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Netherlands Institute of Applied Geoscience TNO- National Geological Survey
E&P Decision & Risk Analysisby Christian Bos
D&RA for improved performance of the E&P industry
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 2
Contents
1. Objectives of RA, tools and methods
2. Features - Events - Processes (FEP) analysis• Objective: HSE impact assessment
3. E&P Best Practice project• FUN forum for Forecasting and Uncertainty; decision-making, etc.
4. “E&P Decision & Risk Analysis”• Objective: improved economic performance• History, past performance E&P industry• Continuous & Discrete uncertainties• Hierarchical constrained optimization under uncertainty• Options modelling• Decision analysis• Modelling: degree of holistic processing, degree of probabilistic processing
3. Conclusions
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 3
RA objectives, methods
1. Optimizing economic performance • Internal company capital investment decision-making process• Method / tools : D&RA + similar methods
2. License application / continuation• External orientation on government authorities• Focus on HSE, commerciality may have to be demonstrated• Method / tools : FEP analysis, perhaps D&RA-like approaches,
monitoring methods (Value of Information in terms of Risk)
3. Operational planning• External + internal focus: operational control• Method / tools: HAZOP
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 4
FEP methodologyFeature-Events-Processes
a scenario-based, qualitative approachusing a mental, not physical, model of FEP interrelations +
empirical evidence / expert elicitation to assess probabilities
Feature: system propertyEvent: (exogenous) disturbance of system equilibrium
Process: reaction of system due to disturbance
(Reaction may be subject to feedback loops, delayed, through chain of effects, non-linear: System Dynamics)
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 5
Qualitative scenario analysis
FEP identification
FEP classification
FEP selection and interaction
Scenario definition and selection
Model concept
Model buildingCons
e-quen
ceanaly
sis
SA ofkey factors
Qualitative Scenario Definition
FEP Analysis
Safety Assessment Model Development
Quantitative Impact Modelling
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 6
Mo
del
dev
elo
pm
ent
Risk identification / classification
Risk ranking /
screening
Risk interaction/ grouping
Scena rio (element) formation
Conceptual model
development
3D <> 2D numerical
model
Probabilistic 2D numerical
simulation
Statistical processing/ assessment
Testing with (natural)
analogues
Qu
ali
tati
ve
Sce
nar
io a
nal
ysis
C
on
seq
uen
ce
anal
ysis
Qu
an
tita
tiv
e
0. Definition of assessment basis
Mo
del
dev
elo
pm
ent
Risk identification / classification
Risk ranking /
screening
Risk interaction/ grouping
Scena rio (element) formation
Conceptual model
development
3D <> 2D numerical
model
Probabilistic 2D numerical
simulation
Statistical processing/ assessment
Testing with (natural)
analogues
Qu
ali
tati
ve
Sce
nar
io a
nal
ysis
C
on
seq
uen
ce
anal
ysis
Qu
an
tita
tiv
e
0. Definition of assessment basis
Mo
del
dev
elo
pm
ent
Risk identification / classification
Risk ranking /
screening
Risk interaction/ grouping
Scena rio (element) formation
Conceptual model
development
3D <> 2D numerical
model
Probabilistic 2D numerical
simulation
Statistical processing/ assessment
Testing with (natural)
analogues
Qu
ali
tati
ve
Sce
nar
io a
nal
ysis
C
on
seq
uen
ce
anal
ysis
Qu
an
tita
tiv
e
0. Definition of assessment basis
Risk assessment workflow (scenario approach)
0.0E+00
5.0E-03
1.0E-02
1.5E-02
2.0E-02
2.5E-02
0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000
time [years] since start injection
[kg/
d/m
2]
0
200
400
600
800
1000
1200
1400
mean CO2 fluxradius
1300 3800
storage efficiency:90.0%
Mo
del
dev
elo
pm
ent
Risk identification / classification
Risk ranking /
screening
Risk interaction/ grouping
Scena rio (element) formation
Conceptual model
development
3D <> 2D numerical
model
Probabilistic 2D numerical
simulation
Statistical processing/ assessment
Testing with (natural)
analogues
Qu
ali
tati
ve
Sce
nar
io a
nal
ysis
C
on
seq
uen
ce
anal
ysis
Qu
an
tita
tiv
e
0. Definition of assessment basis
1
2
3
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 7
1. Identification and classification of risk factors –- Database with risk factors (FEPs)
