关键词: 油田产量, 预测, 功能模拟, 时变系统

Abstract: One advantage of neural network forecast is the good historical matching between forecast indices and influence factors indices,while the differential simulation forecast pays more attention to the change trend of forecast indices.In this paper,these tow methods are organically combined.At first，the inputoutput relation between oilfield output and their influence factors is viewed as a timevarying system,then the BP neural network is introduced to parameter identification of differential simulation to obtain a new forecast method of functional simulation based on timevarying system.This new forecast model owns good selfadaptability since its parameters change with time.Moreover,it has better effect in midlong term forecast because the nonconvergence problem appeared in the coupling process between it and differential simulation can be overcome in the training process of neural network by variable learning rate.In the end,a practical output forecast case in a certain oilfield in China is given.The computational results show that the forecast is in good agreement with the reality,even much better than the results obtained by other forecast methods.

Key words: oilfield output, forecast, functional forecast, timevarying system