Development of predictive model for determination of paraffin deposition in Kazakh crude oil

2024

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DOI 10.15587/1729-4061.2024.306971
Data confidence 70 medium
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Abstract

Kazakhstan's crude oil contains significant amounts of heavy hydrocarbons that solidify as wax at lower temperatures, leading to reduced flow rates and potential blockages in pipelines. Paraffin, a substantial component of crude oil, crystallizes below its pour point, posing challenges for maintaining efficient crude oil transportation. Addressing wax deposition is essential for optimizing oil flow and meeting global energy demand. This article presents an innovative method for forecasting paraffin deposition in Kazakh crude oil, using its chemical composition and thermobaric conditions. The methodology encompassed data analysis, ASTM-standard laboratory tests, and computations employing modified equations for fusion properties calculation. Outcomes comprise computed temperatures, enthalpy, and heat capacity related to melting, along with revised correlations for melting and pour points specific to Kazakh crude oils. The melting point correlation was modified to fit the properties of Kazakh crude oils, resulting in standard deviation of 0.55 %. Computed pour points for hydrocarbons improved by 17 % respectively. As a result of the research a novel software tool was developed and evaluated, highlighting the project’s contribution in providing an adjusted thermodynamic model for paraffin deposition forecasting. Comparisons between the developed software, PVTSim, and field data showed the wax appearance temperature (WAT) predictions closely aligned, with 0.74 % of error. This numerical tool shows potential in predicting wax deposition, thus aiding in the planning of oil production and refining processes in Kazakhstan. The importance of this work extends to its potential economic impact on companies and the nation, as well as its environmental benefits by facilitating eco-friendly planning of oil production facilities. Future research could expand on these findings to further enhance predictive models of paraffin precipitation across diverse conditions