Chaotic Time Series Analysis Research And Application

The most import task for oilfield is to ensure a high and stable output. The accuracy of prediction is the basis of oilfield management. Now, most oilfields have held a set of output data including single well data. And they urgently want to use these data for the development of oilfield.In fact, the process of oilfield development is a complicated nonlinear dynamics. In one word, the prediction for oilfield output is nonlinear. At present, the relative error of the existing prediction methods for oilfield output is about 10%, and these methods can not fit to stochastic noises. We have the oilfield output data, so we can use time series analysis tool for modeling and prediction.The research object of this thesis is the time series of some oil wells outputs. The research fields of the thesis include RLS algorithm, the methods for detection chaos in time series, and the methods for time series prediction.First, the characteristics of RLS algorithm are analyzed deeply, and an improved RLS algorithm is proposed. With the proposed algorithm, oil well outputs are predicted. Simulation results show that, RLS algorithm can not make long time prediction.Second, in order to understand the characteristics of time series of oilfield outputs more clearly, phase space reconstruction is used to reconstruct the attractor of the time series. The maximum Lyapunov exponent and the dimension of the attractor show that the time series of oil well outputs are chaotic.Studies show that the method of false neighbors is not effective, so a quick falseneighbor method that can improve the time complexity from O(M(M-1)) to O(3M).To avoid selecting work set of sequential minimal optimization algorithmrandomly, a new method based on GA is proposed. The work set selected by theproposed method can make the change of target function as large as possible.Simulation results show that the proposed method can speed the training process.And to select suitable parameters with support vector machine, a GA-basedmethod for parameter selection is proposed, too. Simulation results show that thisnew method can reduce generalization error with little increase in support vectors.Finally, based on the identification of characteristics of oil well outputs time...