Single-phase fluid flow classification via learning models

This article may be used for research, teaching and private study purposes. Any substantial or systematic reproduction, redistribution , reselling , loan or sub-licensing, systematic supply or distribution in any form to anyone is expressly forbidden. The publisher does not give any warranty express or implied or make any representation that the contents will be complete or accurate or up to date. The accuracy of any instructions, formulae and drug doses should be independently verified with primary sources. The publisher shall not be liable for any loss, actions, claims, proceedings, demand or costs or damages whatsoever or howsoever caused arising directly or indirectly in connection with or arising out of the use of this material. This paper applies learning models, such as support vector machines (SVM), neural networks, and mixed-integer programming kernel classifiers (MIPKC) to classify the flow pattern of a non-Newtonian fluid in an annulus/pipe. Classification of flow patterns is characterized by six attributes that represent the parameters that determine the fluid flow in the annulus/pipe. The SVM and MIPKC learning models construct a separating hyperplane in the feature space. The weights of the hyperplane represent a scaled level of importance for each of the parameters. Preliminary results show that the most efficient model with respect to computation time favours the SVM model with a polynomial kernel of degree 2. However, with respect to low error rates and sparseness of solution, one of the MIPKC models with a polynomial kernel of degree 2 outperforms the other methods. 1. Introduction Prediction of flow patterns is very important in the design of a variety of engineering systems. In many drilling operations, the engineers have to investigate the flow of drilling fluids and cements down the circular bore of the drill string and up the circular annular space between the drill string and the casing or open hole, so as to obtain accurate estimates of their frictional pressure losses. To obtain accurate pressure drop estimates, the pattern of the fluid flow needs to be established. Hence, knowledge of the flow pattern is vital information on drilling fluids and cement slurries, which is essential in well planning. Fluid flow pattern is determined by the generalized Reynolds number (N re) thanks to the experimental work of Osborne Reynolds who determined the parameters that influence fluid flow (Bared 1990). If N re , 2100, then the flow is defined as laminar flow and if N …