Prediction of CO2 solubility in NaCl brine under geological conditions with an improved binary interaction parameter in the Søreide-Whitson model

Liu, Zhiwei; Cui, Peixuan; Cui, Xincheng; Wang, Xinrong; Du, Dongxing

doi:10.1016/j.geothermics.2022.102544

Cited by 13 publications

(3 citation statements)

References 72 publications

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“…This improved model accommodates a relatively wide range of salinity (1−6 m NaCl), along with temperature (313−433 K) and pressure (5− 300 bar). 42 Ji and Zhu developed a statistical associating fluid theory (SAFT) EOS to calculate the phase behaviors for the CO 2 −H 2 S and CO 2 −H 2 S−H 2 O−(NaCl) systems. 35,43 Li et al predicted the mutual solubilities in the CO 2 −CH 4 −H 2 S−brine system using two models: the SW and Peng−Robinson EOS.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Liu et al proposed enhancements to binary interaction parameter (BIP) within the Søreide–Whitson (SW) model, aimed at predicting CO 2 solubility in the aqueous NaCl solution. This improved model accommodates a relatively wide range of salinity (1–6 m NaCl), along with temperature (313–433 K) and pressure (5–300 bar) . Ji and Zhu developed a statistical associating fluid theory (SAFT) EOS to calculate the phase behaviors for the CO 2 –H 2 S and CO 2 –H 2 S–H 2 O–(NaCl) systems. , Li et al predicted the mutual solubilities in the CO 2 –CH 4 –H 2 S–brine system using two models: the SW and Peng–Robinson EOS .…”

Section: Introductionmentioning

confidence: 99%

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Advanced Machine Learning Models for CO₂ and H₂S Solubility in Water and NaCl Brine: Implications for Geoenergy Extraction and Carbon Storage

Wei,

Lu,

Zhu

et al. 2024

Energy Fuels

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Accurate determination of CO2 and H2S pure gas and mixture solubility of CO2 and H2S in water and brine is important to predict the chemical reactions, phase behavior, and solubility trapping of the sour gases in petroleum reservoir engineering and underground carbon storage. In this study, three machine learning (ML) models, backpropagation neural networks (BPNN), generalized regression neural network (GRNN), and eXtreme Gradient Boosting (XGBoost) models, were implemented to predict gas solubility. In addition, a fourth model called fusion model has been developed for higher accuracy by stacking the three aforementioned models. The models were trained and validated with a database of experimental data with a wide range of temperature, pressure, NaCl salinity, and initial H2S concentration (2784 data points). The results from the four models are highly consistent and agree well with the experimental data. Among these models, the fusion model shows the best performance in estimating the gas solubility with a root-mean-square error (RMSE) and an adjusted R 2 of 0.0271 and 0.9995, respectively, which are better than previous ML and correlation methods in the literature. In addition, a software was developed in this study for visually analyzing the gas solubility trends with different variables. Sensitivity analyses show that pressure and temperature are the most sensitive parameters. The gas solubility in water and brine increases monotonically with temperature, but logarithmically with increasing pressure. The results predict that the CO2 + H2S mixture is more soluble than the pure CO2. Salinity has an inhibitory effect on the solubility. At high salinity, the solubility increases with increasing pressure or temperature compared to that at low salinity. The CO2 and H2S solubility modeling is essential for several applications such as reservoir souring, CO2 sequestration, CO2-enhanced oil recovery (CO2-EOR), thermal recovery/steam-assisted gravity drainage via aquathermolysis, and corrosion-related issues in sour gas fields. A library of charts and tables of solubility data were generated for the relevant gas solubility in a wide range of conditions, which provides a ready reference for geoscientists and chemical and petroleum engineers to acquire CO2 and H2S solubilities at desired conditions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Advanced Machine Learning Models for CO₂ and H₂S Solubility in Water and NaCl Brine: Implications for Geoenergy Extraction and Carbon Storage