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 8
1. Assign quantitative probability of occurrence (expert opinion) – An example –
FEP Group
(node in relational diagram)
Probability of occurrence
in 100 years
Changes natural system 0.02
Geochemical processes &
conditions
0.086
Geomechanical human induced 0.031
Gas composition 0.036
Geomechanical, natural 0.027
Geomechanical, geochemically 0.011
Leaking seal 0.02
Leaking fault 0.01
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 9
1. Building a consistent probability framework withBayesian Belief Network (BBN)
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 10
2. Some FEPs may be selected for modelling fluxes and concentrations (II); Well leakage scenario: A realisation of CO2 saturation after 10 000 yrs
• Average values at -300 m: 23% released from reservoir
Maximum flux after 1500 years
Affected area:0.18 km2
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 11
E&P Performanceunderperformance due to bias &
unwillingness to learn from past & accept new methods
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 12
FUN - Forum for forecasting and uncertainty evaluation (1997 – 2004)
• The Forum is an effort by the authorities and
industry in Norway to determine best practice and
methods for hydrocarbon resource and emissions
estimation, forecasting, uncertainty evaluation and
decision-making.
• 18 member companies plus Norwegian Petroleum
Directorate (NPD)
• Info www.fun-oil.org
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 13
FUN - Members• A/S Norske shell U & P• Amerada Hess Norge A/S• BP Amoco Norge UA• RWE-DEA Norge A/S• Elf petroleum Norge A/S• Enterprise oil Norge ltd.• Esso Norge AS• Idemitsu petroleum Norge a.S.• Mobil exploration Norway inc.• Fortum petroleum A/S• Norsk Agip A/S• Norsk chevron A/S• Norsk Hydro ASA• Norske Conoco AS
• Norwegian Petroleum Directorate • Phillips Petroleum Company
Norway• Saga Petroleum a.s.• Den norske stats oljeselskap
(Statoil)• Total Norge A.S
Observers• Ministry of Petroleum & Energy• OLF• Miljøsok
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 14
The appreciation factor in relation to discovery volumes (NPD)
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 15
Oil Production Forecast NCS22 fields in production
0
20
40
60
80
100
120
140
160
180
1971
1974
1977
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
2019
Oil
pro
duct
ion
(mill
Sm
3 /år)
PDO - Forecast Actual production Fall 90 Fall 95 Fall 98
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 16
Comparison of investment forecasts for fields approved before 1997
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 17
Cost & Schedule risk
• Schedule uncertainty usually poorly managed, incl. correlation to
costs!• Opex only treated superficially: we tend to forget implications!• Later, incremental investments not properly planned: real options,
corrective actions etc. + incremental costs not formally included.
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 22
The context:decision-making under quantified uncertainty
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 23
D
FUN Benchmark study 2004
AA BB
CC
EEFF GG
HHII
0
2
4
6
8
10
12
0 2 4 6 8 10 12
Probabilistic processing
Inte
gra
tio
n
Benchmarking study (bp, ChevTex, ConPhil, ENI, Exxon, Hydro, RWE, Statoil, Total)
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 24
Integrated uncertainty analysis helps improving company performance
Ranking improves after introducing D&RA
0
2
4
6
8
10
12
14
16
1990 1992 1994 1996 1998 2000
Year (5 year period ending)
Ra
nk
Conoco
Chevron
Introduction of D&RA
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 25
The D&RA Process, how mgt & staff create synergy: team work!
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 26
Decision-making under uncertainty:full life-cycle perspective
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 27
Decisions and Levels of Aggregation
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 28
Using “Risk-tolerance” as optimisation constraint
• Project Risk = ∫IRR * pdf (IRR) d(IRR)
• i.e. cum.prob. x average IRR, if it is <WACC
• Project Risk = ∫NPV * pdf (NPV) d(NPV)
• i.e. cum.prob. x average NPV, if it is <0
• The decision-maker should then specify his/her risk-tolerance: for the project in question, and given other (portfolio) considerations, which cumprob x average NPV, i.e. if it is <0, am I prepared to accept?
