Abstract:

Obtaining the optimal decision parameters by intelligent production systems’ autonomous analysis and decision has significant meanings to deal with the low efficiency and high energy consumption in the oil extraction process. However, it is quite difficult to conduct and optimize the mechanism relationships among the operation parameters, the environment variables and the production mode settings, due to the mechanical, geological and artificial factors. Therefore, a novel autonomous decision method of oil extraction system by preference driven multi-objective optimization based on dynamic evolution models is proposed. The potential law of the pumping systems and then establish the dynamic model by unscented Kalman filter neural network (UKFNN) is found. The preference multi-objectives are constructed according to the actual production mode. The optimal decision parameters are obtained by improved non-dominated sorting genetic algorithm (NSGA2). The experimental results show that after the proposed optimization the energy consumptions of the system decrease 15.87%, as well as the system efficiency improves over 4.9%, which illustrate the feasibility and the effectiveness of the proposed method.

Key words: decision parameters, dynamic model, preference driven multi-objective optimization, intelligent oil extraction system