SlideShare ist ein Scribd-Unternehmen logo
1 von 52
Risk Modelling in the E&P industry

                             - the value of flexibility -




                                          Dr. Bart J.A. Willigers, MBA




Copyright © Palantir Economic Solutions
Structure presentation


  The importance of DA


               Value of flexibility


                                 Risk types


                                          Decision trees


                                                      Real options


                                                                     Wrap up
A new reality:
the high oil price is here to stay

٠ The E&P industry is slowly coming to terms with a new reality, after two
   decades during which oil prices fluctuated around $25 per barrel: the
   present high oil price is here to stay.


                                                                          Brent spot price

                100
                 90
                 80
                 70
      USD/bll




                 60
                 50
                 40
                 30
                 20
                 10
                  0
                      02/01/1986


                                   02/01/1988


                                                02/01/1990


                                                             02/01/1992


                                                                            02/01/1994


                                                                                         02/01/1996


                                                                                                      02/01/1998


                                                                                                                   02/01/2000


                                                                                                                                02/01/2002


                                                                                                                                             02/01/2004


                                                                                                                                                          02/01/2006
   Source: Energy Information Administration
High energy demands drives
high risk investments

٠ Unprecedented energy demand
   ٠ A key driver for the all-time high oil prices is a global increase in the
        demand for energy.
٠   Unprecedented high risk investments
     ٠ In order to satisfy this demand the E&P industry has intensified their
        search for Greenfield opportunities and initiated a large number of
        highly technical challenging projects.
     ٠ The investments and risks associated with these projects are
        unprecedented
     ٠ …..hence the need for high quality DA processes
DA process as a key success factor

٠ Decision making as competitive advantage
   ٠ Differentiation in technologies and data collection is difficult
   ٠ A competitive advantage can be achieved by superior data management
        and decision making processes
٠   Decision analysis is ultimately driven by economic metrics
The challenge of
 accurate decision making




   Enhancing the            turnaround time and
 accuracy invariably
    means more                  consistency.
     complexity
The accepted practise of
risk modelling

٠ Enhanced financial modelling
   ٠ The oil and gas industry has started to realise the value of enhanced
        financial modelling.
           • Probabilistic modelling
           • Monte Carlo simulations

٠ However, the value of flexibility is rarely recognised as a key value driver.
Structure presentation


  The importance of DA


               Value of flexibility


                                 Risk types


                                          Decision trees


                                                      Real options


                                                                     Wrap up
Reacting on unfolding uncertainty:
The concept of optimisation

Decision one:
To drill or not



       Drill well             Build platform   Oil sales




                      Decision two:
                     Build platform if
                    drill is successful
NPV’s major restraint:
Capitalising on the value of flexibility

٠ Modelling uncertainty
   ٠ Economic results are typically presented as expected net present values
        and probability distributions.
٠   The failure to react on new information
     ٠ The net present valuation method assumes that once an investment is
        made, the project will run its course without intervention.
Managing uncertainty:
Investing in options

٠ Managing uncertainty
   ٠ Decision makers manage uncertainty by evaluating different feasible
       outcomes and plan for fall-back opportunities.
٠   Modelling uncertainty management
     ٠ Decision tree analysis and real option valuation.
An example of optimisation

No optimisation procedure
                         Oil         Build platform       Oil sales
          Drill well     50%             ($40)             $100

                        No oil       Build platform       Oil sales
                         50%             ($40)              $0

             ENPV = 50% * (-$40 + $100) + 50% * (-$40 + $0) = $10

Optimisation procedure
                         Oil        Build platform        Oil sales
          Drill well     50%             ($40)             $100

                        No oil    Do not build platform   Oil sales
                         50%              $0                $0

             ENPV = 50% * (-$40 + $100) + 50% * ($0 + $0) = $30
Structure presentation


  The importance of DA


               Value of flexibility


                                 Risk types


                                          Decision trees


                                                      Real options


                                                                     Wrap up
Two sources of risk:
Market - and technical risk

٠ For the purpose of risk optimisation a distinction is made
  between market- and technical risk
٠ These risks have different characteristics and require
  different modelling techniques
   ٠ Decision tree analysis and real option valuation
Market - and technical risk:
Characteristics


٠ Risk control
   ٠ Technical risk is, within limits, controlled by the assets
      owner
   ٠ Market risk is outside the control of the assets owner
٠ Risk mitigation
   ٠ Technical risk can be mitigated by investing in a large
      portfolio of assets.
   ٠ Market risk will affect the entire portfolio
Market - and technical risk:
Risk modelling

٠ Total number of possible outcomes
   ٠ Market risk often have a very large number of possible
     outcomes
   ٠ Technical risk is typically resolved in a limited number of
     possible outcomes
Examples of
Market – and technical risk


