Novel mechanistic approach for modeling multiphase flow behavior in crude oil wells
| dc.contributor.advisor | Vargas Arreola, Francisco M. | en_US |
| dc.creator | Khemka, Yash | en_US |
| dc.date.accessioned | 2020-12-21T18:30:16Z | en_US |
| dc.date.available | 2022-05-01T05:01:11Z | en_US |
| dc.date.created | 2021-05 | en_US |
| dc.date.issued | 2020-12-21 | en_US |
| dc.date.submitted | May 2021 | en_US |
| dc.date.updated | 2020-12-21T18:30:16Z | en_US |
| dc.description.abstract | In this work, the development and validation of a multiphase flow and pressure drop modeling framework which combines physical models for multiphase flow phenomena, phase behavior and fluid properties are presented. This state-of-the-art mechanistic method offers an improved representation of the physical phenomena over the conventional approaches which rely on empirical correlations. Physical models have a fundamental basis and provide reliable estimations even outside their fitting range, unlike empirical correlations. The results of simulated trials indicate that the mechanistic model can correct the underprediction tendencies in pressure drop observed with the conventional approaches. This framework could ultimately be used for analyzing the hydrocarbon flow and estimating accurate pressure drop and fluid properties which are important in the design of production pipeline, liquid loading in wells and implementing artificial lift technologies to improve well productivity. In addition, physical methodologies for modeling viscosity, a key property in studying hydrocarbon flow, are presented. Three different crude oil characterization methods are combined with the one-parameter friction theory model for the Peng-Robinson equation of state to predict the viscosity of live crude oils. Their predictive capabilities are further investigated for modeling the viscosity of crude oils under gas injection, a method frequently employed to increase oil recovery rate. The models’ strong predictive capabilities are demonstrated by the fact that while only the live oil viscosity at saturation pressure is required to fit simulation parameters, each model predicts the viscosity of 23 blends in the single-phase region with only 7% average error, which is satisfactory for practical applications. Even in the two-phase region, the predictions are within experimental uncertainty. These viscosity models are integrated into the mechanistic pressure drop framework. Last, a one-parameter friction theory model for the cubic-plus-chain equation of state is presented to describe the viscosity behavior of n-alkanes. This model yielded only 1.51% average error in modeling the viscosity of 15 n-alkanes and by using simple mixing rules, the model is extended to predict the viscosity of 13 n-alkane mixtures with 3.77% average error, within the bounds of experimental uncertainty, making it a promising tool for modeling the viscosity of crude oil systems. | en_US |
| dc.embargo.terms | 2022-05-01 | en_US |
| dc.format.mimetype | application/pdf | en_US |
| dc.identifier.citation | Khemka, Yash. "Novel mechanistic approach for modeling multiphase flow behavior in crude oil wells." (2020) Diss., Rice University. <a href="https://hdl.handle.net/1911/109762">https://hdl.handle.net/1911/109762</a>. | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/109762 | en_US |
| dc.language.iso | eng | en_US |
| dc.rights | Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder. | en_US |
| dc.subject | multiphase flow | en_US |
| dc.subject | thermodynamics | en_US |
| dc.subject | viscosity | en_US |
| dc.subject | asphaltene precipitation | en_US |
| dc.title | Novel mechanistic approach for modeling multiphase flow behavior in crude oil wells | en_US |
| dc.type | Thesis | en_US |
| dc.type.material | Text | en_US |
| thesis.degree.department | Chemical and Biomolecular Engineering | en_US |
| thesis.degree.discipline | Engineering | en_US |
| thesis.degree.grantor | Rice University | en_US |
| thesis.degree.level | Doctoral | en_US |
| thesis.degree.name | Doctor of Philosophy | en_US |
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