SPE DISTINGUISHED LECTURER SERIESis funded principally

through a grant of thethrough a grant of the

SPE FOUNDATIONThe Society gratefully acknowledges

those companies that support the programby allowing their professionals

to participate as Lecturers.

And special thanks to The American Institute of Mining, Metallurgical,

1

p g gand Petroleum Engineers (AIME) for their contribution to the program.

SPE DISTINGUISHED LECTURER SERIESBridging over Uncertainty: dg g o e U ce ta ty

Past Performance into Forecasting

Dr. Sameh MacaryChevron-Australia

2

(previously, with IPR Group of Comp.)

“Th i k d t t“There are risks and costs to a program of actions. But they are far l th th l i k dless than the long-range risks and costs of comfortable inaction”.

J h F K dJohn F. Kennedy

“You want a valve that doesn’t leak and you try everything possible to develop one. But the real world provides you with a leaky valve. You’ve to determine how much leaking you can tolerate’

3

you can tolerate .

NASA Scientist-Colombia Project

O tliOutline• Risk vs uncertainty• Probabilistic approach• Different toolsDifferent tools• What’s past?• What’s forecasting?

4

Simple Definition: Uncertainty vs Risk?Simple Definition: Uncertainty vs Risk?

Uncertainty: range of Risk: potential gains or possibleevents

losses associated withparticular events

5

Probabilistic ApproachProbabilistic ApproachComplexity of The SystemComplexity of The System

Probabilistic ApproachProbabilistic ApproachComplexity of The System(incomplete Understanding)Complexity of The System(incomplete Understanding)

“Achievement is largely the“Achievement is largely the product of steadily raising one's levels of aspiration and expectation” Jack Nicklaus American Golfer

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expectation . Jack Nicklaus, American Golfer

Please Bring Probabilistic ApproachTo The “P” Part of E&P Industry!To The P Part of E&P Industry!

• Engineers: data is constraining!!!

M i & ff l !

• Geoscientists: data-departure point!!!

• More time, money & effort to get closer!– Conservative estimates;– Frequent surprises;– False precision

“The more an E&P Co. integrates its workflow and the more probabilistic its approach in decision making, the better the Co will perform”

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better the Co. will perform”

Schroeder Bank, Prudential Financial Research

Views of E&P WorkViews of E&P WorkConventional Tech. Probabilistic

$$$ uncertainty uncertainty

e &

$Ti

me

P10 P90 T

STOPUse time & $$ to

8

DeterminismUse time & $$ to find other projects

Monte Carlo Simulation

Variable 1Variable 2

Variable 3

Variable 4

Variable 3

Variable 6Variable 5

9

Try To Follow This Way!Try To Follow This Way!Unification

SensitivitySensitivityDiagnostic

Decision TreesInformation ValueInformation Value

Efficient Frontier

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Cumulative and Reverse Cumulative ChartsUnification

1 00CumulativeReverse Cumulative

.75

1.00

.50

or P

90

or P

90

M 23 02

. P10

P10

.25

Mean = 23.02Mean = 23.02.00

10.00 16.88 23.75 30.63 37.50

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Certainty is 80% from 17 to 30

Unification (Cont.) SPE # 68588, 2001

D illi C t $K 425 I iti l R t 100 BOPDDrilling Cost $K 425; Initial Rate 100 BOPD;Effective Decline Rate 50 %/yr, Exp. Decline;Price $ 17 5/bbl escalated 3 %/yr; OpExp $K 2/mo

12

Price $ 17.5/bbl, escalated 3 %/yr; OpExp $K 2/mo,escalated 3%/yr; Severance + Ad Valorem Tax total = 10%

Try To Follow This Way!U ifi ti

Try To Follow This Way!Unification

SensitivitySensitivityDiagnostic

Decision TreesInformation ValueInformation Value

Efficient Frontier13

K – ф Plot: Possible Example of Dependency Misleading

Sensitivity/Dependencies

ф p p y gm

Dea

bilit

y, m

Perm

e

14

Porosity, fraction

Sensitivity/Dependencies (Cont.)

Real Experiment Could Put an End to a Story!0.20

hm-m

0.10stiv

ity, o

hte

r Res

is

Sand B

S d A0.00

0 50 100M d W t I i (%)

Wat Sand A

Mud Water Invasion (%)

Resistivity of spun-out water from fresh core samples t k i S d A & B Th ff t f d t i i i

15

taken in Sand A & B. The effect of mud water invasion is evident from Tritium content in the water samples.

