Optimisation of Polymer Flooding in a Heterogeneous ReservoirConsidering Geological and History Matching Uncertainties

Abstract

Polymer flooding offers the potential to recover more oil from reservoirs but requires significant investmentswhich necessitate a robust analysis of economic upsides and downsides. Key uncertainties in designinga polymer flood are often reservoir geology and polymer degradation. The objective of this study is tounderstand the impact of geological uncertainties and history matching techniques on designing the optimalstrategy and quantifying the economic risks of polymer flooding in a heterogeneous clastic reservoir. We applied two different history matching techniques (adjoint-based and a stochastic algorithm) to matchdata from a prolonged waterflood in the Watt Field, a semi-synthetic reservoir that contains a wide rangeof geological and interpretational uncertainties. An ensemble of reservoir models is available for the WattField, and history matching was carried out for the entire ensemble using both techniques. Next, sensitivitystudies were carried out to identify first-order parameters that impact the Net Present Value (NPV). Theseparameters were then deployed in an experimental design study using a Latin Hypercube to generate trainingruns from which a proxy model was created. The proxy model was constructed using polynomial regressionand validated using further full-physics simulations. A particle swarm optimisation algorithm was then usedto optimize the NPV for the polymer flood. The same approach was used to optimise a standard water floodfor comparison. Optimisations of the polymer flood and water flood were performed for the history matchedmodel ensemble and the original ensemble. The sensitivity studies showed that polymer concentration, location of polymer injection wells and timeto commence polymer injection are key to optimizing the polymer flood. The optimal strategy to deploythe polymer flood and maximize NPV varies based on the history matching technique. The average NPVis predicted to be higher in the stochastic history matching compared to the adjoint technique. The variancein NPV is also higher for the stochastic history matching technique. This is due to the ability of thestochastic algorithm to explore the parameter space more broadly, which created situations where the oilin place is shifted upwards, resulting in higher NPV. Optimizing a history matched ensemble leads to anarrow variance in absolute NPV compared to history matching the original ensemble. This is because theuncertainties associated with polymer degradation are not captured during history matching. The result ofcross comparison, where an optimal polymer design strategy for one ensemble member is deployed to the other ensemble members, predicted a decline in NPV but surprisingly still shows that the overall NPV ishigher than for an optimized water food. This indicates that a polymer flood could be beneficial comparedto a water flood, even if geological uncertainties are not captured properly.