamic monitoring, and ensure that each index is stable within a reasonable range without additional control. Qualified control level: Based on the forecast results, the energy allocation plan is slightly optimized, and the power and water consumption of high-energy-consuming links such as gathering and transportation and wellhead are adjusted first. The oil pressure and production volume are fine-tuned without shutdown, ensuring that the comprehensive green and intelligent control index remains stable in the qualified range, while gradually moving towards the superior control level.

[0020] Level of control to be optimized: Immediately activate the emergency control mode, prioritize the control of key parameters affecting the comprehensive operating status value, eliminate potential production anomalies, and simultaneously adjust the operating parameters of environmental protection equipment, optimize the energy consumption structure, reduce energy consumption, and improve the environmental compliance rate; during the control process, collect data in real time and dynamically correct the control strategy until the comprehensive green and intelligent control index returns to the qualified range.

[0021] The second aspect of the present invention provides a method for executing the intelligent management system for petroleum production, characterized in that it includes: Step 1, multi-source production data association acquisition and processing: acquiring production condition parameters and equipment health parameters in the entire petroleum production process, establishing multi-source spatiotemporal correlation data, and constructing a dual-index dynamic evaluation model to evaluate the comprehensive operating status value in the entire petroleum production process, determining whether any abnormal conditions occur in the entire petroleum production process, issuing an early warning if any abnormal conditions occur, and performing green analysis if no abnormal conditions occur.

[0022] Step 2: Energy Consumption and Environmental Green Analysis: Environmental data is collected through gas sensors and video monitoring systems. An environmental green index dynamic calibration model is constructed to assess the environmental compliance value in the entire oil production process. It is determined whether the environmental protection of the entire oil production process is compliant. If compliant, combined with real-time monitoring of electricity, water, and gas energy consumption, an energy consumption dataset is formed. Through an energy consumption-output linkage algorithm, the energy consumption compliance value in the entire oil production process is analyzed.

[0023] Step 3, Decision Implementation and Effect Feedback: The comprehensive operating status value, environmental compliance value, and energy consumption compliance value of the entire oil production process are weighted and integrated from multiple dimensions to construct a comprehensive green and intelligent management and control index for the entire oil production process. Based on the comprehensive green and intelligent management and control index, oil production operations are graded and evaluated, and classified into excellent, qualified, and management and control levels that need optimization. At the same time, a strategy effect prediction model is embedded to predict the changing trend of each index after the control strategy is implemented, and then automatically match the corresponding control strategy for different management and control levels.

[0024] The beneficial effects of this invention are as follows: First, by acquiring multi-source spatiotemporal correlation data and using a dual-index dynamic evaluation model, it achieves comprehensive monitoring and integrated operational status assessment of the entire oil production process's operating conditions and equipment parameters, identifies production anomalies and provides timely warnings, effectively reduces the risks of equipment failure and production fluctuations, ensures continuous and stable operation of the entire production process, reduces unplanned downtime losses, and improves production operation stability and anomaly prevention capabilities; Second, relying on an environmental green index dynamic calibration model, it integrates multi-dimensional environmental monitoring data and combines the latest environmental policies and standards to achieve dynamic assessment of environmental compliance values, controlling key environmental aspects such as harmful gas emissions and wastewater treatment, and strengthening... The system has improved environmental compliance and green production levels; third, through an energy consumption-output linkage algorithm, it constructs a linkage model between energy consumption and output, eliminates invalid energy consumption data, calibrates energy consumption deviations, accurately analyzes energy consumption compliance values, optimizes energy allocation schemes for electricity, water, gas, etc., prioritizes the control of high energy consumption links, effectively reduces energy consumption per unit output, and improves energy utilization efficiency; fourth, through multi-dimensional weighted integration, it constructs a comprehensive green intelligent management and control index, conducts hierarchical management and control of production operations, combines a strategy effect prediction model, predicts the control effect in advance, optimizes control strategies, automatically matches appropriate solutions for different control levels, reduces manual intervention, and improves management efficiency. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 This is a schematic diagram of the system structure connection of the present invention.

