Soft Sensing Method for Water Cut of Crude Oil Based on Random Forest Algorithm
WANG Peng1,2,3,GONG Pan1,2,FENG Ding1,2,3,TU Yi-liu1,2,3
1.College of Mechanical Engineering, Yangtze University, Jingzhou, Hubei 434023, China
2.Hubei Engineering Research Center for Oil & Gas Drilling and Completion Tools, Jingzhou, Hubei 434023, China
3.Hubei Cooperative Innovation Center of Unconventional Oil and Gas, Wuhan, Hubei 430100, China
Abstract：Real time measurement of wellbore output fluid is a key step in obtaining information from the downhole liquid production and an important prerequisite for the realization of intelligent well. However, there is no better way to measure the content of each component of the output liquid, because of the complexity of downhole environment and the uncertainty of oil-water mixture state. Aimed at the problem of mixed measurement of oil and water in the downhole, a soft sensing method for water cut of crude oil based on machine learning algorithm was presented. Combined with the needs of oil production engineering, water cut of crude oil was divided into 11 categories at 10% intervals. Taking physical properties corresponding to different water cut of crude oil as measurement object, the relationship between physical properties and water cut of crude oil was obtained and then the purpose of measuring the water cut of crude oil was realized, by learning and classifying known samples using random forest algorithm. Finally, the soft sensing method was verified by a designed indoor experiment. The results show that the predicted results by using the mentioned method agree with the experimental values , which can preliminarily meet the requirement of engineering for measuring water cut of underground crude oil and providing a new concept for downhole measurement.
王鹏,龚盼,冯定,涂忆柳. 基于随机森林算法的井下原油含水率软测量方法[J]. 计量学报, 2019, 40(5): 835-841.
WANG Peng,GONG Pan,FENG Ding,TU Yi-liu. Soft Sensing Method for Water Cut of Crude Oil Based on Random Forest Algorithm. Acta Metrologica Sinica, 2019, 40(5): 835-841.
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2019, Vol. 40 
