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An Overview of Using Dynamic Discounted Cash Flow and Real Options to Value and Manage Petroleum Projects Michael Samis, Ph.D., P.Eng. AMEC Americas Limited David Laughton, Ph.D. David Laughton Consulting Disclaimer This presentation was prepared for a valuation workshop presented to the Calgary Chapter of the Professional Risk Managers International Association by AMEC Americas Limited (AMEC) and David Laughton Consulting Limited. The quality of information, conclusions and estimates contained herein is consistent with the level of effort involved in the services provided by AMEC and David Laughton Consulting Limited based on: i) information available at the time of preparation, ii) data supplied by outside sources, and iii) the assumptions, conditions and qualifications set forth in this presentation. This presentation is intended only for educational purposes as an overview of market based valuation methods such as real options. The case studies presented in this workshop were constructed for illustrative purposes based on inputs and models that were chosen to support these purposes, rather than their detailed resemblance to actual economic environments or particular asset structures current or past. Any such resemblance is purely coincidental. These case studies are expressly not a professional opinion on the economic viability or value of any natural resource project discussed in this presentation. Any other use of, or reliance on, this presentation by a third party is at that party’s sole risk. © 2007 [email protected] [email protected] [email protected] 2 Presentation agenda Valuation in the petroleum industry Valuation influences: Uncertainty, structure and value estimation A simple demonstration of DCF and RO value mechanics Modelling output and input prices Case study #1: Long-term cash flows at a SAGD project Case study #2: Equity and government cash flows at a coal bed methane project Case study #3: Valuing a dual-fuel boiler at a SAGD project Final comments © 2007 [email protected] [email protected] [email protected] 3 Valuation in the petroleum industry Valuation influences: Uncertainty, structure and value estimation A simple demonstration of DCF and RO value mechanics Modelling output and input prices Case study #1: Long-term cash flows at a SAGD project Case study #2: Equity and government cash flows at a coal bed methane project Case study #3: Valuing a dual-fuel boiler at a SAGD project Final comments Economic assessment – Financial market and real asset disconnect An economically viable project generates after-tax operating profits sufficient to pay capital and financing costs and provide a return compensating for the project’s unique uncertainty profile. Each project has its own uncertainty and risk characteristics that should be recognized in an economic analysis. There are many methods of assessing economic viability including net present value, internal rate of return, payback period, present value index, breakeven analysis etc. Net present value (NPV) is the most robust method of determining economic viability. NPV is the value added to an organization by investing in the project and represents the value of the project on an open market. © 2007 [email protected] [email protected] [email protected] 5 Evolution of valuation – Financial market and real asset disconnect Valuation methods for financial assets have experienced monumental changes since the early 1970’s – Introduction of derivative valuation methods, and new products and markets (e.g. credit derivatives) Valuation of real assets in the natural resource industries has not experienced the same degree of change – Important advances have been made on the technical side but valuation has only experienced incremental changes © 2007 [email protected] [email protected] [email protected] 6 Evolution of valuation – Financial markets and assets Valuing uncertainty (risk adjustment) Modeling uncertainty and flexibility At net cash-flow (DCF) Dynamic quantitative At source (real options) 1990 Traded derivatives of many types Static quantitative 1970 Qualitative Old style DCF analysis David Laughton (2004). “Determining petroleum and mineral asset values. Where we have been, where we might go”, CIM AGM, Edmonton. © 2007 [email protected] [email protected] [email protected] 7 Evolution of valuation – Natural resource industries Modeling uncertainty and flexibility Valuing uncertainty (risk adjustment) At net cash-flow (DCF) Dynamic quantitative Static quantitative Qualitative At source (real options) Integrated DCF Monte Carlo and Simple DCF decision trees decision trees DCF simulation Monte Carlo with true distributions DCF simple scenarios Simple scenarios DCF 1-point forecasts Static cash flows with true prices David Laughton (2004). “Determining petroleum and mineral asset values. Where we have been, where we might go”, CIM AGM, Edmonton. © 2007 [email protected] [email protected] [email protected] 8 Why question “status quo” valuation? Six reasons The low equity returns in the natural resource industries in the 1990s may in part be linked to poor investment and asset management decisions – Return on equity improves when we become more productive allocating and managing capital. Current valuation methods often rely on professional intuition (e.g. special project discount rates) or inconsistent reasoning to assess risk and calculate value – We need a reasoned valuation approach to test intuitive conclusions and