[0027] Figure 2 This is a schematic diagram of the implementation steps of the method of the present invention. Detailed Implementation

[0028] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Reference Figure 1 As shown, the present invention provides an intelligent management system for petroleum production, including a multi-source production data association acquisition and processing module, an energy consumption and environmental protection green analysis module, and a decision execution and effect feedback module.

[0030] It should be noted that the multi-source production data association acquisition and processing module is connected to the energy consumption and environmental protection green analysis module, and the energy consumption and environmental protection green analysis module is connected to the decision execution and effect feedback module; the multi-source production data association acquisition and processing module, the energy consumption and environmental protection green analysis module, and the decision execution and effect feedback module are each connected to the database.

[0031] Multi-source production data association acquisition and processing module: Collects production condition parameters and equipment health parameters throughout the entire oil production process, establishes multi-source spatiotemporal correlation data, and constructs a dual-index dynamic evaluation model to evaluate the comprehensive operating status value throughout the entire oil production process, determine whether any abnormal conditions have occurred in the entire oil production process, issue early warnings if abnormal conditions occur, and perform green analysis if no abnormal conditions occur.

[0032] Energy Consumption and Environmental Green Analysis Module: Through gas sensors and video monitoring systems, environmental data is collected, a dynamic calibration model for the environmental green index is constructed, the environmental compliance value of the entire oil production process is evaluated, and it is determined whether the environmental protection of the entire oil production process is compliant. If compliant, it combines real-time monitoring of electricity, water, and gas energy consumption to form an energy consumption dataset. Through an energy consumption-output linkage algorithm, the energy consumption compliance value of the entire oil production process is analyzed.

[0033] The decision execution and effect feedback module integrates the comprehensive operating status value, environmental compliance value, and energy consumption compliance value of the entire oil production process through multi-dimensional weighted fusion to construct a comprehensive green and intelligent management and control index for the entire oil production process. Based on the comprehensive green and intelligent management and control index, oil production operations are graded and evaluated, and classified into excellent, qualified, and management and control levels that need optimization. At the same time, a strategy effect prediction model is embedded to predict the changing trend of each index after the control strategy is implemented, and then automatically matches the corresponding control strategy for different management and control levels.

[0034] In the above embodiments, the specific analysis method for collecting production condition parameters and equipment health parameters throughout the entire oil production process and establishing multi-source spatiotemporal correlation data is as follows: Based on the collection of the entire oil production process, multi-source real-time equipment data from wellheads, pipelines, gathering and transportation stations, and reservoirs are collected and divided into four categories according to production scenario rules: wellhead production scenario, pipeline transportation scenario, gathering and transportation processing scenario, and reservoir dynamic scenario. Each scenario in the entire oil production process is obtained. The production rule matching method is used to establish data linkage within the scenario and data correlation between equipment. Data correlation processing is completed to obtain multi-source spatiotemporal correlation data. The multi-source spatiotemporal correlation data includes: oil pressure, casing pressure, production volume, water content, current, load, and equipment vibration, temperature, current fluctuation, and lubricating oil quality values ​​for each scenario in the entire oil production process.

[0035] It should be noted that the process of establishing the multi-source spatiotemporal correlation data is as follows: the system acquires production condition parameters and equipment health parameters in real time through sensors and IoT acquisition terminals deployed at wellheads, pipelines, gathering and transportation stations, and reservoirs. All parameters are synchronized in time, and the sampling frequency is uniformly adjustable from 1 time / minute to 1 time / 5 minutes. At the same time, spatial location is identified according to well number, pipeline number, station ID, and reservoir block number to achieve alignment in both time and space dimensions.

[0036] For equipment and parameters in each scenario, a production pattern matching method is used to establish correlations: Intra-scenario data linkage: Based on the physical transmission law of reservoir-wellhead-pipeline-gathering and transportation, time-series correspondences are established for parameters such as pressure, flow rate, temperature, and load in the same scenario to form a scenario-level dataset; Inter-equipment data association: Based on the upstream and downstream driving relationship of equipment, the operating parameters of equipment such as pumping units, screw pumps, electric submersible pumps, valves, compressors, and oil pumps are associated and bound to form an equipment-level association dataset.