• Risk-tolerance criterion can then be used as optimisation constraint to cut out bad decision-alternatives
WACC
- ∞
- ∞
0
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 29
……
mo
de
llin
g
……
mo
de
llin
g
……
mo
de
llin
g
……
mo
de
llin
g
……
mo
de
llin
g
Aggregated data integration along value chain
StaticModelling
DynamicModelling
Ge
om
ech
an
ica
l mo
de
llin
g
Ge
och
em
ica
l mo
de
llin
g
Se
ism
ic m
od
elli
ng
Ge
olo
gic
al m
od
elli
ng
WellModelling
FacilitiesEngineering
Co
nce
ptu
al D
esi
gn
Co
st E
ng
ine
erin
g
EconomicModelling
Pro
ject
/ass
et li
fe-c
ycle
mod
el
Ta
x /
PS
C m
od
el
Ve
rtic
al F
low
Pe
rfo
rma
nce
Ge
om
ech
. /
fra
ctu
ring
mo
de
l
Pdf’s of KPIs• per activity• per project• per asset
• per portfolio(in time domain)
Related to“value”:
• KPI-Targets• Optim. criteria• Constraints
• Risk tolerance
Proxymodel
Proxymodel
Proxymodel
Proxymodel
Fullmodel
Mu
lti-
dis
cip
lin
ary
da
ta a
gg
reg
ati
on
Multi-disciplinary data aggregation & model integration along value chain
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 30
Decision-making = value optimisation =hierarchical constrained optimisation under uncertainty given targets
Op
tim
isat
ion
cr
iter
ia
Op
timis
atio
n
co
ns
train
ts
Δvalue == Δprobability of meeting a set of pre-of meeting a set of pre-defineddefined time-series targets at the next hierarchical time-series targets at the next hierarchical decision-leveldecision-level
Corporate / portfolio level
Asset / field level
Project level
Operational level
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 31
KPIs – Key Performance Indicators to be optimised• Corporate, e.g.
• EPS, ROACE, ROCE, RRR, Production Income; Quality of Earnings; Production Replacement Ratios, Excluding Acquisitions & Divestments; Finding & Development Costs, Including Acquisitions & Divestments; Discounted Future Net Cash Flow; Upstream Returns
• Asset, e.g. • NPV (EMV); IRR; UTC; P/I; POT; proved developed reserves;
expected reserves; etc.
• Project, e.g. • Capex minimisation within time constraint
• Appraisal, e.g.• Value of Information (EMV)
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 32
Optimisation constraints• Usually, cost-related KPIs
• UTC, Maximum exposure, POT, RRR
• To be used as hurdle rate• E.g. WACC as hurdle rate for IRR, zero for NPV
• In the probabilistic mind-set, a risk-tolerance criterion should be added to act as optimisation (meta-)constraint:
• E.g. “I accept a probability-weighted NPV, if it is <0, of n $MM”• Then any project with a risk > n will be rejected.
• Other constraints:• Manpower, opportunities, HSE, time
• Integrated business models attempt to model “constrained KPI optimisation process”
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 33
Corporate Production Planning
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018
2020
2022
2024
Pro
duct
ion projects
developments
Producing fields
GAP
Target production
Probability of meeting portfolio multi-criteria objectives in time
Ref. SPE 68576 (Howell, Tyler): Using Portfolio Analysis to Develop Corporate Strategy
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 34
Corporate Net Cash Flow Planning
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018
2020
2022
2024
NC
F
projects
developments
Producing fields
NCF constraint
Probability of exceeding portfolio multi-criteria constraints in time
Risk tolerance to be specified: acceptable probability of not-meeting hurdle rate
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 35
Portfolio time-domain feedback mechanism to be included in asset decision-making
SF1 SF2
Objective function & constraints
(outside time domain!)
Verify contribution of “optimised” asset decision against portfolio objectives. If necessary, override stand-
alone asset decision.