      Technical risks          Market risk

      Geological uncertainty   Hydrocarbon price


      Downtime operations      Fiscal terms

      Drilling                 Geopolitics

      Capital investment       Rig rates
Structure presentation


  The importance of DA


               Value of flexibility


                                 Risk types


                                          Decision trees


                                                      Real options


                                                                     Wrap up
Decision tree valuation


٠ Modelling technical risk
   ٠ The impact of technical risk is addressed by developing a series of
       scenarios.
     ٠ Risk optimisation in a decision tree is achieved by making an optimal
       choice an uncertainty is resolved.
٠   Modelling market risk
     ٠ Market risk is accounted for by a risk premium which is incorporated in
       the discount rate.
     ٠ Thus no risk optimisations opportunities are modelled that relate to the
       resolving of market uncertainty during the completion of a project.
Decision tree valuation:
An example

٠ Assume an exploration project
   ٠ With uncertain future production volumes
   ٠ In which a processing plant has to be constructed
         • There is a choice between
             – an expensive efficient large plant
             – and a cheaper but less efficient plant
         • The decision which plant to build can be postponed after the
           uncertainty regarding the production volumes is resolved
Decision tree modelling: assessing different scenarios

                                                   Incremental
                                         No. bll                 Revenue   Investment   NPV
                                                     revenue
                           Large plant
      Low case reserves
                                         100 bll     2 $/bll      200 $     (250 $)     (50 $)

           25%             Small plant

                                         100 bll    0.5 $/bll      50$       (50 $)      0$




                           Large plant
     Mid case reserves                   150 bll     2 $/bll      300 $     (250 $)     50 $


           50%             Small plant
                                         150 bll    0.5 $/bll      75$       (50 $)     25 $




                           Large plant
      High case reserves
                                         250 bll     2 $/bll      500 $      (250 $)    250 $

           25%             Small plant

                                         250 bll    0.5 $/bll     125$       (50 $)     75 $
Decision tree modelling: optimisation and roll back

                                                        NPV

            Low case reserves    Large plant

                  0$
                                               (50 $)

                 25%             Small plant             Max((50 $),0$)

                                                0$




            Mid case reserves    Large plant
   87.5 $        50 $                          50 $

                                                         Max(50 $,25 $)
                 50%             Small plant
                                               25 $



            High case reserves   Large plant
                 250 $
                                               250 $

                 25%             Small plant            Max(250 $, 75 $)

                                               75 $
Comparison of results

                Large plant                                         Small plant
              Low case reserves                                   Low case reserves

                 25%        (50 $)                                   25%         0$

     75 $     Mid case reserves                     31.25 $       Mid case reserves


                 50%            50 $                                 50%        25 $


            High case reserves                                 High case reserves

                 25%        250 $                                    25%        75 $




                                Optimisation Large/ small plant
                                         Low case reserves

                                            25%         0$

                       87.5 $            Mid case reserves


                                            50%        50 $


                                       High case reserves

                                            25%        250 $
Structure presentation


  The importance of DA


               Value of flexibility


                                 Risk types


                                          Decision trees


                                                      Real options


                                                                     Wrap up
Real option valuation


٠ Modelling market risk
   ٠ A mathematical process describes a range of gross project values at
       timex and the development of the range over time.
     ٠ Given a project value at timex, an optimal decision is made whether or
       not to proceed with the project.
     ٠ There is no need for a risk premium in the discount rate because market
       risk is explicitly modelled.
٠   Modelling technical risk
     ٠ Development of a series of scenarios as is done in decision tree
       analysis.
Real option valuation using the
Binominal approach

٠ Modelling a range of asset values with the binominal ROV
   ٠ At time zero the value of an asset has a certain value, when moving
        from period zero to period one this value can increase or decrease with
        an equal likelihood
    ٠   The change occurs as a discrete jump
    ٠   The jump process is repeated when moving from period one to period
        two, and continues going forward into the future. Over time a range of
        possible outcomes are generated
Binominal Real Option Valuation:
Building a lattice


t0     t1     t2         t3


                        Value
Binominal Real Option Valuation:
Building a lattice


t0     t1     t2         t3


                        Value




      110

100

      90.9
Binominal Real Option Valuation:
Building a lattice


t0     t1     t2         t3


                        Value   Cost

                        131     100

             123

      110               110     100

100          100

      90.9              90.9    100

             82.6

                        75.1    100
Binominal Real Option Valuation:
 Optimisation


t0     t1     t2         t3


                              Value

                         Max(131-100,0)



                         Max(110-100,0)



                        Max(90.9-100,0)



                        Max(75.1-100,0)
Binominal Real Option Valuation:
 Optimisation


t0     t1     t2         t3


                              Value

                         Max(131-100,0)



                         Max(110-100,0)



                        Max(90.9-100,0)



                        Max(75.1-100,0)
Binominal Real Option Valuation:
Discounting


t0          t1                           t2


                                                 Value

                               p              31/(1+0.015)

                                   1-p

                               p              10 /(1+0.015)

                                   1-p
                               p              0 /(1+0.015)