[0037] During the association process, the system automatically completes data preprocessing, including outlier removal, noise filtering, missing value interpolation and imputation, physical unit unification, and data normalization preprocessing, ultimately forming multi-source spatiotemporal associated data with time synchronization, spatial positioning, scene layering, and device association.

[0038] In the above embodiments, the specific analysis method for evaluating the comprehensive operating status value of the entire oil production process is as follows: Based on the obtained oil pressure, casing pressure, liquid production, water content, current, load, and equipment vibration, temperature, current fluctuation, and lubricating oil quality values ​​for each time period of each scenario in the entire oil production process, and combined with the reference operating matrix data stored in the database, the offset of the operating matrix data for each time period of each scenario in the entire oil production process is obtained through numerical fitting processing. Then, the comprehensive operating status value of the entire oil production process is analyzed, and the following dual-exponential dynamic evaluation model is used. The processing yields comprehensive operational status values ​​for the entire oil production process. , among which, among which The first in the entire oil production process The first scenario The offset of the running matrix data for each time period. , These are the left and right endpoints of the allowed offset interval for the running matrix data defined in the database. This represents the runtime impact factor corresponding to a unit offset of the runtime matrix data defined in the database. Number each scene. The times 1, 2, 3, and 4 correspond to the wellhead production scenario, pipeline transportation scenario, gathering and transportation processing scenario, and reservoir dynamic scenario, respectively. This is represented by the number of each time period. , It can be any integer greater than 2.

[0039] It should be noted that the larger the comprehensive operating status value, the more stable the production operation status; the smaller the comprehensive operating status value, the greater the operating deviation and the higher the abnormal risk.

[0040] In the above embodiments, the specific analysis method for determining whether an abnormal situation occurs in the entire oil production process is as follows: based on the obtained comprehensive operating status value of the entire oil production process, the comprehensive operating status value of the entire oil production process is compared with the comprehensive operating status reference value of the entire oil production process stored in the database. If the comprehensive operating status value of the entire oil production process is less than the comprehensive operating status reference value, it indicates that an abnormal situation has occurred. If the comprehensive operating status value of the entire oil production process is greater than or equal to the comprehensive operating status reference value, it indicates that there is no abnormal situation.

[0041] In the above embodiments, the specific analysis method for assessing the environmental compliance value of the entire oil production process is as follows: Based on the dynamic calibration model of the environmental green index, the latest local environmental compliance standards and industry environmental regulations stored in the database are synchronized to extract key environmental assessment indicators. These key environmental assessment indicators include hazardous gas concentration indicators, leakage early warning indicators, illegal operation indicators, sewage discharge indicators, and solid waste disposal indicators. The hazardous gas concentration data collected by gas sensors, the leakage, open flame, and illegal operation identification data collected by the video monitoring system, and the real-time monitoring data of sewage discharge and solid waste disposal in the entire oil production process are standardized and normalized according to preset standards to obtain standardized values ​​for each key environmental assessment indicator. Based on the environmental impact weight of each key environmental assessment indicator, a weighted summation algorithm is used to calculate the basic environmental green value of the entire oil production process. Through the dynamic calibration model of the environmental green index, combined with the adjustment coefficients of various scenarios in the entire oil production process and the latest environmental policies, the basic environmental green value is dynamically calibrated to finally obtain the environmental compliance value of the entire oil production process.

[0042] It should be noted that the environmental compliance value is calculated using the following formula: ,in, For the first The weights of key environmental assessment indicators, For the first Standardized values ​​for key environmental assessment indicators. When the values ​​are 1, 2, 3, 4, and 5 respectively, they correspond to the hazardous gas concentration index, leak warning index, violation operation index, sewage discharge index, and solid waste disposal index. To provide dynamic calibration coefficients for environmental protection policies.

[0043] It should be noted that the weights are adaptively allocated based on the environmental risk level classification standards stored in the database, with the concentration of harmful gases and leakage early warning indicators having the highest weights.

[0044] It should be noted that the aforementioned environmental green index dynamic calibration model is used to evaluate the environmental protection level of oil production in real time, and can be dynamically adjusted according to environmental policies, production scenarios, and compliance standards.

