CO₂ Modeling in a Deep Saline Aquifer: A Predictive Uncertainty Analysis Using Design of Experiment

Abstract

When field data are limited, stratigraphic models are used instead of detailed, fully heterogeneous models (FHM) to represent deep saline aquifers in numerical simulations of CO2 storage. This study evaluates parameter sensitivity and prediction uncertainty of three stratigraphic models of decreasing complexity (i.e., facies, layered, formation) against that of a FHM. For select simulation outcomes (i.e., CO2 mass profiles, gas plume shape, brine leakage), parameter sensitivity and associated prediction uncertainty are compared among the models, with the FHM serving as a reference. The analysis is conducted using the computationally efficient design of experiment (DoE) and response surface (RS) methodology. Results suggest that when a competent caprock exists (permeability <1 × 10(-4) mD), the facies and layered models are capable of capturing the most important sensitivity parameters of the FHM, that is, residual gas saturation, heterogeneity variance, and salinity. Using the important parameters identified by DoE, RS modeling then suggests that the same two models also capture the ranges of predictions in mobile gas, trapped gas, and brine leakage. The formation model is less accurate in capturing the sensitivity and prediction ranges of the FHM, although it is accurate in predicting brine leakage into the overlying formation.

Uncertainty analysis of carbon sequestration in an active CO2-EOR field

“…Geological uncertainties associated with heterogeneity have attracted significant attention in recent years, with many studies suggesting heterogeneity as the primary source of uncertainty in model forecasts of CO 2 storage capacity, CO 2 plume extent, CO 2 migration and storage performance, and CO 2 leakage (Deng et al, 2012;Li and Zhang, 2014;Tian et al, 2014;Dai et al, 2014a;Liu and Zhang, 2011). Probabilistic analysis is a robust approach to quantify such geological uncertainties and how they propagate to risk assessment results, typically in the form of specific probability density functions (PDFs) for key uncertain input parameters.…”

“…A RSM illustrates the relationship between the response variable yield (i.e., performance measure or quality characteristics of the products or processes) and one or more independent variables (i.e., input variables) (Myers and Montgomery, 2002;Bacon et al, 2015). The RSM has been recently applied to geologic CO 2 storage processes, including caprock failure assessment due to CO 2 storage (Rohmer and Bouc, 2010;Wriedt et al, 2014); CO 2 flooding design optimization for CO 2 -EOR (Ghomian et al, 2010;Yao and Ji, 2010;Dai et al, 2014b); uncertainty assessment of CO 2 storage in deep saline aquifers (Liu and Zhang, 2011); and uncertainty quantification of CO 2 sequestration with oil recovery (Dai et al, 2014a,c). Compared to the computational-expensive conventional Monte Carlo approach and the relatively difficult implementation of the PCE approach, the RSM was adapted in this study to quantify uncertainties of key parameters associated with CO 2 sequestration and oil recovery.…”

Simulation of industrial-scale CO2 storage: Multi-scale heterogeneity and its impacts on storage capacity, injectivity and leakage

“…GCS is a very complex system Friedmann, 2007;Celia and Nordbotten, 2009;Han et al, 2010;Ide et al, 2010), and many geologic (Ambrose et al, 2008;Michael et al, 2010), hydrologic (Bachu et al, 1994;Tsang et al, 2008;Birkholzer and Zhou, 2009;Person et al, 2010), geochemical Kharaka et al, 2006;Gaus, 2010;Wilkin and Digiulio, 2010;Deng et al, 2010;Carroll et al, 2010;Lu et al, 2009;Shiraki and Dunn, 2000), and geomechanical (Lucier et al, 2006;Rutqvist et al, 2008) factors impact CO 2 storage. Heterogeneity in porosity and permeability of geologic reservoirs has a strong influence on CO 2 injection rate (Stauffer et al, 2009a), CO 2 plume migration (Flett et al, 2007), storage capacity (CSLF Task Force, 2008), potential leakage and risk assessment (Viswanathan et al, 2008;Stauffer et al, 2009bStauffer et al, , 2011bFlett et al, 2007;Liu and Zhang, 2011;Zhou et al, 2009;Deng et al, 2009;Wilson et al, 2003). Field-scale simulations of GCS particularly need to incorporate heterogeneity structure (Doughty and Pruess, 2004;Doughty et al, 2008;Schnaar and Digiulio, 2009) to gain insights into sitespecific issues.…”

“…However, less attention has been paid to CO 2 leakage through cap-rocks. Liu and Zhang (2011) used a heterogeneous reservoir model combined with design of experiment and response surface analysis to study predictive uncertainty in GCS, including brine leakage. Uncertainty analysis of CO 2 leakage can supply important information for how, where and when to monitor for both brine and CO 2 leakage.…”

Statistical based hydromechanical models to estimate poroelastic effects of CO2 injection into a closed reservoir

“…Four poroelastic responses were recorded at the simulations representing a combination of low, medium, and high values of independent values using the experimental design. Deterministic data using numerical simulations have been used for developing statistical‐based models and response surfaces for objectives such as CO 2 ‐EOR optimization . The experimental design can be applied to select the optimal number of unique deterministic runs on which the proxy model is built.…”