(736e) How Do You Make Key Risk Management Decisions for Engineered Geologic Carbon Storage Systems in Face of Uncertainties?

Authors: 
Pawar, R. - Presenter, Los Alamos National Laboratory

Risk assessment and risk management of engineered geologic CO2 storage systems is an area of active investigation. The potential geologic CO2 storage systems currently under consideration primarily include deep saline aquifers, depleted oil/gas reservoirs and deep un-mineable coal seams, etc. These geologic systems are inherently heterogeneous and have limited to no characterization data. Effective risk management decisions to ensure safe, long-term CO2 storage requires assessing and quantifying risks while taking into account the uncertainties in a storage site’s characteristics. The key decisions are typically related to definition of area of review, effective monitoring strategy and monitoring duration, potential of leakage and associated impacts, etc. To date, risk assessments performed at sequestration field sites (both pilot and large-scale) have been mostly qualitative, based primarily on FEPs (Features, Events and Processes) analysis, or semi-quantitative. A quantitative methodology for predicting a sequestration site’s long-term performance is critical for making key decisions necessary for successful deployment of commercial scale geologic storage projects where projects will require quantitative assessments of potential long-term liabilities. Quantitative assessment typically incorporates site-specific conceptual models for critical scenarios which are defined based on FEPs analysis followed by quantitative predictions of long-term evolution of a storage site in response to CO2 injection incorporating computational models that can range from process level models to system level models.

The National Risk Assessment Partnership (NRAP) is a US-Department of Energy (US-DOE) effort focused on developing a defensible, science-based methodology for quantifying long-term performance of geologic CO2 storage sites. The quantitative approach is based on integrated assessment modeling (IAM) paradigm which treats a geologic CO2 storage site as a system made up of various linked subsystems.  The subsystems include storage reservoir, seals, potential leakage pathways (such as wellbores, natural fractures/faults) and receptors (such as shallow groundwater aquifers).  CO2 movement within each of the subsystems and resulting interactions are captured through reduced order models (ROMs). The ROMs capture the complex physical/chemical interactions resulting due to CO2 movement and interactions but are computationally extremely efficient. The computational efficiency allows for performing Monte Carlo simulations necessary for quantitative probabilistic risk assessment. The approach used to develop ROMs as part of NRAP included performing detailed numerical simulations of CO2 storage reservoirs, wellbores and aquifers using multiple models and numerical simulators. The simulators included LBNL’s TOUGH2 suite, LANL’s FEHM, PNNL’s STOMP and LLNL’s NUFT. Multiple (100s to 1000s) sets of simulation runs with detailed numerical models were performed to capture effects of various uncertain parameters. The numerical simulation results for each of the subsystems were used to develop reduced order models in form of abstracted functions or lookup tables. The resulting ROMs were subsequently incorporated in an IAM using a system modeling platform.

We have used the IAM to predict long-term performance of geologic CO2 sequestration systems and to answer questions related to probability of leakage of CO2 through wellbores, impact of CO2/brine leakage into shallow aquifer, etc.  Answers to such questions are critical in making key risk management decisions. A systematic uncertainty quantification approach has been used to understand how uncertain parameters associated with different subsystems (e.g., reservoir permeability, wellbore cement permeability, wellbore density, etc.) impact the overall site performance predictions and ultimately affect key risk management decisions.