LAPSE:2024.1945
Published Article
LAPSE:2024.1945
Study of Methane Solubility Calculation Based on Modified Henry’s Law and BP Neural Network
August 28, 2024
Abstract
Methane (CH4), a non-polar molecule characterized by a tetrahedral structure, stands as the simplest organic compound. Predominantly constituting conventional natural gas, shale gas, and combustible ice, it plays a pivotal role as a carbon-based resource and a key raw material in the petrochemical industry. In natural formations, CH4 and H2O coexist in a synergistic system. This interplay necessitates a thorough examination of the phase equilibrium in the CH4-H2O system and CH4’s solubility under extreme conditions of temperature and pressure, which is crucial for understanding the genesis and development of gas reservoirs. This study synthesizes a comprehensive solubility database by aggregating extensive solubility data of CH4 in both pure and saline water. Utilizing this database, the study updates and refines the key parameters of Henry’s law. The updated Henry’s law has a prediction error of 22.86% at less than 40 MPa, which is an improvement in prediction accuracy compared to before the update. However, the modified Henry’s law suffers from poor calculation accuracy under certain pressure conditions. To further improve the accuracy of solubility prediction, this work also trains a BP (Back Propagation) neural network model based on the database. In addition, MSE (Mean-Square Error) is used as the model evaluation index, and pressure, temperature, compression coefficient, salinity, and fugacity are preferred as input variables, which finally reduces the mean relative error of the model to 16.32%, and the calculation results are more accurate than the modified Henry’s law. In conclusion, this study provides a novel and more accurate method for predicting CH4 solubility by comparing modified Henry’s law to neural network modeling.
Methane (CH4), a non-polar molecule characterized by a tetrahedral structure, stands as the simplest organic compound. Predominantly constituting conventional natural gas, shale gas, and combustible ice, it plays a pivotal role as a carbon-based resource and a key raw material in the petrochemical industry. In natural formations, CH4 and H2O coexist in a synergistic system. This interplay necessitates a thorough examination of the phase equilibrium in the CH4-H2O system and CH4’s solubility under extreme conditions of temperature and pressure, which is crucial for understanding the genesis and development of gas reservoirs. This study synthesizes a comprehensive solubility database by aggregating extensive solubility data of CH4 in both pure and saline water. Utilizing this database, the study updates and refines the key parameters of Henry’s law. The updated Henry’s law has a prediction error of 22.86% at less than 40 MPa, which is an improvement in prediction accuracy compared to before the update. However, the modified Henry’s law suffers from poor calculation accuracy under certain pressure conditions. To further improve the accuracy of solubility prediction, this work also trains a BP (Back Propagation) neural network model based on the database. In addition, MSE (Mean-Square Error) is used as the model evaluation index, and pressure, temperature, compression coefficient, salinity, and fugacity are preferred as input variables, which finally reduces the mean relative error of the model to 16.32%, and the calculation results are more accurate than the modified Henry’s law. In conclusion, this study provides a novel and more accurate method for predicting CH4 solubility by comparing modified Henry’s law to neural network modeling.
Record ID
Keywords
BP neural network, Henry’s law, methane, prediction, solubility
Suggested Citation
Zhao Y, Yu J, Shi H, Guo J, Liu D, Lin J, Song S, Wu H, Gong J. Study of Methane Solubility Calculation Based on Modified Henry’s Law and BP Neural Network. (2024). LAPSE:2024.1945
Author Affiliations
Zhao Y: College of Information Science and Engineering/College of Artificial Intelligence, China University of Petroleum-Beijing, Changping District, Beijing 102249, China; Kunlun Digital Technology Co., Ltd., Dongcheng, Beijing 100010, China
Yu J: National Engineering Research Center of Oil and Gas Pipeline Transportation Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Changping, Beijing 1
Shi H: Kunlun Digital Technology Co., Ltd., Dongcheng, Beijing 100010, China
Guo J: National Engineering Research Center of Oil and Gas Pipeline Transportation Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Changping, Beijing 1
Liu D: National Engineering Research Center of Oil and Gas Pipeline Transportation Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Changping, Beijing 1
Lin J: National Engineering Research Center of Oil and Gas Pipeline Transportation Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Changping, Beijing 1
Song S: National Engineering Research Center of Oil and Gas Pipeline Transportation Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Changping, Beijing 1 [ORCID]
Wu H: National Engineering Research Center of Oil and Gas Pipeline Transportation Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Changping, Beijing 1
Gong J: College of Information Science and Engineering/College of Artificial Intelligence, China University of Petroleum-Beijing, Changping District, Beijing 102249, China; National Engineering Research Center of Oil and Gas Pipeline Transportation Safety/MOE Key
Yu J: National Engineering Research Center of Oil and Gas Pipeline Transportation Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Changping, Beijing 1
Shi H: Kunlun Digital Technology Co., Ltd., Dongcheng, Beijing 100010, China
Guo J: National Engineering Research Center of Oil and Gas Pipeline Transportation Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Changping, Beijing 1
Liu D: National Engineering Research Center of Oil and Gas Pipeline Transportation Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Changping, Beijing 1
Lin J: National Engineering Research Center of Oil and Gas Pipeline Transportation Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Changping, Beijing 1
Song S: National Engineering Research Center of Oil and Gas Pipeline Transportation Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Changping, Beijing 1 [ORCID]
Wu H: National Engineering Research Center of Oil and Gas Pipeline Transportation Safety/MOE Key Laboratory of Petroleum Engineering/Beijing Key Laboratory of Urban Oil and Gas Distribution Technology, China University of Petroleum-Beijing, Changping, Beijing 1
Gong J: College of Information Science and Engineering/College of Artificial Intelligence, China University of Petroleum-Beijing, Changping District, Beijing 102249, China; National Engineering Research Center of Oil and Gas Pipeline Transportation Safety/MOE Key
Journal Name
Processes
Volume
12
Issue
6
First Page
1091
Year
2024
Publication Date
2024-05-26
ISSN
2227-9717
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Original Submission
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PII: pr12061091, Publication Type: Journal Article
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LAPSE:2024.1945
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https://doi.org/10.3390/pr12061091
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Aug 28, 2024
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