highlight inconsistencies. © 2007 [email protected] [email protected] [email protected] 9 Why question “status quo” valuation? Six reasons Conventional DCF valuation methods need to be supplemented with add-ons such as “strategic value” or value multiples or are simply not used in certain valuation problems because they just don’t produce reasonable numbers – Earn-in options, exploration, royalties, certain capital expansions, loss-making operations, staged investments, gold mines, world class assets, leases, market capitalizations. Renewed emphasis on professionally validated valuation models and project assessments – Valuation codes (CIMVal) and financial market regulations (NI43-101) will ultimately require valuation models that explicitly recognize the influences of project uncertainty and structure on project value. © 2007 [email protected] [email protected] [email protected] 10 Why question “status quo” valuation? Six reasons Two significant biases using current methods: Against long-term production Against investing in future cost reduction Current methods do not support as well as possible high quality discussions about: Sources of value The creation and management of future flexibility © 2007 [email protected] [email protected] [email protected] 11 Valuation in the petroleum industry Valuation influences: Uncertainty, structure and value estimation A simple demonstration of DCF and RO value mechanics Modelling output and input prices Case study #1: Long-term cash flows at a SAGD project Case study #2: Equity and government cash flows at a coal bed methane project Case study #3: Valuing a dual-fuel boiler at a SAGD project Final comments Three value influences – Uncertainty, structure, and estimation Project Uncertainty, Project Structure, and Value Estimation Management flexibility (expansion / closure) Cash flow structure (unit margin, non-linear CF, timing) Characteristics (distribution / resolution) Project uncertainty Source or type (economic / physical / other) Project structure Stakeholders (equity / debt / government) Elements of a valuation model Valuing uncertainty (aggregate: DCF / source: RO) Value estimation Modeling uncertainty/flexibility (scenarios / Monte Carlo / DT) © 2007 [email protected] [email protected] [email protected] 13 Project uncertainty – Uncertainty resolution and updating Commodity prices exhibit reversion to a long-term equilibrium due to market forces - uncertainty growth slows with term. 120 120 120 WTIprice price($/bbl) ($/bbl) WTI WTI price ($/bbl) 100 100 100 80 80 80 60 60 60 40 40 40 Long-term expected price = US$50/bbl Long-termexpected expected price price == US$50/bbl US$50/bbl Long-term 20 20 20 0 00 0 00 1 11 2 22 3 33 4 44 5 6 55 66 Project time (year) Project time time (year) (year) Project 7 77 8 88 9 99 10 10 10 © 2007 [email protected] [email protected] [email protected] 14 Project uncertainty – Uncertainty resolution and updating Non-reverting processes are used for investment assets like stocks and gold - uncertainty continues growing in the long-term. 1200 1100 Mineral price ($/unit) 1000 900 800 700 600 500 400 300 200 0 1 2 3 4 5 6 Project time (year) 7 8 9 10 © 2007 [email protected] [email protected] [email protected] 15 Project structure – Unit costs and operating leverage Unit operating costs vary between petroleum projects which produces different magnitudes of net cash flow uncertainty – Investors are risk averse and care about uncertainty. Upside Pure WTI Play Low-cost Oil Field High-cost Oil Field Outcome Units=4; UC=$30/bbl Units=8; UC=$45/bbl Units=2 SWTI, U = $70 CFU = 2*$70 CFU = 4*($70–$30) CFU = 8*($70–$45) = $140 = $160 = $200 SWTI, Now=$60 Expected E[CF] = 4*($60–$30) E[CF] = 8*($60–$45) E[CF]= 2*$60 outcome = $120 = $120 $120 CFD = 2*$50 Downside = $100 Outcome SWTI, D = $50 SCu( ) = ±17% CFD = 4*($50–$30) = $80 CF( ) = ±33% CFD = 8*($50–$45) = $40 CF( ) = ±66% © 2007 [email protected] [email protected] [email protected] 16 Project structure – Management flexibility Flexibility allows management to choose the operating policy that maximizes value as uncertainty is resolved. Project Value The dashed lines indicate the prices at which an action is sub-optimal. Develop satellite field Operate main field Shut-in field 0 Current mineral price © 2007 [email protected] [email protected] [email protected] 17 Project structure – Management flexibility Flexibility allows management to limit downside losses and magnify upside cash flows over the project’s lifetime. Net cash flow distribution for project with no flexibility Small cumulative net cash flow Expected net CF with no flexibility Net cash flow distribution for project with upside and downside policy alternatives. Expected net CF with flexibility Large cumulative net cash flow © 2007 [email protected] [email protected] [email protected] 18 Project structure – Decision phase diagrams Decision points determined by comparing value resulting from different production alternatives Scatter plots show different optimal choices (e.g. abandon vs. operate) for a copper mine with some local costs given different copper price / exchange rate pairs at a particular time. Use pattern of decision "phases" to determine value and risk effects of flexibility. 