Gas%20Condensate%20Blockage

1
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.

2
Gas Condensate BlockageProblem 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 wells
  reservoir PI by 50 to 200 (equivalent to a skin
  of 5 to 20).

3
Gas Condensate BlockageProblem 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 wells pressure drop is in
  the tubing.
• Low and moderate-permeability reservoirs ( kh lt
  about 10,000 md.ft ) may be affected by
  condensate blockage.
• Blockage can still be important for fractured and
  horizontal wells

4
Gas Condensate BlockageProblem 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.

5
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
  fields curves or Corey functions if no measured
  data.
• Radial single-well model carefully constructed to
  represent an average well in the full-field model.

6
Gas Condensate Blockage Recommended approach

• Build a single-well radial model
• Scale model to represent an average wells
  drainage area, OGIP, kh, etc.
• Pick gas rate as a wells share of the total
  fields plateau rate.
• Use r-z radial grid with 20-30 cells in r
  direction, with lt1 ft first block radius and
  logarithmic radial spacing. Run implicit.
• Use well tubing tables and THP control

7
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.

8
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.

9
Rock and straight line rel perms used to estimate
possible impact of condensate blockage.
Rock rel perms have crossover value of 0.08
10
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.

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Gas Condensate Blockage Recommended approach

• If the difference in plateau period is not
  significant, youre done. Dont 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.

12
High permeability reservoir - similar results
with rock and straight line rel perms
condensate blockage is not a problem
13
Low permeability reservoir - different results
with rock and straight line rel perms
condensate blockage impacts well deliverability
14
Gas Condensate Blockage Recommended approach

• In the low permeability reservoir
• The length of the plateau is reduced by gt50
  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.

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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.

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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.

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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
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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
19
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
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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

21
Estimating capillary number and choosing
near-well rel perms

1. Choose a time step near or just after the end of
   plateau.
2. Calculate the Capillary Number Nc and the
   interpolation parameter f (Nc) at each grid cell.
3. Take an average value of f e.g. weighted
   according to pressure drop across the cell.
4. Find the krg vs krg/kro curve for this average
   value of f.
5. Choose new kr vs Sg curves which honor the krg vs
   krg/kro relationship for this average value of f.
   

22
Choosing near-well rel perms
Straight line rel perms
From new kr vs Sg curves
Interpolated krg at average Nc
Rock curves
23
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.
24
Condensate blockage skin from single well model
Rock curves skin 20
With near-well curves, skin 7
25
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.

26
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.

27
Simulation exercise

• 1. (optional) Run 1D Sensor model for these
  cases. (Or just use these output files.)

1. 10 md, rock rel perms
2. 10 md, straight line rel perms
3. 100 md, rock rel perms
4. 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?
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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.
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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.
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• 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