Marco A. Satyro

Phase Behaviour and Physical Property Measurements for VAPEX Solvents: Part II. Propane, Carbon Dioxide and Athabasca Bitumen

The solubility of pure carbon dioxide in Athabasca bitumen was measured and compared with the literature data. Multiple liquid phases were observed at carbon dioxide contents above approximately 12 wt%. A correlation based on Henrys law was found to fit the saturation pressures at carbon dioxide contents below 12 wt%. The saturation pressure and solubility of carbon dioxide and propane in Athabasca bitumen, as well as the liquid phase densities and viscosities, were measured for three ternary mixtures at temperatures from 10 to 25°C. Two liquid phases (carbon dioxide-rich and bitumen-rich) were observed at 13 wt% carbon dioxide and 19 wt% propane. Only liquid and vapour-liquid regions were observed for the other two mixtures (13.5 wt% propane and 11.0 wt% carbon dioxide; 24.0 wt% propane and 6.2 wt% carbon dioxide). The saturation pressures for the latter mixtures were predicted using the correlation for the carbon dioxide partial pressure and a previously developed correlation for the propane partial pressure. The mixture viscosities were predicted with the Lobe mixing rule.

On the applicability of the Sandler-Wong mixing rules for the calculation of thermodynamic excess properties—VE, HE, SE, CpE

Abstract Investigation of derived thermodynamic properties using Sandler-Wong mixing rules shows that predicted excess properties can be unreliable since they are a complex function of the original Gibbs free energy model and the a and b terms. Sometimes the mixing rule will predict a different qualitative behaviour for the heat of mixing when compared to the one predicted by the original Gibbs excess model. Moreover the mixing rules can produce thermodynamic inconsistency at elavated pressures and lead to the prediction of negative heat capacities.

Thermodynamics and the Simulation Engineer

In this paper a brief, commented introduction to thermodynamics highlighting its connections with the simulation of chemical processes is presented. With these connections established I proceed to comment on current challenges faced by process and product modelers and conclude with a peek into the future of process and product design and the role of thermodynamics as the unifying discipline for engineers.

Development of an Open Source Chemical Process Simulator.

Process simulators are a key software tool used in the fluid processing industries for day to day process calculations, for process design, for process optimization and for the debottlenecking of existing processes. Current commercial process simulators do not provide the simulator’s source code; the users must rely on a closed black-box approach for the unit process models. This historical approach to software development and usage is diametrically opposite to that of open source software. The open source paradigm provides a near optimal solution for the development of robust and reliable software applications while using minimal formal resources. It is recognized that the open source approach is a feasible software development solution that overcomes some of the shortcomings of the currently available commercial process simulators. This paper presents the development of a state of the art open source process simulator; as well, it also provides sample plant simulations.

A correction to Sandler–Wong mixing rules

Abstract Due to the temperature dependence of the covolume created by the use of Sandler–Wong mixing rules, thermodynamically inconsistent results will be predicted at very high pressures, including the prediction of negative heat capacities. In this paper, we propose a simple modification to the mixing rule, which prevents this problem and which performs slightly better in the prediction of the Gibbs excess energy.

Compositional Simulation Using an Advanced Peng-Robinson Equation of State

During compositional reservoir simulations where underground fluid composition strongly affects the modeling of recovery processes, flash calculations are commonly employed to help determine the correct number of equilibrium phases, the corresponding compositions, and the phase amount of each phase. Cubic equations of state (EOS) are widely used in the representation of volumetric and phase equilibria due to their simplicity and solvability. Commonly used cubic EOS such as Peng-Robinson (PR) and Soave-Redlich-Kwong (SRK) have well known limitations in predicting liquid phase properties for polar compounds. In this paper, we present a compositional reservoir simulator equipped with the advanced Peng-Robinson EOS and an efficient and robust multiphase flash algorithm that can accurately predict the phase equilibrium. This method utilizes Michelsen’s stability test (Michelsen, 1982) and a combination of accelerated successive substitution and a minimum-variable Newton-Raphson (MVNR) method for fast convergence. The advanced Peng-Robinson (APR) EOS adds volume translation and a flexible attractive temperature-dependent term to the original PR EOS for accurate PVT and saturation property correlation for polar compounds. Examples of pure compounds and mixtures are tested. Computational results show that the developed simulator provides a more detailed description and better understanding of complex dynamic underground fluid phase behavior that may occur during oil recovery processes. Introduction Compositional models are commonly used to simulate complex multiphase flow in a reservoir where phase compositions at equilibrium change with space and time, and an equation of state (EOS) is employed in the models to determine the correct number of equilibrium phases and the corresponding compositions in each phase in each grid block. Since the late 1970s, many isothermal compositional models using cubic equations of state and taking into account up to three phases (water, gas and oil) have been developed. They are different in how the primary equations and unknowns are selected (Cao, 1999). Fussell and Fussell (1979) published a technique which used a minimum variable Newton-Raphson method to solve a system consisting of fugacity equations and a saturation constraint equation for primary variables: pressure, liquid phase mole fraction, liquid phase composition or pressure, vapor phase mole fraction, and vapor phase composition. Coats (1980) described a fully implicit compositional model which solved material balance equations for hydrocarbon components and water simultaneously. Nghiem et al. (1981) developed an implicit-pressure, explicit-composition, and explicit-saturation model with an EOS. These equations were solved using an iterative-sequential method. Pressure was first obtained by solving a material balance equation and the other unknowns were updated thereafter. Young and Stephenson (1983) presented a more efficient Newton-Raphson method-based procedure which differed from Fussell and Fussell’s in the ordering of the equations and unknowns. In summary, a fully implicit model provides better stability; it, however, requires higher computational cost. For a partially implicit model, the implicitness varies with the selection of primary unknowns to be solved for and the choice of reasonable time steps becomes the key point in controlling convergence of the Newton-Raphson iteration and accelerating simulation process (Chen et al., 2006). In compositional reservoir simulation, an EOS plays a critical role in the representation of volumetric, thermodynamic, and phase equilibrium properties. Since van der Waals first presented his EOS in 1873, a lot of modifications have been presented in the literature. Among these equations, Soave-Redlich-Kwong (Soave, 1972) and Peng-Robinson (1976) equations of state are most popularly used in the petroleum industry due to their simplicity, solvability, and generalization. However, PR and SRK EOS have well known limitations in predicting liquid phase properties especially for polar mixtures. Based on the ideas of Peneloux (1982) and Mathias et al. (1988), Virtual Material Group, Inc. (VMG) implemented an advanced Peng-Robinson

