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Transcript of 07 - Valuation Techniques
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Introduction
Probability, Distributions and Correlation
Estimating Under Uncertainty
Tight Clastics / Carbonate Assessment
Shale Assessment
Reservoir Flow
Valuation Techniques
Risk, Uncertainty & Economic Analysis
for Resource Assessment and Production
Forecasting in Shale and Tight Reservoirs
Decline Curve Analysis Approaches• Arps with best fit b
• Modified Hyperbolic Approach
• Hyperbolic followed by exponential
• Hyperbolic ‘b’ exceeds 1, then by hyperbolic ‘b’ less than 1
• Variable ‘b’ with time methods
• Stretched Exponential Production Decline (SEPD)
• Power Law Loss-Ratio
• Duong method – Assume all flow is linear
• The 5 Year rule for Modified Hyperbolic
• Reservoir Modeling
• Great progress but simulators require SRV input
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Arps Equation:
(1+bDit)1/b
Exponential decline b = 0
Harmonic decline b = 1
Hyperbolic decline b is between 0 and 1
This is a good approximation technique when we have steady
state flow conditions, with an unchanging drainage area.
Unfortunately in Resource Plays this is not the case!
q(t) = qi * eDit
q(t) = qi / (1+ Dit )
qi
Reserve Determination From Production Data
q(t) =
Arps Decline Equation
q(t) = Production rate at time t
qi = Initial Production rate
Di = The initial decline rate, in percent per year
t = Cumulative time in days, since the start of production
b = Arps’ decline constant
Arps Equation:
(1+bDit)1/b
qiq(t) =
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Arps’ Decline Models
John Lee, SPE Short Course
Critique of Arps’ Model
Requires: – Constant bottom-hole pressure
– Unchanging drainage area
– A fixed skin factor
– Stabilized (boundary dominated) flow for validity
Recent Observations – Best-fit ‘b’ values almost always >1 for recent gas wells
– Extrapolation to economic limit with high ‘b’ value leads to
unrealistically large reserves estimates – Linear flow likely for most of the economic life
of the well in ultra-low permeability reservoirs
Reserves → ∞ as rate → 0 (time → ∞) for b ≥ 1
After John Lee (2011)
q i
(1+bDit)1/bq(t) =
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10 YearsNo Boundary
SPE 138987 Brent Hale, W. Cobb & Associates
Barnett Vertical Wells
Linear Flow Plots
• Straight line on a 1/q vs sqrt of t plot
• ½ slope on a log rate vs log time plot
Performance of Very Low Perm Fractured Wells
Hough and McClurg (2011)
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0.1
1
10
0.00 5.00 10.00 15.00 20.00 25.00
R a t e M M S C F D
Time in Years
Exponential
Decline used
b = 0
Assumed
Dmin
Hyperbolic
decline used
b > 1
Common approach (i.e. PhD Win, Aries, PEEP):‘exponential tail-off’ after a hyperbolic decline:
Hyperbolic decline used until annual decline rate reaches
a pre-defined min, called Dmin., which in turn is based on
analogs or the experience of the engineer.
