LAPSE:2020.0787
Published Article
LAPSE:2020.0787
Integrating Support Vector Regression with Genetic Algorithm for Hydrate Formation Condition Prediction
Jie Cao, Shijie Zhu, Chao Li, Bing Han
July 2, 2020
To predict the natural gas hydrate formation conditions quickly and accurately, a novel hybrid genetic algorithm−support vector machine (GA-SVM) model was developed. The input variables of the model are the relative molecular weight of the natural gas (M) and the hydrate formation pressure (P). The output variable is the hydrate formation temperature (T). Among 10 gas samples, 457 of 688 data points were used for training to identify the optimal support vector machine (SVM) model structure. The remaining 231 data points were used to evaluate the generalisation capability of the best trained SVM model. Comparisons with nine other models and analysis of the outlier detection revealed that the GA-SVM model had the smallest average absolute relative deviation (0.04%). Additionally, the proposed GA-SVM model had the smallest amount of outlier data and the best stability in predicting the gas hydrate formation conditions in the gas relative molecular weight range of 15.64−28.97 g/mol and the natural gas pressure range of 367.65−33,948.90 kPa. The present study provides a new approach for accurately predicting the gas hydrate formation conditions.
Keywords
gas hydrate, Genetic Algorithm, outlier detection, support vector machine
Suggested Citation
Cao J, Zhu S, Li C, Han B. Integrating Support Vector Regression with Genetic Algorithm for Hydrate Formation Condition Prediction. (2020). LAPSE:2020.0787
Author Affiliations
Cao J: School of Petroleum Engineering, Chongqing University of Science and Technology, Chongqing 401331, China; Sinopec Henan Oilfield Branch Company, Henan, Nanyang 473132, China
Zhu S: School of Petroleum Engineering, Chongqing University of Science and Technology, Chongqing 401331, China [ORCID]
Li C: China National Petroleum Corporation Chuanqing Security Inspection Institute, Guang Han 618300, China
Han B: State Key Laboratory of Oil & Gas Reservoir and Exploitation Engineering, Southwest Petroleum University, Chengdu 610500, China
Journal Name
Processes
Volume
8
Issue
5
Article Number
E519
Year
2020
Publication Date
2020-04-27
Published Version
ISSN
2227-9717
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PII: pr8050519, Publication Type: Journal Article
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LAPSE:2020.0787
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doi:10.3390/pr8050519
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Jul 2, 2020
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Calvin Tsay
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