Process Simulation - From Large Computers and Small Solutions to Small Computers and Large Solutions

This paper reviews the evolution of process simulation in conjunction with the evolution of computer hardware and software technologies from a chemical engineering perspective. A brief history of this hardware evolution is presented and points to exponential growth of computing power. The current personal computers or full function workstations have at least 1GB of memory, 100GB hard drive and run at 4 GHz. We as chemical engineers can only benefit from this continued hardware evolution, as the personal computer has become our full function slide rule.Concurrent with this hardware evolution there has been a proliferation of operating systems and applications software. Some of the applications software has migrated from mainframes and minicomputers and some has been specifically written to take advantage of the user-friendly features available on todays personal computers. A review of this software is presented with particular emphasis on process simulation software. The currently available fifth generation, non-sequential interactive process simulator does make process modeling and design a truly rewarding experience. This software has been designed to allow the personal computer and the engineer to do what each does best, namely the personal computer performing the systematic number crunching and the engineer the intuitive aspect of process simulation and design. The paper will conclude with a look into the future development of process simulation and its interaction with chemical engineering practice.

Phase Behavior and Properties of Heavy Oils

The phase behaviors and thermophysical and transport properties of heavy oils not only share numerous features with light oils and reservoir fluids, but also exhibit substantial differences because of their more complex chemistry and fluid physics associated with this chemistry. As a consequence, conventional understanding, experimental methods, and property/phase behavior computation tools are stretched to their limits. They often fail to provide needed data and insights for process development, design, and operation for production, transport, or refining applications. Heavy oils are inherently multiphase and exhibit time-dependent and polymorphic behaviors that depend not only on temperature and pressure, but also on thermal and shear history as well. Simple properties such as density at fixed temperature and pressure have irreducible uncertainty, and one must think in terms of rheological response rather than viscosity, particularly at low temperatures. Heavy oil characterization poses challenges as well because available analytical tools can provide composition information for less than 50 wt% of typical heavy oils and there is much guess work and correlation needed even to generate composition/property estimates with significant – and hard to quantify uncertainties. In this chapter, we draw upon our diverse experiences to survey the current state of the experimental and computational landscapes related to heavy oil thermophysical and transport properties and phase behavior. Differing and occasionally opposing approaches are presented. Limitations of experimental and computational methods and knowledge needs are discussed, and practical recommendations for specific calculations are noted, typically with caveats. There caveats arise because the landscape is changing rapidly and best practices are evolving continuously, even though we have been producing hydrocarbon resources, transporting, and refining them into diverse products at an industrial scale worldwide for more than a century.

Case Study: Modeling the Phase Behavior of Solvent Diluted Bitumen

The design of solvent-based and solvent assisted heavy oil recovery processes requires accurate predictions of phase behavior as straightforward as saturation pressures and as potentially complex as vapour-liquid-liquid equilibria and asphaltene precipitation. In this case study, saturation pressures of dead and live bitumen were measured in a Jefri PVT cell at different concentrations of a multi-component solvent at temperatures from 20 to 180°C. Saturation pressures and the onset of asphaltene precipitation were also measured for the bitumen diluted with n-pentane. The onset of precipitation was determined by titrating the bitumen with pentane and periodically circulating the mixture past a high pressure microscope. The data were modeled with the Advanced Peng-Robinson equation of state (APR EoS). The maltene fraction of the bitumen was characterized into pseudo-components based on extrapolated distillation data. The asphaltenes were characterized based on a Gamma distribution of the molecular weights of selfassociated asphaltenes. The APR EoS was tuned to match the saturation pressures by adjusting the binary interaction parameter between the solvent and the pseudo-components via a correlation based on critical temperatures. Rather than adjusting the interaction parameters for each pair of components, only the exponent in the correlation was adjusted. The role of mixing rules in correctly predicting the onset and amount of asphaltene precipitation is discussed. Introduction In Western Canada, thermal recovery methods such as cyclic steam stimulation and steam assisted gravity drainage are the methods of choice to recover heavy oil and bitumen with viscosities exceeding 10,000 mPa.s. These methods require significant volumes of natural gas and water to generate steam: approximately 34 m3 of natural gas and 0.2 m3 of groundwater (assuming 90 to 95% recycle) to produce one barrel of bitumen (1). Solvent based and solvent assisted recovery methods are a potential alternative to reduce or replace steam usage. However, potential solvents, such as light n-alkanes , are expensive relative to heavy oil and the success of process depends on how much solvent can be recovered. Predicting the performance of solvent-based and solvent-assisted processes (including both oil and solvent recovery) is challenging because the introduction of a solvent can lead to complex phase behavior. For any given heavy oil and solvent mixture, it may be necessary to predict the phase boundaries, amounts and compositions for liquid-liquid (LL), vapour-liquid (VL), vapour-liquid-liquid (VLL), and asphaltene precipitation regions.