Corporate Production Planning
2000
2002
2004
2006
2008
2010
2012
2014
2016
2018
2020
2022
2024
Pro
duct
ion projects
developments
Producing fields
GAP
Target production
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 36
D&RA - 5 main steps
1. Frame the
problem
2. Set-up quantitat.
models
3. Generate range of outcomes
4. Perform Sensitivity
Analysis
5. Apply Decision
Criteria
•Agree dec. crit.•Agree decisions•Agree scenarios•Construct tree•Prune tree•Agree tree
•Agree models•Populate model•Agree stoch. parameter pdf’s & scenario prob.•Agree / est. correlations•Agree KPIs•Agree risk def.
•Est. MC run parameters•Pdf’s of KPI’s•Quantify risks•Assess impact on portfolio•Est. utility fct, risk tolerance
•Tornado etc•Fine-tune decision altern.• Test robust-ness of decis: - model input - process par - utility fct - dec.sequence•VoI, VoF, ROV
•Describe process•Propose optim. solution+ impact on portfolio•Report•Monitor•Update model
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 37
Decision-Making framework required for valuation (“No impact? No value!”)
Modelling decisions and uncertainties in a combined frameworkModelling decisions and uncertainties in a combined framework
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 38
D&RA step 1: Pruning the tree (1)
• 96 end-nodes
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 39
Pruning the tree (2)
• 48 end-nodes : reduced by half
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 40
The ‘Value Loop’ (Shell)©
Execu
tion
Execu
tion
Data
Data
Physical Physical AssetAsset
ModelsModels
Dec
isio
ns
Dec
isio
ns
& P
lan
s&
Pla
ns
Asset Value Drivers &
Constraints
Data acquisition
Data acquisition
Interpre
tation
Interpre
tation
& Modelin
g
& Modelin
g
Generate &
Generate &
Evaluate
Evaluate
Options
Options
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 41
A typical scenario / decision tree
• Decision nodes, chance nodes, end-nodes (or leaves)• What happens in end-node?
• How is total statistical information used at decision-node?
Decision nodes
Chance nodes
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 42
Integrated, nested models to be run using Monte Carlo sampling process
1 pdf for each KPI
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 43
Options modelling to capture value-upside and mitigate value-risk: flexibility has value!
I1
yr1 yr2
SE_I1Type 3
0
yr3
-10
PlatformConstr.Type 2
1000
1000
300
700
WellDrilling
abandon
100
wait100
100
vertical
horizontal
100
100
I1
yr1 yr2
SE_I1Type 3
0
yr3
-10
PlatformConstr.Type 2
1000
1000
300
700
WellDrilling
abandon
100
wait100
100
vertical
horizontal
100
100
optionsample year 1 year 2 year 3 year 4 year 5 year 6 year 7 year 8 year 9 NPV
1 continue continue continue continue continue continue continue continue continue 5002 continue abandon -103 continue continue continue continue continue continue continue continue continue 3004 continue continue continue special special special special special special 8005 continue continue continue wait wait continue continue continue continue 3006 continue continue continue abandon 1007 continue continue continue continue continue continue continue continue continue 2008 continue continue continue continue continue continue wait wait wait 1009 continue continue continue continue continue continue continue continue continue 200
10 continue continue continue continue continue abandon 30252
• Using automatic “triggers” in
time-series (dynamic DT)• E.g., oil price expectation after
time-step n until end of project• Triggers can be combined
using Boolean operators, e.g.
