Post on 15-Apr-2020
10/10/2014
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REGIONAL ASSOCIATION OFOIL, GAS & BIOFUELS SECTOR COMPANIES
IN LATIN AMERICA AND THE CARIBBEAN
A New Probabilistic Approach to Proved Reserves Estimation in
Mature Fields
Brayam Valqui OrdoñezPETROPERÚ86th ARPEL Experts Level Meeting (RANE) “Management of Reservoirs” October 6-7, 2014 – Buenos Aires, Argentina
86th ARPEL Experts Level Meeting (RANE) “Management of Reservoirs“ – October 6-7 – Buenos Aires, Argentina
Outline
� Reserves Estimation Framework
� Conventional Deterministic Approach
� New Probabilistic approach to Reserves Evaluation in Mature Fields
� Case Study
o Block UNI: Field Development Plan and Reserves Estimation
� Conclusions
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10/10/2014
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86th ARPEL Experts Level Meeting (RANE) “Management of Reservoirs“ – October 6-7 – Buenos Aires, Argentina 3
Reserves Estimation FrameworkThe volume of existing hydrocarbons can be classified and categorized following PRMS guidelines in two
main factors: Degree of technical uncertainty (X axis) and Opportunity of commerciality (Y axis).
Incr
ea
sin
gC
ha
nce
of
Co
me
rcia
bil
ity
Range of Uncertainty
Represent Uncertainty in Our Estimates by Assigning Volumes to Categorize
86th ARPEL Experts Level Meeting (RANE) “Management of Reservoirs“ – October 6-7 – Buenos Aires, Argentina 4
Reserves Estimation FrameworkResources and Reserves Calculation Procedures
Decline curve analysis and Material Balance Estimations are the preferred
methods to calculate reserves in Brown Fields.
Appraisal and Initial
Development Stages
Volumetric Analysis
(Exploration, Appraisal and
Development Stages)
Material Balance
(Early Production)
Reservoir Simulation
(Early Decline)
Decline Curves
(Late Decline)Election
Oil Project Maturity and Availability of Information
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86th ARPEL Experts Level Meeting (RANE) “Management of Reservoirs“ – October 6-7 – Buenos Aires, Argentina 5
Conventional Deterministic Approach:
Deterministic Decline Curve Analysis
Deterministic Field Production Profile
Based in Experts’ Opinion
Case Type Decline Model
High Case Armónica
Base Case Hiperbólica
Low Case Exponencial
Low Case
Base Case
High Case
Companies usually assess three scenarios of performance
Pro
ve
dR
ese
rve
s
Un
cert
ain
ty
86th ARPEL Experts Level Meeting (RANE) “Management of Reservoirs“ – October 6-7 – Buenos Aires, Argentina 6
New Probabilistic Approach - Guidelines:• It is recommended to be used for providing consistency and confidence to proved reserves estimation in
mature fields.
• Three deterministic scenarios (Low, Base and High) were conceived based on expert opinion to initialize the
performance model.
• Ranges of uncertainty and statistical distribution functions of the decline rate (Di) and “b” exponent were
estimated.
• Rank correlation factors need to be carefully defined by expert opinion in order to set dependency between
b and D.
• Probabilistic production profiles were obtained by applying Monte Carlo Simulation; an expectation curve of
proved reserves is eventually built and conventional low, base and high scenarios can be extracted to
deterministic cash flow analysis.
• For undeveloped proved reserves, the methodology can be applied in the same way by estimating a type
curve for each field and/or reservoir. In addition, a hyperbolic model is associated to this type curve to be
treated under probabilistic approach by applying Monte Carlo Analysis.
• Total proved reserves are obtained by adding corresponding PDP and PUD production profiles.
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86th ARPEL Experts Level Meeting (RANE) “Management of Reservoirs“ – October 6-7 – Buenos Aires, Argentina 7
New Probabilistic Approach:
Flowchart - Holistic Probabilistic Model
Evaluation of the Production
and Pressure History
Performance Method
Selection: MBE or DCA
Verification of the Productive behavior
with Dynamic Reservoir Model
Built a Type Curve for each Field
and/or Reservoir
Assigning PDFs for Model
Parameters
Production Forecasting
under Uncertainty
86th ARPEL Experts Level Meeting (RANE) “Management of Reservoirs“ – October 6-7 – Buenos Aires, Argentina 8
New Probabilistic Approach
With the production history and using a decline curve analysis(DCA) software, we plot three scenarios of declination (2hyperbolic and 1 exponential).
