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Setting the scene

Stochastic Handling of Uncertainties in the Decision Making Process SPE London, 26 th October 2010 Dag Ryen Ofstad, Senior Consultant, IPRES Norway. Setting the scene. NPD 2009. NPD 2009. Mature areas: Production decline and marginal discoveries

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Setting the scene

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  1. Stochastic Handling of Uncertainties in the Decision Making ProcessSPE London, 26th October 2010Dag Ryen Ofstad, Senior Consultant, IPRES Norway

  2. Setting the scene NPD 2009 NPD 2009 • Mature areas: Production decline and marginal discoveries • New areas: Risks and uncertainties may be high • offshore ultra deep water • unconventional resources • use of new technology Increasing Need for Proper Decision Analyses

  3. DECISIONS Decision Theory Decision parametersProject optimizationDecision treesPortfolio management METHODOLOGY Top Management Basic Economics Systematic, unsystematic riskNPV, discount rateTax systems, price simulation Portfolio Management Basic Probabilistics SOFTWARE TOOLS Monte Carlo simulationMean, Mode, P10, P50, P90Correlations Project Managers Quantifying Uncertainty Economic Analysts Geology, geophysicsproduction, drainagedrilling, facilities, timing WORK PROCESSES Technical Disciplines • Drill exploration wells • Choose field development concepts • Choose drainage strategies • Rank and drill production wells • Buy/sell assets • Include/exclude projects from portfolio DECISIONSITUATIONS

  4. Decision analysis Quantify Key Measures Decision Basis for Management Structure Problem Capture Uncertainties Develop discovery? Area Plan?How? Negotiations -Licensees -Government Buy licence? Sell? At which price? Drill exploration well? Strategy and planning processes DECISION GATE 1 LIFECYCLE DG4 DG2 DG3 Exploration / Early feasibility LIFECYCLE Concept Screening ConceptOptimization Production, EOR Re-development projects Project Execution FEED PDO

  5. Decision Analyses - Project Examples Discovery A Area Development & Concept Selection Export route B Prospect A Discovery B Prospect C Field A With oil rim Prospect B Export route A • Facts • One existing platform • Exploration well, discovered gas with a thin oil column (>10 m) • Enough gas for development, but uncertain for oil development • Total of three discoveries and 3 prospects in the area

  6. Decision Analyses - Project Examples Well B Well C Well A A’ A Field A Field B ? ? Oil Leg ? • Produce oil leg? • Additional appraisal well? • Drainage strategy? • Facts • 3 exploration wells • Gas-condensate + Oil leg • 3 development scenarios

  7. Decision Analyses - Project Examples 2012 Differences in: Production start date Build-up CAPEX / OPEXLease / Tariffs Liquid CapacityContract Period Which option to choose given the uncertainty in reserves and productivity 2014 Tie-in to A 2010 Tie-in to B FPSO1 FPSO2 FPSO3 FPSO4 • Facts • Oil + Associated gas • 2 segments, one proven • 6 development scenarios

  8. Probability NPV (10^6 USD) Decision Analyses - Methodology Concept 1 Concept 1, 2, 3, 4, 5 2 3 4 5 Highest NPV, but also largest uncertainty

  9. Success criteria • Decision tools • Integrated work approach • Methodology • => Need all! DECISION-MAKING PROCESS DG1 DG2 DG3 DG4 DG5 CONSISTENCY

  10. EXPERTS Tools, Work Approach and Methodology PROJECTS DATA ANALYSES DECISIONS Method x  Analysis 1   Method y   Analysis 2  Analysis 3  Method z  Analysis 4 Analysis 6 DECISION-MAKING PROCESS CONSISTENCY PORTFOLIO

  11. Semi-Deterministic work approach Sub-Surface, Production, Drilling Parameters CAPEX / OPEX and Schedule SENSITIVITES Economic Parameters Decision?

  12. Integrated and Stochastic work approach UNCERTAINTIES AND RISKS Economic Uncertainties MONTE-CARLO Sub-Surface Production Drilling SIMULATION CAPEX, OPEX and Schedule

  13. Portfolio effects on risk Relevant risk Size of portfolio Portfolio x Unsystematic risk Systematic risk Cannot be reduced by diversification. Price, currency, inflation, material cost. Can be reduced in a portfolio of assets through diversification. Exploration risks, reserves,recovery, production, drilling and operations. Portfolio risk Unsystematicrisk Systematicrisk

