CN117684928B - A coordinated optimization control system for oil and water well production and injection under different production modes - Google Patents
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Abstract
Description
技术领域Technical Field
本发明属于信息技术领域,具体涉及一种不同生产模式下油水井采注协同优化控制系统。The present invention belongs to the field of information technology, and in particular relates to a coordinated optimization control system for oil and water well production and injection under different production modes.
背景技术Background technique
采油井与注水井是水驱开发油田的核心生产单元,也是实现油田高效开发人为可控的、最为关键的操作管理对象。随着油田开发工作的持续进行,我国大部分油田已经进入开发中后期,油藏非均质性严重,注采矛盾突出,生产成本居高不下,严重影响油田开发效益。因此,针对不同油田、不同区块或不同开发阶段,建立不同生产模式下油水井采注协同优化系统,对实现油田节能降耗、降本增效以及提高油田经济效益具有重要意义。Oil production wells and water injection wells are the core production units of water-driven oilfields, and are also the most critical operational management objects that can be controlled by humans to achieve efficient development of oilfields. With the continuous development of oilfields, most of my country's oilfields have entered the middle and late stages of development. The reservoir heterogeneity is serious, the contradiction between injection and production is prominent, and the production cost remains high, which seriously affects the benefits of oilfield development. Therefore, for different oilfields, different blocks or different development stages, it is of great significance to establish a coordinated optimization system for oil and water well production and injection under different production modes to achieve energy conservation and consumption reduction, reduce costs and increase efficiency, and improve the economic benefits of oilfields.
目前,油田主要是在单一生产模式下,通过油藏数值模拟来建立采油井和注水井之间的关联,从而实现对采油井和注水井的优化控制,优化周期长、速度慢、计算复杂度高,无法实现油水井系统的全局实时优化。近年来,随着大数据、人工智能技术的快速发展,采用大数据驱动的方式、利用机器学习技术进行不同生产模式下油水井采注协同优化,可极大降低系统复杂性,提高系统快速响应能力,实现油水井系统的实时快速全局优化。At present, oil fields mainly establish the relationship between oil production wells and water injection wells through reservoir numerical simulation under a single production mode, so as to achieve the optimization control of oil production wells and water injection wells. The optimization cycle is long, the speed is slow, and the calculation complexity is high, and it is impossible to achieve the global real-time optimization of the oil and water well system. In recent years, with the rapid development of big data and artificial intelligence technology, the use of big data driven methods and machine learning technology to carry out the coordinated optimization of oil and water well production and injection under different production modes can greatly reduce the complexity of the system, improve the system's rapid response capability, and achieve real-time and rapid global optimization of the oil and water well system.
发明内容Summary of the invention
针对上述问题,本发明提供一种不同生产模式下油水井采注协同优化控制系统。本方法可根据油田实际开发需求,在不同阶段、不同区块、不同厂站进行不同生产模式下的油水井系统采注协同优化,操作简单,易于实现,同时综合考虑了动静态数据对油水井系统的影响,进一步提高油水井系统全局优化水平,适合在油田推广应用。In view of the above problems, the present invention provides a control system for oil and water well production and injection coordination optimization under different production modes. This method can be used to coordinate the optimization of oil and water well system production and injection under different production modes in different stages, different blocks, and different plants and stations according to the actual development needs of the oil field. It is simple to operate and easy to implement. At the same time, it comprehensively considers the impact of dynamic and static data on the oil and water well system, further improves the global optimization level of the oil and water well system, and is suitable for promotion and application in oil fields.
本发明为实现上述目的所采用的技术方案是:一种不同生产模式下油水井采注协同优化控制系统,包括油水井系统生产模式选择模块、油水井系统智能决策模块、油水井系统智能控制模块;The technical solution adopted by the present invention to achieve the above-mentioned purpose is: a coordinated optimization control system for oil and water well production and injection under different production modes, including an oil and water well system production mode selection module, an oil and water well system intelligent decision-making module, and an oil and water well system intelligent control module;
所述油水井系统生产模式选择模块,用于接收选择的不同生产模式;The oil-water well system production mode selection module is used to receive different selected production modes;
所述油水井系统智能决策模块,用于根据所述油水井系统生产模式选择模块所选择的生产模式进行智能决策,并将决策结果传输给所述油水井系统智能控制模块;The oil-water well system intelligent decision-making module is used to make intelligent decisions according to the production mode selected by the oil-water well system production mode selection module, and transmit the decision results to the oil-water well system intelligent control module;
所述油水井系统智能控制模块,用于根据决策结果控制采油井的产液量和注水井的注水量。The oil-water well system intelligent control module is used to control the liquid production of the oil production well and the water injection volume of the water injection well according to the decision result.