195254 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98200002 04 06 08 10 12 14 16 18 20 22 2410
-2
10-1
100
101
102
103
104
105
pe
tday
, bb
l/d
Date
Working Forecast ParametersPhase : OilCase Name : reservesb : 0.867662Di : 0.0186723 M.e.qi : 76.9357 bbl/dt i : 01/31/2014te : 12/31/2025Final Rate : 19.0487 bbl/dCum. Prod. : 0.0769357 bbl/dCum. Date : 01/31/2014Reserves : 157.063 bbl/dReserves Date : 12/31/2025EUR : 157.14 bbl/dForecast Ended By : TimeDB Forecast Date : Not SavedReserve Type : Proven-Developed
195254 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98200002 04 06 08 10 12 14 16 18 20 22 2410
-2
10-1
100
101
102
103
104
105
pet
day,
bb
l/d
Date
Working Forec as t ParametersPhase : OilCase Name : reser vesb : 1.4264Di : 0.0135676 M.e.qi : 76.9357 bbl/dti : 01/31/2014te : 12/31/2025Final Rate : 30.1019 bbl/dCum. Prod. : 0.0769357 bbl/dCum. Date : 01/31/2014Reser ves : 195.881 bbl/dReser ves Date : 12/31/2025EUR : 195.958 bbl/dForecast E nded By : TimeDB For ecast Date : Not SavedReser ve Type : Proven-Developed
195254 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98200002 04 06 08 10 12 14 16 18 2010
-2
10-1
100
101
102
103
104
105
petd
ay,
bbl
/d
Date
Working Forecast ParametersPhase : OilCase Name : reservesb : 0Di : 0.0236751 M.e.qi : 76.9357 bbl /dti : 01/31/2014te : 03/31/2021Final Rate : 9.8129 bbl/dCum. Prod. : 0.0769357 bbl/dCum. Date : 01/31/2014Reserves : 85.2696 bbl /dReserves Date : 03/31/2021EUR : 85.3465 bbl /dForecast Ended By : RateDB Forecas t Date : Not SavedReserve Type : Proven-Developed
Probabilistic density functions are chosen to modeluncertainty in decline curve parameters accordingly tohistorical production performance and expert opinion
b D
The Monte Carlo method is applied, then the simulation is donefor every month until 2027 (A hyperbolic decline model is used)
Año 2014Año 2014
Año 2027Año 2027
biiO tbDQQ
1
)1(−
+=
The results of the monthly production forecasts areranged in percentiles; as a result, probabilisticcumulative production curves are obtained.
0
50
100
150
200
250
300
350
Jan
-14
Jul-1
4
Feb
-15
Sep
-15
Ma
r-16
Oct-1
6
Ap
r-17
No
v-17
Ma
y-18
De
c-1
8
Jul-1
9
Jan
-20
Au
g-2
0
Feb
-21
Sep
-21
Ma
r-22
Oct-2
2
Ma
y-23
No
v-23
Jun
-24
De
c-2
4
Jul-2
5
Jan
-26
Au
g-2
6
Ma
r-27
MS
TB
Pronóstico de Producción Probabilístico
Reservas Probadas: SPE Field
90%
85%
80%
75%
70%
65%
60%
55%
50%
45%
40%
35%
30%
25%
20%
15%
10%
0
50
100
150
200
250
300
350
Jan
-14
Jul-1
4
Feb
-15
Sep
-15
Ma
r-16
Oct-1
6
Ap
r-17
No
v-17
Ma
y-18
De
c-1
8
Jul-1
9
Jan
-20
Au
g-2
0
Feb
-21
Sep
-21
Ma
r-22
Oct-2
2
Ma
y-23
No
v-23
Jun
-24
De
c-2
4
Jul-2
5
Jan
-26
Au
g-2
6
Ma
r-27
MS
TB
Pronóstico de Producción Probabilístico
Reservas Probadas: SPE Field
90%
85%
80%
75%
70%
65%
60%
55%
50%
45%
40%
35%
30%
25%
20%
15%
10%
−
−= −− )1()1(
11)1( bb
ii
bi
P qqDb
qN
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86th ARPEL Experts Level Meeting (RANE) “Management of Reservoirs“ – October 6-7 – Buenos Aires, Argentina
� Using the production history and a decline curve analysis software, three declinescenarios are plot (2 hyperbolic and 1 exponential). Each decline scenario yieldscorresponding b and D parameters with a Qi= 76.9 bpd).