Typically Dmin of 4-10% is being used
An unconstrained extrapolation with best-fit b > 1 is unrealistic
Decline Curves Approaches: Modified Hyperbolic
Advantages
• Widely used for 20+ years
• Uses Industry standard Arps empirical model – Hyperbolic based on history matching followed by Exponential once
a predefined minimum decline rate is reached (terminal decline rate)
• Keeps reserves estimates conservative but within reason
• The two errors in this approach compensate each other – The imperfect ‘b’ value (exceeds 1) causes an over prediction of
reserves
– Applying a exponential terminal decline results in a negative error,
which causes a conservative estimate
Disadvantage
• Appropriate Analog data is required to estimate theminimum terminal decline rate
Decline Curves Approaches: Modified Hyperbolic
John Lee, SPE Short Course
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Terminal Decline & ‘b’ Value Variation
-Bakken Elm Coulee Field
SPE 149273, B. Kurtoglu, C.A. Cox, H. Kazemi
10 20 30 40 50
Years
10
100
1,000
Oil Rate, B / D
Dmin, (%/yr): 10 8 6 5 4
Forecasting EUR: Vertical Devonian Shale, KY
D(20 YRS) = 2.25%Field Average
169 Wells > 30
yrs prod history
D(20 YRS) = 2.37%
CUM = 704MMCF
EUR = 807MMCF
D(20 YRS) = 2.88%
CUM = 296MMCF
EUR = 715MMCF
D(20 YRS) = 3.2%
CUM = 199MMCF
EUR = 638MMCF
5 YEARS PROD
FED GAS OIL & COAL CO
(808685)
15 YEARS PROD
FED GAS OIL & COAL CO
(808685)
50 YEARS PROD
FED GAS OIL & COAL CO
(808685)
b = 2.0
Di = 23%
b = 1.94
Di = 31%
b = 1.6
Di = 29%
b = 1.4
Di = 34%
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Production Type Curves
The type curve approach is ideal when we have:d
• Random per-well recovery, not related, in a predictablemanner, to geological or operating parameters.
• Observed frequently:Shale in general
Cordilleran tight gas trends, Wamsutter, Jonah, Pinedale,
Canadian Deep basin
Neuquen Basin, Argentina
Spraberry Field , Canyon Sand– W Texas
But why not use reservoir models?
Kuuskraa (2007)
Estimates of well drainage
can be developed from
type-curve matching of
early production data once
reservoir properties are
established
in low perm sands
Type curve matching determines drainage area
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Steps to Generate Production ‘Type Curves’
1. Normalize rate time data to a common date.
2. Develop Type well curves for each distinct geological trend
with the same lithology.
3. Develop Type Curves which reflect your anticipated well
spacing.
4. Technology levers cause shifts in rate. Generate plots by
Time periods (30+ data before you subdivide), by Operator
and by Completion technology.
5. Determine the Correlation between the Initial and Average
30, 60 and 90 day Rate and the projected EUR for each
well.
Design of Production Type Curves
1. Normalize rate time data to a common date.
• Do not eliminate any wells unless it can be clearly demonstrated thatyou will never realize that outcome again. For example a failed newfrac fluid or early days when only 3 of 15 planned stages areexecuted.
• Extrapolate all wells using a best fit of the data. If in linear flow usethe 5 year rule for shale and 3 year rule for tight gas and tight oil
sands.
• Do NOT look for a typical P90 or P10 well and then use itsperformance to deliver the P90-P50-P10 Type curves. Wells aredynamic in their delivery. P10 wells become P90 wells etc as different
frac stages fluctuate in their delivery.
• Type curves should be based on well capability against planned backpressures. You will then need to account for operational downtimeas a separate line item. A good discipline in production optimization.
• Use monthly data. Using Excel statistics functions is good for largewell counts. Remember, Excel uses the data not the fitted curve.
Design of Production Type Curves
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Why Not Use The Nearest Offset?
SWN Energy July 2012 Investor Presentation
PetroHawk Investor Presentation May 2011
Honour All Wells in Developing Your Type Curves
- Extrapolate limited life wells and then normalize
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Steps to Generate Production ‘Type Curves’
1. Normalize rate time data to a common date.
2. Develop Type well curves for each distinct geological trend
with the same lithology.
3. Develop Type Curves which reflect your anticipated well
spacing.
4. Technology levers cause shifts in rate. Generate plots by
Time periods (30+ data before you subdivide), by Operator
and by Completion technology.
5. Determine the Correlation between the Initial and Average
30, 60 and 90 day Rate and the projected EUR for each
well.
