This paper outlines a technique based on 13C nmr that relates the chemical structure of the demulsifier chemical to demulsification performance. Principal component analysis methods were used to handle the extensive data generated from nmr and bottle test results. Over one hundred demulsifiers grouped or clustered into only a few distinctly different chemical groups as characterized by the nmr. The similar chemical types fwd similar demulsification performance which means that demulsifier evaluations can be made on the basis of demulsifier chemistry and that only a few of the distinctly different ones need to be tested before optimization can begin.
Since the nmr chemical clwracterization takes only a fraction of the time of a bottle test it is now possible to more rapidly focus on optimization of demulsifier dosage. The method is useful for operators as a quality control evaluation of the demulsifier chemicals they use and for the suppliers as a way to significantly decrease the number of demulsifiers that are evaluated in the field before work begins on optimization.
Chemical demulsification is commonly used to separate water from heavy oils in order to produce a fluid suitable for pipelining (typically less than 0.5 percent solids and water). A wide range of chemical demulsifier are available in order to effect this separation. In principle, a complete chemical and physical characterization of both the demulsifier and the emulsion to be separated would allow one to develop a fundamental understanding of the demulsification mechanism and therefore to optimize the demulsifier selection or allowsynthesis of tailored demulsifiers for separation of particular emulsions.
In practice, this is not possible because of the wide range of factors that can affect demulsifier performance. Aside from demulsifier chemistry, factors such as oil type, the presence and wettability of solids, oil viscosity and the size distribution of the dispersed water phase can all influence demulsifier effectiveness.
Over 121 different demulsifiers (or demulsifier bases) and six different produced oil samples were evaluated. Clearly, it would be prohibitive to develop detailed chemical and physical analyses of such a large number of demulsifiers and as indicated above, such detailed analyses of the demulsifiers may not completely account for their performances on different oil emulsion samples. However, by elating performance on a given emulsion with chemical composition, it would be possible to rapidly optimise demulsifier selection by testing selected members of chemically distinct groups and doing more detailed bottle tests on members of the groups that showed the best results.
Principal component analysis (PCA) is one method that allows one to relatively quickly develop correlations between similar members of large data sets. These groupings or clusters of members of data sets are based on an analysis of the variances (in the measured data set) amongst them and precludes the need for extensive fundamental analyses of each of the members.
Principal Component Analysis may be performed on data sets where the samples are described by a variety of independent or dependent variables.
