Integration of generalized discriminant analysis and classification technique for identification well test interpretation model

Abstract

This paper presents a hybrid method that combines generalized discriminant analysis and machine learning technique for identifying well test model. The proposed method consists of three stages: (1) nonlinear combination of features spaces to maximize the separability among the class models through generalized discriminant analysis. (2) Construction a set of classifier and classify the new data points by a plurality vote of their prediction. This method is described in detail to ensure successful replication of results. The required training and test dataset were generated by using analytical solution models. In our case, there were used 600 samples: 70% for training, 15% for validation, and 15% for testing. We notice that the generalized discriminant analysis is an effective strategy in reducing the number of input features, simplifying the network structure, and lowering the training time of the ANN. The results obtained show that the proposed model provides better performance when predicting new data with a coefficient of correlation approximately equal to 99%.