https://cyberleninka.org/article/n/22310.pdf

Pore-scale imaging and modelling

Enhanced oil recovery techniques for heavy oil and oilsands reservoirs after steam injection

Diffusion in fractured reservoirs, unlike in unfractured reservoirs, can affect significantly the efficiency of gas injection in oil reservoirs and recycling in gas/condensate reservoirs. The physical diffusion, similar to gravity, results in the change of the path of the injected gas species from the fractures to the matrix, giving rise to late breakthrough. In this work, we present, for the first time, a consistent model to incorporate physical diffusion of multicomponent mixtures for gas-injection schemes in fractured reservoirs. The multicomponent diffusion flux is related to multicomponent diffusion coefficients, which are dependent on temperature, pressure, and composition. These coefficients are calculated from a model based on irreversible thermodynamics. Current simulation models of fractured reservoirs that include diffusion are based on inconsistent models of gas-to-liquid diffusion at the fracture/matrix interface. We avoid this deficiency by assuming that the gas and liquid phases are in equilibrium at the interface. The concept of crossflow equilibrium (i.e., vertical equilibrium) is invoked in our model to avoid the use of transfer functions. In this work, we use the combined discontinuous Galerkin and mixed methods on 2D structured grids to calculate fluxes accurately and to have low numerical dispersion to study physical diffusion. Four examples are presented. In one of the examples, a field-scale study is performed to investigate gas injection in a fractured reservoir away from miscibility pressure and close to miscibility pressure. Results show a significant effect of diffusion on recovery performance away from miscibility pressure. In another example, recycling in a fractured gas/condensate reservoir is presented to demonstrate that diffusion has a significant effect on condensate recovery.

Numerical Modeling of Diffusion in Fractured Media for Gas-Injection and -Recycling Schemes

We simulate flow and transport directly on pore-space images obtained from a microcomputed-tomography (micro-CT) scan of rock cores. An efficient Stokes solver is used to simulate lowReynolds-number flows. The flow simulator uses a finite-difference method along with a standard predictor/corrector procedure to decouple pressure and velocity. An algebraic multigrid technique solves the linear systems of equations. We then predict permeability, and the results are compared with lattice-Boltzmannmethod (LBM) numerical results and available experimental data. For solute transport, we apply a streamline-based algorithm that is similar to the Pollock algorithm common in field-scale reservoir simulation, but which uses a novel semianalytic formulation near solid boundaries to capture, with subgrid resolution, the variation in velocity near the grains. A random-walk method accounts for molecular diffusion. The streamline-based algorithm is validated by comparison with published results for Taylor-Aris dispersion in a single capillary with a square cross section. We then predict accurately the available experimental data in the literature for the longitudinal dispersion coefficient for a range of Peclet numbers (10 to 10). We introduce a characteristic length on the basis of the ratio of volume to pore/grain surface area that can be used for consolidated porous media to calculate the Peclet number.

Simulation of Flow and Dispersion on Pore-Space Images

This paper (SPE 113936) was accepted for presentation at the SPE/DOE Symposium on Improved Oil Recovery, Tulsa, 20–23 April 2008, and revised for publication. Original manuscript received for review 20 February 2008. Revised manuscript received for review 23 March 2009. Paper peer approved 8 September 2009. Summary Design of an alkaline/surfactant/polymer (ASP) process requires knowledge of the amount of soap formed under alkaline conditions from naphthenic acids in the crude oil. We show here for several crude oils that, when substantial acid is present, the acid number determined by nonaqueous-phase titration is approximately twice that found by hyamine titration of a highly alkaline aqueous phase used to extract soaps from the crude oil. This acid number by soap extraction should provide a better estimate than nonaqueous-phase titration because the extracted soap interacts with the injected surfactant to form surfactant films and microemulsion droplets during an ASP process. In a previous paper (Liu et al. 2008), an unusually wide range of salinities of ultralow oil/water interfacial tensions (IFTs) was found for one alcohol-free crude-oil/anionic-surfactant system under alkaline conditions where naphthenic soaps were present. Solubilization results indicate that this favorable behavior exists with the same surfactant blend and another crude oil. In the same paper, a 1D simulator for the ASP process was presented. Here, this ASP simulator has been used for various acid contents, injected-surfactant concentrations, slug sizes, and salinities to show that high recoveries of waterflood residual oil (> 90%) can be expected for a wide range of near-optimal (Winsor III) and underoptimum (Winsor I) conditions for a constant-salinity process, even with relatively small slug sizes. A key factor leading to this good performance is development of a gradient in soap/surfactant ratio, which ensures that a displacement front with ultralow IFT forms and propagates through the formation. Similar high recoveries can be attained for certain Winsor II conditions but only for much larger slug sizes, owing to the tendency for surfactant to partition into the oil phase and become retarded. Large dispersion, such as might be expected for field conditions, can reduce recovery significantly for small surfactant slugs even for near-optimal and underoptimum conditions. However, this problem can be overcome by injecting the slug or drive at salinities below reservoir salinity, thereby creating a salinity gradient.