[0045] In the above embodiments, the specific analysis method for determining whether the environmental protection compliance of the entire oil production process is as follows: based on the obtained environmental compliance value of the entire oil production process, the environmental compliance value of the entire oil production process is compared with the environmental green reference value of the entire oil production process stored in the database. If the environmental compliance value of the entire oil production process is greater than or equal to the environmental green reference value, it indicates that the environmental protection compliance of the entire oil production process is compliant; if the environmental compliance value of the entire oil production process is less than the environmental green reference value, it indicates that it is non-compliant.

[0046] In the above embodiments, the specific analysis method for analyzing the energy consumption compliance value in the entire oil production process is as follows: combining real-time monitoring of electricity, water, and gas energy consumption, real-time data of electricity consumption, water resource consumption, and gas consumption in various scenarios throughout the entire oil production process are collected synchronously. After eliminating invalid data, a complete energy consumption dataset is formed. Through an energy consumption-production linkage correlation algorithm, real-time liquid production and oil production data of each scenario are introduced to establish an energy consumption-production linkage correlation model. Abnormal deviations in energy consumption caused by production fluctuations are eliminated. The energy consumption dataset is calibrated, and key energy consumption assessment indicators are extracted. Standardization processing is performed according to preset standards to obtain standardized values ​​of each key energy consumption assessment indicator. Based on the energy-saving impact weight of each key energy consumption assessment indicator, a weighted summation algorithm is used to calculate the energy consumption compliance value in the entire oil production process.

[0047] It should be noted that invalid data includes no-load energy consumption data when the equipment is stopped.

[0048] It should be noted that key indicators for energy consumption assessment include electricity consumption per unit of output, water consumption per unit of output, gas consumption per unit of output, and energy consumption fluctuation coefficient.

[0049] It should be noted that the energy-saving impact weights of each key energy consumption assessment indicator are adaptively allocated by the energy consumption optimization standards stored in the database, with the weight of electricity consumption per unit output being the highest.

[0050] It should be noted that the formula for calculating the energy consumption value in the entire oil production process is as follows: ,in, For the first The weights of key energy consumption assessment indicators For the first Standardized values ​​for key energy consumption assessment indicators. When the values ​​are 1, 2, 3, and 4, they correspond to the electricity consumption per unit output, water consumption per unit output, gas consumption per unit output, and energy consumption fluctuation coefficient, respectively.

[0051] In the above embodiments, the specific analysis method for classifying and evaluating oil production operations according to the comprehensive green and intelligent management and control index, and classifying them into excellent, qualified, and management and control levels that need optimization, is as follows: the comprehensive operating status value, environmental compliance value, and energy consumption compliance value in the entire oil production process are weighted and integrated in multiple dimensions. Based on the actual needs of oil production, the integration weights of the comprehensive operating status value, environmental compliance value, and energy consumption compliance value are adaptively allocated according to the management and control priority standards stored in the database. The comprehensive green and intelligent management and control index in the entire oil production process is calculated through a weighted summation algorithm.

[0052] The comprehensive green and intelligent management index of the entire oil production process is compared with the management level classification thresholds stored in the database. When the comprehensive green and intelligent management index of the entire oil production process is less than the minimum value, it is classified as a management level that needs optimization. When the comprehensive green and intelligent management index of the entire oil production process is greater than or equal to the minimum value and less than or equal to the maximum value, it is classified as a qualified management level. When the comprehensive green and intelligent management index of the entire oil production process is greater than the maximum value, it is classified as an excellent management level.

[0053] It should be noted that the actual demand for oil production prioritizes stable production, energy conservation, and environmental protection.

[0054] It should be noted that the adaptive allocation of the integrated operating status value, environmental compliance value, and energy consumption compliance value has the highest weight, ensuring safe and stable production.

[0055] It should be noted that the specific formula for calculating the comprehensive green and intelligent management and control index of the entire oil production process is as follows: ,in, These are the fusion weights for the comprehensive operating status value, the environmental compliance value, and the energy consumption compliance value, respectively. .

