Alkaline Flooding: Enhanced Oil Recovery
During alkaline flooding, the indigenous natural acids present in the crude oil - tend to migrate towards the oil-water interface; and tend to chemically react with alkali – in order to produce ‘organic salts’, which may remain to be ‘water-soluble’ and ‘surface-active’. In this context, why does ‘the reduction in IFT’ get stopped abruptly – as soon as – ‘the mobilization of residual oil globules’ become immobile?
Feasible to capture the transition of the above physics @ laboratory-scale using experimental investigations (where, the distances well as times remain not long enough to achieve local equilibrium)?
In other words, would it remain feasible to capture the ‘dynamic-tension minima’ for such acidic oils (during alkaline flooding) – caused by both ‘mass transfer’ and ‘interfacial sorption resistances’ – that will enable the estimation of - ‘dynamic IFTs’ and in turn, the ‘critical capillary number’ - for the displacement of oil globules - as a function of space and time – on continual injection of an alkaline solution?
Could ML/AI enhance the benefits of alkaline flooding by optimizing the reaction between sodium hydroxide and the naturally occurring organic acids present in crude oil that result in soap production @ oil-water interface?
How exactly ML/AI would be able to minimize the use of strong alkalis,
which is generally associated with 'production capacity loss' and 'scaling issues'?
Feasible to deduce the 'threshold TAN' by ML/AI for a given reservoir?
Also, how efficient ML/AI would remain to be - in deducing appropriate co-solvents and co-surfactants – in order to reduce IFT and mobility ratio during ASP flooding?
To what extent, the role of ‘low-salinity’ and ‘nano-particles’ be coupled with alkali-flooding towards enhancing the performance of heterogeneous reservoirs under adverse conditions?
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