1.05 1.00 0.95 0.90 0.85 0.80 0.75 Green dot: Continue mining Red dot: Early closure 0.70 1.10 Foreign exchange rate (Host/US$) Monte Carlo simulation can be combined with decision trees to analyze the value and risk-mitigation effects of flexibility. Foreign exchange rate (Host/US$) 1.10 1.05 1.00 0.95 0.90 0.85 0.80 0.75 Blue dot: High capacity mining Green dot: Low capacity mining Red dot: Early closure 0.70 © 2007 [email protected] [email protected] [email protected] 19 Value estimation – DCF and RO are methods of calculating NPV DCF: traditional discounted cash flow analysis. RO: Real options, named in the 1980s when financial option pricing techniques were applied to the valuation of real assets (factories, mines, forests, oil fields). The main emphasis of real options was modeling uncertainty and valuing management flexibility, though here we are interested in its implications for valuation with or without flexibility. Both DCF and RO calculate Net Present Value. Valuation analysts often speak of RO project value as being something different to NPV – it is not. © 2007 [email protected] [email protected] [email protected] 20 Value estimation – Differentiating between DCF and RO DCF (risk adjust net cash flow) Real options (risk adjust at source) Uncertainty Risk adjustment Project structure Project net cash-flow Risk-adjusted discount rate Time adjustment Project value © 2007 [email protected] [email protected] [email protected] 21 Value estimation – DCF uses an aggregate risk/time adjustment Conventional static DCF applies an aggregate average risk and time adjustment to net cash flows and ignores flexibility . S= oil price (only source of uncertainty in this example) Aggregate risk and time adjustment applied to the net cash flow stream (i.e. discounting with RADR or WACC discount rate). E S ⋅ Oil Amount =Revenue − OpCost Operating profit − CAPEX Net cash flow ∗ Time and risk adj. Present Value net cash flow Base alternative © 2007 [email protected] [email protected] [email protected] 22 Value estimation – RO separates risk and time adjustments Real options applies a risk adjustment to the source of uncertainty and a time adjustment to net cash flow. Risk adjustment applied to expected oil price (i.e. pure oil risk discounting). Time adjustment applied here (i.e. discounting at the risk-free rate). E S ⋅ Risk adj.= E RA S ∗ Oil Amount Risk adjusted revenue − OpCost Risk adj. operating profit − CAPEX Risk adjusted net cash flow ∗ Time adj. Present Value net cash flow Base alternative © 2007 [email protected] [email protected] [email protected] 23 Value estimation – Choosing between single-rate DCF and RO The choice between single-rate DCF and RO valuation methods is a matter of selecting the method that is best able to recognize the unique risk characteristics of a particular project. They both recognize uncertainty variation but differ on how to calculate the compensation an investor requires for exposure to project uncertainty (i.e. a risk-adjustment). RO recognizes the dynamic risk variation within the project environment while single-rate DCF does not. RO applies an adjustment at source based on pure risk characteristics and filters this through to the net cash flow stream. DCF uses an aggregate risk adjustment representing the interaction of all uncertainties and flexibilities. This is difficult to do. © 2007 [email protected] [email protected] [email protected] 24 Consider the following You are in an E&P organisation that has been operating primarily in the Canadian western sedimentary basin, and are part of a team looking at prospects off the west coast of Africa. As part of the analysis, your colleagues suggest that, without further study, you should approximate the well productivity in any of these prospects to be the average (weighted by production) of all the wells in which your corporation has an interest. Would you agree with this course of action? © 2007 [email protected] [email protected] [email protected] 25 Valuation in the petroleum industry Valuation influences: Uncertainty, structure and value estimation A simple demonstration of DCF and RO value mechanics Modelling output and input prices Case study #1: Long-term cash flows at a SAGD project Case study #2: Equity and government cash flows at a coal bed methane project Case study #3: Valuing a dual-fuel boiler at a SAGD project Final comments A simple 1-period production asset Asset cash-flow model 1 year from now net cash-flow = output * output price - cost High cost Low cost Asset information Output Cost 100 160 120 © 2007 [email protected] 27 [email protected] [email protected] 27 A simple 1-period production asset Corporate information Output price forecast 2.00 Discount rate 0.20 Discount factor 0.833 = 1/(1+0.20) © 2007 [email protected] 28 [email protected] [email protected] 28 DCF analysis High cost Low cost Forecast cash-flows Revenue Cost Net DCF value 200 =100*2.00 160 40 =200 -160 33.3=40*0.833 120 80 =200-120 66.7 =80*0.833 © 2007 [email protected] 29 [email protected] [email protected] 29 Now add capital Asset cash-flow model now net cash-flow = - capital cost High cost Low cost 15 50 18.3 = 33.3-15 16.7 = 66.7-50 Asset information Capital cost DCF value © 2007 [email protected] 30 [email protected] [email protected] 30 Valuing components of a linear cash-flow If cash-flow is linear in underlying uncertain variables, e.g. Cash-flow = A * output price + B Value of the claim to the cash-flow can be determined by using value additivity Value = A * value of claim to output price + B * value of claim to a unit of risk-free cash-flow © 2007 [email protected] 31 [email protected] [email protected] 31 Forward pricing Value = = A * value of claim to output price + B * value of claim to a unit of risk-free cash-flow A * output