Gas Condensate Blockage A worked example using a simplified simulation model to demonstrate Impact of condensate blockage in a lean gas condensate reservoir. Sensitivity to near-well relative permeabilities. How to estimate near-well relative permeabilities, taking account of velocity-dependent effects.

Gas Condensate Blockage Problem Description In a gas condensate well, FBHP dropping below the dewpoint causes a significant condensate saturation buildup near the wellbore, resulting in lowered gas relative permeability. This reduced gas permeability is called ‘condensate blockage’. It can lower a well’s reservoir PI by 50 to 200% (equivalent to a skin of 5 to 20).

Gas Condensate Blockage Problem Description (continued) Condensate blockage adds pressure drop which can be important to low- and moderate-permeability (kh) wells. High permeability (kh) wells show little effect because most of the well’s pressure drop is in the tubing. Low and moderate-permeability reservoirs ( kh < about 10,000 md.ft ) may be affected by condensate blockage. Blockage can still be important for fractured and horizontal wells

Gas Condensate Blockage Problem Description (continued) Fine grid simulation studies using measured ‘rock’ rel perm curves often predict a significant loss in gas deliverability due to condensate blockage. Recently, numerous authors have shown from field data that the use of rock curves in radial simulations overstates the condensate blockage effect – a different modeling approach is needed. Lab experiments show that rel perms in gas condensate systems increase at high velocity. The rel perms in simulation models need to take account of this effect.

Gas Condensate Blockage Recommended approach The following data will be used: PVT data for the reservoir fluid – black oil or EoS. Relative permeability curves from full-field simulation model (rock curves); use another field’s curves or Corey functions if no measured data. Radial single-well model carefully constructed to represent an average well in the full-field model.

Gas Condensate Blockage Recommended approach Build a single-well radial model Scale model to represent an average well’s drainage area, OGIP, kh, etc. Pick gas rate as a well’s share of the total field’s plateau rate. Use r-z radial grid with 20-30 cells in r direction, with <1 ft first block radius and logarithmic radial spacing. Run implicit. Use well tubing tables and THP control

Gas Condensate Blockage Recommended approach The first single-well radial model run should use rock curves. The gas & oil curves should cross at about 0.1 (0.05 - 0.12 usually); use Corey exponents 2-3 if core data are not available. Rock curves are considered the worst case for condensate blockage.

Gas Condensate Blockage Recommended approach The second single-well radial model run should use straight line (‘miscible’) curves. The straight-line miscible curves are considered the best case for condensate blockage.

Rock and straight line rel perms used to estimate possible impact of condensate blockage. Rock rel perms have crossover ‘value’ of ~ 0.08

Gas Condensate Blockage Recommended approach Check - The plateau period for the single-well radial run using straight line relative permeability curves should be about that seen for the full-field model; little blockage. Compare - The plateau period of gas production of the two single-well radial runs using rock and straight-line relative permeability curves.

Gas Condensate Blockage Recommended approach If the difference in plateau period is not significant, you’re done. Don’t worry about condensate blockage! If the difference in plateau period is significant and the correct period is important to the economics of the project, ‘engineer’ the condensate blockage problem.

High permeability reservoir - similar results with rock and straight line rel perms – condensate blockage is not a problem

Low permeability reservoir - different results with rock and straight line rel perms – condensate blockage impacts well deliverability

Gas Condensate Blockage Recommended approach In the low permeability reservoir The length of the plateau is reduced by >50% between the ‘best’ and ‘worst’ case scenarios for condensate blockage. In the worst case scenario, we would need more wells, more compression, etc. In practice, we will end up somewhere between the two extremes because of the increase in relative permeabilities at high capillary number.

How can we calculate the change in relative permeabilities at high velocity? Experimental data suggest that the changes can be correlated as a function of the Capillary Number. The Capillary Number (Nc) is a dimensionless number which measures the ratio between viscous and capillary forces.

Definition of Capillary Number Nc = velocity * viscosity / IFT ( Nc = DP(viscous)/Pc ) ‘velocity’ is the superficial pore gas velocity – Darcy velocity / porosity / (1-Swc) Data must be in consistent units – simplest is to use SI units - velocity in m/s, viscosity in Pa.s, IFT in N/m.