Design of Production Type Curves
Meek and Levine (2005)
TPA 1: mod
to low; low rank
coal area
TPA 2:
TPA 3:
TPA 4: mod
to high; meteoric
rechargeColorado
v. lowoily coal
v. high
TPA = Type Producing Area
New Mexico
Fruitland
Coal
Outcrop
Fruitland Coals
Type Curves for Each Sub-trend
10 miles
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No flow boundary, as
coals begin to pinch out
and lose perm
Moderately
productive area w
abundant recharge
Structural
hingeline
Type Curves for Each Sub-trend
Lithology, setting and recharge impact production
Fruitland
Coal
Outcrop
10 miles
1,000
2,000
3,000
4,000
40 80 1200
0
M C F G / D
Months
TPA 3
n = 52
TPA4, n = 58
1, n = 65
2, n = 74
Peak water production
Fruitland Coals
Here the sweet spot of the trend is obvious, but
we will return for the need to compare geology to rate
Type Curves for Each Geologic Sub-trend
Meek and Levine (2005)
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Steps to Generate Production ‘Type Curves’
1. Normalize rate time data to a common date.
2. Develop Type well curves for each distinct geological trend
with the same lithology.
3. Develop Type Curves which reflect your anticipated well
spacing.
4. Technology levers cause shifts in rate. Generate plots by
Time periods (30+ data before you subdivide), by Operator
and by Completion technology.
5. Determine the Correlation between the Initial and Average
30, 60 and 90 day Rate and the projected EUR for each
well.
Design of Production Type Curves
Slick-Water Fracs
Time in Days
100
1 0 0 10
100500 150 200
SPE 107435, Russell K. Hall
Fayetteville Shale
D
a i l y G a s P r o d u c t i o n ,
M M C F D
1
0.1
Nitrogen Enhanced Fracs
Technology Levers Cause Shift in Rates
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Barnett StimulatedRock Volume for
Cross-linked Gel vs
Slick Water.
Increased SRV
validated by
doubling of the gas
production rates
Cipolla et al SPE 125532
Technology Levers Cause Shift in Rates
Southwestern Energy August 2010 Investor Presentation
Technology Levers Cause Shift in Rates
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Southwestern Energy July 2011 Investor Presentation
Technology Levers Cause Shift in Rates
Southwestern Energy July 2012 Investor Presentation
Technology Levers Cause Shift in Rates
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Steps to Generate Production ‘Type Curves’
1. Normalize rate time data to a common date.
2. Develop Type well curves for each distinct geological trend
with the same lithology.
3. Develop Type Curves which reflect your anticipated well
spacing.
4. Technology levers cause shifts in rate. Generate plots by
Time periods (30+ data before you subdivide), by Operator
and by Completion technology.
5. Determine the Correlation between the Initial and Average
30, 60 and 90 day Rate and the projected EUR for each
well.
Design of Production Type Curves
The term ‘Wolfberry’ was coined by Van Temple of Henry Resources
Shale Oil Exercise: WolfberryVariation in the Mean of the ERU based on Early Rate Data
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Assignment: Your Management has made a few simple requests:
1. What is the your estimate of the 80% confidence range in the average well
EUR for the upcoming 40 wells given the 7 wells drilled to date and the
analog selected?
2. You have two new wells with 30 day production rates of 160 BOE/D and
300 BOE/D respectively. You are asked to provide a best technical
estimate of the EUR. Using only the initial 7 well data set provide a range
for the EUR of these new wells.
3. Your budget calls for 10 wells based on a P50 rate of 150 BOE/D or more.
How large of a drilling program is required to realize an 90% confidence
that the program will average 150 BOE/D (the minimum to meet our hurdlerates)?
Shale Oil Exercise: WolfberryVariation in the Mean of the ERU based on Early Rate Data
SPEE (2011) Monograph 3
a n a l o g s
North American Unconventional Resource Plays P10/P90
of
Peak
Production
Month
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Assignment: Your Management has made a few simple requests:
a) Determine your assumed P10 / P90 ratio when more data are available.
