Prediction of the wall factor of arbitrary particle settling through various fluid media in a cylindrical tube using artificial intelligence.
Considering the influence of particle shape and the rheological properties of fluid, two artificial intelligence methods (Artificial Neural Network and Support Vector Machine) were used to predict the wall factor which is widely introduced to deduce the net hydrodynamic drag force of confining boundaries on settling particles. 513 data points were culled from the experimental data of previous studies, which were divided into training set and test set. Particles with various shapes were divided into three kinds: sphere, cylinder, and rectangular prism; feature parameters of each kind of particle were extracted; prediction models of sphere and cylinder using artificial neural network were established. Due to the little number of rectangular prism sample, support vector machine was used to predict the wall factor, which is more suitable for addressing the problem of small samples. The characteristic dimension was presented to describe the shape and size of the diverse particles and a comprehensive prediction model of particles with arbitrary shapes was established to cover all types of conditions. Comparisons were conducted between the predicted values and the experimental results.
College of Petroleum Engineering, China University of Petroleum, B405 Engineering Building, No. 66 Changjiang West Road, Qingdao 266580, China.
Mingzhong
M
Li
College of Petroleum Engineering, China University of Petroleum, B405 Engineering Building, No. 66 Changjiang West Road, Qingdao 266580, China.
Guodong
0000-0001-9716-9045
G
Zhang
College of Petroleum Engineering, China University of Petroleum, B405 Engineering Building, No. 66 Changjiang West Road, Qingdao 266580, China.
Jianquan
J
Xue
Down Hole Company, Chuanqing Drilling Company, CNPC, Chengdu 610051, China.
Yanchao
Y
Li
College of Petroleum Engineering, China University of Petroleum, B405 Engineering Building, No. 66 Changjiang West Road, Qingdao 266580, China ; Dongsheng Group Co., Ltd. of Shengli Oilfield, Dongying 257000, China.
Shukai
S
Tang
D000465:Algorithms; D001185:Artificial Intelligence; D008962:Models, Theoretical