forward price * time discount factor + B * time discount factor = (A * output forward price + B) * time discount factor Recall Cash-flow = A * output price + B © 2007 [email protected] 32 [email protected] [email protected] 32 MBV valuation of the production asset Market information Forward output price 1.80 Cash bond price (time discount factor) Output bond value 0.95 1.71 =1.80 * 0.95 How can we value a claim to the cash-flow output * output price - cost ? © 2007 [email protected] 33 [email protected] [email protected] 33 MBV valuation High cost Low cost Market information Output bond value 1.71 =1.80 * 0.95 Valuation Revenue Cost Net 171 =100 * 1.71 152 =160*0.95 19 =171-152 114 =120*0.95 57 =171-114 © 2007 [email protected] 34 [email protected] [email protected] 34 MBV discounting High cost Low cost Forecast cash-flows Revenue 200 Cost 160 Net 40 =200-160 120 80 =200-120 MBV value : Discount factor (value / forecast cash-flow) Revenue Cost Net 171 : 0.855 152 : 0.95 19 : 0.475 114 : 0.95 57 : 0.7125 © 2007 [email protected] 35 [email protected] [email protected] 35 MBV discounting (cont'd) High cost Low cost Risk discount factor (discount factor / time discount factor) Revenue 0.90 Cost 1.00 1.00 Net 0.50 0.75 Risk discount (1- risk discount factor) revenue 0.10 Cost 0.00 0.00 Net 0.50 0.25 © 2007 [email protected] 36 [email protected] [email protected] 36 Uncertainty and risk discounting High cost Low cost Corporate information Output price realisations Price uncertainty 2.00 ± 0.50 0.25 =0.50/2.00 What is the uncertainty in the net cash-flow? How does the risk discount relate to the uncertainty? © 2007 [email protected] 37 [email protected] [email protected] 37 Uncertainty and risk discounting High cost Low cost Corporate information Output price realisations Price uncertainty 2.00 ± 0.50 0.25 =0.50/2.00 Uncertainty (absolute : proportional) Revenue Net cash-flow 200 ± 50 : 0.25 40 ± 50 : 1.25 80 ± 50 : 0.625 © 2007 [email protected] 38 [email protected] [email protected] 38 Uncertainty and risk discounting High cost Low cost Risk discount and proportional uncertainty Revenue Net cash-flow 0.10 = 0.4*0.25 0.50=0.4*1.25 0.25=0.4*0.625 Price of output price risk = 0.4 © 2007 [email protected] 39 [email protected] [email protected] 39 Forward price and price of risk Forward price = Expectation * risk discount factor = Expectation * (1 - risk discount) = Expectation * (1 - price of risk * amount of uncertainty) © 2007 [email protected] 40 [email protected] [email protected] 40 Prices of risk For the same level of uncertainty, the greater the price of risk, the greater the risk discount Price of risk measures how averse the marginal investor is to bearing this particular type of uncertainty Price of risk = 0 means no risk discounting Typical of local, nonsystematic, diversifiable uncertainty Price of risk negative means risk mark-up Marginal investor want to bear this uncertainty Usually hedges other uncertainties © 2007 [email protected] 41 [email protected] [email protected] 41 CAPM and prices of risk CAPM is actually a model of prices of risk Price of risk = economy price of risk * correlation with economy Annual economy price of risk is roughly 0.5 Annual prices of risk typically between -0.5 and 0.5 Empirical determination of price of risk equivalent to determination of an equity beta risk premium in a WACC calculation © 2007 [email protected] 42 [email protected] [email protected] 42 Back to the example, add capital Asset cash-flow model now net cash-flow = - capital cost High cost Low cost Asset information Capital cost 15 50 DCF value 18.3 = 33.3-15 16.7 = 66.7-50 MBV value 4.0 =19-15 7.0 = 57-50 © 2007 [email protected] 43 [email protected] [email protected] 43 Insights Different assets, different uncertainties, different risk discounting Greater discountable uncertainty => greater risk discount Effect of uncertainty on value governed by "prices of risk" Risk discount = price of risk * amount of uncertainty We can think systematically about prices of risk Equivalent to WACC determination Risk discounting still determined centrally MBV, if anything, increases consistency and central control © 2007 [email protected] 44 [email protected] [email protected] 44 Valuation in the petroleum industry Valuation influences: Uncertainty, structure and value estimation A simple demonstration of DCF and RO value mechanics Modelling output and input prices Case study #1: Long-term cash flows at a SAGD project Case study #2: Equity and government cash flows at a coal bed methane project Case study #3: Valuing a dual-fuel boiler at a SAGD project Final comments Forward contracts – Gold forward curves Gold Futures Contracts as of 1st Trading Day of Each Month, 2004 550.0 525.0 500.0 475.0 450.0 425.0 400.0 375.0 r-0 4 Ju l-0 4 O ct -0 4 Ja n05 Ap r-0 5 Ju l-0 5 O ct -0 5 Ja n06 Ap r-0 6 Ju l-0 6 O ct -0 6 Ja n07 Ap r-0 7 Ju l-0 7 O ct -0 7 Ja n08 Ap r-0 8 Ju l-0 8 O ct -0 8 Ja n09 Ap r-0 9 Ju l-0 9 O ct -0 9 Ap Ja n- 04 350.0 Delivery Date © 2007 [email protected] [email protected] [email protected] 46 Forward contracts – Copper forward curves showing reversion 150.0 140.0 130.0 120.0 110.0 100.0 90.0 80.0 70.0 03 0 03 1 0 03 3 0 03 5 0 03 7 09 03 1 04 1 0 04 1 0 04 3 0 04 5 0 04 7 09 04 1 05 1 0 05 1 0 05 3 0 05 5 0 05 7 09 05 1 06 1 01 60.0 Forward Contract Expiry Date © 2007 [email protected] [email protected] [email protected] 47 03/26/1998 04/26/1998 05/26/1998 © 2007 [email protected] [email protected] [email protected] Forward Contract