[0056] In the above embodiments, the specific analysis method for predicting the changing trends of each index after regulation and then automatically matching corresponding regulation strategies for different control levels is as follows: Based on the strategy effect prediction model, the comprehensive operating status value, environmental compliance value, and energy consumption compliance value of the current oil production process are input, along with the preset regulation strategy parameters for the corresponding control level. Combined with historical regulation data and production scenario matching data stored in the database, a regression analysis algorithm is used to predict the change range and final value of the comprehensive operating status value, environmental compliance value, and energy consumption compliance value after regulation, and to determine whether the regulation strategy can make each index reach the preset reasonable range. If the prediction result meets expectations, the corresponding regulation strategy is executed; if the prediction result does not meet expectations, the regulation strategy parameters are adjusted, and the prediction is repeated until it meets expectations before execution.

[0057] Automatic matching control strategies for different control levels: Superior control level: Maintain the current production parameters, equipment operation mode and energy distribution plan of the pumping unit, pump, valve and compressor equipment, perform routine intelligent inspections, collect data of each index every preset period, conduct dynamic monitoring, and ensure that each index is stable within a reasonable range without additional control. Qualified control level: Based on the forecast results, the energy allocation plan is slightly optimized, and the power and water consumption of high-energy-consuming links such as gathering and transportation and wellhead are adjusted first. The oil pressure and production volume are fine-tuned without shutdown, ensuring that the comprehensive green and intelligent control index remains stable in the qualified range, while gradually moving towards the superior control level.

[0058] Level of control to be optimized: Immediately activate the emergency control mode, prioritize the control of key parameters affecting the comprehensive operating status value, eliminate potential production anomalies, and simultaneously adjust the operating parameters of environmental protection equipment, optimize the energy consumption structure, reduce energy consumption, and improve the environmental compliance rate; during the control process, collect data in real time and dynamically correct the control strategy until the comprehensive green and intelligent control index returns to the qualified range.

[0059] It should be noted that the strategy effect prediction model predicts the changes and final values ​​of the comprehensive operating status value, environmental compliance value, and energy consumption compliance value after regulation. The regression analysis algorithm is built based on historical regulation data. It takes the comprehensive operating status value, environmental compliance value, energy consumption compliance value, regulation parameter increment, and production scenario type as input features and the predicted value of each index after regulation as output. It is trained through historical successful regulation samples and the output prediction accuracy is ≥95%.

[0060] It should be noted that, when determining whether the control strategy can bring each index to a preset reasonable range, the system inputs the current index and the control parameters to be executed into the model, combines it with scenario matching data, and calculates the predicted value and change range of each index after control through regression analysis algorithm. The predicted value is then compared with the reasonable range of the index stored in the database. If all predicted values ​​fall within the reference reasonable range, the prediction is deemed to be in line with expectations, and the system is directly issued for execution. If the predicted values ​​do not fall within the reference reasonable range, the prediction is deemed to be in line with expectations, and the system automatically adjusts the control parameters according to the step size and re-predicts. To avoid infinite loops, the maximum number of predictions is set to 3. If all 3 predictions fail to meet the target, the emergency strategy for the level to be optimized is directly triggered.

[0061] It should be noted that the key parameters that affect the overall operating status value, such as load, current, and pressure, are subject to priority control.

[0062] It should be noted that the environmental protection equipment includes waste gas treatment and wastewater recycling equipment.

[0063] Reference Figure 2 As shown, the present invention provides a method for an intelligent management system for petroleum production, characterized by including: Step 1, multi-source production data association acquisition and processing: acquiring production condition parameters and equipment health parameters in the entire petroleum production process, establishing multi-source spatiotemporal correlation data, and constructing a dual-index dynamic evaluation model to evaluate the comprehensive operating status value in the entire petroleum production process, determining whether any abnormal conditions occur in the entire petroleum production process, issuing early warnings if abnormal conditions occur, and performing green analysis if no abnormal conditions occur.

[0064] Step 2: Energy Consumption and Environmental Green Analysis: Environmental data is collected through gas sensors and video monitoring systems. An environmental green index dynamic calibration model is constructed to assess the environmental compliance value in the entire oil production process. It is determined whether the environmental protection of the entire oil production process is compliant. If compliant, combined with real-time monitoring of electricity, water, and gas energy consumption, an energy consumption dataset is formed. Through an energy consumption-output linkage algorithm, the energy consumption compliance value in the entire oil production process is analyzed.

