Effect of GOR on Gas Diffusivity in Reservoir-Fluid Systems

Summary Molecular diffusion plays a dominant role in various reservoir processes, especially in the absence of convective mixing. In general, gas diffusion in oils depends on several factors such as pressure, temperature, oil viscosity, and gas/oil ratio (GOR). Out of these factors, the effects of GOR and live-oil-compositional changes on diffusivity are rare or not available in the literature. The current work fills this gap and presents the experimental observations on the effect of GOR on gas diffusivity in reservoir-fluid systems. Synthetic live oils were created by combining stock-tank oil (STO) and methane in various ratios. Constant-composition-expansion (CCE) experiments were performed with these oils to obtain their bubblepoints and liquid densities in relation to GOR. Methane diffusivity in these oils was obtained from pressure-decay (PD) tests at high-pressure/high-temperature (HP/HT) conditions. The diffusion and solubility parameters were estimated from PD data using the diffusion model and integral-based linear regression presented in previous work (Ratnakar and Dindoruk 2015, 2018). The experimental and modeling methodologies are presented here in sufficient detail to allow readers to replicate and evaluate the results. In this work, we experimentally investigated the effect of GOR on methane diffusivity in oils at HP/HT conditions using PD tests. In particular, We present experimental data for bubblepoints and liquid density of synthetic oils with various GOR values. For the range of GORs considered, these measurements show that the bubblepoint pressure increases linearly with GOR. Late-transient solution (LTS) of the PD model was used to obtain diffusivity parameters by regressing against experimental data. It is found that as the GOR value increases (that is, when oil becomes lighter), the diffusivity value increases, which is in accordance with the Stokes-Einstein relation. Most importantly, an empirical correlation is developed on the basis of a limited data set to describe the variation in diffusivity values with GOR. This can be important when experimental data are available for the STO but not for the live oils. It can also be extremely useful in gas-injection processes where the amount of gas dissolved in the oil varies, leading to variations in diffusivity.

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Numerical Investigation of Gravitational Compositional Grading in Hydrocarbon Reservoirs Using Centrifuge Data

SPE Reservoir Evaluation & Engineering ◽

10.2118/116243-pa ◽

2009 ◽

Vol 12 (05) ◽

pp. 793-802 ◽

Cited By ~ 3

Author(s):

P. David Ting ◽

Birol Dindoruk ◽

John Ratulowski

Keyword(s):

Experimental Data ◽

Phase Behavior ◽

Rock Properties ◽

Model Parameters ◽

Fluid Composition ◽

Bulk Fluid ◽

Reservoir Fluid ◽

Fluid Systems ◽

Fluid Properties ◽

The Impact

Summary Fluid properties descriptions are required for the design and implementation of petroleum production processes. Increasing numbers of deep water and subsea production systems and high-temperature/high-pressure (HTHP) reservoir fluids have elevated the importance of fluid properties in which well-count and initial rate estimates are quite crucial for development decisions. Similar to rock properties, fluid properties can vary significantly both aerially and vertically even within well-connected reservoirs. In this paper, we have studied the effects of gravitational fluid segregation using experimental data available for five live-oil and condensate systems (at pressures between 6,000 and 9,000 psi and temperatures from 68 to 200°F) considering the impact of fluid composition and phase behavior. Under isothermal conditions and in the absence of recharge, gravitational segregation will dominate. However, gravitational effects are not always significant for practical purposes. Since the predictive modeling of gravitational grading is sensitive to characterization methodology (i.e., how component properties are assigned and adjusted to match the available data and component grouping) for some reservoir-fluid systems, experimental data from a specially designed centrifuge system and analysis of such data are essential for calibration and quantification of these forces. Generally, we expect a higher degree of gravitational grading for volatile and/or near-saturated reservoir-fluid systems. Numerical studies were performed using a calibrated equation-of-state (EOS) description on the basis of fluid samples taken at selected points from each reservoir. Comparisons of measured data and calibrated model show that the EOS model qualitatively and, in many cases, quantitatively described the observed equilibrium fluid grading behavior of the fluids tested. First, equipment was calibrated using synthetic fluid systems as shown in Ratulowski et al. (2003). Then real reservoir fluids were used ranging from black oils to condensates [properties ranging from 27°API and 1,000 scf/stb gas/oil ratio (GOR) to 57°API and 27,000 scf/stb GOR]. Diagnostic plots on the basis of bulk fluid properties for reservoir fluid equilibrium grading tendencies have been constructed on the basis of interpreted results, and sensitivities to model parameters estimated. The use of centrifuge data was investigated as an additional fluid characterization tool (in addition to composition and bulk phase behavior properties) to construct more realistic reservoir fluid models for graded reservoirs (or reservoirs with high grading potential) have also been investigated.

