ANN prediction of the CO2 solubility in water and brine under reservoir conditions
Having accurate knowledge on CO2 solubility in reservoir liquids plays a pivotal role in geoenergy harvest and carbon capture, utilization, and storage (CCUS) applications. Data-driven works leveraging artificial neural networks (ANN) have presented a promising tool for forecasting CO2 solubility. I...
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| Format: | Article |
| Language: | English |
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AIMS Press
2025-03-01
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| Series: | AIMS Geosciences |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/geosci.2025009 |
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| _version_ | 1850125179388166144 |
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| author | Shuo Yang Dong Wang Zeguang Dong Yingge Li Dongxing Du |
| author_facet | Shuo Yang Dong Wang Zeguang Dong Yingge Li Dongxing Du |
| author_sort | Shuo Yang |
| collection | DOAJ |
| description | Having accurate knowledge on CO2 solubility in reservoir liquids plays a pivotal role in geoenergy harvest and carbon capture, utilization, and storage (CCUS) applications. Data-driven works leveraging artificial neural networks (ANN) have presented a promising tool for forecasting CO2 solubility. In this paper, an ANN model was developed based on hundreds of documented data to predict CO2 solubility in both pure water and saline solutions across a broad spectrum of temperatures, pressures, and salinities in reference to underground formation conditions. Multilayer perceptron (MLP) models were constructed for each system, and their prediction results were rigorously validated against the the literature data. The research results indicate that the ANN model is suitable for predicting the solubility of carbon dioxide under different conditions, with root mean square errors (RMSE) of 0.00108 and 0.00036 for water and brine, and a coefficient of determination (R2) of 0.99424 and 0.99612, which indicates robust prediction capacities. It was observed from the ANN model that the saline water case could not be properly expanded to predict the CO2 solubility in pure water, underscoring the distinct dissolution mechanisms in polar mixtures. It is expected that this study could provide a valuable reference and offer novel insights to the prediction of CO2 solubility in complex fluid systems. |
| format | Article |
| id | doaj-art-2152ae845a8a4c01ac878a8153f95c9a |
| institution | OA Journals |
| issn | 2471-2132 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | AIMS Geosciences |
| spelling | doaj-art-2152ae845a8a4c01ac878a8153f95c9a2025-08-20T02:34:10ZengAIMS PressAIMS Geosciences2471-21322025-03-0111120122710.3934/geosci.2025009ANN prediction of the CO2 solubility in water and brine under reservoir conditionsShuo Yang0Dong Wang1Zeguang Dong2Yingge Li3Dongxing Du4Geo-Energy Research Institute, College of Electromechanical Engineering, Qingdao University of Science and Technology, Gaomi 261550, ChinaGeo-Energy Research Institute, College of Electromechanical Engineering, Qingdao University of Science and Technology, Gaomi 261550, ChinaGeo-Energy Research Institute, College of Electromechanical Engineering, Qingdao University of Science and Technology, Gaomi 261550, ChinaGeo-Energy Research Institute, College of Electromechanical Engineering, Qingdao University of Science and Technology, Gaomi 261550, ChinaGeo-Energy Research Institute, College of Electromechanical Engineering, Qingdao University of Science and Technology, Gaomi 261550, ChinaHaving accurate knowledge on CO2 solubility in reservoir liquids plays a pivotal role in geoenergy harvest and carbon capture, utilization, and storage (CCUS) applications. Data-driven works leveraging artificial neural networks (ANN) have presented a promising tool for forecasting CO2 solubility. In this paper, an ANN model was developed based on hundreds of documented data to predict CO2 solubility in both pure water and saline solutions across a broad spectrum of temperatures, pressures, and salinities in reference to underground formation conditions. Multilayer perceptron (MLP) models were constructed for each system, and their prediction results were rigorously validated against the the literature data. The research results indicate that the ANN model is suitable for predicting the solubility of carbon dioxide under different conditions, with root mean square errors (RMSE) of 0.00108 and 0.00036 for water and brine, and a coefficient of determination (R2) of 0.99424 and 0.99612, which indicates robust prediction capacities. It was observed from the ANN model that the saline water case could not be properly expanded to predict the CO2 solubility in pure water, underscoring the distinct dissolution mechanisms in polar mixtures. It is expected that this study could provide a valuable reference and offer novel insights to the prediction of CO2 solubility in complex fluid systems.https://www.aimspress.com/article/doi/10.3934/geosci.2025009co2 solubilitybrinewaterartificial neural networkmultilayer perceptron |
| spellingShingle | Shuo Yang Dong Wang Zeguang Dong Yingge Li Dongxing Du ANN prediction of the CO2 solubility in water and brine under reservoir conditions AIMS Geosciences co2 solubility brine water artificial neural network multilayer perceptron |
| title | ANN prediction of the CO2 solubility in water and brine under reservoir conditions |
| title_full | ANN prediction of the CO2 solubility in water and brine under reservoir conditions |
| title_fullStr | ANN prediction of the CO2 solubility in water and brine under reservoir conditions |
| title_full_unstemmed | ANN prediction of the CO2 solubility in water and brine under reservoir conditions |
| title_short | ANN prediction of the CO2 solubility in water and brine under reservoir conditions |
| title_sort | ann prediction of the co2 solubility in water and brine under reservoir conditions |
| topic | co2 solubility brine water artificial neural network multilayer perceptron |
| url | https://www.aimspress.com/article/doi/10.3934/geosci.2025009 |
| work_keys_str_mv | AT shuoyang annpredictionoftheco2solubilityinwaterandbrineunderreservoirconditions AT dongwang annpredictionoftheco2solubilityinwaterandbrineunderreservoirconditions AT zeguangdong annpredictionoftheco2solubilityinwaterandbrineunderreservoirconditions AT yinggeli annpredictionoftheco2solubilityinwaterandbrineunderreservoirconditions AT dongxingdu annpredictionoftheco2solubilityinwaterandbrineunderreservoirconditions |