Expiry Date 06/26/1998 07/26/1998 08/26/1998 09/26/1998 10/26/1998 11/26/1998 12/26/1998 01/26/1999 02/26/1999 03/26/1999 04/26/1999 05/26/1999 06/26/1999 07/26/1999 08/26/1999 09/26/1999 10/26/1999 $4.00 02/26/1998 $3.50 01/26/1998 $3.00 12/26/1997 $2.50 11/26/1997 $2.00 10/26/1997 $1.50 $1.00 09/26/1997 Forward contracts – Natural gas forward curves Gas Price, $/MCF 48 Oil prices – 1970-2005 (real US$/bbl) 100 90 80 2006 US$/b 70 60 50 40 30 20 10 1970 1980 1990 2000 © 2007 [email protected] 49 [email protected] [email protected] 49 Oil prices – Four periods of rising oil prices • • • • 1973-74 1979-81 1991 1998-now • • • Supply-side vs demand-side shocks Permanent vs temporary changes Long-term vs short-term uncertainty • • Financial market information Implications for price models • Most sustained Reversed Short-lived ??? © 2007 [email protected] 50 [email protected] [email protected] 50 Oil prices – Financial market Information • Before the late 1980s Equity prices Predicted much lower prices than the US$100/bbl by 1990 touted by analysts in 1980 • Since then Forward and futures markets © 2007 [email protected] 51 [email protected] [email protected] 51 Oil prices – Oil forward prices 1989-1991 O il F u t u r e s P r ic e s v s T im e 40 35 Oil Price, $/Bbl 30 25 20 15 10 5 0 1 9 8 8 .0 0 1 9 8 9 .0 0 1 9 9 0 .0 0 1 9 9 1 .0 0 1 9 9 2 .0 0 1 9 9 3 .0 0 1 9 9 4 .0 0 1 9 9 5 .0 0 1 9 9 6 .0 0 T im e 19890117 19890203 19890217 19890303 19890403 19890417 19890503 19890517 19890717 19890803 19890817 19891003 19891017 19891103 19891117 19900103 19900117 19900403 19900417 19900503 19900517 19900703 19900717 19900803 19900817 19900917 19901003 19901017 19901203 19901217 19910103 19910117 19910403 19910417 19910503 19910517 19910603 19910617 19910703 19910717 19910903 19910917 19911003 19911017 19911203 19911217 © 2007 [email protected] 52 [email protected] [email protected] 52 Oil prices – Oil forward prices 2002-2005 O il F u tu r e s P r ic e s v s T im e 60 Oil Price, $/Bbl 50 40 30 20 10 0 2000 20020103 20020717 20030303 20030917 20040303 20040917 2002 20020117 20020903 20030317 20031003 20040317 20041103 2004 20020403 20021003 20030403 20031017 20040503 20041117 2006 20020417 20021017 20030417 20031103 20040517 20041203 2008 20020503 20021203 20030603 20031117 20040603 20041217 2010 20020517 20021217 20030617 20031203 20040617 20050103 2012 20020603 20030103 20030703 20031217 20040803 2014 20020617 20030117 20030717 20040203 20040817 20020703 20030203 20030903 20040217 20040903 © 2007 [email protected] 53 [email protected] [email protected] 53 Oil prices – Models • Price = long term factor * short term factor • • Long term factor was fairly stable 1983-2002 Band of US$14-US$26 per bbl • Now long-term factor increased and more uncertain -> Long-term flexibility more valuable © 2007 [email protected] 54 [email protected] [email protected] 54 Oil prices – NYMEX oil forward prices on 27 May 2007 120 110 100 90 80 70 60 50 40 30 20 10 0 0 1 2 3 4 5 6 7 8 9 10 Time horizon (years) Expected price Forward price 90% lower boundary Median price 90% upper boundary Nymex price deflated © 2007 [email protected] 55 [email protected] [email protected] 55 Input prices – Deepwater rig day rate index vs WTI oil Deepwater Rig Day Rate Index vs WTI Oil $/Bbl $70.00 500 Rig index from ODS-PETRODATA DW Rig Rate Index $60.00 Oil $/Bbl $50.00 350 1994 = 100 DWDay Rate Index 400 300 $40.00 250 $30.00 200 150 $20.00 100 WTI $/Bbl Avg for Month 450 $10.00 50 0 $0.00 Jul-01 Jan-02 Jul-02 Jan-03 Jul-03 Jan-04 Jul-04 Jan-05 Jul-05 © 2007 [email protected] 56 [email protected] [email protected] 56 Input prices – Cost index modelling • Rent effects Cost index = 1 + a (Price - Current Price) / Current Price • Quasi-rent effects Cost index = 1 + b (price change-expected price change) © 2007 [email protected] 57 [email protected] [email protected] 57 Input prices – A rough cut at the Alberta SAGD cost index © 2007 [email protected] 58 [email protected] [email protected] 58 Valuation in the petroleum industry Valuation influences: Uncertainty, structure and value estimation A simple demonstration of DCF and RO value mechanics Modelling output and input prices Case study #1: Long-term cash flows at a SAGD project Case study #2: Equity and government cash flows at a coal bed methane project Case study #3: Valuing a dual-fuel boiler at a SAGD project Final comments Case study #1: Alberta oil sands with no flexibility Steam-assisted gravity drainage (SAGD) project Steam forced underground Bitumen pumped to surface Overburden 150m 1. Steam exits injector well and forms a steam chamber in the upper formation. Upper steam injector well 2. Bitumen and condensed water flow by gravity to lower producer well for pumping to surface Lower producer well © 2007 [email protected] [email protected] [email protected] 60 Case study #1: Project background Project background: 2 billion barrels of recoverable reserves at a maximum production rate of 190 thousand b/d (70.6 million b/y). Production increased in phases for a mine life of 35 years. 