195254 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98200002 04 06 08 10 12 14 16 18 20 22 2410
-2
10-1
100
101
102
103
104
105
petd
ay,
bbl/d
Date
Working Forecast ParametersPhase : OilCase Name : reservesb : 0.867662Di : 0.0186723 M.e.qi : 76.9357 bbl/dti : 01/31/2014te : 12/31/2025Final Rate : 19.0487 bbl/dCum. Prod. : 0.0769357 bbl/dCum. Date : 01/31/2014Reserves : 157.063 bbl/dReserves Date : 12/31/2025EUR : 157.14 bbl/dForecast Ended By : TimeDB Forecast Date : Not SavedReserve Type : Proven-Developed
195254 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98200002 04 06 08 10 12 14 16 18 20 22 2410
-2
10-1
100
101
102
103
104
105
petd
ay, b
bl/d
Date
Working Forecast ParametersPhase : OilCase Name : reservesb : 1.4264Di : 0.0135676 M.e.qi : 76.9357 bbl/dti : 01/31/2014te : 12/31/2025Final Rate : 30.1019 bbl/dCum. Prod. : 0.0769357 bbl/dCum. Date : 01/31/2014Reserves : 195.881 bbl/dReserves Date : 12/31/2025EUR : 195.958 bbl/dForecast Ended By : TimeDB Forecast Date : Not SavedReserve Type : Proven-Developed
Hyperbolic Hyperbolic b > 1
195254 56 58 60 62 64 66 68 70 72 74 76 78 80 82 84 86 88 90 92 94 96 98200002 04 06 08 10 12 14 16 18 2010
-2
10-1
100
101
102
103
104
105
petd
ay, b
bl/d
Date
Working Forecast ParametersPhase : OilCase Name : reservesb : 0Di : 0.0236751 M.e.qi : 76.9357 bbl/dti : 01/31/2014te : 03/31/2021Final Rate : 9.8129 bbl/dCum. Prod. : 0.0769357 bbl/dCum. Date : 01/31/2014Reserves : 85.2696 bbl/dReserves Date : 03/31/2021EUR : 85.3465 bbl/dForecast Ended By : RateDB Forecast Date : Not SavedReserve Type : Proven-Developed
Exponential
b=0
D=2.37%
Model Inputs
Case Study: Block UNI
“b” and “D” have a inverse relationship
b=0.86
D=1.87%
b=1.42
D=1.36%
86th ARPEL Experts Level Meeting (RANE) “Management of Reservoirs“ – October 6-7 – Buenos Aires, Argentina
• Beta-pert distributions were used to model b and D under uncertainty.
• Monte Carlo simulation was applied monthly until 2027; probabilistic production profiles and cumulative curves were
developed.
b
D
Probabilistic Hyperbolic Decline Model for Oil Production
Dependency Di and b
Di
b
Case Study: Block UNI
Year: 2014Year: 2014
Year: 2027Year: 2027
|)1(1
biiO tbDQQ
−
+=
−
−= −− )1()1(
11)1( bb
ii
bi
P qqDb
qN
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86th ARPEL Experts Level Meeting (RANE) “Management of Reservoirs“ – October 6-7 – Buenos Aires, Argentina 11
Case Study: Block UNIProbabilistic Production Profile – Base Curve of the Block UNI
• Nine (9) producing wells from Ostrea Fm. were used to recover proved developed reserves (PD).
86th ARPEL Experts Level Meeting (RANE) “Management of Reservoirs“ – October 6-7 – Buenos Aires, Argentina 12
Case Study: Block UNIProbabilistic vs. Deterministic
Production Forecasting
Prob. High Case
Det. High Case
Base Case
Prob. Low Case
Det. Low Case
The best way to evaluate the uncertainty
is using the Probabilistic Approach
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86th ARPEL Experts Level Meeting (RANE) “Management of Reservoirs“ – October 6-7 – Buenos Aires, Argentina 13
Case Study: Block UNIProbabilistic Production Profile – Additional Work Program of the Block UNI
• Five (5) infill wells were agreed to be drilled to Ostrea Fm. to produce proved undeveloped reserves (PUDs).
86th ARPEL Experts Level Meeting (RANE) “Management of Reservoirs“ – October 6-7 – Buenos Aires, Argentina 14
Case Study: Block UNIProbabilistic Production Profile – Total Proved Reserves of the Block UNI
• Fourteen (14) developed wells were used to forecast total proved reserves (PT).
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86th ARPEL Experts Level Meeting (RANE) “Management of Reservoirs“ – October 6-7 – Buenos Aires, Argentina 15
Case Study: Block UNIProbabilistic Production Profile – Cumulative Production of the Block UNI
• From 200 to 300 MSTB of oil would be recovered in an interval confidence of 80%.
86th ARPEL Experts Level Meeting (RANE) “Management of Reservoirs“ – October 6-7 – Buenos Aires, Argentina 16
Conclusions• It is recommended to be used for providing consistency and confidence to proved reserves
estimation in mature fields.
• The methodology allows creating a parametric analytic model based on production history
and reservoir behavior. This production profile is used to forecast a “band” of proved
recoverable volumes under a confidence interval.
• Dependency between model inputs should be set in order to provide consistency to
forecasting probabilistic production profiles.
• To estimate proved undeveloped reserves is recommended first to build a “type curve” by
reservoir (or at least by well) using analogy principles; then, this type curve should be
converted to a “proxy” analytical model (using both/either DCA and/or MBE) to be run under
probabilistic approach by Monte Carlo simulation.
• A probabilistic cash flow analysis is the next step to evaluate the profitability of the project. A
probabilistic production profile should be the input to make a reliable cash flow analysis for
the project.
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Thank you
Questions?