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Alkaline/Surfactant/Polymer Processes: Wide Range of Conditions for Good Recovery

We present results of a detailed investigation of the steam/ solvent-coinjection-process mechanism by use of a numerical model with homogeneous reservoir properties and various solvents. We describe condensation of steam/solvent mixture near the chamber boundary. We present a composite picture of the important phenomena occurring in the different regions of the reservoir and their implications for oil recovery. We compare performances of various solvents and explain the reasons for the observed differences. An improved understanding of the process mechanism will help with selecting the best solvent and developing the best operating strategy for a given reservoir. Results indicate that as the temperature drops near the chamber boundary, steam starts condensing first because its mole fraction in the injected steam/solvent mixture (and hence its partial pressure and the corresponding saturation temperature) is much higher than the solvent’s. As temperature declines toward the chamber boundary and steam continues to condense, the vapor phase becomes increasingly richer in solvent. At the chamber boundary where the temperature becomes equal to the condensation temperature of both steam and solvent at their respective partial pressures, both condense simultaneously. Thus, contrary to steam-only injection, where condensation occurs at the injected steam temperature, condensation of steam/solvent mixture is accompanied by a reduction in temperature in the condensation zone and the farther regions. However, there is little change in temperature in the central region of the steam chamber. The condensed steam/solvent mixture drains outside the chamber, leading to the formation of a mobile liquid stream (drainage region) where heated oil, condensed solvent, and water flow together to the production well. The condensed solvent mixes with the heated oil and further reduces its viscosity. The additional reduction in viscosity by solvent more than offsets the effect of reduced temperature near the chamber boundary. As the steam chamber expands laterally because of continued injection and as temperature in the hitherto drainage region increases, a part of the condensed solvent mixed with oil evaporates. This lowers the residual oil saturation (ROS) in the steam chamber. Therefore, ultimate oil recovery with the steam/solvent-coinjection process is higher than that in steam-only injection. The higher the solvent concentration in oil at a location, the greater is the reduction in the ROS there. Our explanation is corroborated by the experimental results reported in the literature, which show smaller ROS in the steam chamber after a steam/solvent-coinjection process. A lighter solvent has a lower viscosity, a higher volatility, and a higher molar concentration of solvent in the drainage region. Thus, a lighter solvent causes a greater reduction in the viscosity of the heated oil and also leads to a lower ROS. Therefore, the lightest condensable solvent (butane, under the conditions investigated) provides the most favorable results in terms of enhancements in oil rate and oil recovery. This is different from the prior claims in the literature. Introduction Steam-assisted gravity drainage (SAGD) has become the preferred technology for exploiting the huge resource base of bitumen. There are more than 10 commercial SAGD projects in Canada. The field performance indicates that the process offers high production rate and high ultimate recovery. However, it requires a large volume of steam injection. The observed steam/ oil ratio (SOR) in the field is in the range of 3 to 5 cold water equivalent (CWE) bbl/STB (Jimenez 2008). A large usage of steam can affect the project economics adversely and also can have a detrimental impact on the environment. There have been numerous studies that aim to improve SAGD’s performance. These involve variations in the well configuration or changes in the operating methodology. In particular, steam/solvent coinjection appears promising. In this process, a small amount of vaporized but condensable hydrocarbon solvent is added to steam (Nasr et al. 2003). Laboratory investigation (Nasr and Ayodele 2005, 2006) and field trials (Table 1) have demonstrated that compared with steam-only injection, adding solvent to steam results in higher oil rate, reduced SOR, and higher ultimate recovery. Table 1 presents a brief summary of the previous field trials of steam/solvent coinjection. Although none of the pilots [except the LASER (liquid addition to steam for enhanced recovery) pilot by Imperial Oil] has been conducted long enough to provide conclusive results, they generally indicate much-improved performance. This technique may prove invaluable in overcoming some of SAGD’s shortcomings. Steam/solvent coinjection is a complex process. A successful and profitable field implementation requires a judicious decision about solvent type, solvent concentration, and the operating strategy. Selection of the optimum set of parameters is difficult because of a large number of variables involved and their nonlinear effect on the economic performance (Edmunds et al. 2009). A thorough understanding of the oil-recovery mechanism is required for an improved design of the process and to gain maximum benefits of the technology. This topic has attracted much research interest in the industry as well as in academia. Ardali et al. (2012) present a detailed review of prior studies. Although the effect of solvent addition on viscosity reduction is well described in the literature (Gates 2007; Deng et al. 2010), no other mechanistic details have been reported. There is a misunderstanding that a solvent with vaporization temperature comparable to that of steam will condense together with steam at the chamber boundary (Nasr et al. 2003). The misunderstanding of steam/solvent condensation leads to an incorrect selection of the most appropriate solvent. Furthermore, there is little information available about the formation of a mobile liquid stream (or drainage region) outside the steam-chamber boundary and the phenomena occurring in that region. Most importantly, the mechanism of increase in ultimate oil recovery by solvent addition to steam is not well understood (Nasr and Ayodele 2006). It often leads to an inappropriate representation of ROS in numerical-simulation studies and may result in erroneous conclusions. This study fills some of the gaps to develop an improved understanding of the steam/solvent-coinjection process. We have carried out a detailed investigation of the process mechanism by use of a fine-grid numerical model with homogeneous reservoir properties and various solvents. We describe condensation of the Copyright VC 2013 Society of Petroleum Engineers