                                   1-p
                                              0 /(1+0.015)
P = Risk neutral probability
Binominal Real Option Valuation:
Rolling back the tree


t0     t1                    t2


                                     Value

                                     30.5

             0.55*30.5 + 0.45*9.85

                                     9.85

              0.55*9.85 + 0.45*0

                                      0
                0.55*0 + 0.45*0

                                      0
Binominal Real Option Valuation:
Discounting and roll back


t0                   t1                 t2




                                       21.2

        (0.55*21.2+0.45*5.4)/(1.015)

                                       5.4

         (0.55*5.4+0.45*0)/(1.015)

                                        0
Binominal Real Option Valuation:
Discounting and roll back to period 1


t0               t1                      t2




                                        21.2

                13.9

                                        5.4

                2.94

                                         0
Binominal Real Option Valuation:
 Discounting and roll back to period 0


             t0                 t1




                                13.9

(0.55*13.9+0.45*2.94)/(1.015)

                                2.94
Binominal Real Option Valuation:
Determine the option value


               t0

The option value is finally obtained when the
tree is discounted and rolled back to period 0.
The real option value is 8.85




              8.85
Comparison of results:
Optimisation versus no optimisation

  Optimisation             No optimisation
  t0    t1     t3    t4    t0    t1     t3      t4

                     31                         31

              21.2                     21.2

       13.9          10         12.1            10

 8.8          5.4         3.8          1.4

       2.9           0          -6.2           -9.1

               0                       -15.6

                     0                         -24.1
Note of warning!

٠ Several Real Option Valuation techniques have been proposed
   ٠ The binominal method is easy to explain but has some important
        limitations and its use is therefore not recommended
    ٠   Technique like the Least Square Mean Option Valuation are much more
        suited.
Structure presentation


  The importance of DA


               Value of flexibility


                                 Risk types


                                          Decision trees


                                                      Real options


                                                                     Wrap up
The value of flexibility is
project specific

٠ Value of flexibility
   ٠ Value metrics typically increase when risk optimisation is introduced in
         an economic model.
     ٠   The value added to the asset, the value of flexibility, is project specific
         and is determined by
           • the flexibility associated with the project
           • and the structure of the cash flow.
The right tool for the job…




         Real
                      Combined Real Options and
         Options
                      decision tree analysis
         analysis
                                                      Flexibility




Market   NPV                 Decision tree analysis
risk


          Technical risk
Some projects and their preferred
 economic tool


             ROV:               ROV/D-tree:
             Brownfield         Greenfield
             project            exploration
                                project

    Market
    risk
             Deterministic     D-tree:
             analysis:
                               Project with
             Brownfield        contracted oil
             project           price



                      Technical risk
Modelling risk optimisation:
The Prize


         60

         50

         40

         30
 Value




         20                                       NPV
         10                                       ROV

          0
               A   B   C              D   E   F
         -10

         -20

         -30
                           Projects
Modelling risk optimisation:
The Prize

٠ Project Value
   ٠ A project with a staged investment and multiple exit points before the
        final investment has to be made typically has a large value of flexibility.
           • A deterministic net present value of such a project will significantly
             underestimate the true economic value.
٠   Project differentiation
     ٠ Projects that cannot be differentiated on the basis of deterministic net
        present values will yield different decision tree values and projects with
        equal decision tree values can be differentiated using real option
        valuation.
Back up slides
Risk optimisation



                   100%
                   90%
                   80%
 Cum Probability




                   70%
                   60%
                                                                           Non-optimised
                   50%
                                                                           Optimised
                   40%
                   30%
                   20%
                   10%
                    0%
                      -100   -80   -60   -40           -20   0   20   40
                                               Value
Optimised vs non-optimised results
opti vs stoch values
Correlation oil price and rig rates
Resolvable versus Irresolvable Risk

٠ Resolvable risk relate to uncertainties that will be resolved during the early
     stages of a project, during which investments are made
٠    Irresolvable risk refer to uncertainties that persist until all of the investment
     has been made.
٠    The value of managerial flexibility is restricted to resolvable risk

        Project progression

                                     Investment




    Information flow unravelling            Information flow unravelling
          resolvable risk                         irresolvable risk
Value drivers in an uncertain world


  Increasing value                                 Cost and time
                                                    uncertainty
  of flexibility                      American      American
                                       options        options

                                     Compounded    Compounded
                                       options        options

                          Sales         Sales          Sales
                        volatility    volatility     volatility


              CoS          CoS           CoS           CoS


NPV          Decision   Black &       Binominal    LSM ROV
             tree       Scholes

Weitere ähnliche Inhalte

Ähnlich wie Risk modelling in the Exploration and Production Industry

el paso Marketing_Summary
el paso  Marketing_Summaryel paso  Marketing_Summary
el paso Marketing_Summaryfinance49
 
Driller And Dealers July 2010
Driller And Dealers July 2010Driller And Dealers July 2010
Driller And Dealers July 2010vmagdani
 
terex Gabelli120408
terex Gabelli120408terex Gabelli120408
terex Gabelli120408finance42
 
terex Gabelli120408
terex Gabelli120408terex Gabelli120408
terex Gabelli120408finance42
 
energy future holindings 110105
energy future holindings 110105energy future holindings 110105
energy future holindings 110105finance29
 
energy future holindings _110105
energy future holindings _110105energy future holindings _110105
energy future holindings _110105finance29
 
