Well Deliverability Assessment of Libyan Near-Critical Gas Condensate Field - Case Study

‫والغاز‬ ‫للنفط‬ ‫الدولي‬ ‫بنغازي‬ ‫مؤتمر‬Benghazi International Conference of Oil and Gas ---------------------------------- 2018 ------------------------------------------
Well Deliverability Assessment of Libyan Near
Critical Gas Condensate Field – Case Study
Presenter:
Raghd A. Gadrbouh

Contents
• Introduction
• Objectives
• Well A3 Test Analysis
• Simulation Model
• Relative Permeability Curves
• History Match of Well Test Data
• Well Deliverability Assessment
• Conclusions

Introduction
• Gas condensate reservoir is one of the most difficult
reservoirs to simulate and predict its performance.
• Well test modelling is an accurate tool to assess reservoir
parameters to be used in modelling these reservoirs.
• Usually gas condensate deliverability is impacted due to
condensate banking around the wellbore.
• Reduces well productivity and gas relative
permeability.
• Restricts the flow of gas and condensate to the
surface.
• Well A3 of NC98 near critical rich gas condensate field is
a good example of highly damaged heterogeneous gas
condensate bearing well.
Other
restrictions ?!

Objectives
• Assess and evaluate well A3 test data using conventional modern interpretation
techniques with the aim of estimating the average reservoir permeability,
composite skin and absolute open flow (AOF).
• Construct a Cartesian compositional model to simulate well A3 pressure transient
test with the proper description of Well A3 geological and rock data as well as the
phase behaviour modelling.
• Assess the proper relative permeability curves by studying the impact of different
curves on pressure history match, and understand the structure and geological
features of near well area.
• Evaluate the contribution of each skin component in the composite skin factor.
• Project the well to different production scenarios, and identify alternative options
for improving A3 well productivity and field life.

Well A3 Test Analysis
• The test interpretation indicated an
infinite acting system.
• The gas effective permeability is
relatively low of 13 mD for a tested
interval of 320 ft.
• The estimated skin value is around
46.
• This large skin value requires an
accurate analysis to understand the
contribution of each skin component
in the composite skin factor.
2000
3000
4000
5000
6000
7000
8000
0 20 40 60 80 100 120 140 160 180 200
Pressure,psia
Time, hrs
Well A3 DST #3 Pressure History
1
10
100
1000
0.001 0.01 0.1 1 10 100
ΔP'&ΔP(psi)
Δt (Hours)
ΔP vs Δt (DST) ΔP' vs Δt (DST)
5.45
MMscf
10.63
MMscf
13.61
MMscf

‫والغاز‬ ‫للنفط‬ ‫الدولي‬ ‫بنغازي‬ ‫مؤتمر‬Benghazi International Conference of Oil and Gas ---------------------------------- 2018------------------------------------------
Simulation Model
• Nine geological layers were identified, and then
divided into 22 numerical layers of the same
thickness.
• The constructed Cartesian compositional
simulation model consist of (29×29×22) active grid
blocks distributed exponentially.
• The damage and perforation skin are modelled by
a lower permeability zone around the wellbore
according to the Hawkins formula.
• The partial penetration skin was estimated from
Brons and Marting charts to be around 5.
• A3 near critical gas condensate fluid is
characterized by high CGR ~166 STB/MMSCF
with maximum liquid dropout of 25 %.
𝑆 𝑑 =
𝑘
𝑘 𝑑
− 1 l n
𝑟𝑑
𝑟𝑤

Relative Permeability Curves
• Gas-Condensate and Condensate-Water Relative Permeability Curves for the nine
geological layers were generated using Corey equation and SCAL analysis.
• Several relative permeability curves were generated to investigate krgmax, kromax, and
Scc effect on the history match.
𝒌𝒓𝒐 = 𝒌𝒓𝒐 𝒎𝒂𝒙
𝟏 − 𝑺𝒘𝒊 − 𝑺𝒄𝒄 − 𝑺𝒈
𝟏 − 𝑺𝒘𝒊 − 𝑺𝒄𝒄 − 𝑺𝒈𝒄
𝑵 𝒐
𝒌𝒓𝒈 = 𝒌𝒓𝒈 𝒎𝒂𝒙
𝑺𝒈 − 𝑺𝒈𝒄
𝟏 − 𝑺𝒘𝒊 − 𝑺𝒄𝒄 − 𝑺𝒈𝒄
𝑵 𝒈
𝒌𝒓𝒐 = 𝒌𝒓𝒐 𝒎𝒂𝒙
𝟏 − 𝑺𝒘 − 𝑺𝒄𝒄
𝟏 − 𝑺𝒘𝒊 − 𝑺𝒄𝒄
𝑵 𝒐
𝒌𝒓𝒘 = 𝒌𝒓𝒘 𝒎𝒂𝒙
𝑺𝒘 − 𝑺𝒘𝒊
𝟏 − 𝑺𝒘𝒊 − 𝑺𝒄𝒄
𝑵 𝒘
Sgc Scc Krgmax Kromax Krwmax Ng No Nw
0.05 0.20 0.72 0.60 1.00 2.16 2.43 2.40