[0065] Step 3, Decision Implementation and Effect Feedback: The comprehensive operating status value, environmental compliance value, and energy consumption compliance value of the entire oil production process are weighted and integrated from multiple dimensions to construct a comprehensive green and intelligent management and control index for the entire oil production process. Based on the comprehensive green and intelligent management and control index, oil production operations are graded and evaluated, and classified into excellent, qualified, and management and control levels that need optimization. At the same time, a strategy effect prediction model is embedded to predict the changing trend of each index after the control strategy is implemented, and then automatically match the corresponding control strategy for different management and control levels.

[0066] It should be noted that the database is used to store reference operation matrix data, comprehensive operation status reference values ​​in the entire oil production process, the latest local environmental compliance standards and industry environmental protection regulations, environmental green reference values ​​in the entire oil production process, control priority standards, control level classification thresholds, historical control data, production scenario matching data, etc., and is set by relevant personnel in oil management.

[0067] The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. An intelligent management system for oil production, characterized in that, include: Multi-source production data association acquisition and processing module: Collects production condition parameters and equipment health parameters in the entire oil production process, establishes multi-source spatiotemporal correlation data, and constructs a dual-index dynamic evaluation model to evaluate the comprehensive operating status value in the entire oil production process, determine whether there are any abnormal conditions in the entire oil production process, and issue early warnings if abnormal conditions occur; if there are no abnormal conditions, green analysis is performed. Energy Consumption and Environmental Green Analysis Module: Through gas sensors and video monitoring systems, environmental data is collected, a dynamic calibration model of the environmental green index is constructed, the environmental compliance value in the entire oil production process is evaluated, and it is determined whether the environmental protection in the entire oil production process is compliant. If compliant, it combines real-time monitoring of electricity, water and gas energy consumption to form an energy consumption dataset. Through the energy consumption-output linkage algorithm, the energy consumption compliance value in the entire oil production process is analyzed. The decision execution and effect feedback module integrates the comprehensive operating status value, environmental compliance value, and energy consumption compliance value of the entire oil production process through multi-dimensional weighted fusion to construct a comprehensive green and intelligent management and control index for the entire oil production process. Based on the comprehensive green and intelligent management and control index, oil production operations are graded and evaluated, and classified into excellent, qualified, and management and control levels that need optimization. At the same time, a strategy effect prediction model is embedded to predict the changing trend of each index after the control strategy is implemented, and then automatically matches the corresponding control strategy for different management and control levels.

2. The intelligent management system for oil production according to claim 1, characterized in that, The specific analysis method for collecting production condition parameters and equipment health parameters throughout the entire oil production process and establishing multi-source spatiotemporal correlation data is as follows: Based on the entire oil production process, multi-source real-time equipment data from wellheads, pipelines, gathering and transportation stations, and reservoirs are collected and categorized into four main types according to production scenario rules: wellhead production scenario, pipeline transportation scenario, gathering and transportation processing scenario, and reservoir dynamic scenario. This yields various scenarios within the entire oil production process. The production rule matching method is used to establish data linkage within scenarios and data association between equipment. Data association processing is completed to obtain multi-source spatiotemporal correlated data. The multi-source spatiotemporal correlated data includes: oil pressure, casing pressure, production volume, water content, current, load, and equipment vibration, temperature, current fluctuation, and lubricating oil quality values ​​for each production condition in each scenario of the entire oil production process.

3. The intelligent management system for oil production according to claim 2, characterized in that, The specific analysis method for evaluating the comprehensive operational status value of the entire oil production process is as follows: Based on the obtained values ​​of oil pressure, casing pressure, fluid production, water content, current, load, equipment vibration, temperature, current fluctuation, and lubricating oil quality for each time period in each scenario of the entire oil production process, and combined with the reference operation matrix data stored in the database, the offset of the operation matrix data for each time period in each scenario of the entire oil production process is obtained through numerical fitting processing. This allows for the analysis of the comprehensive operating status value of the entire oil production process, and the following dual-exponential dynamic evaluation model is then applied. The processing yields comprehensive operational status values ​​for the entire oil production process. , among which, among which The first in the entire oil production process The first scenario The offset of the running matrix data for each time period. , These are the left and right endpoints of the allowed offset interval for the running matrix data defined in the database. This represents the runtime impact factor corresponding to a unit offset of the runtime matrix data defined in the database. Number each scene. The times 1, 2, 3, and 4 correspond to the wellhead production scenario, pipeline transportation scenario, gathering and transportation processing scenario, and reservoir dynamic scenario, respectively. This is represented by the number of each time period. , It can be any integer greater than 2.