Hint: Let’s agree to use 4. As you will see this is a good start.
b) Plot the data in Table 1 using the Mid-Point Approach
c) Use your P10 / P90 from step (a) to determine a range of EUR outcomes
with increased sampling. For simplicity assume the Mean will be found at
the same percentile and determine the range in Means based on your revised P10 / P90 ratio.
1. What is the your estimate of the 80% confidence range in the average well
EUR for the upcoming 40 wells given the 7 wells drilled to date and the
analog selected?
Shale Oil Exercise: WolfberryVariation in the Mean of the ERU based on Early Rate Data
EUR / Peak Rate: Rank them largest to smallest
Using the mid-point approach, build a Rate or EUR
Type Curve on linear and log vs probability paper:
1. Arrange all data by size, largest first and assign Rank
3. Tabulate data sizes with %tiles
4. Plot the data pairs
Percent ile = 100/n * (Rank – 0.5)
2. Determine the Percentile ValuesData %tile
1
2
3
4
etc
Rank
Largest
…
…
…
Smallest
100/n * (1 – 0.5)
100/n * (2 – 0.5)
100/n * (3 – 0.5)
100/n * (4 – 0.5)
Shale Oil Exercise: WolfberryVariation in the Mean of the ERU based on Early Rate Data
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TABLE 1Source: Drilling Info
Well #
Oil IP
(BO/D)
Gas IP
(MCF/D)
Oil Equiv
IP
(BOE/D)
Max
Month
Oil Rate
BO/D
Max
Month
Gas
Rate
MCF/D
Max
Month
Rate
BOE/D
Cum Oil
(BO)
Cum
Gas
(MCF)
Cum Oil
Equiv
(BOE)
EUR
(MBOE)
G2 80 108 98 86 110 104 11,820 13,258 14,030 92
H1 229 75 242 269 658 379 40,787 83,481 54,701 235
H2 186 114 205 202 298 252 41,486 71,468 53,397 152
H3 124 123 144 122 122 142 10,653 11,524 12,574 177
H4 210 273 256 115 223 152 63,752 167,800 91,719 309
H6 60 89 75 77 147 102 9,662 20,300 13,045 149
S1 64 56 73 145 165 173 43,336 60,681 53,450 137
Wolfberry Eastern Shelf Data
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Rank Order the EUR & Determine the
309 1 7.1
235 2 21.4
177 3 35.7
152 4 50.0
149 5 64.3
137 6 78.6
92 7 92.9
EUR
MBOE Rank
(100/n) *
(Rank - 0.5)
Force fit the Data to Lognormal
with a P10/P90 ratio of 4
Using the P10/P90 ratio of 4 line,
build an envelope where:
The Left side boundary passes
through the point furthest away
from the best fit line.
The right side boundary passesthrough the data point furthest to
the right of the best fit line.
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P10 / P90 of various aggregateper well EUR distributions
P10 / P90
P m e a n ,
%
t i l e
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
0 10 20 30 40 50 60 70
A d j u s t m e n t f r o
m M e a n t o P r o t f o l i o P 9 0
Log-N
Log-N
Log-N
Log-N
Log-N
Log-N
Aggregated Well Count
P e r c e n t a g e o f t h e M e a n V a l u e t o B o o k a s P r
o v e d
Trumpet Chart Approximation Method
For The Determination of The EUR
80% Confidence Interval
The P90 is approximately % of t
with a sample size of 7 wells and a
=
Assuming near symmetry from the
The P10 is approximately % of
a sample size of 7 wells and a P10=
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Assignment: Your Management has made a few simple re
2. You have two new wells with 30 day production rates of
300 BOE/D respectively. You are asked to provide a be
estimate of the EUR. Using only the initial 7 well data s
for the EUR of these new wells.