History Match of Well Test Data
• The initial runs were conducted with a simple
averaged model In order to reduce the complexity
of the real model.
• The model includes one average horizontal and
vertical permeability values, and one averaged
relative permeability curve for both systems.
• The actual DST data were fairly matched. The
simulated derivative curve seems to exhibit
improper match during the mid and late time
regions.
3500
4500
5500
6500
7500
0 40 80 120 160 200
Pressure(psia)
Time (Hours)
DST #3 Simulation Model
Parameter Spp kd/k Sd + Sp Kh, mD Kv, mD Swi, % Krgmax Kromax
Value 5 0.13 11.2 13 3.64 20 1 0.6
0.1
1
10
100
1000
0.001 0.01 0.1 1 10 100
ΔP'&ΔP(psi)
Δt (Hours)
ΔP vs Δt (DST) ΔP vs Δt (Model)
ΔP' vs Δt (DST) ΔP' vs Δt (Model)

• Two transmissibility barriers were introduced
to the model as non-negative transmissibility
multipliers less than unity.
• The locations of these transmissibility barriers
are 261.4 and 107.4 ft in x and y directions
and the applied factors are 0.3 and 0.03,
respectively.
• The transmissibility barriers restrict the flow to
the wellbore which may have reflected the
effect of non-sealing faults present within the
drainage area of the well.
1
10
100
1000
0.001 0.01 0.1 1 10 100
ΔP'&ΔP(psi)
Δt (Hours)
ΔP vs Δt (DST) ΔP vs Δt (Model)
ΔP' vs Δt (DST) ΔP' vs Δt (Model)
Slope Qg Permeability P1hr P1hr Skin
Psi/Cycle Mscf/D mD Psia Psi #
45 13328 13.7 7083 2054.5 46

• In order to simulate the flow behaviour properly, a
sophisticated model with the actual rock
petrophysical data and nine relative permeability
curves were constructed.
• The applied skin values and transmissibility
multipliers were as those obtained from the simple
average model.
• The model experience more pressure drop during
the drawdown periods; this pressure drop is
probably related to the reservoir heterogeneity.
• The imposed damage and perforation skin was
reduced to 7.63 in order to achieve a reasonable
match.
• The impact of relative permeability end-points and
the critical condensate saturation was investigated
through different relative permeability curves.
1500
3000
4500
6000
7500
0 40 80 120 160 200
Pressure(psi)
Time (Hours)
DST Model
0.1
1
10
100
1000
10000
0.001 0.01 0.1 1 10 100
ΔP'&ΔP(psi)
Δt (Hours)
ΔP(DST) ΔP'(DST)
ΔP'(Scc = 0%) ΔP(Scc = 0%)
ΔP'(Scc = 30%) ΔP(Scc = 30%)
ΔP(Scc = 20%) ΔP(Scc = 20%)
3000
4500
6000
7500
0 40 80 120 160 200
Pressure(psia)
Time (Hours)
DST
Model (Scc = 20%)
Model (Scc = 0%)
Model (Scc = 30%)2500
3500
4500
5500
6500
7500
0 40 80 120 160 200
Pressure(psia)
Time (Hours)
DST
Model (krgmax = 0.72)
0.1
1
10
100
1000
10000
0.001 0.01 0.1 1 10 100
ΔP'&ΔP(psi)
Δt (Hours)
ΔP(DST) ΔP'(DST)
ΔP(krgmax = 0.72) ΔP'(krgmax = 0.72)
ΔP'(krgmax = 0.8) ΔP(krgmax = 0.8)
ΔP(krgmax = 0.6) ΔP'(krgmax = 0.6)

