Using analytical and numerical modeling to assess deep groundwater monitoring parameters at carbon capture, utilization, and storage sites

• Main Author: Porse, Sean Laurids
• Format: Others
• Published: 2014
• Subjects: Carbon capture; Utilization and storage; Groundwater; Monitoring network development; Enhanced oil recovery; Analytical modeling; Numerical modeling
• Online Access: http://hdl.handle.net/2152/23913

Carbon Dioxide (CO₂) Enhanced Oil Recovery (EOR) is becoming an important bridge to commercialize geologic sequestration (GS) in order to help reduce anthropogenic CO₂ emissions. Current U.S. environmental regulations require operators to monitor operational and groundwater aquifer changes within permitted bounds, depending on the injection activity type. We view one goal of monitoring as maximizing the chances of detecting adverse fluid migration signals into overlying aquifers. To maximize these chances, it is important to: (1) understand the limitations of monitoring pressure versus geochemistry in deep aquifers (i.e., >450 m) using analytical and numerical models, (2) conduct sensitivity analyses of specific model parameters to support monitoring design conclusions, and (3) compare the breakthrough time (in years) for pressure and geochemistry signals. Pressure response was assessed using an analytical model, derived from Darcy's law, which solves for diffusivity in radial coordinates and the fluid migration rate. Aqueous geochemistry response was assessed using the numerical, single-phase, reactive solute transport program PHAST that solves the advection-reaction-dispersion equation for 2-D transport. The conceptual modeling domain for both approaches included a fault that allows vertical fluid migration and one monitoring well, completed through a series of alternating confining units and distinct (brine) aquifers overlying a depleted oil reservoir, as observed in the Texas Gulf Coast, USA. Physical and operational data, including lithology, formation hydraulic parameters, and water chemistry obtained from field samples were used as input data. Uncertainty evaluation was conducted with a Monte Carlo approach by sampling the fault width (normal distribution) via Latin Hypercube and the hydraulic conductivity of each formation from a beta distribution of field data. Each model ran for 100 realizations over a 100 year modeling period. Monitoring well location was varied spatially and vertically with respect to the fault to assess arrival times of pressure signals and changes in geochemical parameters. Results indicate that the pressure-based, subsurface monitoring system provided higher probabilities of fluid migration detection in all candidate monitoring formations, especially those closest (i.e., 1300 m depth) to the possible fluid migration source. For aqueous geochemistry monitoring, formations with higher permeabilities (i.e., greater than 4 x 10⁻¹³ m²) provided better spatial distributions of chemical changes, but these changes never preceded pressure signal breakthrough, and in some cases were delayed by decades when compared to pressure. Differences in signal breakthrough indicate that pressure monitoring is a better choice for early migration signal detection. However, both pressure and geochemical parameters should be considered as part of an integrated monitoring program on a site-specific basis, depending on regulatory requirements for longer term (i.e., >50 years) monitoring. By assessing the probability of fluid migration detection using these monitoring techniques at this field site, it may be possible to extrapolate the results (or observations) to other CCUS fields with different geological environments. === text