A data driven model for the impact of IFT and density variations on CO2 storage capacity in geologic formations

Nomeli, Mohammad Alizadeh; Riaz, Amir

doi:10.1016/j.advwatres.2017.06.015

Cited by 8 publications

(4 citation statements)

References 31 publications

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“…Under a specific temperature and pressure, the state of CO 2 can switch between gaseous, liquid, solid, and supercritical states. When the temperature is 31.1 °C and the pressure is 7.38 MPa, CO 2 transforms into a supercritical state and turns into a dense, thick liquid (Nomeli and Riaz, 2017;Yuan et al, 2020). Density is the in situ density of pure carbon dioxide at a specific pressure and temperature (Ogawa et al, 2011).…”

Section: Effective Porositymentioning

confidence: 99%

An automatic modeling approach for the potential evaluation of CO2 geological storage in the deep saline aquifer

Jing

Zhou

et al. 2022

Front. Energy Res.

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Geological storage of carbon dioxide is receiving more and more attention as one of the efficient carbon reduction technologies, as China's carbon-neutral strategic plan moves forward. There is an increasing demand for more effective and thorough methodologies to assess the potential of CO 2 storage in deep saline aquifers. This study proposes a method for evaluating the geological storage potential of CO 2 in deep saline aquifers and constructs an automatic evaluation system for the comprehensive potential of CO 2 geological storage using ArcGIS Model Builder visual modeling technology. The automatic evaluation system consists of four functional parts: information collating and database constructing, data pre-processing, model building evaluation and result validation evaluation. First, structured and unstructured data including underlying geology, tectonic geology, oil and gas geology, and drilling data are collated and established in a geodatabase. Second, pre-processing models of the deep saline reservoir-caprock data are established based on the analysis of the geological evolution history of the study area to determine the effective storage thickness, effective porosity, and the influence range of faults; kriging methods are then used to realize the spatial interpolation of the evaluation parameters. Third, the volume coefficient method is adopted to construct the underground storage space model and to establish the density distribution model of the supercritical CO 2 with nonlinear function while taking into account four evaluation factors (i.e. area, effective porosity, effective thickness, effective coefficient) and two limiting factors (i.e. fault, burial depth). Finally, the geological storage potential of CO 2 in the study area is evaluated with the classification of the potential level and compared with the numerical simulation results to verify the model's accuracy. The model is first applied in this paper using a suitable target in China as a case study. The results show that this target area's anticipated storage potential value reaches 52.557 Mt. The total precision error, according to a comparison of the numerical simulation results, is 8.20%. Based on the results obtained, it can be concluded that the automatic GIS-based modeling approach is suitable for a comparable study of potential evaluation of CO 2 geological storage in deep saline aquifers.

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Section: Effective Porositymentioning

confidence: 99%

An automatic modeling approach for the potential evaluation of CO2 geological storage in the deep saline aquifer

Jing

Zhou

et al. 2022

Front. Energy Res.

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“…High salinity levels, as often found in the brines filling deep saline formations, can increase the interfacial tension by up to 10 mN/m (Espinoza and Santamarina, 2010;Saraji et al, 2014). Additionally, CO2 dissolved in the brine may decrease IFT (Nomeli and Riaz, 2017), as may impurities such as CH4 or SO2 (Ren et al, 2000;Saraji et al, 2014). Thus for conditions most likely for storage reservoirs -supercritical CO2 at depths higher than 1200 m with saline brine (Miocic et al, 2016) -CO2-brine IFT will be in the order of 35±5 mN/m (Fig.…”

Section: Predicting Fault Seals For Hydrocarbons and Implications Formentioning

confidence: 99%

Uncertainty in fault seal parameters: implications for CO<sub>2</sub> column height retention and storage capacity in geological CO<sub>2</sub> storage projects

Miocic¹,

Johnson²,

Bond³

2019

Preprint

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Abstract. Faults can act as barriers to fluid flow in sedimentary basins, hindering the migration of buoyant fluids in the subsurface, trapping them in reservoirs and facilitating the build-up of vertical fluid columns. The maximum height of these columns is reliant on the retention potential of the sealing fault with regards to the trapped fluid. Several different approaches for the calculation of maximum supported column height exist for hydrocarbon systems. Here, we translate these approaches to the trapping of carbon dioxide by faults and asses the impact of uncertainties in i) the wettability properties of the fault rock, ii) fault rock composition, and iii) reservoir depth, on retention potential. In similarity to hydrocarbon systems, uncertainties associated with the wettability of a CO2-brine-fault rock system for a given reservoir have less of an impact on column heights than uncertainties of fault rock composition. However, the wettability of the carbon dioxide system is highly sensitive to depth, with a large variation in possible column height predicted at 1000m and 2000m depth, the likely depth range for carbon storage sites. In contrast to hydrocarbon systems higher phyllosilicate entrainment into the fault rock may reduce the amount of carbon dioxide that can be securely retained. Our results show that if approaches developed for fault seal in hydrocarbon systems are translated, without modification, to carbon dioxide systems the capacity of carbon storage sites will be inaccurate, and the predicted security of storage sites erroneous.

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“…In recent years, researchers have shown an increased interest in the application of machine learning techniques for modeling complex systems (Jeong et al, 2018;Nait Amar et al, 2019b;Nait Amar and Zeraibi, 2019;Nomeli and Riaz, 2017;Piotrowski and Napiorkowski, 2012). Machine learning techniques can be divided into computer-aided methods such as support vector regression (SVR) and decision tree, and explicit methods such as gene expression programming (GEP) and group method of data handling (GMDH).…”

Section: Introductionmentioning

confidence: 99%

Prediction of CO2 diffusivity in brine using white-box machine learning

Amar

Ghahfarokhi

2020

Journal of Petroleum Science and Engineering

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A data driven model for the impact of IFT and density variations on CO2 storage capacity in geologic formations

Cited by 8 publications

References 31 publications

An automatic modeling approach for the potential evaluation of CO2 geological storage in the deep saline aquifer

An automatic modeling approach for the potential evaluation of CO2 geological storage in the deep saline aquifer

Uncertainty in fault seal parameters: implications for CO<sub>2</sub> column height retention and storage capacity in geological CO<sub>2</sub> storage projects

Prediction of CO2 diffusivity in brine using white-box machine learning

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A data driven model for the impact of IFT and density variations on CO2 storage capacity in geologic formations

Cited by 8 publications

References 31 publications

An automatic modeling approach for the potential evaluation of CO2 geological storage in the deep saline aquifer

An automatic modeling approach for the potential evaluation of CO2 geological storage in the deep saline aquifer

Uncertainty in fault seal parameters: implications for CO&lt;sub&gt;2&lt;/sub&gt; column height retention and storage capacity in geological CO&lt;sub&gt;2&lt;/sub&gt; storage projects

Prediction of CO2 diffusivity in brine using white-box machine learning

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Uncertainty in fault seal parameters: implications for CO<sub>2</sub> column height retention and storage capacity in geological CO<sub>2</sub> storage projects