Efficient Investigation of Uncertainties in Flood Design Parameters for Coupled CO2 Sequestration and Enhanced Oil Recovery

Abstract

A compositional reservoir simulation study was performed to investigate enhanced oil recovery and sequestration of carbon dioxide. Maximizing profit from oil recovery and maximizing the amount of carbon dioxide stored in the reservoir are competing goals and both will be important in the future. Both depend on large number of parameters and the strategy used to flood the reservoir. A very large number of simulations are required to understand and evaluate different strategies for each reservoir and for each realization of the properties of a particular reservoir. In this study the effects of variety of flood design variables on both EOR and sequestration objectives were investigated in sandstone and carbonate reservoirs separately. Experimental design and the method of response surfaces were used as tools to perform this study in a systematic and efficient way. Design parameters such as well spacing, different injection and production schemes, various well control techniques, and different mobility control methods were selected for study. By applying fractional factorial design and D-optimal methods, simulation cases were selected to study the effect of the parameters for each scenario. The amount of CO2 stored at the end of the oil recovery process and the net present value of the each sensitivity case were considered as the two decision criteria. Economic analysis for all of these cases were performed based on the necessity to account for CO2 storage factors such as capture and transportation costs, and possible CO2 tax credits for storage. Response surface analysis was utilized to determine the best strategy based upon these decision criteria for different type of the reservoirs. The result using this approach was similar to the result from an exhaustive simulation study, but took much less computation time and effort. An approach that is both realistic and feasible, such as the one used in this study, will be needed for future simulation studies because of the increasing importance of CO2 geological storage, the extremely wide variety of reservoir conditions of potential interests, the need to understand and reduce uncertainties, the need to find better operational strategies, and the uncertainty in future economic and regulatory incentives.

Application of Real Options to Valuation and Decision-making in the Petroleum E&P Industry

“…The reservoir porosity heterogeneity field is generated according to the relationship between permeability and porosity for a typical carbonate reservoir as shown in the following equation (Ghomian, 2010):…”

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

“…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.…”

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

“…The next step would be assigning the type of response surface model (e.g., linear and quadratic polynomial models, and Kriging models). Different response surface models have been used as proxies for uncertainty quantification studies relevant to oil and gas as well as CO 2 sequestration . In this study, a quadratic proxy model is used mainly for predicting poroelastic responses.…”

“…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.…”