STEPS:
a) Cross-plot initial production rate versus EUR
b) Determine which Percentile 160 BOE/D and 300 BOE/D
c) Read off the EUR at the same Percentile.
d) Based on the Correlation Coefficient determine the rang
estimate.
Shale Oil Exercise: WolfberryVariation in the Mean of the ERU based on Early Rate D
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R² = 0.6622
0
50
100
150
200
250
300
0 100 200 300
IP vs EUR
EUR ‐ MBOE
I P ‐
B O E / D
30 Day Rate vs EUR
R =
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Rank Order the Rate & Determine the
256 1 7.1
242 2 21.4
205 3 35.7
144 4 50.0
98 5 64.3
75 6 78.6
73 7 92.9
IP
BOE/D Rank
(100/n) *
(Rank - 0.5)
Force Fit to Lognormal with a P10/P90 =
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Track Actuals vs. The Forecasted Aggregate Type CurveWhat Would You Recommend in This Scenario?
Actuals
• Information costs money.How can it add value?
• We can evaluate how this expenditure can addvalue with a decision tree
– One branch includes the expenditure
– One branch does not
• Information adds value if the expected value of the branch with the expenditure is greater than
the expected value of the branch that does not
Let’s look at an example
What is The Value of Information?
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After Both Coins Have Been Tossed,for a Small Fee You Can PEEK atOne Coin. If It Is a HEAD, You PLAY the Game FOR $5.
If the first coin Is a TAIL, You Cannot Win,So You Avoid the $5 LOSS.
How Much Would You Be Willing to Pay for a Peek at the FirstCoin Before You Decide to Play?
How about $1
Two Coin FlipBoth Heads, You Win $25
Any Tails, You Win 0
Expected Value & VOI
$0.00
Two Coin FlipBoth Heads, You Win $25
Any Tails, You Win 0
(0.25)($25 - $6) = +$4.75
(0.25)(-$6) = -$1.50
(0.25)(-$1) = -$0.25
WIN
(0.25)(-$1) = -$0.25Cost of
peek= $1
(0.25)($25-$5) = +$5.00
(0.25)(-$5) = -$1.25
WIN
(0.25)(-$5) = -$1.25
(0.25)(-$5) = -$1.25
HH
HT
TH
TT
HH
HT
TH
TT
Expected Value & VOI
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$0.00
WIN
Cost of peek= $1
WINHH
HT
TH
TT
HH
HT
TH
TT
Two Coin FlipBoth Heads, You Win $25
Any Tails, You Win 0
Solve
Value of peek:$2.75 – $1.25= $1.50
(0.25)($25 - $6) = +$4.75
(0.25)(-$6) = -$1.50
(0.25)(-$1) = -$0.25
(0.25)(-$1) = -$0.25
(0.25)($25-$5) = +$5.00
(0.25)(-$5) = -$1.25
(0.25)(-$5) = -$1.25
(0.25)(-$5) = -$1.25
Expected Value & VOI
Two Coin FlipBoth Heads, You Win $25
Any Tails, You Win 0
Value of the Peek:
A) Increased EV of VentureB) Lowered Exposure on Half the OutcomesC) Increased odds of Final Investment From 1:4 to 1:2
Value of Peek relative to Prize?$1 / $19 = or about 6 Percent
Now, who wil l play this game???I will play until I loose my $25
Expected Value & VOI
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Two Coin FlipPost Appraisal
What is the EV if you plannedto spend the $1 for the peek and $5for the second coin regardless of the firstcoin’s outcome?