Supply Chain Risk Management Tools
Supply Chain Risk Management ToolsSupply Chain Risk Management Tools
Supply Chain Risk Management ToolsSteve_Rosvold
 
Oil Physical and Financial Markets: Economics, Engineering, Pricing, And Regu...
Oil Physical and Financial Markets: Economics, Engineering, Pricing, And Regu...Oil Physical and Financial Markets: Economics, Engineering, Pricing, And Regu...
Oil Physical and Financial Markets: Economics, Engineering, Pricing, And Regu...Kevin Kane
 
Presentation Global Edge Capital Management
Presentation Global Edge Capital ManagementPresentation Global Edge Capital Management
Presentation Global Edge Capital Managementgabrielpellegrini
 
State of the Canadian Oilfield Services Industry and 2015 Outlook Webinar
State of the Canadian Oilfield Services Industry and 2015 Outlook WebinarState of the Canadian Oilfield Services Industry and 2015 Outlook Webinar
State of the Canadian Oilfield Services Industry and 2015 Outlook WebinarMNP LLP
 
Consistent Performance: Reducing the Impacts of Price Uncertainty Through Por...
Consistent Performance: Reducing the Impacts of Price Uncertainty Through Por...Consistent Performance: Reducing the Impacts of Price Uncertainty Through Por...
Consistent Performance: Reducing the Impacts of Price Uncertainty Through Por...Portfolio Decisions
 
Wayne Johnson
Wayne JohnsonWayne Johnson
Wayne Johnson3helix
 
Conco Phillips- Presentations & Conference Calls Howard Weil Annual Energy Co...
Conco Phillips- Presentations & Conference Calls Howard Weil Annual Energy Co...Conco Phillips- Presentations & Conference Calls Howard Weil Annual Energy Co...
Conco Phillips- Presentations & Conference Calls Howard Weil Annual Energy Co...Manya Mohan
 
Oil_Prices_what_do_we_know_What_should_w
Oil_Prices_what_do_we_know_What_should_wOil_Prices_what_do_we_know_What_should_w
Oil_Prices_what_do_we_know_What_should_wPierre Serkine
 
The impact of oil demand and oil supply shocks on the real price of oil and o...
The impact of oil demand and oil supply shocks on the real price of oil and o...The impact of oil demand and oil supply shocks on the real price of oil and o...
The impact of oil demand and oil supply shocks on the real price of oil and o...Fundación Ramón Areces
 
Presentación TechBa - Csoftmty
Presentación TechBa - CsoftmtyPresentación TechBa - Csoftmty
Presentación TechBa - CsoftmtyCsoftmty Monterrey
 
energy future holindings _032905
energy future holindings _032905energy future holindings _032905
energy future holindings _032905finance29
 
energy future holindings 032905
energy future holindings 032905energy future holindings 032905
energy future holindings 032905finance29
 
MeadWestvaco_JPMorgan0608
MeadWestvaco_JPMorgan0608MeadWestvaco_JPMorgan0608
MeadWestvaco_JPMorgan0608finance33
 
MeadWestvaco_JPMorgan0608
MeadWestvaco_JPMorgan0608MeadWestvaco_JPMorgan0608
MeadWestvaco_JPMorgan0608finance33
 

Ähnlich wie Risk modelling in the Exploration and Production Industry (20)

el paso Marketing_Summary
el paso  Marketing_Summaryel paso  Marketing_Summary
el paso Marketing_Summary
 
Driller And Dealers July 2010
Driller And Dealers July 2010Driller And Dealers July 2010
Driller And Dealers July 2010
 
terex Gabelli120408
terex Gabelli120408terex Gabelli120408
terex Gabelli120408
 
terex Gabelli120408
terex Gabelli120408terex Gabelli120408
terex Gabelli120408
 
energy future holindings 110105
energy future holindings 110105energy future holindings 110105
energy future holindings 110105
 
energy future holindings _110105
energy future holindings _110105energy future holindings _110105
energy future holindings _110105
 
Supply Chain Risk Management Tools
Supply Chain Risk Management ToolsSupply Chain Risk Management Tools
Supply Chain Risk Management Tools
 
Oil Physical and Financial Markets: Economics, Engineering, Pricing, And Regu...
Oil Physical and Financial Markets: Economics, Engineering, Pricing, And Regu...Oil Physical and Financial Markets: Economics, Engineering, Pricing, And Regu...
Oil Physical and Financial Markets: Economics, Engineering, Pricing, And Regu...
 
Presentation Global Edge Capital Management
Presentation Global Edge Capital ManagementPresentation Global Edge Capital Management
Presentation Global Edge Capital Management
 
State of the Canadian Oilfield Services Industry and 2015 Outlook Webinar
State of the Canadian Oilfield Services Industry and 2015 Outlook WebinarState of the Canadian Oilfield Services Industry and 2015 Outlook Webinar
State of the Canadian Oilfield Services Industry and 2015 Outlook Webinar
 
Consistent Performance: Reducing the Impacts of Price Uncertainty Through Por...
Consistent Performance: Reducing the Impacts of Price Uncertainty Through Por...Consistent Performance: Reducing the Impacts of Price Uncertainty Through Por...
Consistent Performance: Reducing the Impacts of Price Uncertainty Through Por...
 