所述生产模式包括节能模式、最大产模式和最大效益模式。The production modes include energy-saving mode, maximum production mode and maximum benefit mode.
所述油水井系统智能决策模块包括:The oil and water well system intelligent decision-making module includes:
能耗最小化智能决策单元,用于节能模式下,进行能耗最小化决策,将决策结果传输给所述油水井系统智能控制模块;An energy consumption minimization intelligent decision-making unit is used to make energy consumption minimization decisions in energy-saving mode and transmit the decision results to the oil and water well system intelligent control module;
产量最大化智能决策单元,用于最大产模式下,进行产量最大化决策,将决策结果传输给所述油水井系统智能控制模块;The production maximization intelligent decision-making unit is used to make production maximization decisions under the maximum production mode and transmit the decision results to the oil and water well system intelligent control module;
效益最大化智能决策单元,用于最大效益模式下,进行效益最大化决策,将决策结果传输给所述油水井系统智能控制模块。The benefit maximization intelligent decision-making unit is used to make benefit maximization decisions in the maximum benefit mode and transmit the decision results to the oil and water well system intelligent control module.
所述能耗最小化智能决策单元,执行以下步骤:The energy consumption minimization intelligent decision-making unit performs the following steps:
将注水量ij和采油量qj作为粒子,根据油水井系统吨油能耗最小化的目标函数和约束条件确定适应度函数,使用粒子群算法求解,得到最佳的注水井注水量ij和采油井产液量qj作为决策结果;The water injection volume i j and oil production volume q j are taken as particles, and the fitness function is determined according to the objective function and constraint conditions of minimizing the energy consumption per ton of oil in the oil-water well system. The particle swarm algorithm is used to solve it, and the optimal water injection volume i j of the water injection well and the liquid production volume q j of the oil production well are obtained as the decision results;
其中,目标函数为油水井系统吨油能耗最小化,表达式为:Among them, the objective function is to minimize the energy consumption per ton of oil in the oil-water well system, and the expression is:
式中:Winj为注水系统能耗;Wprod为采油系统能耗;Qoil为给定时间范围内油水井系统产油量;Where: Winj is the energy consumption of the water injection system; Wprod is the energy consumption of the oil production system; Qoil is the oil production of the oil and water well system within a given time range;
其中,注水系统能耗Winj、采油系统能耗Wprod、采油量Qoil分别表示为:Among them, the energy consumption of water injection system Winj , the energy consumption of oil production system Wprod , and the oil production Qoil are expressed as:
式中:ij为第j口注水井的注水量;qj为第j口采油井的采液量;α为注水能耗权重;β为采油能耗权重;ρ为注入水密度;Pj为第j口注水井的注入压力;pj为第j口注水井的井底流压;Hj为第j口采油井的深度;g为重力加速度,fj为第j口采油井的含水率;M为注水井数量;N为采油井数量。Wherein: i j is the injection volume of the jth water injection well; q j is the liquid production volume of the jth oil production well; α is the water injection energy consumption weight; β is the oil production energy consumption weight; ρ is the injected water density; P j is the injection pressure of the jth water injection well; p j is the bottom hole flowing pressure of the jth water injection well; H j is the depth of the jth oil production well; g is the gravitational acceleration, f j is the water content of the jth oil production well; M is the number of water injection wells; N is the number of oil production wells.
所述产量最大化智能决策单元,执行以下步骤:The production maximization intelligent decision-making unit performs the following steps:
将注水量ij和采油量qj作为粒子,根据油水井系统产油量最大化的目标函数和约束条件确定适应度函数,使用粒子群算法求解,得到最佳的注水井注水量ij和采油井产液量qj作为决策结果;The water injection volume i j and the oil production volume q j are taken as particles, and the fitness function is determined according to the objective function and constraint conditions of maximizing the oil production of the oil-water well system. The particle swarm algorithm is used to solve it, and the optimal water injection volume i j of the water injection well and the liquid production volume q j of the oil production well are obtained as the decision results;
其中,目标函数为给定时间范围内油水井系统产油量最大化,表达式为:Among them, the objective function is to maximize the oil production of the oil and water well system within a given time range, and the expression is:
式中:qj为第j口采油井的产油量;N为油水井系统中采油井的数量,t0为初始时刻,t为当前时刻。Where: qj is the oil production of the jth oil well; N is the number of oil wells in the oil-water well system; t0 is the initial time, and t is the current time.
