AN IMPROVED TOUGH2 MODULE TO SIMULATE GEOLOGICAL CO2 STORAGE IN SALINE AQUIFERS

2017 ◽  
Author(s):  
Babak Shabani ◽  
◽  
Javier Vilcaez
2009 ◽  
Vol 32 (1) ◽  
pp. 98-109 ◽  
Author(s):  
C. Chalbaud ◽  
M. Robin ◽  
J-M Lombard ◽  
F. Martin ◽  
P. Egermann ◽  
...  

2013 ◽  
Vol 37 ◽  
pp. 4794-4803
Author(s):  
Gøril Tjetland ◽  
Hallvard Høydalsvik ◽  
Espen Erichsen

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1557
Author(s):  
Amine Tadjer ◽  
Reidar B. Bratvold

Carbon capture and storage (CCS) has been increasingly looking like a promising strategy to reduce CO2 emissions and meet the Paris agreement’s climate target. To ensure that CCS is safe and successful, an efficient monitoring program that will prevent storage reservoir leakage and drinking water contamination in groundwater aquifers must be implemented. However, geologic CO2 sequestration (GCS) sites are not completely certain about the geological properties, which makes it difficult to predict the behavior of the injected gases, CO2 brine leakage rates through wellbores, and CO2 plume migration. Significant effort is required to observe how CO2 behaves in reservoirs. A key question is: Will the CO2 injection and storage behave as expected, and can we anticipate leakages? History matching of reservoir models can mitigate uncertainty towards a predictive strategy. It could prove challenging to develop a set of history matching models that preserve geological realism. A new Bayesian evidential learning (BEL) protocol for uncertainty quantification was released through literature, as an alternative to the model-space inversion in the history-matching approach. Consequently, an ensemble of previous geological models was developed using a prior distribution’s Monte Carlo simulation, followed by direct forecasting (DF) for joint uncertainty quantification. The goal of this work is to use prior models to identify a statistical relationship between data prediction, ensemble models, and data variables, without any explicit model inversion. The paper also introduces a new DF implementation using an ensemble smoother and shows that the new implementation can make the computation more robust than the standard method. The Utsira saline aquifer west of Norway is used to exemplify BEL’s ability to predict the CO2 mass and leakages and improve decision support regarding CO2 storage projects.


2017 ◽  
Vol 57 (1) ◽  
pp. 100 ◽  
Author(s):  
Emad A. Al-Khdheeawi ◽  
Stephanie Vialle ◽  
Ahmed Barifcani ◽  
Mohammad Sarmadivaleh ◽  
Stefan Iglauer

CO2 migration and storage capacity are highly affected by various parameters (e.g. reservoir temperature, vertical to horizontal permeability ratio, cap rock properties, aquifer depth and the reservoir heterogeneity). One of these parameters, which has received little attention, is brine salinity. Although brine salinity has been well demonstrated previously as a factor affecting rock wettability (i.e. higher brine salinity leads to more CO2-wet rocks), its effect on the CO2 storage process has not been addressed effectively. Thus, we developed a three-dimensional homogeneous reservoir model to simulate the behaviour of a CO2 plume in a deep saline aquifer using five different salinities (ranging from 2000 to 200 000 ppm) and have predicted associated CO2 migration patterns and trapping capacities. CO2 was injected at a depth of 1408 m for a period of 1 year at a rate of 1 Mt year–1 and then stored for the next 100 years. The results clearly indicate that 100 years after the injection of CO2 has stopped, the salinity has a significant effect on the CO2 migration distance and the amount of mobile, residual and dissolved CO2. First, the results show that higher brine salinity leads to an increase in CO2 mobility and CO2 migration distance, but reduces the amount of residually trapped CO2. Furthermore, high brine salinity leads to reduced dissolution trapping. Thus, we conclude that less-saline aquifers are preferable CO2 sinks.


2019 ◽  
Vol 82 ◽  
pp. 229-243 ◽  
Author(s):  
Michael U. Onoja ◽  
John D.O. Williams ◽  
Hayley Vosper ◽  
Seyed M. Shariatipour

Sign in / Sign up

Export Citation Format

Share Document