Wayne Johnson
Wayne JohnsonWayne Johnson
Wayne Johnson
 
Conco Phillips- Presentations & Conference Calls Howard Weil Annual Energy Co...
Conco Phillips- Presentations & Conference Calls Howard Weil Annual Energy Co...Conco Phillips- Presentations & Conference Calls Howard Weil Annual Energy Co...
Conco Phillips- Presentations & Conference Calls Howard Weil Annual Energy Co...
 
Oil_Prices_what_do_we_know_What_should_w
Oil_Prices_what_do_we_know_What_should_wOil_Prices_what_do_we_know_What_should_w
Oil_Prices_what_do_we_know_What_should_w
 
The impact of oil demand and oil supply shocks on the real price of oil and o...
The impact of oil demand and oil supply shocks on the real price of oil and o...The impact of oil demand and oil supply shocks on the real price of oil and o...
The impact of oil demand and oil supply shocks on the real price of oil and o...
 
Presentación TechBa - Csoftmty
Presentación TechBa - CsoftmtyPresentación TechBa - Csoftmty
Presentación TechBa - Csoftmty
 
energy future holindings _032905
energy future holindings _032905energy future holindings _032905
energy future holindings _032905
 
energy future holindings 032905
energy future holindings 032905energy future holindings 032905
energy future holindings 032905
 
MeadWestvaco_JPMorgan0608
MeadWestvaco_JPMorgan0608MeadWestvaco_JPMorgan0608
MeadWestvaco_JPMorgan0608
 
MeadWestvaco_JPMorgan0608
MeadWestvaco_JPMorgan0608MeadWestvaco_JPMorgan0608
MeadWestvaco_JPMorgan0608
 

Kürzlich hochgeladen

Do we need a new standard for visualizing the invisible?
Do we need a new standard for visualizing the invisible?Do we need a new standard for visualizing the invisible?
Do we need a new standard for visualizing the invisible?SANGHEE SHIN
 
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UbiTrack UK
 
Linked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond OntologiesLinked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond OntologiesDavid Newbury
 
Bird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemBird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemAsko Soukka
 
Computer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and HazardsComputer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and HazardsSeth Reyes
 
Meet the new FSP 3000 M-Flex800™
Meet the new FSP 3000 M-Flex800™Meet the new FSP 3000 M-Flex800™
Meet the new FSP 3000 M-Flex800™Adtran
 
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...DianaGray10
 
Designing A Time bound resource download URL
Designing A Time bound resource download URLDesigning A Time bound resource download URL
Designing A Time bound resource download URLRuncy Oommen
 
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPAAnypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPAshyamraj55
 
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IES VE
 
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1DianaGray10
 
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve DecarbonizationUsing IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve DecarbonizationIES VE
 
Digital magic. A small project for controlling smart light bulbs.
Digital magic. A small project for controlling smart light bulbs.Digital magic. A small project for controlling smart light bulbs.
Digital magic. A small project for controlling smart light bulbs.francesco barbera
 
Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.YounusS2
 
Empowering Africa's Next Generation: The AI Leadership Blueprint
Empowering Africa's Next Generation: The AI Leadership BlueprintEmpowering Africa's Next Generation: The AI Leadership Blueprint
Empowering Africa's Next Generation: The AI Leadership BlueprintMahmoud Rabie
 
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Will Schroeder
 
UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8DianaGray10
 
Babel Compiler - Transforming JavaScript for All Browsers.pptx
Babel Compiler - Transforming JavaScript for All Browsers.pptxBabel Compiler - Transforming JavaScript for All Browsers.pptx
Babel Compiler - Transforming JavaScript for All Browsers.pptxYounusS2
 
Cybersecurity Workshop #1.pptx
Cybersecurity Workshop #1.pptxCybersecurity Workshop #1.pptx
Cybersecurity Workshop #1.pptxGDSC PJATK
 
Nanopower In Semiconductor Industry.pdf
Nanopower  In Semiconductor Industry.pdfNanopower  In Semiconductor Industry.pdf
Nanopower In Semiconductor Industry.pdfPedro Manuel
 

Kürzlich hochgeladen (20)

Do we need a new standard for visualizing the invisible?
Do we need a new standard for visualizing the invisible?Do we need a new standard for visualizing the invisible?
Do we need a new standard for visualizing the invisible?
 
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
UWB Technology for Enhanced Indoor and Outdoor Positioning in Physiological M...
 
