Evaluation of oil Wells performance ranking in high water cut stage

In order to quantify the performance ranking of oil wells in high water cut reservoir, a quantitative evaluation method of oil wells is established from two perspectives of numerical simulation and machine learning respectively. The backward flight time of oil wells control area is obtained by numerical simulation. The flow heterogeneity of oil wells is evaluated based on Lorentz coefficient, and the concept of potential index is proposed to characterize the oil wells potential and flow capacity. A large number of dynamic data of oilfield development is collected to establish time series model. The production history of oil wells is fitted by using VAR algorithm of machine learning, the impact of oil wells production on the whole reservoir development is evaluated through impulse response analysis and the production capacity is quantified by the cumulative influence coefficient. The scores obtained by the two methods are compared, and the comprehensive performance ranking of oil wells is determined by the entropy weight method. The evaluation method is applied to Gangxi Oilfield. The results show that the two evaluation methods are based on different assumptions, but the scoring trend of each well is basically the same. Due to the comprehensive consideration of the influence of numerical simulation and development dynamic data, the final evaluation score objectively reflects the development status of oil wells. The new method provides a theoretical basis for the effective development of high water cut reservoir.