Research on Determination Method of Oil Viscosity Based on Component Data and Machine Learning Algorithm

Under certain conditions, when crude oil is moved by external forces, the property of internal friction generated between crude oil molecules is called crude oil viscosity. The viscosity of crude oil reflects its complex seepage state in porous media. Underground crude oil with high viscosity, always has great flow resistance in porous media, thus the flowing becomes more difficult. Oil viscosity is an indispensable key parameter in the process of dynamic analysis, reservoir engineering calculation and reservoir numerical simulation, which has critical influence on the field of well production or crude oil storage and transportation. Due to different oil viscosity, recovery approach of oil reservoirs, technical measures for storage and transportation, and the quality of oil products will be affected. The composition of crude oil is complicated, but it is mainly composed of carbon and hydrogen elements. The composition has a crucial effect on oil viscosity. Therefore, according to composition data of the actual oil sample, the determination dataset of oil viscosity is constructed together with other key parameters that affect the viscosity of crude oil within the reservoirs. Based on various machine learning algorithms, like extremely randomized trees and XGBoost, determination methods of oil viscosity based on component data and machine learning algorithms are established. In the construction process of computational model of oil viscosity, whole dataset is parted to the training dataset and the testing dataset in the ratio of 8:2. The training dataset is mainly used to determine the best hyper-parameter combination of machine learning algorithm, while the testing dataset is used to determine the accuracy and adaptability of the corresponding method. Compared with methods such as experimental method and empirical formula method, the determination method of oil viscosity based on component data and machine learning algorithm does not require extra experimental costs and has a considerable degree of accuracy. Once the relevant input parameters are determined, the viscosity determination of multiple groups of oil samples could be completed quickly and accurately.