所述效益最大化智能决策单元,执行以下步骤:The benefit maximization intelligent decision-making unit performs the following steps:
将注水量ij和采油量qj作为粒子,根据油水井系统生产效益最大化的目标函数和约束条件确定适应度函数,使用粒子群算法求解,得到最佳的注水井注水量ij和采油井产液量qj作为决策结果;The water injection volume i j and oil production volume q j are taken as particles, and the fitness function is determined according to the objective function and constraint conditions of maximizing the production efficiency of the oil and water well system. The particle swarm algorithm is used to solve it, and the optimal water injection volume i j of the water injection well and the liquid production volume q j of the oil production well are obtained as the decision results;
其中,目标函数为给定时间范围内油水井系统生产效益最大化,表达式为:Among them, the objective function is to maximize the production efficiency of the oil and water well system within a given time range, and the expression is:
式中:Ro为油价,Rw为注水费用;ij为第j口注水井的注水量;M为油水井系统中注水井数量。Where: Ro is the oil price, Rw is the water injection cost; i j is the water injection volume of the jth injection well; M is the number of injection wells in the oil-water well system.
所述油水井系统智能控制模块,包括:The oil and water well system intelligent control module comprises:
采油井控制单元,用于根据决策结果中的采油井产液量,通过变频器实现采油井电动机变速运行,从而控制采油井冲次,控制油井产量;The oil well control unit is used to realize the variable speed operation of the oil well motor through the frequency converter according to the oil well production volume in the decision result, so as to control the oil well flushing frequency and the oil well production;
注水井控制单元,用于根据决策结果中的注水井的注水量,通过流量自控仪调节控制阀阀门开度,从而控制注水井注水量。The water injection well control unit is used to adjust the opening of the control valve through the flow automatic controller according to the water injection volume of the water injection well in the decision result, so as to control the water injection volume of the water injection well.
一种不同生产模式下油水井采注协同优化控制方法,包括以下步骤:A method for coordinated optimization control of oil and water well production and injection under different production modes comprises the following steps:
油水井系统生产模式选择模块接收选择的不同生产模式;The oil and water well system production mode selection module receives different selected production modes;
油水井系统智能决策模块根据所述油水井系统生产模式选择模块所选择的生产模式进行智能决策,并将决策结果传输给所述油水井系统智能控制模块;The intelligent decision-making module of the oil-water well system makes an intelligent decision according to the production mode selected by the production mode selection module of the oil-water well system, and transmits the decision result to the intelligent control module of the oil-water well system;
油水井系统智能控制模块根据决策结果控制采油井的产液量和注水井的注水量。The intelligent control module of the oil and water well system controls the liquid production of the oil production well and the water injection volume of the water injection well according to the decision results.
本发明的有益效果如下:The beneficial effects of the present invention are as follows:
(1)本发明基于井间连通关系建立油水井系统采注协同优化控制模型,将采油井系统和注水井系统进行一体化分析和全局优化,不需要复杂的精细化油藏描述和数值模拟过程,仅通过注采数据就可以定量计算井间连通系数,方法简单易于实现,计算复杂度低,便于油田现场应用;(1) The present invention establishes a coordinated optimization control model for oil and water well system production and injection based on the well-to-well connectivity relationship, integrates the oil production well system and the water injection well system for analysis and global optimization, does not require complex and refined reservoir description and numerical simulation processes, and can quantitatively calculate the well-to-well connectivity coefficient only through injection and production data. The method is simple and easy to implement, has low computational complexity, and is convenient for on-site application in oil fields;
(2)本发明所建立的不同生产模式下油水井采注协同优化控制系统,能够满足油田在不同开发阶段的实际生产需求,可针对油田不同区块、不同场站、不同井组,选择所需的生产模式,适合在油田大面积推广应用;(2) The oil-water well production and injection coordinated optimization control system under different production modes established by the present invention can meet the actual production needs of oil fields at different development stages, and can select the required production mode for different blocks, different stations, and different well groups in the oil field, which is suitable for large-scale promotion and application in oil fields;
(3)根据所选择的油田生产模式,基于油气生产大数据,可快速定制协同优化控制方案,实现油田节能降耗和提质增效,提高油田采收率和经济效益。(3) According to the selected oil field production mode and based on the big data of oil and gas production, a collaborative optimization control plan can be quickly customized to achieve energy conservation and consumption reduction, improve quality and efficiency, and increase oil field recovery rate and economic benefits.