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Diffusivity of Gas Into Bitumen: Part II—Data Set and Correlation

SPE Journal ◽

10.2118/195575-pa ◽

2019 ◽

Vol 24 (04) ◽

pp. 1667-1680 ◽

Cited By ~ 2

Author(s):

W. D. Richardson ◽

F. F. Schoeggl ◽

S. D. Taylor ◽

B.. Maini ◽

H. W. Yarranton

Keyword(s):

Oil Recovery ◽

Oil Viscosity ◽

Data Set ◽

Gas Concentration ◽

Recovery Processes ◽

Gas Diffusivity ◽

Constant Diffusivity ◽

Mixture Viscosity ◽

Gaseous Hydrocarbons

Summary The oil-production rate of in-situ heavy-oil-recovery processes involving the injection of gaseous hydrocarbons partly depends on the diffusivity of the gas in the bitumen. Data for gas diffusivities, particularly above ambient temperature, are relatively scarce because they are time consuming to measure. In this study, the diffusion and solubilities of gaseous methane, ethane, propane, and n-butane in a Western Canadian bitumen were measured from 40 to 90°C and pressures from 300 to 2300 kPa, using a pressure-decay method. The diffusivities were determined from a numerical model of the experiments that accounted for the swelling of the oil. In Part I of this study (Richardson et al. 2019), it was found that both constant and viscosity-dependent diffusivities could be used to model the mass of gas diffused and the gas-concentration profile in the bitumen; however, the constant diffusivity was different for each experiment and mainly depended on the oil viscosity. In this study, a correlation for the constant diffusivity to the oil viscosity is developed as a tool to quickly estimate the gas diffusivity. A correlation of diffusivity to the mixture viscosity is also developed for use in more-rigorous diffusion models. The maximum deviations in the mass diffused over time predicted with the constant and viscosity-dependent (mixture viscosity) correlations at each condition are on average 7.4 and 8.7%, respectively.

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Determination of Concentration-Dependent Gas Diffusivity in Reservoir Fluid Systems

Industrial & Engineering Chemistry Research ◽

10.1021/acs.iecr.0c01847 ◽

2020 ◽

Vol 59 (33) ◽

pp. 15028-15047

Author(s):

Hyun Woong Jang ◽

Daoyong Yang

Keyword(s):

Gas Diffusivity ◽

Reservoir Fluid ◽

Fluid Systems

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FRACTAL ANALYSIS OF GAS DIFFUSION IN POROUS NANOFIBERS

Fractals ◽

10.1142/s0218348x15400113 ◽

2015 ◽

Vol 23 (01) ◽

pp. 1540011 ◽

Cited By ~ 21

Author(s):

BOQI XIAO ◽

JINTU FAN ◽

ZONGCHI WANG ◽

XIN CAI ◽

XIGE ZHAO

Keyword(s):