Linked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond OntologiesLinked Data in Production: Moving Beyond Ontologies
Linked Data in Production: Moving Beyond Ontologies
 
Bird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystemBird eye's view on Camunda open source ecosystem
Bird eye's view on Camunda open source ecosystem
 
Computer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and HazardsComputer 10: Lesson 10 - Online Crimes and Hazards
Computer 10: Lesson 10 - Online Crimes and Hazards
 
Meet the new FSP 3000 M-Flex800™
Meet the new FSP 3000 M-Flex800™Meet the new FSP 3000 M-Flex800™
Meet the new FSP 3000 M-Flex800™
 
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
Connector Corner: Extending LLM automation use cases with UiPath GenAI connec...
 
Designing A Time bound resource download URL
Designing A Time bound resource download URLDesigning A Time bound resource download URL
Designing A Time bound resource download URL
 
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPAAnypoint Code Builder , Google Pub sub connector and MuleSoft RPA
Anypoint Code Builder , Google Pub sub connector and MuleSoft RPA
 
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
IESVE Software for Florida Code Compliance Using ASHRAE 90.1-2019
 
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1UiPath Platform: The Backend Engine Powering Your Automation - Session 1
UiPath Platform: The Backend Engine Powering Your Automation - Session 1
 
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve DecarbonizationUsing IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
Using IESVE for Loads, Sizing and Heat Pump Modeling to Achieve Decarbonization
 
Digital magic. A small project for controlling smart light bulbs.
Digital magic. A small project for controlling smart light bulbs.Digital magic. A small project for controlling smart light bulbs.
Digital magic. A small project for controlling smart light bulbs.
 
Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.Basic Building Blocks of Internet of Things.
Basic Building Blocks of Internet of Things.
 
Empowering Africa's Next Generation: The AI Leadership Blueprint
Empowering Africa's Next Generation: The AI Leadership BlueprintEmpowering Africa's Next Generation: The AI Leadership Blueprint
Empowering Africa's Next Generation: The AI Leadership Blueprint
 
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
Apres-Cyber - The Data Dilemma: Bridging Offensive Operations and Machine Lea...
 
UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8UiPath Studio Web workshop series - Day 8
UiPath Studio Web workshop series - Day 8
 
Babel Compiler - Transforming JavaScript for All Browsers.pptx
Babel Compiler - Transforming JavaScript for All Browsers.pptxBabel Compiler - Transforming JavaScript for All Browsers.pptx
Babel Compiler - Transforming JavaScript for All Browsers.pptx
 
Cybersecurity Workshop #1.pptx
Cybersecurity Workshop #1.pptxCybersecurity Workshop #1.pptx
Cybersecurity Workshop #1.pptx
 
Nanopower In Semiconductor Industry.pdf
Nanopower  In Semiconductor Industry.pdfNanopower  In Semiconductor Industry.pdf
Nanopower In Semiconductor Industry.pdf
 

