Transport Diffusion Behaviors and Mechanisms of CO<sub>2</sub>/CH<sub>4</sub> in Shale Nanopores: Insights from Molecular Dynamics Simulations

Zhou, Wenning; Zhu, Jiadan; Wang, Haobo; Kong, Debin

doi:10.1021/acs.energyfuels.2c02197

Cited by 9 publications

(3 citation statements)

References 49 publications

(98 reference statements)

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“…Via MD simulations, the diffusion of confined CO 2 has been investigated as part of various mixtures, such as shale gas, CH 4 , − n -C 4 H 10 , n -C 7 H 16 , n -C 8 H 18 , and ionic liquids. , The diffusivity of pure CO 2 has also been investigated under confinement by different materials, such as MOF, − graphene sheets, zeolites, calcites, , silicalites , and clays . In this review, we focus only on the results related to diffusion of the confined mixture of CO 2 and H 2 O.…”

Section: Aqueous Co2 Diffusion In Confined Mediamentioning

confidence: 99%

Diffusivity of CO₂ in H₂O: A Review of Experimental Studies and Molecular Simulations in the Bulk and in Confinement

Polat,

Coelho,

Vlugt

et al. 2024

J. Chem. Eng. Data

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Section: Aqueous Co2 Diffusion In Confined Mediamentioning

confidence: 99%

Diffusivity of CO₂ in H₂O: A Review of Experimental Studies and Molecular Simulations in the Bulk and in Confinement

Polat,

Coelho,

Vlugt

et al. 2024

J. Chem. Eng. Data

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“…The structural changes during maturation directly affect the mechanical properties of kerogen, such as microscopic pores and fracture mechanisms, and play a vital role in establishing oil/gas transport channels in shale oil/gas reservoirs. − However, kerogen exists at the nanoscale, and the mechanical properties through experiments are challenging to be measured. Therefore, molecular simulation is widely used to the mechanical properties of kerogen.…”

Section: Necessity Of Kerogen Molecular Modelmentioning

confidence: 99%

Perspectives of Machine Learning Development on Kerogen Molecular Model Reconstruction and Shale Oil/Gas Exploitation

Kang

Zhao

2022

Energy Fuels

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The shale revolution has provided abundant shale oil/gas resources for the world, but the efficient, sustainable, and environmentally friendly exploitation of shale oil/gas is still challenging. Kerogen is the primary hydrocarbon source of shale oil/gas. The research on the kerogen chemo-mechanical properties significantly influences the development of shale oil/gas extraction technology. Rapid reconstruction of the kerogen molecular models is the most effective way to study the generation mechanism of shale oil/gas from the bottom-up molecular level. However, due to the combinatorial explosion problem, the reconstruction complexity of kerogen increases sharply because of the kerogen’s characteristics of complex origin, large molecular weight, and diverse functional groups. The traditional kerogen molecular reconstruction methods require professionals to comprehensively analyze various experimental information to approximate the actual kerogen molecular models through trial-and-error. So, the traditional methods are time and material-consuming and extremely inefficient. These shortcomings make researchers spend too much strength on the reconstruction of kerogen molecular models and cannot focus on the study of kerogen chemo-mechanical properties. For the past few years, state-of-the-art machine learning (ML) methods have been applied to intelligently reconstruct the kerogen molecular models through high-throughput and predict shale oil/gas production mechanisms. Although the current work is still in the infancy stage, ML methods are believed to be the most promising way to solve the drawbacks of traditional methods and reconstruct kerogen in reliable and large molecular weight. Hence, mechano-energetics is proposed to study the efficient development and utilization of energy based on mechanics and ML. This paper briefly reviews the development history of kerogen molecular model reconstruction methods and the research of ML in the fields of kerogen reconstruction and shale oil/gas exploitation. Some recommendations for further ML-based work are also suggested. We are convinced that the ML methods will accelerate the research of kerogen and promote the significant development of unconventional oil/gas exploitation technologies.

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“…It also forms the basis for gas fields' scientific and efficient development. Shale gas reservoirs have nanoscale pore characteristics, distinguishing them from conventional natural gas [5]. The production mode for gas wells is "non-constant pressure, non-constant production".…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Research for Predicting Shale Gas Well Production

Qi,

Li,

et al. 2024

Symmetry

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The estimated ultimate recovery (EUR) of a single well must be predicted to achieve scale-effective shale gas extraction. Accurately forecasting EUR is difficult due to the impact of various geological, engineering, and production factors. Based on data from 200 wells in the Weiyuan block, this paper used Pearson correlation and mutual information to eliminate the factors with a high correlation among the 31 EUR influencing factors. The RF-RFE algorithm was then used to identify the six most important factors controlling the EUR of shale gas wells. XGBoost, RF, SVM, and MLR models were built and trained with the six dominating factors screened as features and EUR as labels. In this process, the model parameters were optimized, and finally the prediction accuracies of the models were compared. The results showed that the thickness of a high-quality reservoir was the dominating factor in geology; the high-quality reservoir length drilled, the fracturing fluid volume, the proppant volume, and the fluid volume per length were the dominating factors in engineering; and the 360−day flowback rate was the dominating factor in production. Compared to the SVM and MLR models, the XG Boost and the RF models based on integration better predicted EUR. The XGBoost model had a correlation coefficient of 0.9 between predicted and observed values, and its standard deviation was closest to the observed values’ standard deviation, making it the best model for EUR prediction among the four types of models. Identifying the dominating factors of shale gas single-well EUR can provide significant guidance for development practice, and using the optimized XGBoost model to forecast the shale gas single-well EUR provides a novel idea for predicting shale gas well production.

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Transport Diffusion Behaviors and Mechanisms of CO₂/CH₄ in Shale Nanopores: Insights from Molecular Dynamics Simulations

Cited by 9 publications

References 49 publications

Diffusivity of CO₂ in H₂O: A Review of Experimental Studies and Molecular Simulations in the Bulk and in Confinement

Diffusivity of CO₂ in H₂O: A Review of Experimental Studies and Molecular Simulations in the Bulk and in Confinement

Perspectives of Machine Learning Development on Kerogen Molecular Model Reconstruction and Shale Oil/Gas Exploitation

Machine Learning-Based Research for Predicting Shale Gas Well Production

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Transport Diffusion Behaviors and Mechanisms of CO2/CH4 in Shale Nanopores: Insights from Molecular Dynamics Simulations

Cited by 9 publications

References 49 publications

Diffusivity of CO2 in H2O: A Review of Experimental Studies and Molecular Simulations in the Bulk and in Confinement

Diffusivity of CO2 in H2O: A Review of Experimental Studies and Molecular Simulations in the Bulk and in Confinement

Perspectives of Machine Learning Development on Kerogen Molecular Model Reconstruction and Shale Oil/Gas Exploitation

Machine Learning-Based Research for Predicting Shale Gas Well Production

Contact Info

Product

Resources

About

Transport Diffusion Behaviors and Mechanisms of CO₂/CH₄ in Shale Nanopores: Insights from Molecular Dynamics Simulations

Diffusivity of CO₂ in H₂O: A Review of Experimental Studies and Molecular Simulations in the Bulk and in Confinement

Diffusivity of CO₂ in H₂O: A Review of Experimental Studies and Molecular Simulations in the Bulk and in Confinement