A SYNTHETIC COMPUTATIONAL-INTELLIGENCE-BASED METHOD AND ITS APPLICATION IN IDENTIFYING WATER-FLOODED ZONES IN OIL FIELD

摘要

Inspired by the natural features of the variable size of the population, an improved genetic algorithm with variable population-size (VPGA) is presented in this paper. Based on the VPGA, a novel synthetic computational-intelligence-based method (SCIBM) is proposed and applied to the identification of water-flooded zones in the oil field. This method integrates evolutionary neural networks and recognition technique for multi-point-data neural networks. Simulation results applying the SCIBM to actual logging data in Daqing Oil Field show that the proposed method works well in the identification of water-flooded zones.