Gas injection processes suffer from poor volumetric sweep efficiency due to phenomena such as viscous fingering caused by density contrast between the injected gas and the displaced fluid, channeling due to heterogeneities in the reservoir and gravity override due to the inherent lower density of gas. One possible way to overcome these negative effects is by foaming the gas. Foam can be defined as a dispersion of gas separated by lamellae in a continuous liquid phase. Lamellae stability is enhanced by adding surfactants to the aqueous phase (Lake, 2014). The underlying idea is that the pitfalls of gas injection can be overcome by trapping the gas and therefore reducing its mobility. The benefits of foam flooding have been reported by several authors (Almaqbali et al., 2017, Li et al., 2009, Chalbaud et al., 2002, Patil et al., 2018 ) however, to implement field scale projects, an accurate representation of foams in models that can be applied to reservoir simulators is of paramount importance. In this sense, two main families of foam modelling exist today. One family referred as population balance models, treat foam texture and bubble size explicitly and another family, that treats foam texture implicitly by applying a mobility reduction factor to gas mobility.
In the first part of this document, a model that treats foam texture implicitly will be used to evaluate foam performance in a heterogenous reservoir consisting of two layers with different permeabilities. The model known as STARS (Computer Modelling Group, 2010) reduces gas mobility using semiempirical functions that represent the physics governing foam texture. One of the functions used by the model is the dryout function, which represents the effects of water saturation on foam coalescence. This function is tuned by a couple parameters, namely, fmdry and epdry that control the water saturation at which foam dries out and how fast it dries out, in other words, how abrupt its collapse is. Farajzadeh et. al, (2015) found that these parameters were permeability dependent.
Inspired by these findings, in the modelling section of this project, foam performance in a two layer reservoir with different permeabilities under varying injection conditions is assessed. Each layer will have its unique set of parameters, namely, fmmob, epdry and fmdry. We are interested in seeing the effects that epdry has on foam performance and the effects of having vastly contrasting foam strengths between layers (large fmmob contrast). The results suggest that epdry can have significant impact on foam performance in the high quality regime. Interestingly, it was found that large fmmob contrast reduce vertical conformance and can reduce recovery efficiency in the high quality regime.
It is known that oil can have negative impact on foam stability, however most studies treat gas and oil as different phases. Under miscible conditions, oil and gas become one phase and the extent to how oil destabilizes foam is not clearly understood. Kahrobaei et. al (2017) found in core flood experiments a unique rheological behavior in foams with miscible mixtures of Carbon Dioxide – Decane. Their experiments showed three distinguishable apparent viscosity regimes depending on Carbon dioxide fraction. Regime 1, in which apparent viscosity increased with increasing CO2 fraction; Regime 2 in which apparent viscosity decreased with increasing CO2 fraction; and Regime 3 in which apparent viscosity is constant independently of the CO2 molar composition.
Inspired by Kahrobaei et. al, (2017) findings, the second part of the project attempts to investigate, using microfluidics, the effects in foam texture caused by compositional changes in an AOS surfactant solution and a miscible Carbon Dioxide – Decane mixture. Unfortunately, due to set-up limitations and inability to reliably mix the components, the experiments were inconclusive. Nonetheless, valuable lessons learnt are presented that will facilitate the experimental approach if someone decides to continue with this line of investigation.