Wei,

Lu,

Zhu

et al. 2024

Energy Fuels

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“…They determined Henry's constant by fitting the experimental CO 2 solubility in pure water at the same pressure and temperature range. Moreover, multiple thermodynamic models of CO 2 solubility have been constructed based on various pressures, temperatures, and water salinity conditions 25 , 35 , 36 . Table 2 provides an informative summary of theoretical models found in the literature.…”

Section: Introductionmentioning

confidence: 99%

Modeling CO2 solubility in water using gradient boosting and light gradient boosting machine

Mahmoudzadeh,

Amiri-Ramsheh,

Atashrouz

et al. 2024

Sci Rep

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The growing application of carbon dioxide (CO2) in various environmental and energy fields, including carbon capture and storage (CCS) and several CO2-based enhanced oil recovery (EOR) techniques, highlights the importance of studying the phase equilibria of this gas with water. Therefore, accurate prediction of CO2 solubility in water becomes an important thermodynamic property. This study focused on developing two powerful intelligent models, namely gradient boosting (GBoost) and light gradient boosting machine (LightGBM) that predict CO2 solubility in water with high accuracy. The results revealed the outperformance of the GBoost model with root mean square error (RMSE) and determination coefficient (R2) of 0.137 mol/kg and 0.9976, respectively. The trend analysis demonstrated that the developed models were highly capable of detecting the physical trend of CO2 solubility in water across various pressure and temperature ranges. Moreover, the Leverage technique was employed to identify suspected data points as well as the applicability domain of the proposed models. The results showed that less than 5% of the data points were detected as outliers representing the large applicability domain of intelligent models. The outcome of this research provided insight into the potential of intelligent models in predicting solubility of CO2 in pure water.

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Geochemical and Hydrogeochemical Processes Determining Arsenic Presence in Rocks and Groundwater in the Southeastern Portion of El Bajío Guanajuatense, Guanajuato, Mexico

Morales-Arredondo,

Armienta Hernández,

Lugo-Dorantes

et al. 2024

J. Earth Sci.

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Prediction of CO2 solubility in NaCl brine under geological conditions with an improved binary interaction parameter in the Søreide-Whitson model

Cited by 13 publications

References 72 publications

Advanced Machine Learning Models for CO₂ and H₂S Solubility in Water and NaCl Brine: Implications for Geoenergy Extraction and Carbon Storage

Advanced Machine Learning Models for CO₂ and H₂S Solubility in Water and NaCl Brine: Implications for Geoenergy Extraction and Carbon Storage

Modeling CO2 solubility in water using gradient boosting and light gradient boosting machine

Geochemical and Hydrogeochemical Processes Determining Arsenic Presence in Rocks and Groundwater in the Southeastern Portion of El Bajío Guanajuatense, Guanajuato, Mexico

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Prediction of CO2 solubility in NaCl brine under geological conditions with an improved binary interaction parameter in the Søreide-Whitson model

Cited by 13 publications

References 72 publications

Advanced Machine Learning Models for CO2 and H2S Solubility in Water and NaCl Brine: Implications for Geoenergy Extraction and Carbon Storage

Advanced Machine Learning Models for CO2 and H2S Solubility in Water and NaCl Brine: Implications for Geoenergy Extraction and Carbon Storage

Modeling CO2 solubility in water using gradient boosting and light gradient boosting machine

Geochemical and Hydrogeochemical Processes Determining Arsenic Presence in Rocks and Groundwater in the Southeastern Portion of El Bajío Guanajuatense, Guanajuato, Mexico

Contact Info

Product

Resources

About

Advanced Machine Learning Models for CO₂ and H₂S Solubility in Water and NaCl Brine: Implications for Geoenergy Extraction and Carbon Storage

Advanced Machine Learning Models for CO₂ and H₂S Solubility in Water and NaCl Brine: Implications for Geoenergy Extraction and Carbon Storage