Modeling and simulation of water displacing oil based on improved simulated annealing neural network

The underground water content rate of the oil reserve is the most important parameter in the oil exploitation. The physical experimental model is constructed as the practical oil field in geological structure and geological parameter according to the similarity theory in this paper. Based on a new data acquisition system, a mass of data which would be taken as the sample data is collected through stimulating the water displacing oil experiment in different conditions. Construct the ANN model of the water displacing oil process with the simulated annealing algorithm which is improved by the Powell arithmetic. Then forecast the underground water content rate in different area at certain time with the model. The method makes some instructional sense to the practical production.