Data Acquisition And Treatment In The Design Of On-Line Viscometer

With the popularity of viscosity measurement technique, how to achieve the viscosity data's online and real-time measurement has become a difficulty. In addition, the on-line viscometer has the characteristics such as expensive, maintenance hardly, lower measurement accuracy, etc. So the research to the on-line viscometer is very significant.The object of study in this paper is the Polyacrylamide solution which is paly a special role in the enhanced oil recovery. This paper designs a device to collect and treatment the flow of the flowmeter, then the data collected as the modeling perameter to forecast the viscosity value.By analyzing the properties of Polyacrylamide and the viscosity data that is measured in laboratory, we can use tube viscometer to calibrate the Polyacrylamide's viscosity values. Then the calibration values can be used as the true data to compared with the forecasting data obtained after modeling. Due to there are many errors and noises in the flow data, we can use wavelet analysis to denose. Because neural network has its superiority in data processing and modeling, we propose to use the neural network system to modeling and forecast data.According to test conditions of the university, parameters to be monitored in the viscosity test is determined, and corresponding viscosity test system is build, then viscosity test of PAM is implemented. After analyzing detected data of each parameter, the flow of target-type and electromagnetic flowmeter are ascertained as main vicosity prediction parameters. According to the condition and characteristics of viscosity test , a new method based on the wavelet analysis and neural network model is chosen and applied to the viscosity test. After that, the result of viscosity prediction is given and predictive methods are believable, and tested data are used to validate. By compared prediction data with calibration data, it is showed that the predictive results are believable.In the summery, it is sincerely hoped the research of this article may be useful as reference for the modeling and prediction in the viscosity of Non-Newton fluid and for other fields outside the oil production in our country.