The channel prediction of Fuyu reservoir has been always highly emphasized. Using the conventional seismic attribute to predict channel is difficult to achieve the precision requirements because of the complex fault and the fast lithofacies phase in Fuyu reservoir. Aiming at this weak problem, the method of combing gray correlation analysis and support vector machines is used to establish a set of technical process which is suitable for the prediction of fluvial reservoirs under complicated geological conditions. In Fuyu test area in X reservoir of Daqing oilfield as an example, firstly, conventional seismic attribute of sedimentary unit dimensionless, obtained by the method of gray correlation analysis of the seismic attribute correlation factor, the greater the degree of correlation, indicating that the response probability of attribute river is higher. On this basis, the optimal correlation factor sequence a accumulation of large properties, first order accumulative sequence is generated, used as the input into the support vector machine training sample, so as to complete construction of support vector machine river forecast model. Drilling confirms that the prediction based on Gray Correlation Support Vector Machine has a larger coincidence rate of drilling. Combined with the superiority of seismic inversion to predict the channel sand boundary, supplemented by data of core, well logging and mud logging data to complete sedimentary microfacies in X Test Area Fuyu reservoir. Meanwhile, drilling further confirms the reliability of predicting channel and then the industrial oil flow well is successfully obtained. The results of comprehensive research show that this method is suitable for high channel prediction accuracy. It can be used as a better channel prediction method under complicated geological conditions.

Influence of round-off error and truncation error on convergence property of iterative sequences is an important topic in scientific computing. Using functional theory, the rate of convergence for the contraction mapping iterative sequences with errors in metric space was analyzed. The optimal estimate for the order of convergence was obtained.