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

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Presentation on theme: "Stochastic Handling of Uncertainties in the Decision Making Process SPE London, 26th October 2010 Dag Ryen Ofstad, Senior Consultant, IPRES Norway."— Presentation transcript:

1 Stochastic Handling of Uncertainties in the Decision Making Process SPE London, 26th October Dag 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 Technical Disciplines
DECISIONS Decision Theory Decision parameters Project optimization Decision trees Portfolio management METHODOLOGY Basic Economics Top Management Systematic, unsystematic risk NPV, discount rate Tax systems, price simulation Portfolio Management Basic Probabilistics Monte Carlo simulation Mean, Mode, P10, P50, P90 Correlations SOFTWARE TOOLS Project Managers Quantifying Uncertainty Economic Analysts Geology, geophysics production, drainage drilling, 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 DECISION SITUATIONS

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 LIFE CYCLE DG2 DG3 DG4 LIFE CYCLE Exploration / Early feasibility Concept Screening FEED Concept Optimization Project Execution Production, EOR Re-development projects PDO

5 Decision Analyses - Project Examples
Discovery A Export route B Area Development & Concept Selection 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 A Well B Well C A A’ Field A Field B ? ? Oil Leg ? Facts 3 exploration wells Gas-condensate + Oil leg 3 development scenarios Produce oil leg? Additional appraisal well? Drainage strategy?

7 Decision Analyses - Project Examples
2012 Differences in: Production start date Build-up CAPEX / OPEX Lease / Tariffs Liquid Capacity Contract 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 Decision Analyses - Methodology
Concept 1 Concept 1, , , 4, 5 Probability NPV (10^6 USD) 2 3 4 5 Highest NPV, but also largest uncertainty

9 Success criteria Decision tools Integrated work approach Methodology
=> Need all! DECISION-MAKING PROCESS DG DG DG DG DG5 CONSISTENCY

10         Tools, Work Approach and Methodology CONSISTENCY
EXPERTS 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
Systematic risk Unsystematic 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 Size of portfolio Relevant risk Portfolio x Unsystematic risk Systematic risk

14 Nr. & type of production/ Oil/gas price forecast
Field development planning Provide clear insight into complex projects Economic indicators: EMV,NPV,IRR, etc. Project cash flow Prospect(s) Tax Producing Reserves Discovery? Inflation & Discount rate Market considerations Nr. & type of production/ Injection wells Well CAPEX & OPEX Process capacity Process & Transport EPCI time Oil/gas price forecast Drill rate Well CAPEX schedule Well/Process Capacities Production profiles In any field development a huge range of variables must be considered. Many factors are dependant on others. It is essential to structure clearly a complex situation so we can build a model that can generate all necessary decision support for the entire project team. Using IPRISKfield it is possible to handle very complex development scenarios. Production OPEX build up CAPEX & OPEX Market prognosis Production & Transport Facilities CAPEX Well uptime Process CO2 fee uptime CAPEX schedule Revenue, oil & gas Tariffs Gas price Oil price

15 Capturing the Uncertainties
Rock Volume Parameters Rock & Fluid Characteristics Recovery Factor PROBABILITY Oil and Gas Reserves / Resources NPV RESERVES Capacity Constraints Facilities & Wells, Schedule Production Profiles PRODUCTION TIME CAPEX OPEX Revenue Tariff Prod.start 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 / Close Save / Save As / Exit Drilling cost and timing Risk factors and cost implications Run simulation Project description Responsibilities Change Records CAPEX / OPEX Phasing Transportation and tariffs Logistics and insurance Inspect results Comparisons Export to STEA Model initialisation System set-up Generate reports Production profiles Production constraints Available capacity Profile preview Exploration risks Reserves calculations May include different: Geological scenarios Seismic interpretations Several sediment.models etc. Economics input (Oil price, gas price, discount rate, fiscal regime) Separate analyses of field projects, concepts and sensitivites Analysis A Analysis B Analysis C Analysis D Analysis E

17 Integrated Field Development Model

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

19 BACK-UP SLIDES

20 Deterministic vs. probabilistic approach
How can input risk and uncertainty be quantified? DETERMINISTIC PROBABILISTIC 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’ PARAMETER Distribution PARAMETER Distribution PARAMETER Distribution PARAMETER Distribution PARAMETER Distribution Base case High case Low case 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 ?
PRO: The mean: Performs right "in the long run" Decisions based on the mean has the lowest expected error Caters for occasional large surprises Is additive across reservoirs, fields and portfolios Maximises the value of the portfolio CON: The mean: Is possibly more complicated to comprehend and explain May give "infeasible" values Mean number of eyes of a dice is 3.5 Sum of 100 dice: Makes sense The mean is most companies’ preferred basis for decisions !

22 Statistical Measures Mode P50 Mean
Mean The same as expected value. Arithmetic average of all the values in the distribution. The preferred decision parameter. Mode Most 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 Drilling campaign example
PROBABILITY DETERMINISTIC BASE STOCHASTIC MEAN DRILLING TIME PER WELL n EQUAL WELLS P90 P10 # WELLS TIME n erlend 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 PESSIMISTIC OPTIMISTIC EXPECTED CASE???

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

27 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 WAG injector Reserves P

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