  14. Field development planning Provide clear insight into complex projects Economic indicators: EMV,NPV,IRR, etc. Project cash flow Prospect(s) Tax Producing Reserves Inflation & Discount rate Discovery? Market considerations Well CAPEX & OPEX Nr. & type of production/ Injection wells Process capacity Oil/gas priceforecast Process & Transport EPCI time Drill rate Well CAPEX schedule Production profiles Well/Process Capacities Production CAPEX & OPEX Market prognosis build up OPEX Production & Transport Facilities CAPEX Process Well uptime CO2 fee uptime CAPEX schedule Revenue, oil & gas Tariffs Gas price Oil price

  15. NPV Capturing the Uncertainties Rock Volume Parameters Rock & Fluid Characteristics Recovery Factor PROBABILITY Oil and Gas Reserves / Resources RESERVES Capacity Constraints Facilities & Wells, Schedule Production Profiles PRODUCTION TIME Prod.start CAPEX OPEX Tariff Revenue Cash flow Cash Flow Cut off P&A Abandonment Fiscal Regime Probability Plots Time Plots Decision Trees Tornado Plots Summary Tables Results

  16. Integrated Field Development Model New / Open / CloseSave / Save As / Exit Drilling cost and timing Risk factors and cost implications Run simulation Inspect results Comparisons Export to STEA CAPEX / OPEX Phasing Transportation and tariffs Logistics and insurance Project descriptionResponsibilitiesChange Records Generate reports Model initialisation System set-up Production profilesProduction constraints Available capacity Profile preview Exploration risks Economics input (Oil price, gas price, discount rate, fiscal regime) • Reserves calculations • May include different: • Geological scenarios • Seismic interpretations • Several sediment.models etc. Separate analyses of field projects, concepts and sensitivites Analysis A Analysis C Analysis D Analysis E Analysis B

  17. Integrated Field Development Model

  18. E’ E E Compare and rank Optimum path basis for decisions Analyses Optimize and update Compare and rank A H C CONCEPTS B E D HIGHEST EMV F G

  19. BACK-UP SLIDES

  20. Low case Base case High case Deterministic vs. probabilistic approach How can input risk and uncertainty be quantified? DETERMINISTIC PROBABILISTIC PARAMETER 1 Distribution PARAMETER 2 Distribution PARAMETER 3 Distribution PARAMETER 4 Distribution PARAMETER 5 Distribution PARAMETER 1 ’high’ ’base’ ’low’ PARAMETER 2 ’high’ ’base’ ’low’ PARAMETER 3 ’high’ ’base’ ’low’ PARAMETER 4 ’high’ ’base’ ’low’ PARAMETER 5 ’high’ ’base’ ’low’ Simulation • Three discrete outcomes • Base Case Expected for the project • High case and low case are extremely unlikely to occur • Full range of possible outcomes • True expected NPV • True P90 • True P10 • Correct comparison and ranking of options

  21. Why use "Mean" for decision-making ? • CON: The mean: • Is possibly more complicated tocomprehend and explain • May give "infeasible" values • Mean number of eyes of a dice is 3.5 • Sum of 100 dice: Makes sense PRO: The mean: • Performs right "in the long run" • Decisions based on the meanhas the lowest expected error • Caters for occasional largesurprises • Is additive across reservoirs, fields and portfolios • Maximises the value of the portfolio The mean is most companies’ preferred basis for decisions !

  22. Mode P50 Mean Statistical Measures Mean The same as expected value. Arithmetic average of all the values in the distribution. The preferred decision parameter. ModeMost likely value. The peak of the frequency distribution. Base case? P50 Equal probability to have a higher or lower value than the P50 value. Often referred to as the Median.

  23. n EQUAL WELLS DETERMINISTIC BASE P90 STOCHASTIC MEAN STOCHASTIC MEAN P10 PROBABILITY TIME DETERMINISTIC BASE DRILLING TIME PER WELL n # WELLS Drilling campaign example Deterministic base: Underestimates drilling cost Overestimates # wells drilled per year Overestimates production first years Courtesy of IPRES

  24. Probabilistic approach PRODUCTION DEV.COST DRILLING RESERVES GRV N/G Ø Sw Rc Bo NEXT TARGET

  25. Example Contact Uncertainties - Cases Non-communication Communication 2577 2577 2625 2625 2647 2647 2688 2731 2731 2800 EXPECTED CASE??? PESSIMISTIC OPTIMISTIC

  26. Depth conversion adjustment Probability for Communication Probability of Gas-Cap Random Number Generator GOC OWC GRV N/G Ø Sw Bg Rf Fault location adjustment Monte Carlo - Principle

  27. P Reserves Development scenarios (1) Pure depletion • Long curved horizontal producer (2) Water injection • Short horizontal producer • Vertical injector (3) Gas injection • Long horizontal producer • Vertical gas injector (4) WAG injection • Short horizontal producer • WAG injector    

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