Advanced Correlations for Predicting Wax Precipitation in Crude Oil: A Study on Melting Point and Solid-State Transition Temperatures - Khussainova, Alfiya; Ismailova, Jamilyam; Moldabayeva, Gulnaz; Bekbau, Bakbergen; Delikesheva, Dinara; Zhumanbetova, Nargiz; Ismailov, Abdulakhat; Bakesheva, Aigul (Engineering, Technology & Applied Science Research, 2025)
This study presents an in-depth investigation into the fusion properties, specifically the melting point and solid-state transition temperature, of crude oil samples from five distinct fields in Kazakhstan. These properties are critical for understanding and predicting wax precipitation, which poses significant challenges in the petroleum industry, particularly in cold climates where wax deposition can obstruct pipelines. Using advanced analytical techniques, including gas chromatography and pour point testing, new correlations were developed to more accurately predict these fusion properties for Kazakhstani crude oil. The proposed correlations outperform the existing models, offering closer alignment with the experimental data across a wide range of hydrocarbon compounds. The novelty of this research lies in its tailored approach, which integrates experimental data, existing predictive models, and Python programming to develop a region-specific solution for Kazakhstani crude oil. By addressing the limitations of generalized models, the study highlights the importance of adapting predictive frameworks to specific oil compositions and regional conditions. These findings have substantial implications for the optimization of crude oil transportation and storage in cold environments, where wax deposition is a prevalent issue. The improved accuracy of the proposed correlations enables better predictability of wax-related flow assurance problems, contributing to more efficient and safer operations in the oil and gas industry. Furthermore, this work establishes a robust methodological framework that can be extended to other crude oil types and operational scenarios, paving the way for advancements in predictive modeling of wax precipitation under diverse environmental conditions.