An enhanced-oil-recovery (EOR) pilot test has multiple goals, among them to be profitable (if possible), demonstrate oil recovery, verify the properties of the EOR agent in situ, and provide the information needed for scaleup to an economical process. Given the complexity of EOR processes and the inherent uncertainty in the reservoir description, it is a challenge to discern the properties of the EOR agent in situ in the midst of geological uncertainty. We propose a numerical case study to illustrate this challenge: a polymer EOR process designed for a 3D fluvial-deposit water/oil reservoir. The polymer is designed to have a viscosity of 20 cp in situ. We start with 100 realizations of the 3D reservoir to reflect the range of possible geological structures honoring the statistics of the initial geological uncertainties. For a population of reservoirs representing reduced geological uncertainty after 5 years of waterflooding, we select three groups of 10 realizations out of the initial 100, with similar water-breakthrough dates at the four production wells. We then simulate 5 years of polymer injection. We allow that the polymer process might fail in situ and viscosity could be 30% of that intended. We test whether the signals of this difference at injection and production wells would be statistically significant in the midst of geological uncertainty. Specifically, we compare the deviation caused by loss of polymer viscosity with the scatter caused by the geological uncertainty using a 95% confidence interval. Among the signals considered, polymer-breakthrough time, minimum oil cut, and rate of rise in injection pressure with polymer injection provide the most-reliable indications of whether a polymer viscosity was maintained in situ.

An enhanced-oil-recovery (EOR) pilot test has multiple goals, among them to demonstrate oil recovery, verify the properties of the EOR agent in-situ, and provide the information needed for scale-up to an economic process. Given the complexity of EOR processes and the inherent uncertainty in the reservoir description, it is a challenge to discern the properties of the EOR agent in-situ in the midst of geological uncertainty. We propose a general workflow and present a case study to illustrate this challenge: a polymer EOR process in a 2D layer-cake reservoir. The polymer is designed to have a viscosity of 60 cp in-situ. There is uncertainty in the reservoir description, represented here by a range of values of Dykstra Parsons coefficient and different spatial arrangements of layers. We allow that the polymer process might fail in-situ and viscosity could be 20% of that intended. We test whether the signals of this difference at injection and production wells would be statistically significant in the midst of the geological uncertainty. Specifically, we compare the deviation caused by loss of polymer viscosity to the scatter caused by the geological uncertainty using the statistical 95% confidence interval. Among the signals considered, the ‘rate of rise in injection pressure with polymer injection’ and ‘maximum injection pressure in the injector’ give the most reliable indications of whether a polymer viscosity was maintained in-situ. If unintended and uncontrolled fracturing of the injection well is considered likely during polymer injection, however, injection pressure may be an unreliable indicator of in-situ polymer viscosity. In that case a diagnostic fracture-injection/falloff test could produce the needed indication of polymer viscosity in-situ. ‘Polymer breakthrough time’ and ‘cumulative oil production at the end of process’ give indications of polymer in-situ loss in some of the cases. With a more severe viscosity loss, e.g. 90% or worse, these signals give a statistically significant indication of loss of polymer viscosity in all of the cases.