50% of operating costs are from natural gas. Two design options: No on-site upgrader (third party refinery). • Development and sustaining CAPEX: CAD$7.6b; US$24/bbl refining penalty; Net CF: CAD$410m/yr. Build on-site upgrader / refinery CAPEX. • Development and sustaining CAPEX: CAD$15.6b; No refining penalty; Net CF: CAD$775m/yr. A non-linear royalty and CIT tax regime. © 2007 [email protected] [email protected] [email protected] 61 Case study #1: Sources of uncertainty WTI / synthetic crude oil price Moderate levels of uncertainty (25%) with strong reversion to a long-term equilibrium price of US$50.00/bbl. Natural gas price High levels of uncertainty (50%) with strong reversion to a longterm equilibrium price of CAD$6.00 mmbtu. Light-heavy differential (heavy oil refining penalty) High levels of uncertainty (50%) with strong reversion to a longterm equilibrium price of US$24.00/bbl. Low level of systematic uncertainty. Correlations between uncertainties: WTI - NatGas: 0.7; WTI - LHDiff: 0.7; NatGas - LHDiff: 0.5 © 2007 [email protected] [email protected] [email protected] 62 Case study #1: WTI / synthetic crude oil price WTI / Synthetic crude oil price 90 80 WTI price (US$/bbl) 70 60 50 40 30 20 10 0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 Project time (years) Expected WTI price Upper confidence bdy Risk-adjusted WTI price Lower confidence bdy © 2007 [email protected] [email protected] [email protected] 63 Case study #1: Natural gas price Natural gas price (CAD$/mmcf) 12 10 8 6 4 2 0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 Project time (years) Expected NatGas price Upper confidence bdy Forward NatGas price Lower confidence bdy © 2007 [email protected] [email protected] [email protected] 64 Case study #1: Light-heavy differential (refining penalty) Light-heavy differential (US$/bbl) 45 40 35 30 25 20 15 10 5 0 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 Project time (years) Expected light-heavy differential Upper confidence bdy Risk-adjusted light-heavy differential Lower confidence bdy © 2007 [email protected] [email protected] [email protected] 65 Case study #1: Monte Carlo DCF and RO valuation results Cumulative net cash flow SAGD: CAD$ 7201m SAGD+Upgrader: CAD$ 12309m • Discounted cash flow and real options make conflicting Time-adj. cumulative net cash flow SAGD: CAD$ 3450m design recommendations. SAGD+Upgrader: CAD$ 5092m Discounted cash flow DCF net present value SAGD: CAD$ 277m SAGD+Upgrader: CAD$ -697m Real options RO net present value SAGD: CAD$ 1104m SAGD+Upgrader: CAD$ 3515m DCF risk-adj. value reduction SAGD: CAD$3173m SAGD+Upgrader: CAD$5789m RO risk-adj. value reduction SAGD: CAD$ 2346m SAGD+Upgrader: CAD$ 1577m © 2007 [email protected] [email protected] [email protected] 66 Case study #1: Project total unit operating costs Effective unit operating costs (CAD$/bbl) 80 70 60 50 40 30 Development horizon 20 10 0 0 5 10 15 20 25 30 35 40 Project time SAGD SAGD + Upgrader Expected WTI (CDN$) © 2007 [email protected] [email protected] [email protected] 67 Case Study #1 – CF deviations, NCFDFs, and NCFRDFs Cash flow deviations indicate average cash flow variability. CF Deviationt, i = Standard deviation (Stakeholder CFt) ⋅ Expected CFt Net cash flow risk discount factors (NCFRDFs) indicate the size of the risk adjustment applied to a cash flow. Present value of cash flow t NCFDF t = Expected cash flow t Present value of cash flow t NCFRDFt = Expected cash flow t ⋅ Time discount factort NCFDFs and NCFRDFs profile should change with variations in cash flow uncertainty since both adjustments applied to the project cash flows reflect investor sensitivity to uncertainty. © 2007 [email protected] [email protected] [email protected] 68 Case study #1: Equity coefficient of variation 120% Development horizon Coefficient of variation (%) 100% 80% 60% 40% 20% 0% 0 5 10 15 20 25 30 35 40 Project time SAGD SAGD + Upgrader © 2007 [email protected] [email protected] [email protected] 69 Case study #1: Expected CF and CF boundaries 3000 Development horizon Net cash flow (CAD$ million) 2500 2000 1500 1000 500 0 0 5 10 -500 15 20 25 30 35 40 Project time (year) SAGD E[CF] SAGD+Upgrader E[CF] SAGD 90% CB SAGD+Upgrader 90% CB SAGD 10% CB SAGD+Upgrader 10% CB © 2007 [email protected] [email protected] [email protected] 70 Case study #1: Net CF time and risk discount factors Net cash flow discount factor 1.2 1.0 0.8 0.6 0.4 Developmen t horizon 0.2 0.0 0 5 10 15 20 25 30 35 40 Project time DCF (SAGD & SAGD+Upgrader) RO SAGD RO SAGD + Upgrader © 2007 [email protected] [email protected] [email protected] 71 Case study #1: Net CF risk discount factors Net cash flow risk discount factor 1.2 1.0 0.8 0.6 Note that the horizontal structure of the RO NCF-RDF is consistent with reversion in oil and natural gas prices. 0.4 Developmen t horizon 0.2 0.0 0 5 10 15 20 25 30 35 40 Project time DCF (SAGD & SAGD+Upgrader) RO SAGD RO SAGD + Upgrader © 2007 [email protected] [email protected] [email protected] 72 Case study #1 – SAGD project conclusions Interaction of project design and uncertainty has important value effects – especially in the long-term. Conventional DCF assumes project uncertainty grows at a fixed rate; RO recognizes project uncertainty is non-linear. Project cash flow risk is dependent upon design (sometimes in surprising ways). RO respects this while a constant DCF discount rate does not. Real option analysis helps focus valuation analysts on explicit recognition of project characteristics. Avoids hiding features in DCF discount rates and circular debates about discount rates (10% or 12%). Facilitates a detailed discussion about the interaction between the economic environment and the project. © 2007 [email protected] [email protected] [email protected] 73 Case study #1 – SAGD project conclusions A key parameter in this analysis may be the correlation of the LHDiff with the economy, which determines the amount of risk discounting in its forward prices Assumed