Impact of Reservoir Heterogeneity
• The additional pressure drop during the drawdown
periods shall bring a question on which parameter
would result such drop.
• If all other parameters were fixed, reservoir
heterogeneity would have been the only difference
between the two previous models.
• In order to estimate the value of heterogeneity skin
for well A3 simulation model, the critical condensate
saturation is assumed to be zero (Sb ~0) and the
two models were compared.
• The comparison results indicated an additional
pressure drop due to reservoir heterogeneity which
is proportional to the gas rate during the drawdown
periods, it varies from 450 psia to 1000 psia
depending on the increase in gas rate.

Well Deliverability Assessment
• Well Inflow Performance (IPR)
• This could be attributed to either bad
reservoir characteristics or the
induced damage around the well or
both factors together.
• The well inflow performance curve
indicated a low potential production
value (AOF) despite the large tested
interval.
• If the induced damage will be taken
out; the measured composite skin
can be reduced from 46 to 10.
• Applying skin of 10 can improve the
actual IPR curve and the resulted
AOF will be around 70 MMscf/day.
0
3000
6000
9000
0 10000 20000 30000 40000 50000 60000 70000 80000
Pwf(psia)
Gas rate (Mscf/d)
Actual IPR
DST
Ideal IPR (Model)
Ideal IPR (Real Data)

• As the transient test data was fairly matched, we are
confident that the parameters used in this model represent
the actual well A3 properties.
• Two scenarios of natural depletion versus gas cycling were
compared. The natural depletion case was simulated with
a target production rate of 15 MMscf/d for 30 years.
• Since the well is severely damaged, the production
scenario was conducted for both cases (with and without
damage) as the well can be stimulated.
• Taking the damage effect into consideration, the well
showed almost no plateau period at the target flow rate
and 11 years plateau without damage.
• The gas recovery 73% and condensate recovery 30%
does not change with the different production scenarios.
0
1000
2000
3000
4000
5000
6000
7000
8000
0% 20% 40% 60% 80%
Pressure(psia)
Recovery Factor %
Condensate Recovery
Gas Recovery

• Dry gas is injected from four injectors – five spot
pattern – into the reservoir to maintain the reservoir
pressure and enhance the condensate production.
• Increasing the cycling duration from 5 to 10 years
enhances the condensate production and recovery
from 47% to 58% with no change in the gas recovery.
• Make-up gas is essential to balance the voidage
replacement, maintain the reservoir pressure, and
enhance the condensate sweep efficiency. The amount
is estimated to be 25 % of the reinjected dry gas.
• Condensate recovery improved significantly by a factor
of 6% with the make-up gas addition. 51% of the
condensate can be recovered in 5 years, rises to 64%
for 10 years cycling duration.
0
3000
6000
9000
0% 20% 40% 60% 80%
Pressure(psia)
Recovery Factor %
Cycling at Pi for 5 Years (Condensate RF)
Cycling at Pi for 10 Years (Condensate RF)
0
2000
4000
6000
8000
10000
0% 20% 40% 60% 80%
Pressure(psia)
Recovery Factor %
Condensate Recovery (5 Years Cycling)
Condensate Recovery (10 Years Cycling)

Conclusions
• Analysis of well A3 DST indicated a large skin value (~46) and a relatively low effective
permeability value of around 13 mD.
• History match of well A3 test data was used to generate the pseudo gas-condensate
relative permeability that reasonably represent the average dynamic flow around well A3.
• Single well compositional simulation model confirmed that the large composite skin is
mainly contribute by the well damage/perforation skin and reservoir heterogeneity skin
and the damage skin is found to have a compounding effect on other skin factors.
• Simulation model identified two flow barriers near the wellbore area, the barriers were
assumed to reflect the effect of non-sealing faults within the drainage area of the well.
• The simulated natural depletion case of well A3 cannot sustain a plateau rate of 15
MMscd/d due to the large damage around the well. However, if the well undergoes
stimulation process prior the production scheme, this target rate is expected to be
sustained for 11 years.
• Condensate recovery can be significantly enhanced by performing gas recycling up to 58
% for 10 years cycling duration. This recovery can be more enhanced (64%) with using
make-up gas.

Thank you!

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