4. The intelligent management system for petroleum production according to claim 3, characterized in that, The specific analytical method for determining whether any abnormalities occur in the entire oil production process is as follows: Based on the obtained comprehensive operating status value of the entire oil production process, the comprehensive operating status value of the entire oil production process is compared with the comprehensive operating status reference value of the entire oil production process stored in the database. If the comprehensive operating status value of the entire oil production process is less than the comprehensive operating status reference value, it indicates that an abnormal state has occurred. If the comprehensive operating status value of the entire oil production process is greater than or equal to the comprehensive operating status reference value, it indicates that there is no abnormal state.

5. The intelligent management system for petroleum production according to claim 1, characterized in that, The specific analytical method for assessing the environmental compliance values ​​throughout the entire oil production process is as follows: Based on the dynamic calibration model of the environmental green index, and by synchronizing the latest local environmental compliance standards and industry environmental regulations stored in the database, key indicators for environmental assessment are extracted. These key indicators include hazardous gas concentration indicators, leak warning indicators, illegal operation indicators, wastewater discharge indicators, and solid waste disposal indicators. Hazardous gas concentration data collected by gas sensors, leak, open flame, and illegal operation identification data collected by video monitoring systems, and real-time monitoring data of wastewater discharge and solid waste disposal throughout the entire oil production process are standardized and normalized according to preset standards to obtain standardized values ​​for each key environmental assessment indicator. Based on the environmental impact weights of each key environmental assessment indicator, a weighted summation algorithm is used to calculate the basic environmental green value for the entire oil production process. Through the dynamic calibration model of the environmental green index, combined with various scenarios in the entire oil production process and the adjustment coefficients of the latest environmental policies, the basic environmental green value is dynamically calibrated to finally obtain the environmental compliance value for the entire oil production process.

6. The intelligent management system for petroleum production according to claim 5, characterized in that, The specific analytical method for determining whether environmental protection is compliant throughout the entire oil production process is as follows: Based on the obtained environmental compliance values ​​in the entire oil production process, the environmental compliance values ​​in the entire oil production process are compared with the environmental green reference values ​​in the entire oil production process stored in the database. If the environmental compliance value in the entire oil production process is greater than or equal to the environmental green reference value, it indicates that the entire oil production process is environmentally compliant. If the environmental compliance value in the entire oil production process is less than the environmental green reference value, it indicates that it is non-compliant.

7. The intelligent management system for petroleum production according to claim 6, characterized in that, The specific analytical method for analyzing the energy consumption compliance value in the entire oil production process is as follows: By combining real-time monitoring of electricity, water, and gas energy consumption, real-time data on electricity consumption, water consumption, and gas consumption in various scenarios throughout the entire oil production process are collected synchronously. After removing invalid data, a complete energy consumption dataset is formed. Through an energy consumption-production linkage algorithm, real-time liquid production and oil production data from various scenarios are introduced to establish a linkage model between energy consumption and production. Abnormal deviations in energy consumption caused by production fluctuations are eliminated, the energy consumption dataset is calibrated, and key energy consumption assessment indicators are extracted. These indicators are then standardized according to preset standards to obtain standardized values ​​for each key energy consumption assessment indicator. Based on the energy-saving impact weights of each key energy consumption assessment indicator, a weighted summation algorithm is used to calculate the energy consumption compliance value for the entire oil production process.