Change in Rel Perms with Capillary Number - Eclipse 300 model Needs at least 7 empirical parameters – suitable values not published in open literature! Increasing Nc Increasing Nc

Change in Rel Perms with Capillary Number - Fevang-Whitson Model Needs only 2 empirical parameters – suitable values published in open literature. Based on krg as a function of krg/kro – this is the fundamental rel perm relationship which controls condensate blockage. Increasing Nc

Change in Rel Perms with Capillary Number - Fevang-Whitson Model Interpolates between rock and miscible (straight line) rel perms at fixed values of krg/kro

How can we run a single-well simulation with the ‘correct’ relative permeabilities for the near-well region? EITHER Use a compositional simulator (e.g. Eclipse 300) with a model for velocity dependent rel perms. Need to know the parameters for the E300 correlation OR Estimate capillary number and rel perms manually. Described in the next 2 slides This will give a first approximation of the importance of the Nc effect

Estimating capillary number and choosing near-well rel perms Choose a time step near or just after the end of plateau. Calculate the Capillary Number Nc and the interpolation parameter f (Nc) at each grid cell. Take an ‘average’ value of f – e.g. weighted according to pressure drop across the cell. Find the krg vs krg/kro curve for this average value of f. Choose new kr vs Sg curves which honor the krg vs krg/kro relationship for this average value of f.

Choosing near-well rel perms Straight line rel perms From ‘new’ kr vs Sg curves Interpolated krg at ‘average’ Nc Rock curves

Repeat simulation using ‘near well’ rel perm curves For this example, the use of velocity-dependent rel perms has a significant impact, and more detailed study is justified.

Condensate blockage skin from single well model Rock curves – skin ~ 20 With ‘near-well’ curves, skin ~ 7

Full field simulation where condensate blockage is an important issue (1) Three levels of modeling (in increasing order of complexity) Coarse grid model with condensate blockage skin from single well models. Coarse grid model with generalized pseudopressure (GPP) model for well inflow. GPP model accounts for condensate blockage in the well inflow equations Use local grid refinement around the wells.

Full field simulation where condensate blockage is an important issue (2) Coarse grid with generalized pseudopressure (GPP) model is the recommended approach in almost all cases. GPP model only requires a small overhead GPP model can include velocity-dependent rel perms Including LGRs increases run time and affects numerical stability. LGR only recommended for very lean gas condensates in models with very large grid cells. In this case LGR does not treat blockage per se, but provides accurate flowing GORs to the GPP model.

Simulation exercise 1. (optional) Run 1D Sensor model for these cases. (Or just use these output files.) 10 md, rock rel perms 10 md, straight line rel perms 100 md, rock rel perms 100 md, straight line rel perms 2. Look at development of the condensate bank with time – radius of bank and gas rel perms in the bank. 3. Plot gas production rates and look at impact of condensate banking. Is it important for the 100 md reservoir? For the 10 md reservoir?

Simulation exercise (continued) For the 10md, rock rel perms case 4. Calculate condensate blockage skin, and compare with results from simple spreadsheet. 5. Calculate capillary number for each grid cell near end of plateau. 6. Find a typical value of the parameter f for interpolating between ‘rock’ and straight line rel perms. Assume a = 4000, n = 0.7. Calculate the krg vs krg/kro relationship for the condensate bank. 7. Find rel perm curves which give similar krg/kro behaviour for the range of krg/kro values that occur in the condensate. 8. Repeat simulations with these new ‘interpolated near-well’ rel perm curves, and calculate condensate blockage skin.

Simulation exercise (results) Gas production profiles show little difference between st line and rock curve results for 100 md reservoir, but a significant difference for 10 md. The calculations of capillary number and relative permeability interpolation give an average value of f of about 0.75. A rel perm calculation shows that rel perm curves with Corey exponents of 1.9 the same krg vs krg/kro relationship the interpolated curves with f = 0.75. Gas production profiles shows a plateau of about 3.5 years, compared with 1.5 years using rock rel perms and 4.5 years using straight line rel perms.

References Gas Condensate Relative Permeability for Well Calculations Measurement of Relative Permeability for calculating Gas Condensate Well Deliverability Calculating Well Deliverability in Gas condensate Reservoirs Notes