This paper provides specific guidelines for choosing the PVT model, black-oil or equation of state (EOS), for full-field reservoir simulation of volatile/near-critical oil and gas condensate fluid systems produced by depletion and/or gas injection. In the paper we have used a "generic" reservoir from the North Sea containing a fluid system with compositional grading from a medium-rich gas condensate upstructure, through an undersaturated critical mixture at the gas-oil contact, to a volatile oil downstructure. A component pseudoization procedure is described which involves a stepwise automated regression from the original 22-component EOS. We found that a six-component pseudoized EOS model described the reservoir fluid system with good accuracy and, for the most part, this EOS model was used in the study. Methods are proposed for generating consistent black-oil PVT tables for this complex fluid system. The methods are based on consistent initialization and accurate in-place surface gas and surface oil volumes when compared with initialization with an EOS model. We also discuss the trade-off between accurate initialization and accurate depletion performance (oil and gas recoveries). Each "reservoir" is simulated using black-oil and compositional models for various depletion and gas injection cases. The simulated performance for the two PVT models is compared for fluid systems ranging from a medium rich gas condensate to a critical fluid, to slightly volatile oils. The initial reservoir fluid composition is either constant with depth or exhibits a vertical compositional gradient. Scenarios both with saturated and undersaturated GOC are considered. The reservoir performance for the two PVT models is also compared for different permeability distributions. Reservoir simulation results show that the black-oil model can be used for all depletion cases if the black-oil PVT data are generated properly. In most gas injection cases, the black-oil model is not recommended—with only a few exceptions. We also show that black-oil simulations using solution oil/gas ratio equal to zero (rs=0) does not always define a conservative ("P10") sensitivity for gas injection processes. If gravity segregation is strong, the incremental loss of oil recovery due to "zero vaporization" is more than offset by exaggerated density differences caused by erroneous gas densities. Introduction Reservoir simulation is a versatile tool for reservoir engineering. Usually CPU-time is the limiting factor when the simulation model is made. The objective of this paper is to provide guidelines for choosing black-oil or compositional reservoir simulators. The paper also recommends procedures for generation of black-oil PVT tables and for initialization of black-oil and pseudoized EOS simulation models. Furthermore, a stepwise component pseudoization procedure in order to minimize the number of component when a compositional simulator is required. Simulated production performance both for injection and depletion from black-oil and compositional are compared for a variety of reservoir fluids ranging a medium rich gas condensate to a critical fluid, to slightly volatile. Both reservoirs with constant composition and compositional grading reservoir with depth have been simulated. Selection of Reservoir Fluid System A fluid sample was selected from a North Sea field. The reservoir is slightly undersaturated with an initial reservoir pressure of 490 bara at the "reference" depth of 4640 m MSL. The selected reference sample contains 8.6 mol-% C7+, it has a two-stage GOR of 1100 Sm3/Sm3 and a dewpoint of 452 bara at 163°C. Table 1 gives the reference fluid composition (Fig. 1). 22-Component SRK EOS Model The Pedersen et al. SRK1 EOS characterization method was used to generate the "base" EOS model. Decanes-plus was split into 9 fractions using the EOS simulation program PVTsim.

This paper presents verification of the Fevang and Whitson (1996) gas-condensate pseudopressure method for layered reservoirs. Layers may be characterized by widely varying permeability and composition, and they may be communicating or noncommunicating. The pseudopressure method is used in well calculations for coarse-grid models with relatively large areal grid dimensions (>50 m), capturing near-well condensate blockage without local grid refinement, thereby reducing run time and model size.The paper presents examples from several field studies and from two synthetic systems using a commercial reservoir simulator. The field studies include rich-and lean-condensate reservoirs.The study was conducted using a commercial compositional reservoir simulator. Three-dimensional multilayer, fine-grid (radial and Cartesian) models and equivalent coarse-grid models were used. Both depletion and gas-injection cases were simulated for a wide range of reservoir fluids. Reservoir performance of fine-grid models and coarse-grid models was compared using the gas-condensate pseudopressure method and showed comparable results in all cases studied, including relative permeabilities with capillarynumber dependence and high-velocity (␤) flow treatment.The coarse-grid model with pseudopressure is somewhat dependent on coarse-grid size, generally requiring ⌬x=⌬y≈50-100 m for lean gas condensates and ⌬x=⌬y≈100-200 m for rich gas condensates.The paper verifies for the first time that the gas-condensate pseudopressure method as proposed by Fevang and Whitson (1996) is valid and accurate for layered systems with significant heterogeneity (permeability variation), with and without crossflow, with and without capillary-number modification of relative permeabilities, and for widely ranging fluid compositions in each layer. Fevang and Whitson (1996) show that the flow toward a gascondensate well producing from a reservoir can be divided into three main flow regions, extending from the wellbore outward: the near-well region, the condensate-buildup region, and the singlephase gas region. Flow Regimes and Flow Behavior in a Gas-Condensate Well Near-Well Region (Region 1).The near-well region is an inner near-wellbore region saturated with oil and gas with both fl owing simultaneously. The fl owing composition [i.e., the gas/oil ratio (GOR)] within this region is constant throughout, and equal to the composition of the produced wellstream mixture. Furthermore, the dewpoint of the producing wellstream mixture equals the reservoir pressure at the outer edge of this region, p*.The gas relative permeability in this region is mainly a function of liquid-saturation distribution. The steady-state saturation distribution in Region 1 is determined (as a function of pressure) specifically to ensure that all liquid condensing from the singlephase gas and entering Region 1 has sufficient mobility to flow through and out of Region 1 without any net accumulation. Condensate-Buildup Region (Region 2). This is a region of condensate buildup in which the liquid con...