low here: based on presumption that it is determined by Venezuelan politics What if Venezuelan political risk is correlated with the economy or if differential is strongly influenced by supply-demand balance of different types of refining capacity which is in turn driven by the economy? MBV highlights the importance of asking these sorts of questions and doing sensitivity analysis around them © 2007 [email protected] [email protected] [email protected] 74 Valuation in the petroleum industry Valuation influences: Uncertainty, structure and value estimation A simple demonstration of DCF and RO value mechanics Modelling output and input prices Case study #1: Long-term cash flows at a SAGD project Case study #2: Equity and government cash flows at a coal bed methane project Case study #3: Valuing a dual-fuel boiler at a SAGD project Final comments Case Study #2 – Coalbed methane project Development CPX of CAD$190m. 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0 0 10 15 Project time (year) 20 25 30 Methane production Real CAD$/ mcf Stable long-term unit costs and profit margins. Average real unit production cost is CAD$3.64/mmcf (includes tax and royalties). Average profit margin is 43% 5 10.00 100% 8.00 80% 6.00 60% 4.00 40% 2.00 20% 0.00 Profit margin (%) Strong initial production rates declining over the next 30 yrs. 8.0 Methane production (million mcf) An undeveloped coalbed methane project containing 104 million mmcf of methane. 9.0 0% 0 5 10 15 20 Project time (year) Expected unit revenue Expected unit operating profit 25 30 Expected unit operating cost Expected profit margin © 2007 [email protected] [email protected] [email protected] 76 Case study #2 – Natural gas price model Reverting natural gas price model with a real long-term expected price of CAD$6.43/mmcf. High levels of volatility with riskadjustment because of correlation to financial market activity. Natural gasprice (CAD$/mmcf) 12 10 8 6 4 2 0 0 5 10 15 20 25 30 Project time (years) Expected NatGas price Upper confidence bdy Risk-adjusted NatGas price Lower confidence bdy © 2007 [email protected] [email protected] [email protected] 77 Case study #2 – Project tax regime A royalty payment dependent on whether pre-production capital has been repaid. Royalty base may be adjusted for field operating costs. 1% royalty rate during capital repayment period. 25% royalty rate after capital repayment period. Corporate income tax rate of 35% on taxable income. Tax losses may be carried forward 7 years. Declining balance depreciation with accelerated schedules for preproduction capital (Class 41a). © 2007 [email protected] [email protected] [email protected] 78 Case study #2 – Expected after-tax equity cash flows Expected equity net cash flow (not adjusted for time and risk) over the life of the project is $477 million. Probability of a negative lifetime net cash flow balance is small using the current price model. Lower 10% confidence boundary is $398 million. Real cash flow (CAD$ million) 100 80 60 40 20 0 0 5 10 15 20 25 30 Project time (year) Expected equity after-tax cash flow 10% confidence bdy 90% confidence bdy © 2007 [email protected] [email protected] [email protected] 79 Case study #2 – Expected royalty cash flows Expected royalty cash flows (not adjusted for time and risk) over the life of the project are $136 million. There is no possibility of a negative lifetime cash flow balance and the lower 10% confidence boundary is $97 million. Narrow histogram when there is no management flexibility. Real cash flow (CAD$ million) 20 15 10 5 0 0 5 10 15 20 25 30 Project time (year) Expected royalty cash flow 10% confidence bdy 90% confidence bdy © 2007 [email protected] [email protected] [email protected] 80 Case study #2 – Expected corporate income tax cash flows Expected corporate income tax cash flows (not adjusted for time and risk) over the life of the project are $142 million. There is no possibility of a negative lifetime cash flow balance; the lower 10% CB is $99 million and the upper 90% CB is $189 million. Real cash flow ($ million) 20 15 10 5 0 0 5 10 15 20 25 30 Project time (year) Expected CIT cash flow 10% confidence bdy 90% confidence bdy © 2007 [email protected] [email protected] [email protected] 81 Case study #2 – Cash flow uncertainty comparison 300% Coefficient of variation 250% 200% 150% 100% 50% 0% 0 5 Equity 10 15 Project time (year) Royalty 20 25 30 Corporate income tax © 2007 [email protected] [email protected] [email protected] 82 Case study #2 – DCF/RO NOFLEX results Stakeholder NPV (CAD$ million) Equity Royalty DCF 73.2 44.5 RO 154.0 75.6 Corporate income tax 45.1 76.9 DCF NPV is calculated with a 12% risk adjusted discount rate. RO NPV is calculated with a 5% risk-free rate and a riskadjusted price curve. Equity IRR is 24.3%. Implied RO cost of capital is 6.6% which is the DCF discount rate that equates DCF NPV to RO NPV. Excess RO return is 17.7%. © 2007 [email protected] [email protected] [email protected] 83 Case Study #2 – Equity CF deviation, NCFDF and NCFRDF 1.0 80% 0.8 80% 0.8 60% 0.6 60% 0.6 40% 0.4 40% 0.4 20% 0.2 20% 0.2 0% 0.0 0 5 Equity CF CoV 10 15 20 Project time (year) DCF NCFDF 25 RO NCFDF 30 Cash flow CoV (%) 100% Net cash flow discount factor 1.0 Cash flow CoV (%) 100% 0% Net cash flow RISK discount factor Cash flow uncertainty stabilizes in the long term due to price reversion and constant real unit operating costs. RO risk adjustments track cash flow variability. DCF NPV lower than RO because NCFDFs are on average smaller (i.e. a larger risk adjustment than RO). 