D&RA for improved performance of the E&P industry E&P Decision & Risk Analysis by Christian Bos Netherlands Institute of Applied Geoscience TNO - National Geological Survey 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 ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 2 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 DRisk) 3. Operational planning • • External + internal focus: operational control Method / tools: HAZOP ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 3 FEP methodology Feature-Events-Processes a scenario-based, qualitative approach using a mental, not physical, model of FEP interrelations + empirical evidence / expert elicitation to assess probabilities Feature: system property Event: (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) ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 4 Qualitative scenario analysis FEP identification FEP classification FEP Analysis FEP selection and interaction Scenario definition and selection Model concept Model building Conse quenc e analys is Qualitative Scenario Definition Safety Assessment Model Development Quantitative Impact Modelling SA of key factors ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 5 Risk assessment workflow (scenario approach) Scenario analysis Risk / identification classification 1 1400 mean CO2 flux radius 1200 2.0E-02 [kg/d/m2] 1000 1.5E-02 800 600 1.0E-02 Consequence analysis 2.5E-02 Quantitative Model development Qualitative 0. 0. Definition Definition of of assessment assessment basis basis 400 5.0E-03 storage efficiency: 90.0% 1300 1000 Risk interaction/ grouping Scenario (element) formation Conceptual model development 2 Testing with 3D <> 2D numerical model (natural) analogues Probabilistic 2D numerical simulation Statistical processing/ assessment 3 3800 0.0E+00 0 200 Risk ranking/ screening 2000 3000 4000 5000 6000 7000 time [years] since start injection 8000 9000 0 10000 ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 6 1. Identification and classification of risk factors – - Database with risk factors (FEPs) ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 7 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 ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 8 1. Building a consistent probability framework with Bayesian Belief Network (BBN) ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 9 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 ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 10 E&P Performance underperformance due to bias & unwillingness to learn from past & accept new methods ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 11 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 ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 12 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 ENGINE Workshop 7, Leiden (C. Bos) • • • • • 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 November 8th, 2007 13 The appreciation factor in relation to discovery volumes (NPD) ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 14 Oil Production Forecast NCS 22 fields in production 160 3 Oil production (mill Sm /år) 180 140 120 100 80 60 40 20 0 PDO - Forecast Actual production ENGINE Workshop 7, Leiden (C. Bos) Fall 90 Fall 95 Fall 98 November 8th, 2007 15 Comparison of investment forecasts for fields approved before 1997 ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 16 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. ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 17 The context: decision-making under quantified uncertainty ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 22 Benchmarking study (bp, ChevTex, ConPhil, ENI, Exxon, Hydro, RWE, Statoil, Total) FUN Benchmark study 2004 12 Integration 10 A B 8 6 F G E 4 H D C I 2 0 0 2 4 6 8 10 12 Probabilistic processing ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 23 Integrated uncertainty analysis helps improving company performance Ranking improves after introducing D&RA Ye ar (5 ye ar pe riod e nding) 1990 0 1992 1994 1996 1998 2000 2 4 Conoco Rank 6 Chevron 8 10 Introduction of D&RA 12 14 16 ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 24 The D&RA Process, how mgt & staff create synergy: team work! ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 25 Decision-making under uncertainty: full life-cycle perspective ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 26 Decisions and Levels of Aggregation ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 27 Using “Risk-tolerance” as optimisation constraint WACC ∫ • Project Risk = IRR * pdf (IRR) d(IRR) -∞ • i.e. cum.prob. x average IRR, if it is <WACC ∫ 0 • 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 risktolerance: 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 ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 28 Multi-disciplinary data aggregation & model integration along value chain Aggregated data integration along value chain Proxy model Proxy model Proxy model Full model ENGINE Workshop 7, Leiden (C. Bos) Pdf’s of KPIs • per activity • per project • per asset • per portfolio Tax / PSC model (in time domain) …… modelling Project/asset life-cycle model Cost Engineering …… modelling Conceptual Design Geomech. / fracturing model …… modelling Vertical Flow Performance Geochemical modelling …… modelling Geomechanical modelling Geological modelling …… modelling Static Dynamic Well Facilities Economic Modelling Modelling Modelling Engineering Modelling Seismic modelling Multi-disciplinary data aggregation Proxy model Related to “value”: • KPI-Targets • Optim. criteria • Constraints • Risk tolerance November 8th, 2007 29 Decision-making = value optimisation = Optimisation constraints Corporate / portfolio level Asset / field level Project level Operational level Optimisation criteria hierarchical constrained optimisation under uncertainty given targets Δvalue = Δprobability of meeting a set of predefined time-series targets at the next hierarchical decision-level ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 30 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; Dproved developed reserves; Dexpected reserves; etc. • Project, e.g. • Capex minimisation within time constraint • Appraisal, e.g. • Value of Information (DEMV) ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 31 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” ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 32 Probability of meeting portfolio multicriteria objectives in time Ref. SPE 68576 (Howell, Tyler): Using Portfolio Analysis to Develop Corporate Strategy Corporate Production Planning Production Target production projects GAP developments ENGINE Workshop 7, Leiden (C. Bos) 2024 2022 2020 2018 2016 2014 2012 2010 2008 2006 2004 2002 2000 Producing fields November 8th, 2007 33 Probability of exceeding portfolio multi-criteria constraints in time Corporate Net Cash Flow Planning NCF constraint NCF projects developments 2024 2022 2020 2018 2016 2014 2012 2010 2008 2006 2004 2002 2000 Producing fields Risk tolerance to be specified: acceptable probability of not-meeting hurdle rate ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 34 Portfolio time-domain feedback mechanism to be included in asset decision-making Verify contribution of “optimised” asset decision against portfolio objectives. If necessary, override standalone asset decision. SF1 SF2 ENGINE Workshop 7, Leiden (C. Bos) Corporate Production Planning Production Target production projects GAP developments November 8th, 2007 2024 2022 2020 2018 2016 2014 2012 2010 2008 2006 2004 2002 Producing fields 2000 Objective function & constraints (outside time domain!) 35 D&RA - 5 main steps 1. Frame the problem •Agree dec. crit. •Agree decisions •Agree scenarios •Construct tree •Prune tree •Agree tree 2. Set-up quantitat. models •Agree models •Populate model •Agree stoch. parameter pdf’s & scenario prob. •Agree / est. correlations •Agree KPIs •Agree risk def. 3. Generate range of outcomes •Est. MC run parameters •Pdf’s of KPI’s •Quantify risks •Assess impact on portfolio •Est. utility fct, risk tolerance ENGINE Workshop 7, Leiden (C. Bos) 4. Perform Sensitivity Analysis •Tornado etc •Fine-tune decision altern. • Test robustness of decis: - model input - process par - utility fct - dec.sequence •VoI, VoF, ROV 5. Apply Decision Criteria •Describe process •Propose optim. solution + impact on portfolio •Report •Monitor •Update model November 8th, 2007 36 Decision-Making framework required for valuation (“No impact? No value!”) Modelling decisions and uncertainties in a combined framework ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 37 D&RA step 1: Pruning the tree (1) • 96 end-nodes ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 38 Pruning the tree (2) • 48 end-nodes : reduced by half ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 39 The ‘Value Loop’ (Shell)© Data Asset Value Drivers & Constraints Decisions & Plans Physical Asset Models ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 40 A typical scenario / decision tree Decision nodes Chance nodes • Decision nodes, chance nodes, end-nodes (or leaves) • What happens in end-node? • How is total statistical information used at decision-node? ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 41 Integrated, nested models to be run using Monte Carlo sampling process 1 pdf for each KPI ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 42 Options modelling to capture value-upside and mitigate value-risk: flexibility has value! • 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. sample 1 2 3 4 5 6 7 8 9 10 option year 1 continue continue continue continue continue continue continue continue continue continue year 2 continue abandon continue continue continue continue continue continue continue continue yr1 yr2 100 300 Plat form Const r. 1000 Type 2 SE_I1 Type 3 1000 I1 -10 700 horizontal Well Drilling 100 vert ical 100 wait 100 abandon 0 year 3 continue year 4 continue year 5 continue year 6 continue year 7 continue year 8 continue year 9 continue continue continue continue