New Insights Into CO2 Injection and Storage in Saline Aquifers

2012 ◽  
Author(s):  
Mehran Sohrabi ◽  
Masoud Riazi ◽  
Christian Bernstone ◽  
Mahmoud Jamiolahmady ◽  
Nils-Peter Christensen
Keyword(s):  
Saline Aquifers ◽  
Co2 Injection ◽  
And Storage

Cement-Sheath Wellbore Integrity for CO2 Injection and Storage Wells

2010 ◽  
Author(s):  
Koji Takase ◽  
Yogesh Ramesh Barhate ◽  
Hiroyuki Hashimoto ◽  
Siddhartha Francois Lunkad
Keyword(s):  
Co2 Injection ◽  
Wellbore Integrity ◽  
Cement Sheath ◽  
And Storage

scholarly journals Analytical Solution to Evaluate Salt Precipitation during CO2 Injection in Saline Aquifers

2009 ◽  
Vol 1 (1) ◽  
pp. 1775-1782 ◽  
Author(s):  
Mehdi Zeidouni ◽  
Mehran Pooladi-Darvish ◽  
David Keith
Keyword(s):  
Analytical Solution ◽  
Saline Aquifers ◽  
Co2 Injection ◽  
Salt Precipitation

scholarly journals Managing Uncertainty in Geological CO2 Storage Using Bayesian Evidential Learning

Energies ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 1557
Author(s):  
Amine Tadjer ◽  
Reidar B. Bratvold
Keyword(s):  
Uncertainty Quantification ◽  
History Matching ◽  
Co2 Storage ◽  
Carbon Capture And Storage ◽  
Monitoring Program ◽  
Co2 Injection ◽  
Drinking Water Contamination ◽  
Model Inversion ◽  
Geological Co2 Storage ◽  
And Storage

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.

A Laboratory Investigation of the Effect of Ethanol-Treated Carbon Dioxide Injection on Oil Recovery and Carbon Dioxide Storage

SPE Journal ◽  
2021 ◽  
pp. 1-17
Author(s):  
Saira ◽  
Emmanuel Ajoma ◽  
Furqan Le-Hussain
Keyword(s):  
Carbon Dioxide ◽  
Oil Recovery ◽  
Carbon Capture ◽  
Molar Fraction ◽  
Co2 Injection ◽  
Carbon Dioxide Injection ◽  
Treated Carbon ◽  
Carbon Dioxide Co2 ◽  
And Storage ◽  
Experimental Runs

Summary Carbon dioxide (CO2) enhanced oil recovery is the most economical technique for carbon capture, usage, and storage. In depleted reservoirs, full or near-miscibility of injected CO2 with oil is difficult to achieve, and immiscible CO2 injection leaves a large volume of oil behind and limits available pore volume (PV) for storing CO2. In this paper, we present an experimental study to delineate the effect of ethanol-treated CO2 injection on oil recovery, net CO2 stored, and amount of ethanol left in the reservoir. We inject CO2 and ethanol-treated CO2 into Bentheimer Sandstone cores representing reservoirs. The oil phase consists of a mixture of 0.65 hexane and 0.35 decane (C6-C10 mixture) by molar fraction in one set of experimental runs, and pure decane (C10) in the other set of experimental runs. All experimental runs are conducted at constant temperature 70°C and various pressures to exhibit immiscibility (9.0 MPa for the C6-C10 mixture and 9.6 MPa for pure C10) or near-miscibility (11.7 MPa for the C6-C10 mixture and 12.1 MPa for pure C10). Pressure differences across the core, oil recovery, and compositions and rates of the produced fluids are recorded during the experimental runs. Ultimate oil recovery under immiscibility is found to be 9 to 15% greater using ethanol-treated CO2 injection than that using pure CO2 injection. Net CO2 stored for pure C10 under immiscibility is found to be 0.134 PV greater during ethanol-treated CO2 injection than during pure CO2 injection. For the C6-C10 mixture under immiscibility, both ethanol-treated CO2 injection and CO2 injection yield the same net CO2 stored. However, for the C6-C10 mixture under near-miscibility,ethanol-treated CO2 injection is found to yield 0.161 PV less net CO2 stored than does pure CO2 injection. These results suggest potential improvement in oil recovery and net CO2 stored using ethanol-treated CO2 injection instead of pure CO2 injection. If economically viable, ethanol-treated CO2 injection could be used as a carbon capture, usage, and storage method in low-pressure reservoirs, for which pure CO2 injection would be infeasible.

scholarly journals Cyclic CO2 – H2O injection and residual trapping: implications for CO2 injection efficiency and storage security

2018 ◽  
Author(s):  
Katriona Edlmann ◽  
Sofi Hinchliffe ◽  
Niklas Heinemann ◽  
Gareth Johnson ◽  
Jonathan Ennis-King ◽  
...  
Keyword(s):  
Co2 Injection ◽  
Injection Efficiency ◽  
Storage Security ◽  
Residual Trapping ◽  
And Storage
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