Design of new soft sensors based on PCA, genetic algorithm and neural network for parameters estimation of a petroleum reservoir

Abstract

A new set of soft sensors is presented, based on principal component analysis (PCA), genetic algorithm (GA) and artificial neural network (ANN) methodologies for parameters estimation of a petroleum reservoir. The crude diagrams of reservoir parameters provide valuable evaluation for petro-physical parameters. These parameters, however, are usually difficult to measure due to limitations insights on cost, reliability considerations, inappropriate instrument maintenance and sensor failures. PCA and genetic algorithm is utilized to develop new soft sensors to incorporate reliability and prediction capabilities of ANN. Genetic algorithms are used to decide the initial weights of the gradient decent methods so that all the initial weights can be searched intelligently. The genetic operators and parameters are carefully designed and set avoiding premature convergence and permutation problems. The proposed algorithm combines the local searching ability of the gradient-based back-propagation (BP) strategy with the global searching ability of genetic algorithms in the PCA subspaces. The developed soft sensors are applied to reconstruct parameters of Marun reservoir located in Ahwaz, Iran, by utilizing the available geophysical well log data.