Simulation And Intelligent Diagnosis Evaluation Of Oilfield Water Injection System

Oilfield water injection pipe network system is mainly composed of water injection station,water injection pump,water distribution room and water injection well.It is connected by pipelines to form a huge and complex network system.The node units and pipeline units in the system do not exist in isolation,they are interrelated and affect each other.In order to monitor the operation status of the whole water injection system,it is necessary to collect the operation data.Only through the data of stations,wells and other nodes is not enough to evaluate the operation effect of the whole pipe network.Because most of the water injection pipelines are buried underground,it is difficult to master the hydraulic status of the whole water injection pipe network system.On the other hand,the long-term operation of water injection network will inevitably lead to scaling,pipe burst and leakage.Due to the complex structure and large scale of the pipe network buried underground,it is difficult to implement quick response remedial measures in case of leakage accidents,resulting in waste of resources and environmental pollution.Therefore,in view of the above problems,this paper studies the operation and digital management of water injection pipe network based on artificial intelligence and big data theory while learning from traditional pipe network research methods.Aiming at the problems of simulation calculation of oilfield water injection pipeline network,the node unit,pipe segment unit,subsidiary unit and overall mathematical model of pipeline network are established,and the simple iterative method is mainly used to solve large-scale nonlinear equations.At the same time,the relevant theoretical methods are transformed,and the simulation software of oilfield water injection pipe network is compiled,including the functions of pipe network structure modeling,operation data extraction and simulation hydraulic calculation,which basically meets the actual engineering requirements,and is convenient for mastering and analyzing the hydraulic condition of water injection pipe network.Based on the experience and method of pressure monitoring point layout in urban water supply network,a more suitable scheme for water injection network is proposed.The whole water injection pipe network is divided into several sub areas according to the radius of water injection,and the suitable positions of pressure monitoring points in each area are studied respectively,and the positions of special points are adjusted.Combining theory with experience,it provides important support for real-time monitoring and intelligent diagnosis of water injection pipe network.Aiming at the problem of pipeline leakage and emergency situation difficult to locate.A three-layer BP neural network structure is built,and the Bayesian regularization optimization method is used to determine the leakage node or leakage area.It can avoid complex calculation and detect quickly,and is suitable for leakage location of large-scale pipe network.By means of data visualization and large screen technology,the visualization platform of water injection pipe network is built.Echarts chart display technology is used to display the operation parameters of the pipe network.It is convenient for the maintenance and operation personnel to grasp the overall situation of the pipe network,and provides a theoretical reference for the decision-makers to optimize the transformation of the pipe network.At the same time,it also lays a foundation for the digital transformation of oilfield.