Netherlands Institute of Applied Geoscience TNO - National Geological Survey E&P Decision & Risk...

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


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


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)


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


Mo

del

dev

elo

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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

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anal

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0. Definition of assessment basis

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Risk ranking /

screening



Conceptual model

development

3D <> 2D numerical

model


simulation



analogues

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anal

ysis

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Risk ranking /

screening



Conceptual model

development

3D <> 2D numerical

model


simulation



analogues

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ysis

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anal

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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]

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mean CO2 fluxradius

1300 3800

storage efficiency:90.0%

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Risk ranking /

screening



Conceptual model

development

3D <> 2D numerical

model


simulation



analogues

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1

2

3


1. Identification and classification of risk factors –- Database with risk factors (FEPs)


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


1. Building a consistent probability framework withBayesian Belief Network (BBN)


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


E&P Performanceunderperformance due to bias &

unwillingness to learn from past & accept new methods


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


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


The appreciation factor in relation to discovery volumes (NPD)


Oil Production Forecast NCS22 fields in production

0

20

40

60

80

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120

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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


Comparison of investment forecasts for fields approved before 1997


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.


The context:decision-making under quantified uncertainty


D

FUN Benchmark study 2004

AA BB

CC

EEFF GG

HHII

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12

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Probabilistic processing

Inte

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Benchmarking study (bp, ChevTex, ConPhil, ENI, Exxon, Hydro, RWE, Statoil, Total)


Integrated uncertainty analysis helps improving company performance

Ranking improves after introducing D&RA

0

2

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1990 1992 1994 1996 1998 2000

Year (5 year period ending)

Ra

nk

Conoco

Chevron

Introduction of D&RA


The D&RA Process, how mgt & staff create synergy: team work!


Decision-making under uncertainty:full life-cycle perspective


Decisions and Levels of Aggregation


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


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Aggregated data integration along value chain

StaticModelling

DynamicModelling

Ge

om

ech

an

ica

l mo

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Ge

och

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Se

ism

ic m

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Ge

olo

gic

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WellModelling

FacilitiesEngineering

Co

nce

ptu

al D

esi

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Co

st E

ng

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erin

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EconomicModelling

Pro

ject

/ass

et li

fe-c

ycle

mod

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x /

PS

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od

el

Ve

rtic

al F

low

Pe

rfo

rma

nce

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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


Decision-making = value optimisation =hierarchical constrained optimisation under uncertainty given targets

Op

tim

isat

ion

cr

iter

ia

Op

timis

atio

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co

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train

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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


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)


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”


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


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


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


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


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


D&RA step 1: Pruning the tree (1)

• 96 end-nodes


Pruning the tree (2)

• 48 end-nodes : reduced by half


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


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


Integrated, nested models to be run using Monte Carlo sampling process

1 pdf for each KPI


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


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


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


The “modelling cube”


integration

prec

ision

unce

rtai

nty

Utopia:the dream

Current practice


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


The realistic dream?

integration

prec

ision

unce

rtai

nty

The utopiandream

Current practice

Gradually increase precision (decision-driven)


Discrete uncertainties(scenario trees)


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)

?


Continuous uncertainties(probability density functions)


Integrated Asset Management1 pdf for each KPI for each end-node


*

=

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


*

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

*


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


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


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 !


DSS-Portfolio imports DSS-Asset information …


… and optimises phasing of projects using objective function and EF


Probability vs. time of meeting set of corporate KPIs is optimised


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

Netherlands Institute of Applied Geoscience TNO - National Geological Survey E&P Decision & Risk...

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