8. The intelligent management system for petroleum production according to claim 1, characterized in that, The method for classifying and evaluating petroleum production operations based on a comprehensive green and intelligent management index, categorizing them into excellent, qualified, and management levels requiring optimization, is as follows: The comprehensive operating status value, environmental compliance value, and energy consumption compliance value of the entire oil production process are weighted and integrated in multiple dimensions. Based on the actual needs of oil production, the integration weights of the comprehensive operating status value, environmental compliance value, and energy consumption compliance value are adaptively allocated according to the control priority standards stored in the database. The comprehensive green and intelligent control index of the entire oil production process is calculated through a weighted summation algorithm. The comprehensive green and intelligent management index of the entire oil production process is compared with the management level classification thresholds stored in the database. When the comprehensive green and intelligent management index of the entire oil production process is less than the minimum value, it is classified as a management level that needs optimization. When the comprehensive green and intelligent management index of the entire oil production process is greater than or equal to the minimum value and less than or equal to the maximum value, it is classified as a qualified management level. When the comprehensive green and intelligent management index of the entire oil production process is greater than the maximum value, it is classified as an excellent management level.

9. The intelligent management system for petroleum production according to claim 8, characterized in that, The specific analysis method for predicting the changing trends of various indices after regulation and then automatically matching corresponding regulation strategies for different control levels is as follows: Based on the strategy effect prediction model, the system inputs the comprehensive operating status value, environmental compliance value, and energy consumption compliance value of the current oil production process, as well as the preset control strategy parameters for the corresponding control level. Combining historical control data and production scenario matching data stored in the database, the system uses regression analysis algorithms to predict the change range and final value of the comprehensive operating status value, environmental compliance value, and energy consumption compliance value after control, and to determine whether the control strategy can bring each index to the preset reasonable range. If the prediction result meets expectations, the corresponding control strategy is executed; if the prediction result does not meet expectations, the control strategy parameters are adjusted, and the prediction is repeated until it meets expectations before execution. Automatic matching control strategies for different control levels: Superior control level: Maintain the current production parameters, equipment operation mode and energy distribution plan of the pumping unit, pump, valve and compressor equipment, perform routine intelligent inspections, collect data of each index every preset period, conduct dynamic monitoring, and ensure that each index is stable within a reasonable range without additional control. Qualified control level: Based on the prediction results, the energy allocation plan is slightly optimized, and the power and water consumption of high energy-consuming links such as gathering and transportation and wellhead are adjusted first. The oil pressure and production volume are fine-tuned without shutdown, ensuring that the comprehensive green and intelligent control index remains stable in the qualified range, while gradually moving towards the superior control level. Level of control to be optimized: Immediately activate the emergency control mode, prioritize the control of key parameters that affect the overall operating status value, eliminate potential production anomalies, and simultaneously adjust the operating parameters of environmental protection equipment, optimize the energy consumption structure, reduce energy consumption, and improve the environmental compliance rate; During the regulation process, data is collected in real time, and the regulation strategy is dynamically adjusted until the comprehensive green and intelligent management index returns to the qualified range.

10. A method for implementing the intelligent management system for petroleum production according to any one of claims 1-9, characterized in that, include: Step 1: Multi-source production data association, collection and processing: Collect production condition parameters and equipment health parameters throughout the entire oil production process, establish multi-source spatiotemporal correlation data, and construct a dual-index dynamic evaluation model to evaluate the comprehensive operating status value throughout the entire oil production process, determine whether any abnormal conditions have occurred in the entire oil production process, issue early warnings if abnormal conditions have occurred, and conduct green analysis if no abnormal conditions have occurred. Step 2: Energy Consumption and Environmental Green Analysis: Environmental data is collected through gas sensors and video monitoring systems. An environmental green index dynamic calibration model is constructed to assess the environmental compliance value in the entire oil production process. It is determined whether the environmental protection in the entire oil production process is compliant. If compliant, combined with real-time monitoring of electricity, water, and gas energy consumption, an energy consumption dataset is formed. The energy consumption-output linkage algorithm is used to analyze the energy consumption compliance value in the entire oil production process. Step 3, Decision Implementation and Effect Feedback: The comprehensive operating status value, environmental compliance value, and energy consumption compliance value of the entire oil production process are weighted and integrated from multiple dimensions to construct a comprehensive green and intelligent management and control index for the entire oil production process. Based on the comprehensive green and intelligent management and control index, oil production operations are graded and evaluated, and classified into excellent, qualified, and management and control levels that need optimization. At the same time, a strategy effect prediction model is embedded to predict the changing trend of each index after the control strategy is implemented, and then automatically match the corresponding control strategy for different management and control levels.

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