6510

0.0 0.5

Porosity

6510 0.0

Water SaturationWater Saturation

3810

0.01.0

Porosity

3810

0.00.5

Sensitivity/Dependencies (Cont.)Porosity Water Saturation

66006600

38503850

Sand A (unconsolidated) Sand B (tight)

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Good match between core Sw and the new log derived Sw incorporating Rw obtained from spun-out water.

Monte Carlo AssessmentMonte Carlo AssessmentSensitivity/Dependencies (Cont.)

200 MBPT Jobs (8 years)200 MBPT Jobs (8 years)

Log Normal DistributionLog Normal Distribution

yy

Triangular DistributionTriangular Distribution

yy

requ

ency

requ

ency

requ

ency

requ

ency

FrFrFrFr

Cost/Job, $MCost/Job, $M

1515 4545 10510575

No. of Jobs /YearNo. of Jobs /Year

1010 2727 353518

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Try To Follow This Way!U ifi ti

Try To Follow This Way!Unification

SensitivitySensitivityDiagnostic

Decision TreesInformation ValueInformation Value

Efficient Frontier18

reFailure Patterns!!! What Actions To Improve???

Diagnostic Plots

Source 2 1 0 1 0 4 50%

Migration 3 5 3 0 2 13 38%or F

ailu

re

13

Reservoir 2 7 8 6 5 28 29%

easo

n fo

Closure 1 0 3 6 1 11 55%

Seal 1 1 2 0 17 21 81%ctua

l Re

Seal 1 1 2 0 17 21 81%

Source Migration Reservoir Closure Seal Total Prob.

Ac

Pre-Drill Critical Risk

“If we all worked on the assumption that what is accepted as

19

p ptrue is really true, there would be little hope of advance”.

Orville Wright, 1871-1948, American Co-Inventor of the first practical airplane

Diagnostic Plots (Cont.)

25

75 90 45

100

60

65 40

60

475460 520 455

20

Rank Your Wells by Their RatesDiagnostic Plots (Cont.)

Ln Rank

Field gets mature21

Field gets mature1st Production Phase 2nd Phase 3rd Phase

Try To Follow This Way!U ifi ti

Try To Follow This Way!Unification

SensitivitySe s t tyDiagnostic

D i i TDecision TreesInformation ValueInformation Value

Efficient Frontier22

Decision Trees Built ChronologicallyDecision Trees

Re-develop or abandon?Time moves right

p

23

Re-develop DT CalculationsDecision Trees (Cont.)

p

24

Acid Stimulation-Decision TreeAcid Stimulation-Decision TreeDecision Trees (Cont.)

SuccessSuccessYesYes

CostCostContractorContractor

0.600.60

0.400.400 670 67

YesYes

YesYesNoNo0.40.4

< $ 21 M< $ 21 M

> $ 21 M> $ 21 M0.3100.310

1st1st0.670.67

0.330.330.770.77

YesYes

NoNo

NN

0.60.6

0 50 5

> $ 21 M> $ 21 M

< $ 18 M< $ 18 M

0.0740.0740.3100.310

0.230.230.750.75

0 250 25

YesYes

NoNo

NoNo

0.50.5

0.50.5> $ 18 M> $ 18 M0.1740.1740.4510.451

2nd2ndDecision

0.250.250.330.33

0.670.67

YesYes

NoNo

YesYes

0.60.6< $ 20 M< $ 20 M

3rd3rd

25

0.750.75

0.250.25

YesYes

NoNo0.40.4> $ 20 M> $ 20 M0.0470.047

0.2390.2393rd3rd

Try To Follow This Way!U ifi ti

Try To Follow This Way!Unification

SensitivitySensitivityDiagnostic

D i i TDecision TreesInformation ValueInformation Value

Efficient Frontier26

Efficient FrontierEfficient Frontier

The Efficient Frontier contains those portfolios for which there is: > No higher value for the same risk; and/or > No lower risk for the same value.

d

Increase Value

Rew

ard

Reduce Risk

R

27Risk

Summary-Performance TrackingConclusions/Recommendations

•“Yet, most every corporate effort to graft Army’s After Action Review (AAR) into their culture has failed because people reduce this truly innovative living practices to a sterile technique” Peter Sengeg p q g

•“Maximize the value of your data and incorporate ranges in inputs and outputs” so that “Pick one range-get two free probability distributions”outputs so that Pick one range get two free probability distributions

Sameh Macary

Let LetLearn Plan

Let us

Let us

DoMeasurego this

go this

28

this way

this way