Modelling the Viscosity-Temperature Relationship of Alberta Bitumen

Miadonye, Adango; Singh, Birbal; Puttagunta, V.R.

doi:10.1080/08843759408916182

Cited by 19 publications

(10 citation statements)

References 4 publications

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“…The viscosity of the gas phase is modeled using a standard mixing rule for EOS calculations from Jossi et al (1962). The viscosity of the oil phase is determined using the representation given by Miadonye et al (1994) where b, c, and s are parameters evaluated from measurements for the oil under study. Note that T in Eq.…”

Section: Mass-and Energy-balance Equationsmentioning

confidence: 99%

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Numerical Simulation of the In-Situ Upgrading of Oil Shale

2010

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Oil shale is a highly abundant energy resource, though commercial production has yet to be realized. Thermal in-situ upgrading processes for producing hydrocarbons from oil shale have gained attention recently, however, in part because of promising results reported by Shell using its in-situ conversion process (Crawford et al. 2008). This and similar processes entail heating the oil shale to approximately 700°F (371°C), where the kerogen in the shale decomposes through a series of chemical reactions into liquid and gas products. In this paper, we present a detailed numerical formulation of the in-situ upgrading process. Our model, which can be characterized as a thermal/compositional, chemical reaction, and flow formulation, is implemented into Stanford's General Purpose Research Simulator (GPRS). The formulation includes strongly temperature-dependent kinetic reactions, fully compositional flow and transport, and a model for the introduction of heat into the formation through downhole heaters. We present detailed simulation results for representative systems. The model and heating patterns are based on information in Shell publications; chemical-reaction and thermodynamic data are from previously reported pyrolysis experiments. After a relatively modest degree of parameter adjustment (with parameters restricted to physically realistic ranges), our results for oil and gas production are in reasonable agreement with available field data. We also investigate various sensitivities and show how production is affected by heater temperature and location. The ability to model these effects will be essential for the eventual design and optimization of in-situ upgrading operations.

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Section: Mass-and Energy-balance Equationsmentioning

confidence: 99%

“…Given that we represent kerogen as CH 1.5 N 0.026 O 0.05 , this range corresponds to 0.85-1.7 lbm mol/ft 3 . Thermal conductivity was reported to vary from 3.5 to 8.7 Btu/ ft/day/°F for organic-rich oil-shale formations (Nottenburg et al 1978).…”

Section: Model Components Reactions and Other Parametersmentioning

confidence: 99%

Numerical Simulation of the In-Situ Upgrading of Oil Shale

2010

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“…The parameter s is a measure of variation of viscosity with temperature and 303.15 is derived from the fact that the viscosity of the oil becomes infinite at absolute zero temperature. As reported in previous publications (Puttagunta et al, 1991and Miadonye et al, 1994) the values of the constant and parameters as obtained by a non-linear regression techniques are C = -3.0020 and s = 0.0066940 • b + 3.5364.…”

Section: Viscosity Correlationsmentioning

confidence: 95%

Viscosity Estimation for Bitumen-Diluent Mixtures

Miadonye

Singh

Puttagunta

1995

Fuel Science and Technology International

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“…The rock properties are shown in Table 4 and the fluid properties in Table 5, adapted from [10,20,21]. The viscosity of the gas phase is given by: These data are adapted from [20] and the viscosity of the liquid phase is given by [26]: …”

Section: Test Casementioning

confidence: 99%

Modelling in-situ upgrading of heavy oil using operator splitting method

et al. 2015

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The in-situ upgrading (ISU) of bitumen and oil shale is a very challenging process to model numerically because of the large number of components that need to be modelled using a system of equations that are both highly non-linear and strongly coupled. Operator splitting methods are one way of potentially improving computational performance. Each numerical operator in a process is modelled separately, allowing the best solution method to be used for the given numerical operator. A significant drawback to the approach is that decoupling the governing equations introduces an additional source of numerical error, known as the splitting error. The best splitting method for modelling a given process minimises the splitting error whilst improving computational performance compared to a fully implicit approach. Although operator splitting has been widely used for the modelling of reactive-transport problems, it has not yet been applied to the modelling of ISU. One reason is that it is not clear which operator splitting technique to use. Numerous such techniques are described in the literature and each leads to a different splitting error. While this error has been extensively analysed for linear operators for a wide Julien Maes range of methods, the results cannot be extended to general non-linear systems. It is therefore not clear which of these techniques is most appropriate for the modelling of ISU. In this paper, we investigate the application of various operator splitting techniques to the modelling of the ISU of bitumen and oil shale. The techniques were tested on a simplified model of the physical system in which a solid or heavy liquid component is decomposed by pyrolysis into lighter liquid and gas components. The operator splitting techniques examined include the sequential split operator (SSO), the Strang-Marchuk split operator (SMSO) and the iterative split operator (ISO). They were evaluated on various test cases by considering the evolution of the discretization error as a function of the time-step size compared with the results obtained from a fully implicit simulation. We observed that the error was least for a splitting scheme where the thermal conduction was performed first, followed by the chemical reaction step and finally the heat and mass convection operator (SSO-CKA). This method was then applied to a more realistic model of the ISU of bitumen with multiple components, and we were able to obtain a speed-up of between 3 and 5.

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Modelling the Viscosity-Temperature Relationship of Alberta Bitumen

Cited by 19 publications

References 4 publications

Numerical Simulation of the In-Situ Upgrading of Oil Shale

Numerical Simulation of the In-Situ Upgrading of Oil Shale

Viscosity Estimation for Bitumen-Diluent Mixtures

Modelling in-situ upgrading of heavy oil using operator splitting method

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