The Research On The Optimized Running Of Oilfield Water-injection System Based On The Intelligent Algorithm

As an essential part in the extraction process of China's current onshore oil fields,the Water Driving Oil method can maintain the pressure of the oil layer and help stabilize and increase crude oil production.However,the energy consumption of the oilfield water-injection system(OWS)is enormous,especially as the oilfield enters the later stage of development characterized by high water cut,its energy consumption tends to continue to increase.Therefore,the energy efficiency improvement of the OWS is of great significance for reducing the cost of crude oil development and the sustainable development of the oilfields.In the actual operation of OWS,the adjustment of its operation scheme mainly depends on the subjective experience of the managers.Moreover,with the change of the water injection volume,the adjustment of the water injection pump operating status and parameters often lacks timeliness and reasonable basis,resulting in low efficiency and high energy consumption of OWS.In this case,this research introduces intelligent algorithms to optimize the operation of OWS,thereby improving its system operating efficiency and reducing energy consumption.Specifically,the main research results of this thesis are as follows.First,as a critical work,the simulation of OWS determines its optimization effect.Based on the research and analysis of the structural characteristics of the OWS pipeline network,a mathematical model of each component is established.Subsequently,according to the energy conservation equation,the overall equation set for the OWS pipe network is obtained,which is solved by the simple iterative method.Based on this,the pressure and flow parameters of each node in the OWS pipe network are determined,and the calculation results are verified through the experimental pipe network.Second,according to the simulation results,by taking the minimum energy consumption as the objective function,the water injection volume,pump displacement,pressure of the water injection well and other parameters as constraints,the external penalty function method is used to process the constraint conditions to construct a mathematical model for analyzing and optimizing the operating parameters of OWS.Furthermore,the optimization models through particle swarm algorithm and simulated annealing algorithm are established,respectively.Subsequently,actual production data of Fuyu Oilfield is calculated to verify the feasibility of the proposed method.Third,the optimization of the OWS operation scheme is studied.Specifically,the optimization of the operating scheme includes 0-1 binary discrete variables and continuous variables,which can be formulated into a mixed variable-constrained optimization problem.By taking the minimum water-injection energy consumption as the objective function,the water-injection volume,pump displacement,water-injection well pressure and other parameters as constraints,the external penalty function method is used to deal with the constraints,and the mathematical model for the optimization of the operation scheme of the OWS is established.According to the characteristics of specific problems,the particle swarm algorithm and simulated annealing algorithm are improved,and the annealing and sampling process are added to the particle swarm algorithm to form a hybrid simulated annealing particle swarm algorithm.The actual case calculation of the optimization scheme is also carried out with Fuyu Oilfield as the research sample.The results show that the energy consumption of water injection is reduced,indicating that the proposed method is effective.Finally,on the basis of the above theoretical research results,the software for optimizing the production and operation of OWS is compiled using the C++Builder language.Taking Daqing Oilfield as an example,the proposed software is used to optimize its OWS.The example calculation results show that the proposed algorithm is effective and the proposed software is reliable.