Optimization Of Series Operation Parameters Of Central Gathering Stations

After many years of exploitation,Shengli Oilfield has entered a period of particularly high water cut development and energy consumption of the collection and transmission system has also increased significantly.The original production process can no longer meet actual needs,and it is necessary to improve the process to adapt the need of high water cut production.This article takes the Dabei-Dingwang-Yihe system running in series as the research object and optimizes its operating costs.Based on analysis about process of the Dabei-Dingwang-Yihe system,combined with the characteristics of three-station operation in series,to meet the actual production demand that water cut in crude oil is less than 1% at the Yihe station,this article adjusted water content of exported crude oil at Dabei and Dingwang stations to reduce operating costs.For this reason,with the aim of minimizing daily operating expenses,a mathematical model for the daily operating costs of the three stations was established as well as optimal variables and constraints;in addition,combined with the “time-of-daytariff” policy,a mathematical model for the daily electricity charges of pumps at the Yihe station was established to solve required parameters of inter-station pipeline for optimization.In order to create engineering value of indoor settling and dewatering experiments,this article studied the method of preparing high-water-cut crude oil.The effects of stirring method,stirring temperature,water adding method,stirring intensity and stirring time on the emulsion stability were analyzed.And combined actual operation condition,water content of crude oil at two time points was compared to determine preparation method of water-containing crude oil at each station.Under different settling time,setting temperature and dosage,the dewatering effect of formulated high-water-cut crude oil was quantitatively studied.BP neural network was trained on the basis of considerable experimental data to predict the residual moisture content of crude oil under different conditions.In order to improve prediction accuracy of BP neural network,this article used genetic algorithm(GA)to optimize its initial weights and thresholds,and compared the prediction results of BP and GA-BP from perspectives of fitting degree,error and prediction result to determine the optimized GA-BP predicts more accurately.According to characteristics of mathematical model about daily operation cost at three stations,the Particle Swarm Optimization(PSO)algorithm and GA-BP were used to solve the model.In order to facilitate users,GUI of central gathering stations parameter optimization was written.The optimization results demonstrated that reducing the water content of outbound crude oil at Dabei and Dingwang stations will reduce their operating costs.At the same time,the operating costs of Yihe station can also be reduced by changing its processing conditions.The Genetic Algorithm was used to solve the mathematical model about daily electricity charges of pumps at Yihe station.To prove optimized operation plan consumes least money,transmission plans of optimum volume adjustment,ideal volume adjustment and time sharing equalization were compared and proved that the transmission plan of optimum volume adjustment costs least.