Abstract
One of the problems plaguing many Oil and Gas operators is the formation of stable emulsions in their separation systems. Emulsions are highly specific colloidal mixtures of two immiscible fluids, such as water and oil, in the presence of some stabilizing agent. The aim of this work is to assess and predict the stability of emulsions observed in the North Coast Marine Acreage (NCMA) of Trinidad and Tobago. The specific objectives are to (1) Determine the stabilizing factors of emulsions observed in NCMA via laboratory work, (2) Establish a model for predicting emulsion stability expected of a surfactant stabilized Pickering emulsion system and (3) Compare the results of laboratory testing to model results using NCMA emulsion samples. Two types of emulsions were found in the NCMA samples: (1) a cloudy white emulsion and (2) a thick grey rag emulsion. A series of laboratory testing was conducted to determine the stabilizing factors. Two Machine Learning algorithms were attempted, k Nearest Neighbor and Random Forest Classification, to predict the emulsion result. The Machine Learning models developed were able to predict an outcome of emulsion stability proving the concept that these algorithms can be used for practical assessment. However, the accuracy compared to the laboratory findings was significantly lower than expected at only 45%. This proves that the behaviour of the NCMA emulsion system is unique with few universally applicable trends and warrants further detailed assessment.
NSTL
Elsevier