Fluid characterization and splitting the heavy fraction by using a gamma distribution model and flash calculation based on EOS: a case study of one of the Iranian oil reservoirs
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Kamyar Movagharnejad
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Fatemeh Aali
Abstract
This study explores advanced methods to improve the accuracy of hydrocarbon property predictions in oil reservoir fluids, utilizing novel data from an Iranian oil reservoir. The objectives include calculating molecular weights, specific gravity, and molar ratios of hydrocarbons (C11–C50) while evaluating the performance of models for critical properties and phase equilibria. Key approaches involve the gamma distribution and single carbon number (SCN) techniques for hydrocarbon characterization, alongside flash calculations using cubic and non-cubic equations of state, including Soave-Redlich-Kwong (SRK), Peng-Robinson (PR), and Perturbed Chain-SAFT (PC-SAFT). Empirical data were generated using the Pederson regression method and compared with predictions from the gamma distribution function. Results indicate that the gamma distribution method reduces prediction errors by 50 %, with deviations of 5.19 % compared to 10.39 % for the SCN method. Among the equations of state, PC-SAFT achieved the highest accuracy, predicting vapor phase mole fractions with a deviation of 0.0172 % and gas and liquid densities with deviations of 22.125 % and 0.24 %, respectively. The novelty of this study lies in integrating unique field data from an Iranian reservoir with advanced modeling techniques, providing a reliable framework for reservoir fluid analysis. These findings contribute to optimizing oil recovery and improving hydrocarbon prediction accuracy.
Funding source: Babol Noshirvani University of Technology
Award Identifier / Grant number: BNUT/370675/02
Acknowledgments
This work was supported by the Babol Noshirvani University of Technology [Grant number BNUT/370675/02].
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The contributions of the authors are as follows: Fatemeh Aali: Conceptualization, Methodology, Software, Validation, Writing – Review & Editing, Data Curation, Supervision. Mahnam Ketabi: Conceptualization, Formal Analysis, Writing – Original Draft, Visualization, Supervision. Fatemeh Hejazi: Conceptualization, Methodology, Software. Nastaran Rasoolzadeh: Investigation, Resources, Writing – Original Draft. Sara Gooran: Investigation, Resources, Writing – Original Draft. Sayyed Ali Taheri: Conceptualization, Investigation, Writing – Original Draft, Supervision, Project Administration. Kamyar Movagharnejad: Corresponding Author, Project Administration, Conceptualization, Writing – Review & Editing, Supervision.
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Use of Large Language Models, AI and Machine Learning Tools: The authors used ChatGPT solely to assist with the English translation and language refinement of the manuscript. The scientific content, analysis, and interpretation are entirely the work of the authors.
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Conflict of interest: No potential conflict of interest was reported by the author(s).
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Research funding: This work was supported by the Babol Noshirvani University of Technology.
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Data availability: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.
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