Resolution of water-in-oil emulsion is a major crude oil processing requirement in oil industry. To improve the quality of the oil and fulfill regulatory requirements numerous chemical demulsifiers of varying efficiencies and effectiveness have been developed over the years. In this study, we have investigated the effects of water content, temperature, and different concentrations of Sodium Methyl Ester Sulfonate (SMES) on emulsion viscosity profiles and stability under distinct levels of salinities. The water content was measured with the American Standard Testing Method ASTM D4928 while SARA analysis was conducted using the ASTM D3279 and ASTM D6591 methods. The density and viscosity of the samples were measured following the ASTM D5002 and ASTM D445 techniques respectively while the emulsion stability was evaluated based on the rate of sedimentation, flocculation and coalescence from Turbiscan classic MA 2000. Refractometer with the aid of a light-emitting diode, a sapphire prism and a high-resolution optical sensor was used to measure the refractive index while interfacial tension was measured with spinning drop tensiometer. The emulsion samples were investigated at 25, 50 and 75°C. Analyses show that the interactions of the constituents of a crude oil system, the produced water system and the emulsion system play major roles in the characterization of water-in-crude oil emulsions. Hence, the stability of water-in-crude oil emulsions is related to the viscous force presented by the continuous phase, water cut and salinity.
Part of the book: Science and Technology Behind Nanoemulsions
Emulsions are metastable systems typically formed in the presence of surfactant molecules, amphiphilic polymers, or solid particles, as a mixture of two mutually immiscible liquids, one of which is dispersed as very small droplets in the other. These dispersions are unwanted occurrences in some areas, like those formed during crude oil production, but are also put into many other useful applications in the oil and gas industry, food industry, and construction industry, among others. These emulsions form when two immiscible liquids come together in the presence of an emulsifying agent and sufficient agitation strong enough to disperse one of the liquids in the other. Thermodynamically, these emulsions are unstable and thus would separate into their individual phases when left alone. To be stabilized, surface-active agents (surfactants) or solids (that act in so many ways like surfactants) ought to be used. Like many commercially available products, several pharmaceutical products are usually supplied in the form of emulsions that must be stabilized before they are being administered. Pharmaceutical emulsions used for oral administration either as medications themselves or as carriers come in form of stable emulsions. Either water-in-oil (w/o) or oil-in-water (o/w), these emulsions after formulation must be classified, majorly as stable or unstable. Only formulations that give stable emulsions are used, and the unstable ones reformulated or discarded. Classifying such emulsions using results obtained by visual observation in most cases can be very tedious and inaccurate. This necessitates the use of a more scientific and intelligent method of classification. The objective of this study is to employ support vector machine (SVM) as a new technique to classify synthetic emulsions. The study will assess the effects of nonionic surfactant (sodium monooleate) and Laponite clay (LC) on the stability of synthetic emulsions prepared using a response surface methodology (RSM) based on a Box-Behnken design. The stability of the emulsions was measured using batch test and TurbiScan, and the SVM was used to classify the emulsions into stable, moderately stable and unstable emulsions. The study showed that an increase in surfactant concentration in the presence of moderate to high concentrations of LC can provide a stable emulsion. Also, a clear classification of the emulsion samples was provided by the SVM, with high accuracy and reduced misclassifications due to human error. A higher accuracy in classification would reduce the risk of using the wrong formulation for any pharmaceutical product.
Part of the book: Science and Technology Behind Nanoemulsions