continue continue continue continue continue continue special wait abandon continue continue continue continue continue special wait continue special continue continue special continue continue special continue continue special continue continue continue continue continue continue continue continue wait continue continue abandon continue wait continue continue wait continue ENGINE Workshop 7, Leiden (C. Bos) 100 yr3 NPV 500 -10 300 800 300 100 200 100 200 30 252 E(fut. oil price) < 15 OR CFn, n+5<0 AND Prodn, n+5<3000 November 8th, 2007 43 Problem framing: designing and evaluating options in dynamic decision trees ← “mapping uncertainty space onto decision space” → Quantify uncertainties ← Predict probabilistically when & how these uncertainties may be resolved in time (note 1) Design, for each scenario, options in response to (gradual) unveiling of truth (note 2) option valuation (“Value of Flexibility”) Design, for each option, a decision algorithm based on (expected) state variables / KPIs (to be applied at t=t1 …) Calculate, for each optional decision path in time, the NPV (by including cost of option) → Discontinue / delete any sub-optimal path (strike pull-out option) ← project valuation and ranking (including “Value of Flexibility”) → Calculate, for all dynamically optimized (i.e. filtered), optional decision paths in time, the EMV of the full project 1. 2. Compare this to EMV of alternative project definitions (with D flexibilities) & rank Select optimal project definition Distinguish unveiling of endogenous vs. exogenous information • Endogenous (project-specific): valuation of flexibility using EMV • Exogenous (general market, etc): valuation of flexibility using ROV Unveiling of new info: distinguish model input (e.g. perm.) vs. model output parameters (e.g. q oil, NCF) ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 44 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) by judiciously acquiring new information ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 45 The “modelling cube” ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 46 uncertainty Utopia: the dream integration Current practice ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 47 uncertainty Utopia: the dream The high degree of model precision limits what we can achieve in terms of holistic and probabilistic modelling integration Current practice ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 48 The realistic dream? Gradually increase precision (decisiondriven) uncertainty The utopian dream integration Current practice ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 49 Discrete uncertainties (scenario trees) ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 50 Decision node (with risk&opp. factors) Dead-end node (ltd. calc. of FM) Scenario / decision name Scenario chance Chance node (can be conditional) Optimal decision (branch coloured red) End node (leaf) here calculations in Fast Models are done ? ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 51 Continuous uncertainties (probability density functions) ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 52 Integrated Asset Management 1 pdf for each KPI for each end-node ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 53 Monte Carlo Simulation Methodology, uncorrelated Pr Randomly Sample Pr * * Capital Expenditure Revenue Calculate Pr Pr * Operating Expense ENGINE Workshop 7, Leiden (C. Bos) Grey area = risk of NPV<0 0 = * Cash Flow November 8th, 2007 54 Monte Carlo Simulation Methodology, correlated parameters (here >0) Sample correlated Randomly Sample Pr Pr * * Capital Expenditure Revenue Calculate Pr Pr Lower risk! * Operating Expense ENGINE Workshop 7, Leiden (C. Bos) * 0 = Cash Flow November 8th, 2007 55 Input parameters, output values and key performance indicators KEY PERFORMANCE INDICATORS Indicators Technical Economic STOIIP etc... NPV etc... OUTPUT VALUES SE STOIIP DE UR RDP Qo(t) #Wells SF Qo(t) CAPEX AP Qo(t) OPEX CO INPUT PARAMETERS ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 56 Integrating continuous and discontinuous uncertainties (pdf’s & scenarios) Sample individual KPIpdf’s and time-series at chance nodes and construct merged pdf NP VP / I IR R Establish pdf of KPIs for each end-node Correctly model scenario dependencies ! ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 58 ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 59 ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 60 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 ! ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 62 DSS-Portfolio imports DSSAsset information … ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 63 … and optimises phasing of projects using objective function and EF ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 64 Probability vs. time of meeting set of corporate KPIs is optimised ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 65 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 ENGINE Workshop 7, Leiden (C. Bos) November 8th, 2007 68