附图说明BRIEF DESCRIPTION OF THE DRAWINGS
图1为本发明实施例提供的不同生产模式下油水井采注协同优化控制系统示意图。FIG1 is a schematic diagram of a coordinated optimization control system for oil and water well production and injection under different production modes provided by an embodiment of the present invention.
图2为本发明实施例提供的油水井系统智能控制模块控制方法示意图。FIG. 2 is a schematic diagram of a control method of an intelligent control module of an oil-water well system provided by an embodiment of the present invention.
具体实施方式Detailed ways
下面结合附图及实施例对本发明做进一步的详细说明。The present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.
本发明基于井间连通关系建立油水井系统采注协同优化控制模型,将采油井系统和注水井系统进行一体化分析和全局优化,能够满足油田在不同开发阶段的实际生产需求,可针对油田不同区块、不同场站、不同井组,选择所需的生产模式,适合在油田大面积推广。根据所选择的油田生产模式,基于油气生产大数据,可快速定制优化控制方案,实现油田节能降耗和提质增效,提高油田采收率,降低油气开发成本,提升油田经济效益。The present invention establishes a coordinated optimization control model for oil and water well system production and injection based on the well-to-well connectivity relationship, integrates the oil production well system and the water injection well system for analysis and global optimization, can meet the actual production needs of oil fields at different development stages, can select the required production mode for different blocks, different stations, and different well groups in the oil field, and is suitable for large-scale promotion in oil fields. According to the selected oil field production mode, based on the big data of oil and gas production, the optimization control scheme can be quickly customized to achieve energy saving and consumption reduction, quality improvement and efficiency enhancement in the oil field, improve the oil field recovery rate, reduce the cost of oil and gas development, and enhance the economic benefits of the oil field.
如图1所示,本实施例的油水井采注协同优化控制系统如下所述。As shown in FIG1 , the oil-water well production and injection coordinated optimization control system of this embodiment is described as follows.
本发明所述的不同生产模式下油水井采注协同优化控制系统,包括:The oil and water well production and injection coordinated optimization control system under different production modes of the present invention includes:
油水井系统生产模式选择模块:根据油田实际生产需求,针对油田不同区块、不同场站或不同开发阶段,选择相应的生产模式。生产模式具体包括节能模式、最大产模式和最大效益模式。Oil and water well system production mode selection module: According to the actual production needs of the oil field, select the corresponding production mode for different blocks, different stations or different development stages of the oil field. The production mode specifically includes energy-saving mode, maximum production mode and maximum benefit mode.
油水井系统智能决策模块:包括能耗最小化智能决策单元,产量最大化智能决策单元,效益最大化智能决策单元。根据各生产模式所对应的智能决策单元,确定油水井系统采注协同优化目标函数、优化模型决策变量、油水井系统约束条件和模型求解方法,给出最优决策结果。Intelligent decision-making module for oil and water well system: including intelligent decision-making unit for minimizing energy consumption, intelligent decision-making unit for maximizing output, and intelligent decision-making unit for maximizing benefits. According to the intelligent decision-making units corresponding to each production mode, the oil and water well system production and injection collaborative optimization objective function, optimization model decision variables, oil and water well system constraints and model solution methods are determined to give the optimal decision result.
能耗最小化智能决策单元的目标函数为油水井系统吨油能耗最小化,表达式为:The objective function of the energy consumption minimization intelligent decision-making unit is to minimize the energy consumption per ton of oil in the oil and water well system, and the expression is:
式中:Winj为注水系统能耗,kW·h;Wprod为采油系统能耗,KW·h;Qoil为给定时间范围内油水井系统产油量,t。Where: Winj is the energy consumption of the water injection system, kW·h; Wprod is the energy consumption of the oil production system, kW·h; Qoil is the oil production of the oil-water well system within a given time range, t.
