Conformance improvement is the key to success in most enhanced oil recovery (EOR) processes, especially CO2 foaming or steamflooding. Despite technical and economical restrictions, foam has been used as dispersions of microgas bubbles in the reservoir to help improve mobility. Steam-foam has many applications in the industry, including but not limited to heavy oil reservoirs, which are an important part of the future energy supply. Steam-foam applications have been used to help prevent steam channeling and steam override, thus improving overall sweep efficiency, not only in continuous steam but also in cyclic steam injection processes. Because of the high temperatures achieved during steamfloods, a robust understanding of chemistry, including the thermal stability of surfactants, is important.

The effectiveness and therefore economics of the steam-foam process are strongly dependent on surfactant adsorption and retention. With that in mind, effective sizing of the foam injected requires a good understanding of the process. In this study, a reservoir simulator is used in which surfactant transport is modeled, with surfactant availability determined by a combination of surfactant adsorption, surfactant thermal decomposition, and oil partitioning due to temperature. A robust commercial optimization and uncertainty tool is coupled with the reservoir simulator to generate the scenarios defined by control variables for optimization and uncertainty parameters for sensitivity analysis.

The degree of mobility reduction is interpolated as a product of factors that include aqueous surfactant type and concentration, the presence of an oil phase, and the capillary number. An empirical foam modeling approach is employed with foam mobility reduction treated by means of modified gas relative permeability curves. The simulation results including the sensitivity of the parameters and controlling agents, providing a better understanding on the influence of surfactant adsorption and thus the amount of chemicals to be used are presented and discussed to serve as a guide for future applications.

It is not easy to find documented examples of realistic optimization studies where significance of each control and uncertainty parameter is outlined and discussed using a realistic reservoir model. The simultaneous use of optimization and uncertainty led to a better understanding and thus control of decision variables in varying ranges of uncertainty that will be useful in analyzing prospective assets.

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