Optimizing demulsifier selection for crude oil dehydration: a fuzzy TOPSIS-based multi-criteria decision-making approach

Abstract The effective separation of water from crude oil is essential for maintaining oil quality, optimizing production efficiency, and minimizing operational challenges in the petroleum industry. However, selecting an optimal demulsifier remains a complex problem due to the need to balance separation efficiency, environmental impact, cost-effectiveness, and ease of application. This study addresses this challenge by applying the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (FTOPSIS) method, a robust multi-criteria decision-making (MCDM) approach, to evaluate and rank demulsifiers under uncertain conditions systematically. Four commercial demulsifiers, Alcopol 500, Polymer-based Demulsifier, Nalco Champion EC7135A, and Schlumberger’s ClearPhase, were assessed using fuzzy logic to quantify expert evaluations. The results indicate that Nalco Champion EC7135A achieved the highest closeness coefficient (0.751), making it the top-ranked demulsifier due to its superior separation efficiency and lower environmental impact. Alcopol 500 ranked second (0.708), followed by the Polymer-based Demulsifier (0.692) and Schlumberger’s ClearPhase (0.619). Compared to conventional selection approaches that often rely on trial-and-error or single-criterion assessments, this study demonstrates that Fuzzy TOPSIS provides a structured, quantitative, and transparent approach to demulsifier selection. The novelty of this research lies in its integration of fuzzy logic with MCDM for demulsifier evaluation, offering a more precise and adaptable framework for decision-making in oilfield operations. These findings have significant practical implications for the petroleum industry, as they enable more data-driven and sustainable selection processes, reducing operational costs and improving crude oil dehydration efficiency. Future research should focus on incorporating real-time operational data, expanding the evaluation to emerging eco-friendly demulsifiers, and integrating predictive machine learning models to enhance the accuracy of the selection process.