Viscosity modeling of solvent-water-heavy oil/bitumen systems at high pressures and elevated temperatures

It is widely accepted that enhancing heavy oil/bitumen recovery has become a priority though fundamentally challenging due to the rising oil consumption and gradual depletion of conventional oil. The hybrid solvent-steam injection has been considered to be one of the most important enhanced oil recovery (EOR) methods for a given heavy oil/bitumen reservoir with its main mechanisms related to viscosity reduction resulted from thermal energy as well as solvent dissolution. To quantify the viscosity for such mixtures, various correlations have been proposed but each of them has its own limitations and may lead to large deviations under certain conditions. It is, therefore, important to accurately quantify the viscosity of solvent-water-heavy oil/bitumen systems within a unified, consistent, and efficient framework. First, this work presents a novel framework for dynamically reproducing the measured viscosity of solvent-heavy oil/bitumen systems under varying pressure and temperature conditions by integrating the Peng-Robinson equation of state (PR EOS) with binary interaction parameters (BIPs) and modified alpha functions. By treating heavy oil/bitumen as either as a single pseudocomponent (PC) or multiple PCs, the six most common mixing rules (i.e., Arrhenius’ mixing rule, Cragoe’s mixing rule, double-log mixing rule, Lobe’s mixing rule, power law mixing rule, and Shu’s mixing rule) have been evaluated and compared. By adopting the effective density concept, the volume-based power law, weight-based power law, and weight-based Cragoe’s mixing rules reproduce the experimentally measured viscosity from 4.3-15000.0 mPaꞏs within the pressure and temperature range from 1.1 to 10.9 MPa and from 287.9 to 463.4 K. When utilizing one PC to represent the oil sample, the overall absolute average relative deviation (AARD) for viscosity prediction is found to be 16.0%, 16.5%, and 29.4% for the aforementioned three mixing rules. When treating heavy oil/bitumen as four PCs, the AARDs decrease to 13.9%, 14.8%, and 19.3% for the same mixing rules. Then, such a framework has been expanded to determine the viscosity of solvent-water-heavy oil/bitumen mixtures as a function of thermal energy, solvent dissolution, and water concentration. By treating heavy oil/bitumen as either a single PC or multiple PCs, such a framework along with the volume translation (VT) strategy and effective density for the six mixing rules successfully reproduces the experimentally measured viscosity from 0.7- 566.0 mPaꞏs with an overall AARD of 41.1%, 10.2%, 26.3%, 36.4%, 47.2%, and 47.3% (1 PC) and 30.2%, 9.1%, 19.3%, 35.5%, 40.0%, and 30.1% (4 PCs), respectively. Adding water to a solvent-heavy oil/bitumen mixture can either increase or decrease its viscosity, mainly depending on thermal energy and solvent dissolution. Water concentration in feed plays a crucial role on the mixture viscosity at liquid/vapor (LV) equilibria other than aqueous/ liquid/vapor (ALV) equilibria. Heavier solvents are found to have a superior capacity for diluting heavy oil/bitumen at the same solvent concentration, and water has the similar ability for reducing mixture viscosity when it is in liquid phase. At a higher temperature, water as a vapour shows its better ability in diluting heavy oil/bitumen than some solvents (e.g., CO2 and C3H8). In addition to enabling dynamic and accurate viscosity prediction for complex mixtures, this framework can be seamlessly integrated with any reservoir simulators, facilitating design, evaluation, and optimization of a hybrid solvent-steam process.

Name thesis:degree_name

Master of Applied Science (MASc)

Level thesis:degree_level

Master's

Discipline thesis:degree_discipline

Engineering - Petroleum Systems

Grantor dc:publisher

Faculty of Graduate Studies and Research, University of Regina

Year dc:date.issued

2024

Author dc:creator

• Hu, Bingge

Advisor dc:contributor.advisor

• Yang, Daoyong (Tony)

Committee member dc:contributor.committeemember

• Shirif, Ezeddin

Language dc:language.iso

en

DOI

https://doi.org/10.82465/4244

OAI identifier oai:identifier

oai:uregina.scholaris.ca:10294/16432