A Novel Modeling Enhancement of Emulsion in Condensate with High Aromatic

Abstract

Abstract At surface facilities, a gas field with condensate and a severe oil in water/reverse emulsion problem was observed. Due to the emulsify-prone nature of liquid production, this field suffers from Oil in Water content. Current modeling prediction tool cannot be used to predict the severity of the condensate emulsion problem because its application is restricted to black oil. This paper describes enhancements to the modeling of condensate with high aromatic mixtures. The process flow for testing the enhancements to the modeling is as follows: At first, a large number of laboratory experiments were carried out. These experiments included the measurement and characterization of the mixture's composition, as well as the formation of an emulsion despite variations in the process's parameters, such as shear rate, temperature, pH, and solid content. To develop correlations in order to enhance the applicability of inhouse Emulsion Stability Tool by changing fluid and process parameters in preparation for future emulsion monitoring and the optimization of process facilities at condensate field. The generation of output data of Turbiscan Index (TSI) and Turbidity were accomplished through the development of a multi-equation model of data sets. The following parameters have been agreed upon and will serve as the Key Performance Indicator (KPI): > 80% of measurements in the laboratory should correspond to the model's predictions, and these predictions should be validated with data from the field. Through the multi-correlation method, an improved modeling has been developed for condensate emulsion. During the validation process between the measurements taken in the laboratory and the predictions made by the model, the accuracy of the model reached 94% using 396 sets of trained data. During validation with actual field data, it achieved a perfect score of 8 out of 8 trained data points. It concluded that KPI has been achieved based on those parameters. This modeling enhancement is a novel tool that can predict emulsion in condensate with a high aromatic composition to an exceptionally high degree of accuracy. It can provide early detection of emulsion issues for corrective measurement, also known as the optimization of de-oiler dosage. It has the potential to generate added value by lowering the cost of sampling, reducing the number of laboratory tests required for bottle testing, and preventing improper facility design.