EV = 0.25($25 -$6) + 0.75 (-$6) = 0.25
Without the ‘back-away’ option, this Is notan attractive alternative, relative to fl ippingboth coins
WORTHLESS
Expected Value & VOI
Two Coin FlipPost AppraisalSummary:
Prize: $25.00 - ($5.00+Peek)Pc = 0.25
Peek Cost % of Prize EV of Venture$1.00 6 $2.75
$2.50 14 $1.25 (same as EV w/o peek)
For This Venture, Your Spending Limit For New Data is 14% of the Value of the Prize
Expected Value & VOI
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Prevalent Assumptions:
• Only 1 Well Site Considered• Seismic Data will identif y, with certainty if Closure is high or low• A successful wel l has a PV of $1.0 MM• A dry hole has a PV of - $(0.8) MM• Seismic costs $100 K
The New Seismic Data Will Not:-- Illustrate Other Leads-- Change the Size (and Hence Value) of the Prospect-- Modify the Reservoir Chance
Chance Factors:
Source = 1.00Timing = 1.00Reservoir = 0.80Trap/Closure = 0.75Seal/Containm ent = 1.00 _____________________________
Pg = 0.60
Value of Informationa Ex VOI - 1
Scenario 1
Scenario 2
ProposedSeismic Line
ProposedSeismic Line
Chance Factors:
Source = 1.00Timing = 1.00
Reservoir = 0.80Closure = 0.75
Containment = 1.00
Pg = 0.60
Only 1 Well Site ConsideredSeismic Data Will Identify, with Certainty if Closure is High or Low.
E d g e of 3 D
E d g e of 3 D
owc
owcTop of Structure
Value of Information Ex VOI - 1
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Build tree in chronological order … with a place to
Drill (no seismic)
Shoot seismic
Value of Information
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Pitfalls in The VOI Approa
• Failing to realize that new information wresolve some of your uncertainties.
• When dealing with imperfect informatioassessing values in the wrong order on
decision tree often yields biased results
• Allowing the new information to alter thrange.
• Using intuition to estimate the value of information.
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Types of Informatio
• Perfect – Once you have the information, all d
removed
• Imperfect – Although the information has been
some questions, sometimes all of thquestions, still remain
– We will need to address this issue, have tools to do that
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2nd Stage Pilot CoCD
With encouraging information from analysis of past wells in an area, ybelieves that Pc for developing this UCR trend is 0.60; i.e. Pc = 0.60
(Option 1, time to commercial declaration = 2 years)
Upon additional assessment, your team believes that:1. An ini tial pi lot (IP = 4 wells), if successful will increase the Pc = 0
(Option 2, delaying commercial declaration by 3 years)2. If the IP is successful, you could conduct a 2nd stage pilot (16 we
further increasing the Pc = 0.9.(Option 3, delaying commercial production by 4 years)
3. PV of commercial success = $200 MM (not including any pilot cos4. PV of commercial failure
1. Option 1 PV (f) = $ -150 MM2. Option 2 PV (f) = $ -100 MM3. Option 3 PV (f) = $ - 25 MM
5. PV (IP) = $ - 7 MM6. PV (2nd) = $ -19 MM if a failure7. PV (2nd) = $ - 5 MM if a success8. Each pilot stage will defer commercial dev by 1 year.
The PV(success) will decrease by $15 MM for each year of dela
Initial PilotIP 2SP
Exercise – UCR Pilot Evaluatio
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P(CD) now = 0.60P(CD) given IP succeeds = PP(CD) given IP and 2nd stage
P(IP)
0.9
0.75
0.1
P(
P(
P(
0
0
P(
P(
proceed to CD (commercial dev)
initialpilot
proceed to commercial dev
proceedto CD
P(2SP)
P(2SP)~
P(IP)~
2nd Stage Pilot CoCDInitial PilotIP 2SP
Option 2 with Init Pilot
P(Ch wt value for Option1 = $60 MM
IP
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P(CD) now = 0.60P(CD) given IP succeeds = PP(CD) given IP and 2nd stage
0.80
0.9
0.75
0.1
CDIP 2SP
P(
P(
0.9
0.
proceed to CD (commercial dev)
initialpilot
proceed to commercial dev
proceedto CD
0.17
0.20
0.83
2nd Stage Pilot CoCDInitial PilotIP 2SP
Determine value at endnodes to determine mostvaluable option
200 (CD) - 7 (IP) - 5(2SP) - 30 (2 Y
- 7 (IP) - 5(2SP) - 25 (
- 7 (IP) - 19 (2
0.
