Reduced Physics Modeling of CO2 Injectivity

Ganesh, Priya Ravi; Mishra, Srikanta

doi:10.1016/j.egypro.2014.11.336

Cited by 15 publications

(4 citation statements)

References 11 publications

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“…Ravi Ganesh and Mishra 3 presented a correlating chart between injectivity index (expressed in Metric Tons or MT/yr/psi) and permeability thickness (expressed in millidarcy or mD-ft) using data from two additional field projects as well as a number of numerical simulation runs for various parameter combinations. They found a strong trend over multiple orders of magnitude and suggested the relationship J ∼0.1 kh as a rough order-of-magnitude type approximation.…”

Section: Motivation and Scopementioning

confidence: 99%

Injectivity index: a powerful tool for characterizing CO₂ storage reservoirs—a technical note

2020

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Injectivity index is a powerful tool that can be easily calculated from field injection and pressure data. Theoretically, injectivity index is linked to reservoir properties such as permeability, thickness of pay, and size of the reservoir. Consequently, injectivity index data can serve as a powerful tool to assess changes in reservoir quality and performance. In this article, we have looked at field data from notable global carbon sequestration projects in order to identify a correlation between injectivity index (J) and the product of reservoir permeability and thickness (kh). Results suggested that there is a linear relationship between J and kh with field data confirming to lower and upper bounds of J = 0.03×kh and J = 0.23×kh respectively. Average trend was calculated to be J = 0.08×kh. This correlative approach can be extremely beneficial when there is a need to estimate pressure buildup corresponding to a target injection or vice versa. The approach can also be used to understand reservoir flowability and performance at a broader scale prior to the need for expensive or time consuming flow and pressure transient testing.

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Section: Motivation and Scopementioning

confidence: 99%

Injectivity index: a powerful tool for characterizing CO₂ storage reservoirs—a technical note

2020

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“…Thus, many experimental and theoretical studies have extensively developed high-performance electrochemical catalyst systems for CO 2 conversion to achieve a minimized overpotential, improved product selectivity, improved stability, etc. [11][12][13][14] And CO 2 is reduced to various products based on a multielectron transfer mechanism. [15,16] The main target products are often classified into C1 product (e.g., CO, CH 4 , CH 3 OH, and HCOOH, Yu Han and Zhijia Zhang are co-first authors.…”

Section: Introductionmentioning

confidence: 99%

DFT Comparison the Performance of Pd₁₀Sn₅ and Pd₁₀Ag₅ Electrocatalyst for Reduction of CO₂

Han

Zhang

Guo

et al. 2020

Applied Organom Chemis

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CO2 electrocatalysis as a hydrocarbon is a promising means of achieving economical CO2‐mediated hydrogen energy cycling. Hydrocarbons are renewable hydrogen storage materials. The development of reliable metal alloy electrocatalysts is an urgent but challenging task associated with such systems, although there is still a lack of precise reaction mechanism design. In this study, the performance of Pd10Ag5 alloy nanoparticles (NPs) and Pd10Sn5 alloy nanoparticles (NPs) on the electrocatalytic reaction of CO2 was compare. The kinetic and density functional theory (DFT) calculations show that the selectivity of the Pd‐based bimetallic catalyst to the C2 product is greater than that of C1, and the stability of Pd10Ag5 is better and less affected by the reaction environment. However, the catalytic performance of the Pd10Sn5 electrocatalyst in the liquid phase is the best. The insight obtained from the calculations is used to develop criteria for identifying new and improved catalysts for electrochemical CO2 reduction.

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“…They conclude that the model developed using Box-Behnken and quadratic polynomials performs the best. Ganesh and Mishra (2014) gives a 2D simplified dimensionless proxy model to characterize CO 2 injectivity in semi-confined layered saline aquifers. Kang et al (2016) develops a statistical kriging model for a CO 2 -capture-enabled power station and use the model as an efficient proxy to optimize the power station process.…”

Section: Introductionmentioning

confidence: 99%

Statistical modeling of geopressured geothermal reservoirs

Ansari¹,

Hughes²,

White³

2017

Computers & Geosciences

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Identifying attractive candidate reservoirs for producing geothermal energy requires predictive models. In this work, inspectional analysis and statistical modeling are used to create simple predictive models for a line drive design. Inspectional analysis on the partial differential equations governing this design yields a minimum number of fifteen dimensionless groups required to describe the physics of the system. These dimensionless groups are explained and confirmed using models with similar dimensionless groups but different dimensional parameters. This study models dimensionless production temperature and thermal recovery factor as the responses of a numerical model. These responses are obtained by a Box-Behnken experimental design. An uncertainty plot is used to segment the dimensionless time and develop a model for each segment. The important dimensionless numbers for each segment of the dimensionless time are identified using the Boosting method. These selected numbers are used in the regression models. The developed models are reduced to have a minimum number of predictors and interactions. The reduced final models are then presented and assessed using testing runs. Finally, applications of these models are offered. The presented workflow is generic and can be used to translate the output of a numerical simulator into simple predictive models in other research areas involving numerical simulation.

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Reduced Physics Modeling of CO2 Injectivity

Cited by 15 publications

References 11 publications

Injectivity index: a powerful tool for characterizing CO₂ storage reservoirs—a technical note

Injectivity index: a powerful tool for characterizing CO₂ storage reservoirs—a technical note

DFT Comparison the Performance of Pd₁₀Sn₅ and Pd₁₀Ag₅ Electrocatalyst for Reduction of CO₂

Statistical modeling of geopressured geothermal reservoirs

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Reduced Physics Modeling of CO2 Injectivity

Cited by 15 publications

References 11 publications

Injectivity index: a powerful tool for characterizing CO2 storage reservoirs—a technical note

Injectivity index: a powerful tool for characterizing CO2 storage reservoirs—a technical note

DFT Comparison the Performance of Pd10Sn5 and Pd10Ag5 Electrocatalyst for Reduction of CO2

Statistical modeling of geopressured geothermal reservoirs

Contact Info

Product

Resources

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Injectivity index: a powerful tool for characterizing CO₂ storage reservoirs—a technical note

Injectivity index: a powerful tool for characterizing CO₂ storage reservoirs—a technical note

DFT Comparison the Performance of Pd₁₀Sn₅ and Pd₁₀Ag₅ Electrocatalyst for Reduction of CO₂