Study On The Optimization Method Of Rotor Speed For Progressing Cavity Pump

As a new artificial lift method, progressing cavity pump (PCP) oil production hasbeen widely used in the oil fields at home and abroad for its characteristics includingsimple structure, high efficiency energy saving, small floor area and so on. PCP isespecially suitable for the oil well of production high viscosity oil, high sand content andhigh gas content. The choice of rotor speed of PCP speed is very important in the oilproduction; the rotor speed will directly affect the pump efficiency and service life. If theselection of rotor speed is unreasonable, it will cause “burn pump”, lower pump efficiency,shorten service life and other problems. Recently, researchers at home and abroad haveused the method of establishing the best speed model to choose reasonable speed. Thedisadvantage of the best speed model is that it does not consider the coupling influence ofgeometric parameters and structure parameters, and the convergence speed of establishedmodel is slower and accuracy is lower. Meanwhile, the researchers have not further studyin the problem of speed intelligent control, and the effect is still not obvious to improvepump efficiency and prolong life. Therefore, this paper studied PCP speed optimizationmethod from the theory and experiment both aspects, and trying to improve the level ofPCP speed control technology.In the first place, the PCP structure and its working principle were analyzed in thearticle. Then, development of the PCP technology both at home and abroad wassummarized. The key problems of oil production technology of PCP were deeply discussedand the breakthrough point of this work was determined.Speed is the dominant factor of affecting the service life of PCP stator rubber;therefore the core of this paper was study the relationship between them. However, theinfluence of speed on rubber wear depends on structure parameters and working conditionparameters and the influence shows a nonlinear coupling relationship. Therefore, on thebasis of combing the two groups of influence factors, the paper made a textual research forthe method of solving nonlinear coupling. The thought of establishing optimization model of PCP speed on the basis of artificial neural network was proposed. In order to simulatethe actual working condition and investigate optimization effect, the oil temperature, oilviscosity, pressure difference of pump ends and wear loss of stator rubber were selected asinput, and PCP speed as output. Four typical artificial neural network models (BP network,RBF network, Elman network and GA-BP network) were described in the paper. From theanalysis results between optimization results and actual value, the ideal optimization modelwas selected.The rotor speed needs to be changed according to the changes of working conditionsin the process of oil production. In order to realize the real-time control for speed, a PCPspeed optimization system was developed. The development environment of hardware andsoftware, algorithm in the software, measurement and real-time control for PCP speed andits influence factors utilizing this system were introduced in the paper.In order to verify the effectiveness of PCP speed optimization model and make furtherstudy on speed optimization, a test platform of PCP speed optimization which can simulatethe actual well conditions was self-designed and developed. The main structure of the testplatform was ring-block type wear testing machine and combining with the developedspeed optimization system, it can complete the effectiveness verification of PCP speedoptimization model and the experimental research for different control modes.On the test platform, PCP speed optimization results obtained by artificial neuralnetwork optimization model were analyzed in experiment. Experiments with differentschemes were carried on, and from the results it can be seen that the wear loss of statorrubber was obviously reduced with the speed which is optimized by the BP neural networkoptimization model.Finally, this paper proposed a constructive idea to improve PCP speed optimizationplatform based on artificial neural network. Moreover, it gave the open architecture ofoptimization system and the subsystem interface between actual signal acquisitions.