Guidelines for Choosing Compositional and Black-Oil Models for Volatile Oil and Gas-Condensate Reservoirs

2000 ◽  
Author(s):  
Øivind Fevang ◽  
Kameshwar Singh ◽  
Curtis H. Whitson
Keyword(s):  
Oil And Gas ◽  
Volatile Oil ◽  
Gas Condensate ◽  
Gas Condensate Reservoirs ◽  
Black Oil

APPLICATION OF THE BLACK OIL PVT REPRESENTATION TO SIMULATION OF GAS CONDENSATE RESERVOIR PERFORMANCE

1987 ◽  
Vol 27 (1) ◽  
pp. 370
Author(s):  
W.H. Goldthorpe ◽  
J.K. Drohm
Keyword(s):  
Oil And Gas ◽  
Volatile Oil ◽  
Gas Condensate ◽  
Phase Behaviour ◽  
Dew Point ◽  
Pressure Drops ◽  
Gas Condensate Reservoirs ◽  
Reservoir Performance ◽  
Black Oil ◽  
Condensate Reservoir

Special attention must be paid to the generation of PVT parameters when applying conventional black oil reservoir simulators to the modelling of volatile oil and gas-condensate reservoirs. In such reservoirs phase behaviour is an important phenomenon and common approaches to approximating this, via the black oil PVT representation, introduce errors that may result in prediction of incorrect recoveries of surface gas and condensate. Further, determination of production tubing pressure drops for use in such simulators is also prone to errors. These affect the estimation of well potentials and reservoir abandonment pressures.Calculation of black oil PVT parameters by the method of Coats (1985) is shown to be preferred over conventional approaches, although the PVT parameters themselves lose direct physical meaning. It is essential that a properly tuned equation of state be available for use in conjunction with experimental data.Production forecasting based on simulation output requires further processing in order to translate the black oil surface phase fluxes into products such as sales gas, LPG and condensate. For gas-condensate reservoirs, such post-processing of results from the simulation of depletion or cycling above the dew point is valid. In principle it is invalid for cycling below the dew point but in practice it can still provide useful information.

Determination of Bubble-Point and Dew-Point Pressure Without a Visual Cell

2014 ◽  
Author(s):  
R.. Hosein ◽  
R.. Mayrhoo ◽  
W. D. McCain
Keyword(s):  
Oil And Gas ◽  
Saturation Pressure ◽  
Volatile Oil ◽  
Phase Region ◽  
Gas Condensate ◽  
Dew Point ◽  
Bubble Point ◽  
Point Pressure ◽  
Dew Point Pressure

Abstract Bubble-point and dew-point pressures of oil and gas condensate reservoir fluids are used for planning the production profile of these reservoirs. Usually the best method for determination of these saturation pressures is by visual observation when a Constant Mass Expansion (CME) test is performed on a sample in a high pressure cell fitted with a glass window. In this test the cell pressure is reduced in steps and the pressure at which the first sign of gas bubbles is observed is recorded as bubble-point pressure for the oil samples and the first sign of liquid droplets is recorded as the dew-point pressure for the gas condensate samples. The experimental determination of saturation pressure especially for volatile oil and gas condensate require many small pressure reduction steps which make the observation method tedious, time consuming and expensive. In this study we have extended the Y-function which is often used to smooth out CME data for black oils below the bubble-point to determine saturation pressure of reservoir fluids. We started from the initial measured pressure and volume and by plotting log of the extended Y function which we call the YEXT function, with the corresponding pressure, two straight lines were obtained; one in the single phase region and the other in the two phase region. The point at which these two lines intersect is the saturation pressure. The differences between the saturation pressures determined by our proposed YEXT function method and the observation method was less than ± 4.0 % for the gas condensate, black oil and volatile oil samples studied. This extension of the Y function to determine dew-point and bubble-point pressures was not found elsewhere in the open literature. With this graphical method the determination of saturation pressures is less tedious and time consuming and expensive windowed cells are not required.

