CO₂ injection into geological formations is considered one way of mitigating the increasing levels of carbon dioxide concentrations in the atmosphere and its effect on and global warming. In regard to sequestering carbon underground, different countries have conducted projects at commercial scale or pilot scale and some have plans to develop potential storage geological formations for carbon dioxide storage. In this study, pure CO₂ injection is examined on a model with the properties of bunter sandstone and then sensitivity analyses were conducted for some of the fluid, rock and injection parameters. The results of this study show that the extent to which CO₂ has been convected in the porous media in the reservoir plays a vital role in improving the CO₂ dissolution in brine and safety of its long term storage. We conclude that heterogeneous permeability plays a crucial role on the saturation distribution and can increase or decrease the amount of dissolved CO₂ in water around ± 7% after the injection stops and up to 13% after 120 years. Furthermore, the value of absolute permeability controls the effect of the K_v/K_h ratio on the CO₂ dissolution in brine. In other words, as the value of vertical and horizontal permeability decreases (i.e., tight reservoirs) the impact of K_v/K_h ratio on the dissolved CO₂ in brine becomes more prominent. Additionally, reservoir engineering parameters, such as well location, injection rate and scenarios, also have a high impact on the amount of dissolved CO₂ and can change the dissolution up to 26%, 100% and 5.5%, respectively.

History matching is the process of modifying a numerical model (representing a reservoir) in the light of observed production data. In the oil and gas industry, production data are employed during a history matching exercise in order to reduce the uncertainty in associated reservoir models. However, production data, normally measured using commercial flowmeters that may or may not be accurate depending on factors such as maintenance schedules, or estimated using mathematical equations, inevitably has inherent errors. In other words, the data which are used to reduce the uncertainty of the model may have considerable uncertainty in itself. This problem is exacerbated for gas condensate and wet gas reservoirs as there are even greater errors associated with measuring small fractions of liquid. The influence of this uncertainty in the production data on history matching has not been addressed in the literature so far. In this paper, the effect of systematic and random flow measurement errors on history matching is investigated. Initially, 14 production data sets with different ranges of systematic and random errors, from 0 to 10%, have been employed in a history matching exercise for an oil reservoir and the results have later been evaluated based on a reference model. Subsequently, 23 data sets with errors ranging from 0 to 20% have been employed in the same process for a wet gas reservoir. The results show that for both cases systematic errors considerably affect history matching, while the effect of random errors on the considered scenarios is seen to be insignificant. Although reservoir model parameters in the wet gas reservoir were not as sensitive to the flow measurement errors as in the oil reservoir, for both cases, the future production forecast was significantly affected by the errors. Permeability was seen to be the most sensitive history matching parameter to the flow measurement errors in the oil reservoir, while for the wet gas reservoir, the most sensitive parameter was the forecast of future oil and gas production. Finally, considering the noticeable effect of systematic errors on both cases, it is suggested that flowmeter calibration and regular maintenance is prioritised, although the subsequent economic cost needs to be considered.