Pattern recognition for water flooded layer based on ensemble classifier

In order to establish an effective water flooded layer recognition model to deal with complex chromatogram data and correctly identify the water flooded layer in the oil and gas reservoirs, this paper proposes a modeling approach based on ensemble classifier. First, the proposed approach utilizes the function fitting method to obtain the effective chromatogram characteristic information (CCIs). Moreover, in order to transform the sparse classification problem into a general classification problem, the synthetic minority over-sampling technique (SMOTE) algorithm is used to process the unbalanced training sample as a general training sample. Compared with the traditional classification approach, the robustness and effectiveness of the ensemble classifier model composed of the model-free classification (MFBC) algorithm, the k-nearest neighbor (KNN) algorithm and the support vector machine (SVM) algorithm were validated through the standard data source from the UCI (University of California at Irvine) repository. Finally, the proposed model is validated through an application in a complex oil and gas recognition system of China petroleum industry. The CCIs and the prediction results are obtained to provide more reliable water flooded layer information, guide the process of reservoir exploration and development and improve the oil development efficiency.