The paper presents a methodology to develop and apply an equation-of-state (EOS) multi-fluid model for a field in Tunisia. The EOS model was developed by matching measured PVT data for a near-critical oil sample. The fluid characterization was used to estimate contamination level in oil-based-mud contaminated MDT samples, calculate decontaminated sample composition, estimate zone composition based on clean-up test measured oil-gas ratio, estimate fluid composition of some layers where samples were not available, and study the effect of gas condensate blockage and capillary number on simulated well performance. In this field, reservoir fluids range from lean gas condensate to rich gas condensate and volatile oil. Clean up tests were conducted for all four zones encountered in the well, and oil-gas ratios were measured. During the clean up test of one zone, a near critical oil sample was collected and standard PVT experiments were conducted. Oil based mud (OBM) contaminated MDT samples were collected from six of the nine non-communicating layers, with OBM contamination levels between 20–65 wt% STO. An EOS model was developed after matching measured PVT data on the near-critical oil sample. The MDT samples were decontaminated using measured mud composition. The calculated decontaminated "clean" sample compositions were used in a reservoir simulation model to initialize the layer from which the MDT sample was taken. The developed EOS model was also used to estimate the fluid composition of different zones and layers without fluid samples. The zone fluid compositions were calculated based on measured test OGR. The EOS model, zone fluid compositions, decontaminated MDT samples, and layer mobilities were used to estimate fluid composition of the layers without samples. This paper provides a methodology that can be used in any other field.

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This paper presents a physically consistent approach to modify black-oil PVT tables for (1) eliminating, in some cases, so-called negative compressibilities, (2) extrapolating saturated and undersaturated properties to conditions beyond the limits of original PVT tables, and (3) guaranteeing physical consistency of gas and oil properties as a critical condition is approached. For a number of reasons a black-oil PVT table may contain inconsistencies that result in non-physical behavior in reservoir simulators, sometimes leading to slow run times and, sometimes, premature run termination. Physical inconsistencies can even arise when the black-oil PVT table is created by a physically-consistent EOS model. Black-oil behavior can also be inconsistent as a result of the method used to create the tables - e.g. correlations, conversion of laboratory data, or using EOS models where gas and oil phases are not in thermodynamic equilibrium. Three main methods are developed in the paper. First, an analysis of negative compressibilities is given and an approach to eliminate the problem is proposed. Negative compressibilities can arise because derivatives are held constant from one pressure table point to the next, inconsistent with the pressure-dependent evaluation of properties themselves. The second contribution of our paper is a method to extrapolate an existing black-oil table to higher saturation pressures than found in the original table. Extrapolation is possible to any higher pressure - including a critical ("convergence") pressure where phase properties become identical. The proposed method uses a piecewise-linear log-log relationship between black-oil (surface gas and surface oil) K-values and pressure. Our final contribution is a consistent method to calculate saturated and undersaturated black-oil PVT properties at interior and extrapolated pressures to those in the original table. A cubic EOS and the LBC viscosity correlation methods are used to provide physical consistency, even approaching a critical condition where gas and oil properties must become equal. Introduction Sometimes black-oil reservoir simulators take unexpectedly-large CPU time and experience numerical instability due to "problem" PVT data - e.g. physically-inconsistent or ill-behaved input, or "fill-in" data (in this paper, the black-oil PVT data are divided in two groups - input and fill-in data. All PVT data in the input table are termed as input data and all calculated (interpolated and extrapolated) PVT data are termed as fill-in data. Thus the fill-in data includes both interpolated and extrapolated data). The problem may be more pronounced for near critical fluid systems, but "bad" black-oil PVT data can be found for even the simplest fluid system. In this study, different types of consistency checks of the black-oil PVT data are described. An existing black-oil PVT table may need to be extrapolated to higher saturation pressures. A method to extrapolate an existing table is described. The extrapolation of the fluid (which is defined by RS or rs) is based on K-values of the surface oil and surface gas. In the paper, the fluids are divided in two groups - input fluids and extrapolated fluids. The fluid is termed "extrapolated" fluid if the solution Rs (or rs) is higher than the maximum solution Rs (or rs) in the input table. An EOS based method is used to calculate saturated and undersaturated fill-in data. The fill-in data are calculated both for input and extrapolated fluids. The LBC correlation is used to calculate oil and gas viscosities. Consistency Checks The conditions of physical and "numerical" consistency are defined for black-oil PVT tables. In general, physical consistency guarantees numerical consistency - i.e. reservoir simulation model stability with respect to phase and volumetric calculations. However, for near-critical conditions we have found that model stability is sometimes jeopardized by PVT properties with large pressure derivatives - e.g. dRs/dp and drs/dp. The reason for model instability may be the fill-in method, or it may be due to inadequate numerical methods.