Large-scale Numerical Modelling of CO2 Injection and Containment Phases for an Italian Near-coast Reservoir Using PFLOTRAN

Orsini, P.; Cantucci, Barbara; Quattrocchi, F.

doi:10.1016/j.egypro.2014.07.040

Cited by 7 publications

(3 citation statements)

References 14 publications

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“…These models can be compared with estimates from natural analogues and engineered sites. We have compiled data from eight modelling studies (Table 2) that report dissolved CO2 over time (Ozah et al 2005;Pickup et al 2011;Sato et al 2011;Bonneville et al 2013;Szulczewski et al 2013;Kempka et al 2014;Orsini et al 2014). All studies are reservoir-scale simulations.…”

Section: Modelled Dissolution Ratesmentioning

confidence: 99%

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Quantification of solubility trapping in natural and engineered CO ₂ reservoirs

Leslie

Cavanagh

Haszeldine

et al. 2021

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Secure retention of CO2 in geological reservoirs is essential for effective storage. Solubility trapping, the dissolution of CO2 into formation water, is a major sink on geological timescales in natural CO2 reservoirs. Observations during CO2 injection, combined with models of CO2 reservoirs, indicate the immediate onset of solubility trapping. There is uncertainty regarding the evolution of dissolution rates between the observable engineered timescale of years and decades, to the >10 kyr state represented by natural CO2 reservoirs. A small number of studies have constrained dissolution rates within natural analogues. The studies show that solubility trapping is the principal storage mechanism after structural trapping, removing 10–50% of CO2 across whole reservoirs. Natural analogues, engineered reservoirs and model studies produce a wide range of estimates on the fraction of CO2 dissolved and the dissolution rate. Analogue and engineered reservoirs do not show the high fractions of dissolved CO2 seen in several models. Evidence from natural analogues supports a model of most dissolution occurring during emplacement and migration, before the establishment of a stable gas-water contact. A rapid decline in CO2 dissolution rate over time suggests that analogue reservoirs are in dissolution equilibrium for most of the CO2 residence time.Supplementary material:https://doi.org/10.6084/m9.figshare.c.5476199Thematic collection: This article is part of the Geoscience for CO2 storage collection available at: https://www.lyellcollection.org/cc/geoscience-for-co2-storage

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Section: Modelled Dissolution Ratesmentioning

confidence: 99%

“…Best-fit lines, dashed, are approximate and plot as exponential decay curves in non-log space. Bravo Dome studies and Kempka et al (2014) (Sathaye et al 2014;Zwahlen et al 2017); and selected simulation case studies (Ozah et al 2005;Pickup et al 2011;Sato et al 2011;Bonneville et al 2013;Kempka et al 2014;Orsini et al 2014). Fig.…”

Section: Figure Captionsmentioning

confidence: 99%

Quantification of solubility trapping in natural and engineered CO ₂ reservoirs

Leslie

Cavanagh

Haszeldine

et al. 2021

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“…Previous studies by the International Energy Agency greenhouse gas emissions (IEA GHG) have shown that DSAs have the highest storage capacity of the various geological storage reservoirs available for CO 2 storage, so substantial decreases in CO 2 emission levels may therefore depend upon using DSAs as carbon storage reservoirs. Researchers have been studying the feasibility and applicability of injecting gas into geological formations, including DSAs 9–12 …”

Section: Introductionmentioning

confidence: 99%

A machine learning‐based approach to the multiobjective optimization of CO₂ injection and water production during CCS in a saline aquifer based on field data

Vaziri

Sedaee

2023

Energy Science & Engineering

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The presence of carbon capture and storage (CCS) projects is important due to the growing production of greenhouse gases, especially carbon dioxide (CO 2 ).Our target functions have been chosen because of the importance of CO 2 storage in CCS projects and the requirement for producing less water in projects requiring water production. As a proxy for reservoir simulations, support vector regression, artificial neural network (ANN), and multivariate adaptive regression spline (MARS) have been used. It was determined that MARS had higher accuracy based on examining these three data-driven models with the available field data. It was, however, very close to the accuracy of the ANN. MARS gave root mean square of error (RMSE), mean absolute error (MAE), and R 2 values of 2.78%, 1.95%, and 0.998, respectively, for predicting CO 2 storage values in test data, yet 3.73%, 3.53%, and 0.995 for blind data. The RMSE, MAE, and R 2 to evaluate the machine learning (ML) model for predicting water production were 3.58%, 2.81%, and 0.997, respectively, while the results for blind data were 3.94%, 2.83%, and 0.997, respectively. By reducing the amount of computational load and time taken to reproduce the simulation data by MARS, it will be beneficial for optimization. This study highlights the application of the ML approach to coupling with genetic algorithms and optimizing CO 2 storage and water production. With the proposed frameworks, preprocessing, feature selection, two-stage validation of the data-driven model, and optimizer are performed in the aquifer and a repository containing a series of optimal solutions is developed to be used in projects.

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A critical review of CO2 mineral trapping in sedimentary reservoirs – from theory to application: Pertinent parameters, acceleration methods and evaluation workflow

Khandoozi

Hazlett

Fustic

2023

Earth-Science Reviews

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Large-scale Numerical Modelling of CO2 Injection and Containment Phases for an Italian Near-coast Reservoir Using PFLOTRAN

Cited by 7 publications

References 14 publications

Quantification of solubility trapping in natural and engineered CO ₂ reservoirs

Quantification of solubility trapping in natural and engineered CO ₂ reservoirs

A machine learning‐based approach to the multiobjective optimization of CO₂ injection and water production during CCS in a saline aquifer based on field data

A critical review of CO2 mineral trapping in sedimentary reservoirs – from theory to application: Pertinent parameters, acceleration methods and evaluation workflow

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Large-scale Numerical Modelling of CO2 Injection and Containment Phases for an Italian Near-coast Reservoir Using PFLOTRAN

Cited by 7 publications

References 14 publications

Quantification of solubility trapping in natural and engineered CO 2 reservoirs

Quantification of solubility trapping in natural and engineered CO 2 reservoirs

A machine learning‐based approach to the multiobjective optimization of CO2 injection and water production during CCS in a saline aquifer based on field data

A critical review of CO2 mineral trapping in sedimentary reservoirs – from theory to application: Pertinent parameters, acceleration methods and evaluation workflow

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Product

Resources

About

Quantification of solubility trapping in natural and engineered CO ₂ reservoirs

Quantification of solubility trapping in natural and engineered CO ₂ reservoirs

A machine learning‐based approach to the multiobjective optimization of CO₂ injection and water production during CCS in a saline aquifer based on field data