E(fut. oil price) < 15
OR
CFn, n+5<0
AND
Prodn, n+5<3000
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 44
Problem framing: designing and evaluating options in dynamic decision trees
Quantify uncertainties
Predict probabilistically when & how theseuncertainties may be resolved in time (note 1)
Design, for each option, a decision algorithm basedon (expected) state variables /KPIs (to be applied at t=t1 …)
1. Distinguish unveiling of endogenous vs. exogenous information• Endogenous (project-specific): valuation of flexibility using EMV• Exogenous (general market, etc): valuation of flexibility using ROV
2. Unveiling of new info: distinguish model input (e.g. perm.) vs. model output parameters (e.g. qoil, NCF)
← “mapping uncertainty space onto decision space” →
Calculate, for each optional decision path in time, the NPV(by including cost of option)
← option valuation (“Value of Flexibility”) →
Design, for each scenario, options in response to (gradual) unveiling of truth (note 2)
Discontinue / delete any sub-optimal path(strike pull-out option)
← project valuation and ranking (including “Value of Flexibility”) →Calculate, for all dynamicallyoptimized (i.e. filtered), optional decision paths in time, the EMV of the full project
Compare this to EMV of alternative project definitions(with flexibilities) & rank
Select optimalproject definition
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 45
The task of all stakeholders in E&P decision making is to
• Correctly quantify, using
the available models, the
uncertainty in the KPIs, • Reduce the associated risk
(i.e. reduce the chance of
obtaining a KPI less than a
given value), • Grasp the associated
opportunity or upside
potential (i.e. maximise the
chance of obtaining a KPI
more than a given value) byjudiciously acquiring new information
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 46
The “modelling cube”
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 47
integration
prec
ision
unce
rtai
nty
Utopia:the dream
Current practice
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 48
integration
prec
ision
unce
rtai
nty
Utopia:the dream
Current practice
The high degree of model precision limits what we can achieve in terms of holistic and probabilistic modelling
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 49
The realistic dream?
integration
prec
ision
unce
rtai
nty
The utopiandream
Current practice
Gradually increase precision (decision-driven)
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 50
Discrete uncertainties(scenario trees)
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 51
Decision node(with risk&opp. factors)
Chance node(can be conditional)
End node (leaf)here calculations in
Fast Models are done
Dead-end node(ltd. calc. of FM)
Scenario / decision name
Scenario chance
Optimal decision(branch coloured red)
?
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 52
Continuous uncertainties(probability density functions)
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 53
Integrated Asset Management1 pdf for each KPI for each end-node
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 54
*
=
Monte Carlo Simulation Methodology, uncorrelated
*
RandomlySample
*
Revenue
Pr
Operating Expense
Pr
Capital Expenditure
Pr
Calculate
*
Cash Flow
Pr
0
Grey area = risk of NPV<0
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 55
*
Monte Carlo Simulation Methodology, correlated parameters (here >0)
*
RandomlySample
Calculate
*
=
Revenue
Pr
Operating Expense
Pr
Capital Expenditure
Pr
Cash Flow
Pr
0
Lower risk!
Samplecorrelated
*
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 56
Input parameters, output values and key performance indicators
SE DE RDP SF AP CO
INPUT PARAMETERS
STOIIP UR Qo(t)
#Wells
Qo(t)
CAPEX
Qo(t)
OPEX
OUTPUT VALUES
Indicators
Technical EconomicNPVetc...
STOIIPetc...
KEY PERFORMANCE INDICATORS
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 58
Integrating continuous and discontinuous uncertainties (pdf’s & scenarios)
Establish pdf of KPIs for each end-nodeNPVP /
I IRR
Correctly model scenario dependencies !
Sample individual KPI-pdf’s and time-series at chance nodes and construct merged pdf
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 59
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 60
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 62
Hierarchical optimization
• Optimized project should also be optimal for the asset’s life-cycle
• Optimized asset life-cycle should also be optimal for the company’s portfolio
• Etc.
• Risk cannot be assessed stand-alone for a project• Risk should be assessed in context of the portfolio
• Methods & tools required to preserve uncertainty relationships intra- & inter-project !
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 63
DSS-Portfolio imports DSS-Asset information …
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 64
… and optimises phasing of projects using objective function and EF
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 65
Probability vs. time of meeting set of corporate KPIs is optimised
November 8th, 2007ENGINE Workshop 7, Leiden (C. Bos) 68
Conclusion• State objective of RA very clearly
• HSE ? Are HSE-norms constraints for economic optimization?• Internal decision-making for capital allocation?• License application?• Operational planning?• Etc.
• Agree to which extent all processes can be modelled quantitatively, and whether models can be integrated
• Can impact models be integrated with economic models?
• See modelling more as a learning environment, rather than predictor of absolute truths
• Geosystem remains mainly poorly known!• Updating models & risk profiles, verifying assumptions…. LEARNING!• Design monitoring activities, design new decision options, strike options