Monitoring the composition of gas condensate well streams based on samples taken from multiphase flow meters

2021 ◽  
pp. 127-139
Author(s):  
E. A. Gromova ◽  
S. A. Zanochuev
Keyword(s):  
Multiphase Flow ◽  
Oil And Gas ◽  
Gas Condensate ◽  
Flow Meters ◽  
Gas Condensate Reservoirs ◽  
Reliable Estimation ◽  
Development Control ◽  
Hydrocarbon System ◽  
Labor Consuming

The article highlights the relevance of reliable estimation of the composition and properties of reservoir gas during the development of gas condensate fields and the complexity of the task for reservoirs containing zones of varying condensate content. The authors have developed a methodology that allows monitoring the composition of gas condensate well streams of similar reservoirs. There are successful examples of the approach applied in Achimov gas condensate reservoirs at the Urengoy oil and gas condensate field. The proposed approach is based on the use of the so-called fluid factors, which are calculated on the basis of the known component compositions of various flows of the studied hydrocarbon system. The correlation between certain "fluid factors" and the properties of reservoir gas (usually determined by more labor-consuming methods) allows one to quickly obtain important information necessary to solve various development control tasks.

An Equation of State Compositional Model

1980 ◽  
Vol 20 (05) ◽  
pp. 363-376 ◽  
Author(s):  
Keith H. Coats
Keyword(s):  
Equation Of State ◽  
Critical Point ◽  
Oil And Gas ◽  
Numerical Models ◽  
Volatile Oil ◽  
Gas Condensate ◽  
Numerical Dispersion ◽  
Stable Convergence ◽  
Compositional Model ◽  
Miscible Flooding

Abstract This paper describes an implicit, three-dimensional formulation for simulating compositional-type reservoir problems. The model treats three-phase flow in Cartesian (x-y-z) or cylindrical (r-theta-z) geometries. Applicability ranges from depletion or cycling of volatile oil and gas condensate to miscible flooding operations involving either outright or multicontact-miscibility.The formulation uses an equation of state for phase equilibrium and property calculations. The equation of state provides consistency and smoothness as gas- and oil-phase compositions and properties converge near a critical point. This avoids computational problems near a critical point associated with use of different correlations for K values as opposed to phase densities. Computational testing with example multicontact-miscibility (MCM) problems indicates stable convergence of this formulation as phase properties converge at a critical point. Results for these MCM problems show significant numerical dispersion, primarily affecting the calculated velocity of the miscible-front advance. Our continuing effort is directed toward reduction of this numerical disperson and comparison of model results with laboratory experiments for both MCM and outright-miscibility cases.We feel that the implicit nature of the model enhances efficiency as well as reliability for most compositional-type problems. However, while we report detailed problem results and associated computing times, we lack similar reported times to compare the overall efficiency of an implicit compositional formulation with that of a semi-implicit formulation. Introduction Many papers have treated increasingly sophisticated or efficient methods for numerical modeling of black-oil reservoir performance. That type of reservoir allows an assumption that reservoir gas and oil have different but fixed compositions, with the solubility of gas in oil being dependent on pressure alone.A smaller number of papers have presented numerical models for simulating isothermal "compositional" reservoirs, where oil and gas equilibrium compositions vary considerably with spatial position and time. With some simplification, the reservoir problems requiring compositional treatment can be divided into two types. The first type is depletion and/or cycling of volatile oil and gas condensate reservoirs. The second type is miscible flooding with MCM generated in situ.A distinction between these types is that the first usually involves phase compositions removed from the critical point, while the second type generally requires calculation of phase compositions and properties converging at the critical point. A compositional model should be capable of treating the additional problem of outright miscibility where the original oil and injected fluid are miscible on first contact.A difficulty in modeling the MCM process is achievement of consistent, stable convergence of gas-and oil-phase compositions, densities, and viscosities as the critical point is approached. A number of studies have reported models that use different correlations for equilibrium K-values as opposed to phase densities. Use of an equation of state offers the advantage of a single, consistent source of calculated K-values, phase densities, and their densities near a critical point. SPEJ P. 363＾

Features of the geological structure of an oil and gas condensate field in Tomsk region

2021 ◽  
pp. 48-60
Author(s):  
S. F. Mulyavin ◽  
A. V. Byakov ◽  
R. A. Neschadimov
Keyword(s):  
Oil And Gas ◽  
Phase State ◽  
Volatile Oil ◽  
Geological Structure ◽  
Gas Condensate ◽  
Gas Content ◽  
Oil Viscosity ◽  
Coastal Marine ◽  
Tomsk Region ◽  
Continental Origin

The X oil and gas condensate field is located in Parabel district of Tomsk region; the field is large in terms of recoverable reserves. Oil and gas content is confined to Jurassic sediments of Tyumen suite and Vasyugan suite. The reservoirs of the Vasyugan suite are marine and coastal-marine sediments, characterized by alternating sandstones, mudstones, siltstones, clays and exhibit complex internal aging. The productive deposits of the Tyumen suite are of continental origin and are distinguished by significant lithological variability. One oil deposit (J11 stratum), one gas and oil deposit (J12 stratum) and three gas condensate deposits (J13-4, J3, J4-5 strata) were identified in the productive formations. The article analyzes the features of the geological structure and conditions of sedimentation of productive strata. In terms of its phase state and physicochemical proper-ties, the fluid of the J11 deposit is a "volatile oil", phase state of which is close to the near-critical. Reservoirs of productive formations are of terrigenous type, porous, low-permeability, while the oil productivity of the formations is high due to the ultra-low oil viscosity.