New Insights Into Steam/Solvent-Coinjection-Process Mechanism

This paper (SPE 115961) was accepted for presentation at the SPE Annual Technical Conference and Exhibition, Denver, 21–24 September 2008, and revised for publication. Original manuscript received for review 7 July 2008. Revised manuscript received for review 11 February 2010. Paper peer approved 3 May 2010. Summary It is known that dispersion in porous media results from an interaction between convective spreading and diffusion. However, the nature and implications of these interactions are not well understood. Dispersion coefficients obtained from averaged cup-mixing concentration histories have contributions of convective spreading and diffusion lumped together. We decouple the contributions of convective spreading and diffusion in core-scale dispersion and systematically investigate interaction between the two in detail. We explain phenomena giving rise to important experimental observations such as Fickian behavior of core-scale dispersion and powerlaw dependence of dispersion coefficient on Peclet number. We track movement of a swarm of solute particles through a physically representative network model. A physically representative network model preserves the geometry and topology of the pore space and spatial correlation in flow properties. We developed deterministic rules to trace paths of solute particles through the network. These rules yield flow streamlines through the network comparable to those obtained from a full solution of Stokes’ equation. Paths of all solute particles are deterministically known in the absence of diffusion. Thus, we can explicitly investigate purely convective spreading by tracking the movement of solute particles on these streamlines. Then, we superimpose diffusion and study dispersion in terms of interaction between convective spreading and diffusion for a wide range of Peclet numbers. This approach invokes no arbitrary parameters, enabling a rigorous validation of the physical origin of core-scale dispersion. In this way, we obtain an unequivocal, quantitative assessment of the roles of convective spreading and diffusion in hydrodynamic dispersion in flow through porous media. Convective spreading has two components: stream splitting and velocity gradient in pore throats in the direction transverse to flow. We show that, if plug flow occurs in the pore throats (accounting only for stream splitting), all solute particles can encounter a wide range of independent velocities because of velocity differences between pore throats and randomness of pore structure. Consequently, plug flow leads to a purely convective spreading that is asymptotically Fickian. Diffusion superimposed on plug flow acts independently of convective spreading (in this case, only stream splitting), and, consequently, dispersion is simply the sum of convective spreading and diffusion. In plug flow, hydrodynamic dispersion varies linearly with the pore-scale Peclet number when diffusion is small in magnitude compared to convective spreading. For a more realistic parabolic velocity profile in pore throats, particles near the solid surface of the medium do not have independent velocities. Now, purely convective spreading (caused by a combination of stream splitting and variation in flow velocity in the transverse direction) is non-Fickian. When diffusion is nonzero, solute particles in the low-velocity region near the solid surface can move into the main flow stream. They subsequently undergo a wide range of independent velocities because of stream splitting, and, consequently, dispersion becomes asymptotically Fickian. In this case, dispersion is a result of an interaction between convection and diffusion. This interaction results in a weak nonlinear dependence of dispersion on Peclet number. The dispersion coefficients predicted by particle tracking through the network are in excellent agreement with the literature experimental data for a broad range of Peclet numbers. Thus, the essential phenomena giving rise to hydrodynamic dispersion observed in porous media are (1) stream splitting of the solute front at every pore, causing independence of particle velocities purely by convection; (2) velocity gradient in pore throats in the direction transverse to flow; and (3) diffusion. Taylor’s dispersion in a capillary tube accounts only for the second and third of these phenomena, yielding a quadratic dependence of dispersion on Peclet number. Plug flow in the bonds of a physically representative network accounts only for the first and third phenomena, resulting in a linear dependence of dispersion on Peclet number. When all the three phenomena are accounted for, we can explain effectively the weak nonlinear dependence of dispersion on Peclet number.

Raman Kumar Jha

Papers

New Insights Into Steam/Solvent-Coinjection-Process Mechanism

Effect of diffusion on dispersion

Effect of Diffusion on Dispersion

Flow Reversal and Mixing

Investigation of Pore-Scale (Local) Mixing