0.
200 (CD) - 7 (IP) - 15 (1 Y
- 7 (IP) - 100 (
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0.80
0.75
CDIP 2SP
P(
P(
proceed to CD (commercial dev)
initialpilot
proceed to commercial dev
proceedto CD
0.17
0.20
0.83
2nd Stage Pilot CoCDInitial PilotIP 2SP
Observations:Prob at each CD = 0.60
0.
0.
0.
0.
Option 1: $60.0 MM
Option 2: $85.4 MM
Option 3: $87.5 MM
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developexplore demonstrate
proceed
to CD
delineate
P10
P90
P50
Fail, exit
Fail, exit
proceed directly to Commercial Dev
Regardless of which stage the project is in, Economics are to be run on a point forward basis.
Assume 40 ac spacing
over 10,000 ac
Cum well count = 293
@ $4MM / well > $1B Let’s recap: where is the portrayal of risk?
Fail, exit
e.g.
Drill 3 wells
check
min suiteand rates
e.g. comm demo
Drill 20 + wells
Uniformity/comm
Optimize frac
e.g.Drill 7 wells
Proof of concept
developexplore demonstrate
proceedto CD
delineate
P10
P90
P50
proceed directly to Commercial Dev
Regardless of which stage the project is in, Economics are to be run on a point forward basis.
Assume 40 ac spacing
over 10,000 ac
Let’s recap: where is the chance?
Fail, exit
Fail, exit
Fail, exit
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developexplore demonstrate
proceed
to CD
delineate
P10
P90
P50
proceed directly to Commercial Dev
Regardless of which stage the project is in, Economics are to be run on a point forward basis.
Assume 40 ac spacing
over 10,000 ac
Let’s recap: where is the uncertainty?
Fail, exit
Fail, exit
Fail, exit
Let’s recap: uncertainty in the type curve
months
RateFrom Modified Arps
Arps Eq (hyperbolic)
Modified Arps
Stretched Exponential
Power Law Loss
Duong Equation
Russell et al hybrid 5 yr
Transient Hyperbolic Exp
Methods described
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Let’s recap: portrayal of avg production
and costs
Play resources
from drilling programNote the symmetry that
comes from
addition
EUR / wellfrom type curve
GIP per wellNote the asymmetry
that comes from
mult.
Let’s recap: Resources involved
EUR / wellfrom analog
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Pg = 100%
Pe = 52%
PV
$296 M
Let’s recap: Distribution vs Decision Tree
Conclusions (1- 7 of 13)
1) Charging creates the pressure anomalies
2) When sample size is limited, rely on first principles
for distribution (population) shape and analogs for
population variability.
3) Reservoirs (except CBM) tend to be low perm.
4) Porosity cut-off considerations lowered with
improved completion techniques
5) In organic rich, thermally mature shales, different
mineral suites can become productive, as long as
clay content is minimal
6) Consider compositing multiple criteria to spatially
high-grade segments
7) Different segments (different geologic ingredients)
likely deserve different type curves.
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8) Consider completion techniques and spacing when
forecasting uncertainty in type curves.9) Type curves are critical for valuation, link play
chance assessments to minimal IP rates
modeled
10) A high play chance can lead to a very low Pe for a
drilling program; integrate engineering work with
geoscience early
11) Staged investments require staged plans.
Assessments should separate geologic chance
from commercial risk
12) From the reference decision point, the product of
all success case probabilities must be the same
for all options that reach that success state.
13) Confidence (chance) of achieving is related to
sample size
Conclusions (8-13 of 13)
Risk, Uncertainty & Economic Analysis
for Resource Assessment and ProductionForecasting in Shale and Tight Reservoirs