Risk modelling in the Exploration and Production Industry

  • 1. Risk Modelling in the E&P industry - the value of flexibility - Dr. Bart J.A. Willigers, MBA Copyright © Palantir Economic Solutions
  • 2. Structure presentation The importance of DA Value of flexibility Risk types Decision trees Real options Wrap up
  • 3. A new reality: the high oil price is here to stay ٠ The E&P industry is slowly coming to terms with a new reality, after two decades during which oil prices fluctuated around $25 per barrel: the present high oil price is here to stay. Brent spot price 100 90 80 70 USD/bll 60 50 40 30 20 10 0 02/01/1986 02/01/1988 02/01/1990 02/01/1992 02/01/1994 02/01/1996 02/01/1998 02/01/2000 02/01/2002 02/01/2004 02/01/2006 Source: Energy Information Administration
  • 4. High energy demands drives high risk investments ٠ Unprecedented energy demand ٠ A key driver for the all-time high oil prices is a global increase in the demand for energy. ٠ Unprecedented high risk investments ٠ In order to satisfy this demand the E&P industry has intensified their search for Greenfield opportunities and initiated a large number of highly technical challenging projects. ٠ The investments and risks associated with these projects are unprecedented ٠ …..hence the need for high quality DA processes
  • 5. DA process as a key success factor ٠ Decision making as competitive advantage ٠ Differentiation in technologies and data collection is difficult ٠ A competitive advantage can be achieved by superior data management and decision making processes ٠ Decision analysis is ultimately driven by economic metrics
  • 6. The challenge of accurate decision making Enhancing the turnaround time and accuracy invariably means more consistency. complexity
  • 7. The accepted practise of risk modelling ٠ Enhanced financial modelling ٠ The oil and gas industry has started to realise the value of enhanced financial modelling. • Probabilistic modelling • Monte Carlo simulations ٠ However, the value of flexibility is rarely recognised as a key value driver.
  • 8. Structure presentation The importance of DA Value of flexibility Risk types Decision trees Real options Wrap up
  • 9. Reacting on unfolding uncertainty: The concept of optimisation Decision one: To drill or not Drill well Build platform Oil sales Decision two: Build platform if drill is successful
  • 10. NPV’s major restraint: Capitalising on the value of flexibility ٠ Modelling uncertainty ٠ Economic results are typically presented as expected net present values and probability distributions. ٠ The failure to react on new information ٠ The net present valuation method assumes that once an investment is made, the project will run its course without intervention.
  • 11. Managing uncertainty: Investing in options ٠ Managing uncertainty ٠ Decision makers manage uncertainty by evaluating different feasible outcomes and plan for fall-back opportunities. ٠ Modelling uncertainty management ٠ Decision tree analysis and real option valuation.
  • 12. An example of optimisation No optimisation procedure Oil Build platform Oil sales Drill well 50% ($40) $100 No oil Build platform Oil sales 50% ($40) $0 ENPV = 50% * (-$40 + $100) + 50% * (-$40 + $0) = $10 Optimisation procedure Oil Build platform Oil sales Drill well 50% ($40) $100 No oil Do not build platform Oil sales 50% $0 $0 ENPV = 50% * (-$40 + $100) + 50% * ($0 + $0) = $30
  • 13. Structure presentation The importance of DA Value of flexibility Risk types Decision trees Real options Wrap up
  • 14. Two sources of risk: Market - and technical risk ٠ For the purpose of risk optimisation a distinction is made between market- and technical risk ٠ These risks have different characteristics and require different modelling techniques ٠ Decision tree analysis and real option valuation
  • 15. Market - and technical risk: Characteristics ٠ Risk control ٠ Technical risk is, within limits, controlled by the assets owner ٠ Market risk is outside the control of the assets owner ٠ Risk mitigation ٠ Technical risk can be mitigated by investing in a large portfolio of assets. ٠ Market risk will affect the entire portfolio
  • 16. Market - and technical risk: Risk modelling ٠ Total number of possible outcomes ٠ Market risk often have a very large number of possible outcomes ٠ Technical risk is typically resolved in a limited number of possible outcomes
  • 17. Examples of Market – and technical risk Technical risks Market risk Geological uncertainty Hydrocarbon price Downtime operations Fiscal terms Drilling Geopolitics Capital investment Rig rates
  • 18. Structure presentation The importance of DA Value of flexibility Risk types Decision trees Real options Wrap up
  • 19. Decision tree valuation ٠ Modelling technical risk ٠ The impact of technical risk is addressed by developing a series of scenarios. ٠ Risk optimisation in a decision tree is achieved by making an optimal choice an uncertainty is resolved. ٠ Modelling market risk ٠ Market risk is accounted for by a risk premium which is incorporated in the discount rate. ٠ Thus no risk optimisations opportunities are modelled that relate to the resolving of market uncertainty during the completion of a project.
  • 20. Decision tree valuation: An example ٠ Assume an exploration project ٠ With uncertain future production volumes ٠ In which a processing plant has to be constructed • There is a choice between – an expensive efficient large plant – and a cheaper but less efficient plant • The decision which plant to build can be postponed after the uncertainty regarding the production volumes is resolved
  • 21. Decision tree modelling: assessing different scenarios Incremental No. bll Revenue Investment NPV revenue Large plant Low case reserves 100 bll 2 $/bll 200 $ (250 $) (50 $) 25% Small plant 100 bll 0.5 $/bll 50$ (50 $) 0$ Large plant Mid case reserves 150 bll 2 $/bll 300 $ (250 $) 50 $ 50% Small plant 150 bll 0.5 $/bll 75$ (50 $) 25 $ Large plant High case reserves 250 bll 2 $/bll 500 $ (250 $) 250 $ 25% Small plant 250 bll 0.5 $/bll 125$ (50 $) 75 $