scholarly journals Evaluating the Challenges in Pressure – Volume – Temperature (PVT) Analysis of Gas Condensate Reservoirs

2020 ◽  
pp. 13-18
Author(s):  
Aniedi B. Usungedo ◽  
Julius U. Akpabio
Keyword(s):  
Gas Condensate ◽  
Production Performance ◽  
Prediction Algorithm ◽  
Pvt Properties ◽  
Volume Depletion ◽  
Volume Factor ◽  
Gas Condensate Reservoirs ◽  
Compositional Model ◽  
Compositional Modeling ◽  
Black Oil

Aims: The variations in production performances of the Black oil and compositional simulation models can be evaluated by simulating oil formation volume factor (Bo), gas formation volume factor (Bg), gas-oil ratio (Rs) and volatilized oil-gas ratio (Rv). The accuracy of these two models could be assessed. Methodology: To achieve this objective some basic parameters were keyed into matrix laboratory (MATLAB) using the symbolic mathematical toolbox to obtain accurate Pressure Volume Temperature (PVT) properties which were used in a production and systems analysis software to generate the production performance and hydrocarbon recovery estimation. Standard black oil PVT properties for a gas condensate reservoir was simulated by performing a series of flash calculations based on compositional modeling of the gas condensate fluid at the prescribed conditions through a constant volume depletion (CVD) path. These series of calculations will be carried out using the symbolic math toolbox. PVT property values obtained from both compositional modeling and black oil PVT prediction algorithm are incorporated to determine the production performance of each method for comparison. Results: The absolute open flow for the black oil PVT algorithm and the compositional model for the Rs value of 500 SCF/STB and Rs value of 720SCF/STB were 130,461 stb/d and 146,028 stb/d respectively showing a 10.66% incremental flow rate. Conclusion: In analyzing PVT properties for complex systems such as gas condensate reservoirs, the use of compositional modeling should be practiced. This will ensure accurate prediction of the reservoir fluid properties.

scholarly journals Reservoir Simulation Analysis of Pressure Depletion Performance in Gas-Condensate Reservoirs: Black-Oil and Compositional Approaches

2021 ◽  
pp. 119-137
Author(s):  
Akinsete O. Oluwatoyin ◽  
Anuka A. Agnes
Keyword(s):  
Relative Permeability ◽  
Reservoir Simulation ◽  
Gas Condensate ◽  
Productivity Index ◽  
Liquid Density ◽  
Gas Condensate Reservoirs ◽  
Compositional Model ◽  
Pressure Depletion ◽  
Black Oil ◽  
Black Oil Model

Pressure depletion in gas-condensate reservoirs create two-phase flow. It is pertinent to understand the behavior of gas-condensate reservoirs as pressure decline in order to develop proper producing strategies that would increase gas and condensate productivity. Eclipse 300 was used to simulate gas-condensate reservoirs, a base case model was created using both black-oil and compositional models. The effects of three Equation of States (EOS) incorporated into the models were analysed and condensate dropout effect on relative permeability was studied. Analysis of various case models showed that, gas production was maintained at 500MMSCF/D for about 18 and 12 months for black-oil and compositional models, respectively. However, the compositional model revealed that condensate production began after a period of two months at 50MSTB/D whereas for the black oil model, condensate production began immediately at 32MSTB/D. Comparison of Peng-Robinson EOS, Soave-Redlich-Kwong EOS and Schmidt Wenzel EOS gave total estimates of condensate production as 19MMSTB, 15MMSTB and 9MMSTB and initial values of gas productivity index as 320, 380 and 560, respectively. The results also showed that as condensate saturation increased, the relative permeability of gas decreased from 1 to 0 while the relative permeability of oil increased from 0.15 to 0.85. The reservoir simulation results showed that compositional model is better than black-oil model in modelling for gas-condensate reservoirs. Optimal production was obtained using 3-parameter Peng-Robinson and Soave-Redlich-Kwong EOS which provide a molar volume shift to prevent an underestimation of liquid density and saturations. Phase behaviour and relative permeability affect the behaviour of gas-condensate reservoirs.

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