OPTIMAL COMPLEXITY IN RESERVOIR MODELING OF AN EOLIAN SANDSTONE FOR CARBON SEQUESTRATION SIMULATION

Geologic Carbon Sequestration (GCS) is a proposed means to reduce atmospheric carbon dioxide (CO₂). At a GCS site, given the type and accessibility of geologic characterization data, different reservoir models can be built ranging from simple to complex. For example, petrophysical properties can be alternatively modeled assuming homogeneity or heterogeneity, the later requiring advanced modeling techniques supported by additional (detailed) data. In GCS where the cost of data collection needs to be minimized, what are the right type of data to be collected & analyzed? In Wyoming, GCS is proposed for the Nugget Sandstone, a deep (>13,000 ft) saline aquifer exhibiting permeability heterogeneity. Using subsets of characterization data, this study builds a suite of increasingly complex reservoir model families, including a homogeneous model (FAM1), a stationary petrophysical model ignoring facies (FAM2), a stationary facies model with sub-facies variability (FAM3), and a non-stationary facies model (with sub-facies variability) conditioned to a soft depositional model (FAM4). These families, representing alternative conceptual models built with increasing amount of data, were simulated with the same CO₂ injection test (50 year duration at 1/10 Mt per year and 3000 year monitor). Based on the Design of Experiment (DOE), an efficient sensitivity analysis (SA) is conducted for all model families, systematically varying uncertain input parameters while assuming identical production scenario (i.e., well configuration, rate, BHP constraint) and boundary condition (i.e., model is part of a larger semi-infinite system where the injected gas can flow out). Results are compared among the families to identify parameters that have 1st order impact on select outcomes (e.g., CO₂ storage ratio). The comparison indicates that the geologic modeling decision influences the important uncertainty factors. Given more complexity, Based on these results, a response-surface (RS) analysis generates prediction envelopes of the same outcomes, which are further compared among the families. Future work will assess other depositional environments in an attempt to derive a set of geologic modeling guidelines, one for each environment.