| QU Zhi-hao,TANG De-zhi,HU Li-hua,CHEN Hong-jian,LI Hui-xin,JIA Hai-yun,WANG Zhu,ZHANG Lei.Prediction of H2S Corrosion Products and Corrosion Rate Based on Optimized Random Forest[J],49(3):42-49 |
| Prediction of H2S Corrosion Products and Corrosion Rate Based on Optimized Random Forest |
| Received:December 19, 2019 Revised:March 20, 2020 |
| View Full Text View/Add Comment Download reader |
| DOI：10.16490/j.cnki.issn.1001-3660.2020.03.006 |
| KeyWord:machine learning random forest H2S corrosion corrosion product corrosion rate prediction model |
| Author | Institution |
| QU Zhi-hao |
1.University of Science and Technology Beijing, Beijing , China |
| TANG De-zhi |
2.Petro China Planning & Engineering Institute, Beijing , China |
| HU Li-hua |
3.CNOOC Research Institute, Beijing , China |
| CHEN Hong-jian |
2.Petro China Planning & Engineering Institute, Beijing , China |
| LI Hui-xin |
3.CNOOC Research Institute, Beijing , China |
| JIA Hai-yun |
1.University of Science and Technology Beijing, Beijing , China |
| WANG Zhu |
1.University of Science and Technology Beijing, Beijing , China |
| ZHANG Lei |
1.University of Science and Technology Beijing, Beijing , China |
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| Abstract: |
| The work aims to investigate the prediction model of iron sulfide corrosion products and corrosion rate under the H2S environment, and provide basis for designing and selecting the corrosion protection of pipelines in gas fields with sour oil. Based on the collected experimental data of H2S corrosion, the priority of each corrosion factor was ranked by the random forest algorithm. On the one hand, the prediction model of the corrosion product category was established by the random forest classification algorithm with the corrosion product types as output. On the other hand, the prediction model of the corrosion rate was established by random forest regression algorithm with corrosion rate as output. The grid search method was used to optimize the super parameters of various algorithms to improve the prediction performance. According to the random forest algorithm, the characteristic importance of H2S corrosion product types was ranked as follows: H2S partial pressure, temperature, pH, experimental period, total pressure and CO2 partial pressure. The cross-validation score of random forest classification model based on grid search optimization exceeded 0.9 and f1 score reached 0.96, which was better than other three common classification models. The mean square error between the prediction result and the actual value of the random forest regression model with grid search optimization was 0.86%. The R value of the correlation coefficient was 0.979, which was better than the other two regression models. The random forest classification and regression models optimized by grid search have high accuracy in predicting corrosion product types and corrosion rates in complex H2S environment, which can provide reference for the corrosion protection pipelines in oil and gas fields. |
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