Effects of inorganic CO2 intrusion on diagenesis and reservoir quality of lacustrine conglomerate sandstones: Implications for geological carbon sequestration

Wei, Wei; Zhu, Xiaomin; Lü, Peng; Tan, Mingxuan

doi:10.1016/j.geoen.2023.212111

Cited by 8 publications

(1 citation statement)

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“…Geological storage of CO 2 in saline aquifers, depleted hydrocarbon reservoirs, or deep coal beds is a desirable measure to slow down and mitigate the trend of global warming. − CO 2 is mainly recovered from natural gas and flue gases, but the coexistence of impurities (e.g., H 2 S) with CO 2 is common . Generally, co-injecting CO 2 and hydrogen sulfide (H 2 S) is proposed as a suitable carbon capture and storage (CCS) scheme due to the high costs of purifying, capturing, and compressing CO 2 . , In addition, there are numerous sour gas reservoirs (with the H 2 S concentration of more than four ppm) around the world, such as the Upper Smackover Formation, Mississippi, USA, and Lower Triassic Feixiangguan Formation, Sichuan, China .…”

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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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%

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

Self Cite

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The distribution and driving mechanism of soil inorganic carbon in semi-arid and arid areas: A case study of Alxa region in China

Zhu,

Si,

et al. 2024

CATENA

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Early marine carbonate cementation in a relict shelf-edge coral reef North of Miami, Florida: Pseudo-coupled depositional and diagenetic modeling

Lu,

Zhang,

Zhang

et al. 2023

Geoenergy Science and Engineering

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Effects of inorganic CO2 intrusion on diagenesis and reservoir quality of lacustrine conglomerate sandstones: Implications for geological carbon sequestration

Cited by 8 publications

References 51 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

The distribution and driving mechanism of soil inorganic carbon in semi-arid and arid areas: A case study of Alxa region in China

Early marine carbonate cementation in a relict shelf-edge coral reef North of Miami, Florida: Pseudo-coupled depositional and diagenetic modeling

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Effects of inorganic CO2 intrusion on diagenesis and reservoir quality of lacustrine conglomerate sandstones: Implications for geological carbon sequestration

Cited by 8 publications

References 51 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

The distribution and driving mechanism of soil inorganic carbon in semi-arid and arid areas: A case study of Alxa region in China

Early marine carbonate cementation in a relict shelf-edge coral reef North of Miami, Florida: Pseudo-coupled depositional and diagenetic modeling

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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