Carbon dioxide sequestration is one of many mitigation tools available to help reduce carbon dioxide emissions while other disposal/repurposing methods are being investigated. Geologic sequestration is the most stable option for long-term storage of carbon dioxide (CO2), with significant CO2 trapping occurring through mineralization within the first 20-50 years. A fiber optic based monitoring system has been proposed to provide real time concentrations of CO2 at various points throughout the geologic formation. The proposed sensor is sensitive to the refractive index (RI) of substances in direct contact with the sensing component. As RI is a measurement of light propagating through a bulk medium relative to light propagating through a vacuum, the extraction of the effects of any specific component of that medium to the RI remains very difficult. Therefore, a requirement for a selective barrier to be able to prevent confounding substances from being in contact with the sensor and specifically isolate CO2 is necessary. As such a method to evaluate the performance of the selective element of the sensor was investigated. Polybenzimidazole (PBI) and VTEC polyimide (PI) 1388 are high performance polymers with good selectivity for CO2 used in high temperature gas separations. These polymers were spin coated onto a glass substrate and cured to form ultra-thin (>10 μm) membranes for gas separation. At a range of pressures (0.14 –0.41 MPa) and a set temperature of 24.2±0.8 °C, intrinsic permeabilities to CO2 and nitrogen (N2) were investigated as they are the gases of highest prevalence in underground aquifers. Preliminary RI testing for proof of concept has yielded promising results when the sensor is exposed exclusively to CO2 or N2. However, the use of both PBI and VTEC PI in these trials resulted in CO2 selectivities of 0.72 to 0.87 and 0.33 to 0.63 respectively, for corresponding feed pressures of 0.14 to 0.41 MPa. This indicates that both of the polymers are more selective for N2 and should not be used in CO2 sensing applications as confounding gas permeants, specifically N2, will interfere with the sensing element. / Graduate / 0428 / 0495 / 0542 / ben.t.davies@gmail.com

The mitigation of greenhouse gases, like CO2 is a challenging aspect for our society. A strategy to hamper the constant emission of CO2 is utilizing carbon capture and storage technologies. CO2 is sequestrated in subsurface reservoirs. However, these reservoirs harbor the risk of leakage and appropriate geophysical monitoring methods are needed. A crucial aspect of monitoring is the assignment of measured data to certain events occurring. Especially if changes in the measured data are small, suitable statistical methods are needed. In this thesis, a new statistical workflow based on cluster analysis is proposed to detect similar transient electromagnetic signals. The similarity criteria dynamic time warping, the autoregressive distance, and the normalized root-mean-square distance are investigated and evaluated with respect to the classic Euclidean norm. The optimal number of clusters is determined using the gap statistic and visualized with multidimensional scaling. To validate the clustering results, silhouette values are used. The statistical workflow is applied to a synthetic data set, a long-term monitoring data set and a repeat measurement at a pilot CO2-sequestration site in Brooks, Alberta.