注水系统能耗Winj、采油系统能耗Wprod、采油量Qoil可分别表示为:The energy consumption of water injection system W inj , the energy consumption of oil production system W prod , and the oil production Q oil can be expressed as:
式中:ij为第j口注水井的注水量,t/d;qj为第j口采油井的采液量,t/d;α为注水能耗权重;β为采油能耗权重;ρ为注入水密度,kg/m3;Pj为第j口注水井的注入压力,MPa;pj为第j口注水井的井底流压,MPa;Hj为第j口采油井的深度,m;h为重力加速度,m/s2,fj为第j口采油井的含水率;M为注水井数量;N为采油井数量。In the formula: i j is the injection volume of the jth water injection well, t/d; q j is the liquid production volume of the jth oil production well, t/d; α is the water injection energy consumption weight; β is the oil production energy consumption weight; ρ is the injected water density, kg/m 3 ; P j is the injection pressure of the jth water injection well, MPa; p j is the bottom hole flowing pressure of the jth water injection well, MPa; H j is the depth of the jth oil production well, m; h is the acceleration of gravity, m/s 2 , f j is the water content of the jth oil production well; M is the number of water injection wells; N is the number of oil production wells.
产量最大化智能决策单元的目标函数为给定时间范围内油水井系统产油量最大化,表达式为:The objective function of the production maximization intelligent decision-making unit is to maximize the oil production of the oil and water well system within a given time range, and the expression is:
式中:qj为第j口采油井的产油量,t/d;N为油水井系统中采油井的数量,t0为初始时刻,t为当前时刻。Where: qj is the oil production of the jth oil well, t/d; N is the number of oil wells in the oil-water well system, t0 is the initial time, and t is the current time.
效益最大化智能决策单元的目标函数为给定时间范围内油水井系统生产效益最大化,表达式为:The objective function of the benefit maximization intelligent decision-making unit is to maximize the production benefit of the oil and water well system within a given time range, and the expression is:
式中:Ro为油价,USD/t,Rw为注水费用,USD/t;ij为第j口注水井的注水量,t/d;M为油水井系统中注水井数量。Where: Ro is the oil price, USD/t, Rw is the water injection cost, USD/t; i j is the water injection volume of the jth injection well, t/d; M is the number of injection wells in the oil-water well system.
进一步地,确定优化模型决策变量。决策变量是指优化问题中所涉及的与约束条件和目标函数有关的待确定的量。上述优化问题中人为可控的决策变量为注水井的注水量和采油井的产液量。Furthermore, the decision variables of the optimization model are determined. Decision variables refer to the quantities to be determined related to the constraints and the objective function involved in the optimization problem. The artificially controllable decision variables in the above optimization problem are the injection volume of the water injection well and the liquid production of the oil production well.
进一步地,确定优化模型约束条件:约束条件是优化问题中与目标函数相关联的各因素的取值范围限制条件。上述优化模型约束条件包括注水系统约束条件、采油系统约束条件和井间连通关系约束条件。Furthermore, the optimization model constraints are determined: the constraints are the value range restrictions of various factors associated with the objective function in the optimization problem. The above optimization model constraints include water injection system constraints, oil production system constraints and well connectivity constraints.
注水系统约束条件包括注水管网流量平衡约束、注水管道流量约束、注水井运行时注入压力约束、注水井井筒流动特性约束,表达式为:The constraints of the water injection system include the flow balance constraint of the water injection network, the flow constraint of the water injection pipeline, the injection pressure constraint when the water injection well is running, and the flow characteristic constraint of the water injection well. The expression is:
式中:I为总注水量,t/d;imin为注水井的最小可行注水量,t/d;imax为注水井的最大可行注水量,t/d;Pmin为注水井运行压力下限,MPa;Pmax为注水井运行压力上限,MPa;Fiw为注水井井筒内管流流动的压降方程组,Qiw为注水井井筒出口处的流量向量,Piw为注水井井筒入口处的压力向量,piw为注水井井筒出口处的压力向量。In the formula: I is the total water injection volume, t/d; i min is the minimum feasible water injection volume of the injection well, t/d; i max is the maximum feasible water injection volume of the injection well, t/d; P min is the lower limit of the operating pressure of the injection well, MPa; P max is the upper limit of the operating pressure of the injection well, MPa; Fiw is the pressure drop equation group of the pipe flow in the wellbore of the injection well, Qiw is the flow vector at the outlet of the wellbore of the injection well, Piw is the pressure vector at the inlet of the wellbore of the injection well, and p iw is the pressure vector at the outlet of the wellbore of the injection well.