  • 22. Decision tree modelling: optimisation and roll back NPV Low case reserves Large plant 0$ (50 $) 25% Small plant Max((50 $),0$) 0$ Mid case reserves Large plant 87.5 $ 50 $ 50 $ Max(50 $,25 $) 50% Small plant 25 $ High case reserves Large plant 250 $ 250 $ 25% Small plant Max(250 $, 75 $) 75 $
  • 23. Comparison of results Large plant Small plant Low case reserves Low case reserves 25% (50 $) 25% 0$ 75 $ Mid case reserves 31.25 $ Mid case reserves 50% 50 $ 50% 25 $ High case reserves High case reserves 25% 250 $ 25% 75 $ Optimisation Large/ small plant Low case reserves 25% 0$ 87.5 $ Mid case reserves 50% 50 $ High case reserves 25% 250 $
  • 24. Structure presentation The importance of DA Value of flexibility Risk types Decision trees Real options Wrap up
  • 25. Real option valuation ٠ Modelling market risk ٠ A mathematical process describes a range of gross project values at timex and the development of the range over time. ٠ Given a project value at timex, an optimal decision is made whether or not to proceed with the project. ٠ There is no need for a risk premium in the discount rate because market risk is explicitly modelled. ٠ Modelling technical risk ٠ Development of a series of scenarios as is done in decision tree analysis.
  • 26. Real option valuation using the Binominal approach ٠ Modelling a range of asset values with the binominal ROV ٠ At time zero the value of an asset has a certain value, when moving from period zero to period one this value can increase or decrease with an equal likelihood ٠ The change occurs as a discrete jump ٠ The jump process is repeated when moving from period one to period two, and continues going forward into the future. Over time a range of possible outcomes are generated
  • 27. Binominal Real Option Valuation: Building a lattice t0 t1 t2 t3 Value
  • 28. Binominal Real Option Valuation: Building a lattice t0 t1 t2 t3 Value 110 100 90.9
  • 29. Binominal Real Option Valuation: Building a lattice t0 t1 t2 t3 Value Cost 131 100 123 110 110 100 100 100 90.9 90.9 100 82.6 75.1 100
  • 30. Binominal Real Option Valuation: Optimisation t0 t1 t2 t3 Value Max(131-100,0) Max(110-100,0) Max(90.9-100,0) Max(75.1-100,0)
  • 31. Binominal Real Option Valuation: Optimisation t0 t1 t2 t3 Value Max(131-100,0) Max(110-100,0) Max(90.9-100,0) Max(75.1-100,0)
  • 32. Binominal Real Option Valuation: Discounting t0 t1 t2 Value p 31/(1+0.015) 1-p p 10 /(1+0.015) 1-p p 0 /(1+0.015) 1-p 0 /(1+0.015) P = Risk neutral probability
  • 33. Binominal Real Option Valuation: Rolling back the tree t0 t1 t2 Value 30.5 0.55*30.5 + 0.45*9.85 9.85 0.55*9.85 + 0.45*0 0 0.55*0 + 0.45*0 0
  • 34. Binominal Real Option Valuation: Discounting and roll back t0 t1 t2 21.2 (0.55*21.2+0.45*5.4)/(1.015) 5.4 (0.55*5.4+0.45*0)/(1.015) 0
  • 35. Binominal Real Option Valuation: Discounting and roll back to period 1 t0 t1 t2 21.2 13.9 5.4 2.94 0
  • 36. Binominal Real Option Valuation: Discounting and roll back to period 0 t0 t1 13.9 (0.55*13.9+0.45*2.94)/(1.015) 2.94
  • 37. Binominal Real Option Valuation: Determine the option value t0 The option value is finally obtained when the tree is discounted and rolled back to period 0. The real option value is 8.85 8.85
  • 38. Comparison of results: Optimisation versus no optimisation Optimisation No optimisation t0 t1 t3 t4 t0 t1 t3 t4 31 31 21.2 21.2 13.9 10 12.1 10 8.8 5.4 3.8 1.4 2.9 0 -6.2 -9.1 0 -15.6 0 -24.1
  • 39. Note of warning! ٠ Several Real Option Valuation techniques have been proposed ٠ The binominal method is easy to explain but has some important limitations and its use is therefore not recommended ٠ Technique like the Least Square Mean Option Valuation are much more suited.
  • 40. Structure presentation The importance of DA Value of flexibility Risk types Decision trees Real options Wrap up
  • 41. The value of flexibility is project specific ٠ Value of flexibility ٠ Value metrics typically increase when risk optimisation is introduced in an economic model. ٠ The value added to the asset, the value of flexibility, is project specific and is determined by • the flexibility associated with the project • and the structure of the cash flow.
  • 42. The right tool for the job… Real Combined Real Options and Options decision tree analysis analysis Flexibility Market NPV Decision tree analysis risk Technical risk
  • 43. Some projects and their preferred economic tool ROV: ROV/D-tree: Brownfield Greenfield project exploration project Market risk Deterministic D-tree: analysis: Project with Brownfield contracted oil project price Technical risk
  • 44. Modelling risk optimisation: The Prize 60 50 40 30 Value 20 NPV 10 ROV 0 A B C D E F -10 -20 -30 Projects
  • 45. Modelling risk optimisation: The Prize ٠ Project Value ٠ A project with a staged investment and multiple exit points before the final investment has to be made typically has a large value of flexibility. • A deterministic net present value of such a project will significantly underestimate the true economic value. ٠ Project differentiation ٠ Projects that cannot be differentiated on the basis of deterministic net present values will yield different decision tree values and projects with equal decision tree values can be differentiated using real option valuation.
  • 47. Risk optimisation 100% 90% 80% Cum Probability 70% 60% Non-optimised 50% Optimised 40% 30% 20% 10% 0% -100 -80 -60 -40 -20 0 20 40 Value
  • 49. opti vs stoch values
  • 50. Correlation oil price and rig rates
  • 51. Resolvable versus Irresolvable Risk ٠ Resolvable risk relate to uncertainties that will be resolved during the early stages of a project, during which investments are made ٠ Irresolvable risk refer to uncertainties that persist until all of the investment has been made. ٠ The value of managerial flexibility is restricted to resolvable risk Project progression Investment Information flow unravelling Information flow unravelling resolvable risk irresolvable risk
  • 52. Value drivers in an uncertain world Increasing value Cost and time uncertainty of flexibility American American options options Compounded Compounded options options Sales Sales Sales volatility volatility volatility CoS CoS CoS CoS NPV Decision Black & Binominal LSM ROV tree Scholes