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

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

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

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

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

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?

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

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

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

        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

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

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

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

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

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

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

Integrated Field Development Model

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

BACK-UP SLIDES

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 1 Distribution PARAMETER 2 Distribution PARAMETER 3 Distribution PARAMETER 4 Distribution PARAMETER 5 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

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 !

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.

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

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

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

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

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    