采油系统约束条件包括采油井流量取值约束、采油系统流动特性约束,表达式为:The constraints of the oil production system include the constraints on the flow value of the oil well and the flow characteristics of the oil production system, and the expression is:
式中:qmin为采油井最小产液量,t/d;qmax为采油井最大产液量,t/d;Foe为采油井井筒多相管流流动方程,Qoe为采油系统入口和出口处的流量向量,Poe为采油系统入口和出口处的压力向量,Toe为采油系统入口和出口处的温度向量。Wherein: q min is the minimum liquid production of the oil well, t/d; q max is the maximum liquid production of the oil well, t/d; F oe is the flow equation of the multiphase flow in the wellbore of the oil well, Q oe is the flow vector at the inlet and outlet of the oil production system, P oe is the pressure vector at the inlet and outlet of the oil production system, and T oe is the temperature vector at the inlet and outlet of the oil production system.
井间连通关系约束条件表达式为:The constraint expression of well connectivity is:
式中:ik(t)为第k口注水井的累积注水量,t/d;qj(t)为第j口采油井的累积产液量,t/d;M表示采油井周围的注水井数量,λkj为第k口注水井和第j口采油井的井间连通系数;τkj为第k口注水井和第j口采油井之间的时间常数;为第j口采油井的井底流压,MPa;tn为开发时间,d;t0为初始开发时刻,d;Δtl表示采样间隔,n为采样数。Where: i k (t) is the cumulative water injection of the kth water injection well, t/d; q j (t) is the cumulative liquid production of the jth oil production well, t/d; M represents the number of water injection wells around the oil production well, λ kj is the well-to-well connectivity coefficient between the kth water injection well and the jth oil production well; τ kj is the time constant between the kth water injection well and the jth oil production well; is the bottom hole flowing pressure of the jth oil production well, MPa; tn is the development time, d; t0 is the initial development time, d; Δtl represents the sampling interval, n is the sampling number.
进一步地,确定优化模型求解方法:针对上述优化问题,采用粒子群算法求解能耗优化问题、最大产问题和最大效益问题。Furthermore, the optimization model solving method is determined: for the above optimization problems, the particle swarm algorithm is used to solve the energy consumption optimization problem, the maximum output problem and the maximum benefit problem.
步骤4:采用粒子群算法求解油水井系统能耗优化问题。对由决策变量、吨油能耗最小化目标函数和注水系统约束、采油系统约束以及井间连通关系约束组成的优化模型,使用粒子群算法进行迭代求解,直至得到最佳的注水井注水量和采油井产液量参数。Step 4: Use particle swarm algorithm to solve the energy consumption optimization problem of oil and water well system. The optimization model composed of decision variables, objective function of minimizing energy consumption per ton of oil, water injection system constraints, oil production system constraints and well connectivity constraints is solved iteratively using particle swarm algorithm until the optimal water injection volume of water injection wells and liquid production volume of oil production wells are obtained.
根据粒子群算法得到的注水量和产液量最优参数组合,控制注水井注水量和采油井产液量,使油水井系统能耗最小。According to the optimal parameter combination of water injection rate and liquid production rate obtained by particle swarm algorithm, the water injection rate of water injection wells and the liquid production rate of oil production wells are controlled to minimize the energy consumption of the oil and water well system.
油水井系统智能控制模块:包括采油井控制单元和注水井控制单元。根据油水井系统智能决策模块制定的最优决策结果,控制采油井的采液量和注水井的注水量,如图2所示。Intelligent control module of oil and water well system: including oil production well control unit and water injection well control unit. According to the optimal decision result made by the intelligent decision module of oil and water well system, the liquid production volume of oil production well and the water injection volume of water injection well are controlled, as shown in Figure 2.
采油井控制单元基于智能决策结果,控制合理的产液量水平,通过变频器实现采油井电动机变速运行,从而控制采油井冲次,控制油井产量。注水井控制单元基于智能决策结果,控制合理的注水量水平,通过流量自控仪调节控制阀阀门开度,从而控制注水井注水量。The oil well control unit controls the reasonable level of liquid production based on the results of intelligent decision-making, and realizes variable speed operation of the oil well motor through the frequency converter, thereby controlling the flushing frequency of the oil well and the output of the oil well. The water injection well control unit controls the reasonable level of water injection based on the results of intelligent decision-making, and adjusts the opening of the control valve through the flow automatic control instrument, thereby controlling the water injection volume of the water injection well.
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