Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/CoDIT.2018.8394853 | 2018 5th International Conference on Control, Decision and Information Technologies (CoDIT) |
Keywords | Field | DocType |
layer recognition model,complex chromatogram data,function fitting method,effective chromatogram characteristic information,sparse classification problem,synthetic minority over-sampling technique,unbalanced training sample,general training sample,traditional classification approach,ensemble classifier model,model-free classification algorithm,support vector machine algorithm,standard data source,complex oil,gas recognition system,reliable water,layer information,reservoir exploration,oil development efficiency,water flooded layer pattern recognition,CCI,SMOTE,China petroleum industry,oil-and-gas reservoirs | Data source,Data modeling,Petroleum industry,Pattern recognition,Recognition system,Computer science,Support vector machine,Robustness (computer science),Artificial intelligence,Statistical classification,Classifier (linguistics) | Conference |
ISSN | ISBN | Citations |
2576-3555 | 978-1-5386-5066-0 | 0 |
PageRank | References | Authors |
0.34 | 6 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiqiang Geng | 1 | 50 | 10.34 |
| Xuan Hu | 2 | 0 | 1.01 |
| Qun-Xiong Zhu | 3 | 13 | 3.93 |
| Yongming Han | 4 | 27 | 5.96 |
| Yuan Xu | 5 | 1 | 1.71 |
| Yan-Lin He | 6 | 12 | 6.96 |