Experimental Data ◽

Fractal Dimension ◽

Fractal Theory ◽

Fractal Dimensions ◽

Fractal Model ◽

Gas Diffusion ◽

Physical Mechanisms ◽

Gas Diffusivity ◽

Model Predictions ◽

Good Agreement

In this study, with the consideration of pore size distribution and tortuosity of capillaries, the analytical model for gas diffusivity of porous nanofibers is derived based on fractal theory. The proposed fractal model for the normalized gas diffusivity (De/D0) is found to be a function of the porosity, the area fractal dimensions of pore and the fractal dimension of tortuous capillaries. It is found that the normalized gas diffusivity decreases with increasing of the tortuosity fractal dimension. However, the normalized gas diffusivity is positively correlated with the porosity. The prediction of the proposed fractal model for porous nanofibers with porosity less than 0.75 is highly consistent with the experimental and analytical results found in the literature. The model predictions are compared with the previously reported experimental data, and are in good agreement between the model predictions and experimental data is found. The validity of the present model is thus verified. Every parameter of the proposed formula of calculating the normalized gas diffusivity has clear physical meaning. The proposed fractal model can reveal the physical mechanisms of gas diffusion in porous nanofibers.

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Determination of Gas Diffusion and Interface-Mass Transfer Coefficients for Quiescent Reservoir Liquids

SPE Journal ◽

10.2118/84072-pa ◽

2006 ◽

Vol 11 (01) ◽

pp. 71-79 ◽

Cited By ~ 32

Author(s):

Faruk Civan ◽

Maurice L. Rasmussen

Keyword(s):

Experimental Data ◽

Mass Transfer ◽

Gas Diffusion ◽

Drilling Mud ◽

Transfer Coefficients ◽

Mass Transfer Coefficients ◽

Gas Diffusivity ◽

Completion Fluids ◽

Interface Mass Transfer

Summary A physically and mathematically rigorous transient-state equilibrium diffusion model is applied for simultaneous determination of the gas-diffusion and interface-mass-transfer coefficients from pressure de-cline by dissolution of gas in quiescent liquids involving petroleum reservoirs. The short- and long-time analytical solutions of this model are reformulated to enable direct determination of the best-estimate values of these parameters by regression of experimental data. Typical experimental data are then analyzed by means of the present improved methods, and the values obtained are compared with the re-ported values. The present methodology is proven practical and yields unique and accurate parameter values. Introduction Gas-diffusivity and interface-mass-transfer coefficients are important parameters determining the rate of dissolution of the injection gases in oil during secondary recovery, and the rate of dissolution and separation of light gases in reservoir oil and brine, water tables associated with depleted-reservoir gas storage, drilling mud, and completion fluids (Hill and Lacey 1934; O'Bryan et al. 1988; O'Bryan and Bourgoyne 1990; Bodwadkar and Chenevert 1997; Bradley et al. 2002; Liu and Civan 2005). In order to develop proper gas-injection strategies, accurate values of these parameters are required for reservoir simulation and prediction of oil recovery by miscible flooding and the optimization of miscibility for best recovery. Laboratory measurement of gas diffusivity in quiescent liquids is usually accomplished through the measurement of the pressure of gas in contact with certain liquids, such as oil, brine, drilling mud, and completion fluids in a closed PVT cell (see Fig. 1) during gas dissolution in the liquid phase. The accuracies of the available models, including those by Riazi (1996), Sachs (1997, 1998), and Zhang et al. (2000), are limited by the inherent simplifying assumptions involved in the analytic treatment and the subsequent interpretation of such experimental data. As judged by the reported studies, there appears to be no consensus among the available analytical approaches used for diffusivity measurement. In addition, the previous studies focused mostly on the determination of gas diffusivity and did not account for interface-mass-transfer effects. The methodology offered by Civan and Rasmussen (2001, 2002, 2003), and further elaborated in the present paper, allows for both interface mass-transfer effects and for bulk diffusivity. It is a novel and practical approach that determines parameters describing both effects from a given set of pressure-decline data. The best estimate of the coefficient of diffusion of gas species (solute) in a given liquid medium (solvent) is usually inferred indirectly by matching the prediction of a suitable mathematical model involving the species transfer by diffusion to experimental data under prescribed conditions. For this purpose, Sachs (1998) resorts to the numerical solution of the nonlinear model equations incorporating the dependency of the diffusion coefficient on concentration without clearly describing the boundary conditions used in the solution.

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