Waterflooding as a secondary oil recovery method is still the most commonly used technique for fluid injection since 1865. Previously the main concern was the amount of water injection into the reservoir while recently researchers have figured out that the quality of the injected water is as important as the quantity and it should be monitored. This technology is known as low salinity water injection, LoSal, Smart Waterflood and Advanced Ion Management in the literature.
Evidence from laboratory studies, supported by several field tests, has distinctly shown that injecting low salinity water has a significant impact on oil recovery. Although there are many LoSal experimental results reported in the literature, the publication on modeling of this process is scarce. However, practical application of IOR processes requires a predictive model.
Since mechanistic modeling, for the low salinity water injection, is not seen in the literature, in this project, we investigate a mechanistic modeling approach for the low salinity water injection. We believe no matter the manner and details of mechanism, it ought to be all about ions. The key objective in this project is to link the water-rock chemistry to changes in the state of a rock. For example, if the dominant mechanism for low salinity water injection is the wettability alteration, it will be viewed as follows in this project:
Since geochemical reactions are the basis for this mechanistic modeling, to properly model LoSal, we would need a geochemical engine to handle geochemical reactions in the reservoir. Toward this goal we are coupling IPhreeqc, available in USGS geochemical module, with UTCHEM, The University of Texas’s in-house chemical flooding simulator.