Prediction of the productivity of steam flooding production wells using Gray Relation Analysis and Support Vector Machine

Abstract

The productivity of steam flooding production wells is determined by numerous factors. In the paper the dominant influence factors of the productivity have been revealed using Gray Relation Analysis (GRA). Based on the Gray Relation Analysis, a model was established for predicting the productivity of steam flooding production wells by Support Vector Machine (SVM), the training algorithm was used to train the model and predict the productivity of test samples. The results show that high precision can be achieved from SVM, the error committed by the model was about ± 10% which is acceptable in field application. The results of SVM were compared with those of Group Method of Data Handling (GMDH) and Backward Propagation (BP) Artificial Neural Network, it indicated that the prediction precision of SVM was obviously higher than that of the two mentioned above. It was demonstrated that the SVM had a good adaptability and practicability for predicting the productivity of steam flooding production wells.