0.0 0 5 Equity CF CoV 10 15 20 Project time (year) DCF NCFRDF 25 30 RO NCFRDF © 2007 [email protected] [email protected] [email protected] 84 Case Study #2 – Royalty CF deviation, NCFDF and NCFRDF 1.0 80% 0.8 80% 0.8 60% 0.6 60% 0.6 40% 0.4 40% 0.4 20% 0.2 20% 0.2 0% 0.0 0 5 Royalty CF CoV 10 15 20 Project time (year) DCF NCFDF 25 RO NCFDF 30 Cash flow CoV (%) 100% Net cash flow discount factor 1.0 Cash flow CoV (%) 100% 0% Net cash flow RISK discount factor Royalty CF uncertainty is initially very high because of uncertainty in capital repayment period and stabilizes once this period is finished. Real option risk adjustments are initially high compared to later years because of high initial royalty uncertainty. 0.0 0 5 Royalty CF CoV 10 15 20 Project time (year) DCF NCFRDF 25 30 RO NCFRDF © 2007 [email protected] [email protected] [email protected] 85 Case Study #2 – CIT CF deviation, NCFDF and NCFRDF 1.0 80% 0.8 80% 0.8 60% 0.6 60% 0.6 40% 0.4 40% 0.4 20% 0.2 20% 0.2 0% 0.0 0 5 10 15 20 Project time (year) Corporate income tax CF CoV DCF NCFDF 25 30 RO NCFDF Cash flow CoV (%) 100% Net cash flow discount factor 1.0 Cash flow CoV (%) 100% 0% Net cash flow RISK discount factor High volatility of CIT in early years due to uncertainty in the time necessary to depreciate development capital. RO risk adjustments recognize changes in CIT uncertainty. 0.0 0 5 10 Corporate income tax CF CoV 15 20 Project time (year) DCF NCFRDF 25 30 RO NCFRDF © 2007 [email protected] [email protected] [email protected] 86 Case Study #2 – Final comments Highlighted that the level of cash flow uncertainty can vary greatly between project stakeholders and during the project. RO was able to explicitly recognize this variation in its risk adjustment whereas constant discount rate DCF does not. This analysis can be extended to analyze the impact of financing terms or tax policy on project development. Flexible models can estimate the increased probability that some areas of a petroleum project are not developed because of onerous terms. © 2007 [email protected] [email protected] [email protected] 87 Valuation in the petroleum industry Valuation influences: Uncertainty, structure and value estimation A simple demonstration of DCF and RO value mechanics Modelling output and input prices Case study #1: Long-term cash flows at a SAGD project Case study #2: Equity and government cash flows at a coal bed methane project Case study #3: Valuing a dual-fuel boiler at a SAGD project Final comments Case study #3: Dual-fuel boiler SAGD with fuel switching Similar SAGD project to that in Case Study #1: 2 billion barrels of recoverable reserves at a maximum production rate of 190 thousand bbl/d (70.6 million bbl/y). Production increased in phases for a field life of 38 years. Transport cost: $3.00/bbl Two design options: gas-fired boiler CAPEX ($b) Nat. gas(mcf/bbl) Bitumen (bbl/bbl) Other OPEX ($/bbl) 8.2 1.1 5.50 dual-fuel boiler gas-fired bitumen-fired 9.2 1.1 0.033 0.179 6.00 8.00 Annual decision with no switch cost © 2007 [email protected] [email protected] [email protected] 89 Case study #3: Production profile © 2007 [email protected] [email protected] [email protected] 90 Case study #3: CAPEX profile ! " # $ " © 2007 [email protected] [email protected] [email protected] 91 Case study #3: Sources of uncertainty WTI / synthetic crude oil price Moderate levels of uncertainty (25%) with strong reversion to a long-term equilibrium price of $55.54/bbl; current $75.00/bbl Natural gas price High levels of uncertainty (50%) with strong reversion to a longterm equilibrium price of $7.00/mcf; current $7.50/mcf WTI-bitumen differential High levels of uncertainty (50%) with strong reversion to a longterm equilibrium price of $26.66/bbl; ; current $39.00/bbl Correlations between uncertainties: WTI - NatGas: 0.7; WTI - LHDiff: 0.7; NatGas - LHDiff: 0.5 © 2007 [email protected] [email protected] [email protected] 92 Case study #3: WTI model %&' ! (# ) ! ' ' ' © 2007 [email protected] [email protected] [email protected] 93 Case study #3: WTI-bitumen differential model %&' ! (# ) ! ' ' ' © 2007 [email protected] [email protected] [email protected] 94 Case study #3: Natural gas price model %&' ! (# ) ! ' ' ' © 2007 [email protected] [email protected] [email protected] 95 Case study #3: Taxes Royalty Pre-payout : 1% of plant gate revenue Post-payout: max (pre_payout royalty, 25% of cash-flow) Losses carried forward at long Canada bond rate CIT 28.5% rate 30% declining balance on lagged sustaining capital 25% declining balance on half-year lagged development capital 41a: 100% declining balance up to accounting income limit Large other income so losses claimed immediately © 2007 [email protected] [email protected] [email protected] 96 Case study #3: Effects of uncertainty Uncertainty relevant only because of "non-linear cash-flows" Taxes Flexibility Analysis without uncertainty Analysis with varying level of correlation between bitumen and natural gas prices Stong, weak, none Lower correlation, more uncertainty in net Value without flexibility down with uncertainty in net Value of flexibility up with uncertainty in the effects of the choice © 2007 [email protected] [email protected] [email protected] 97 Case study #3: Computation: DCF value of asset = max over policies p (sum over realisations r (probabilityr * sum over times t (asset net cashflow t (p,r) * corporate risk discount factor t * time discount factor t))) © 2007 [email protected] [email protected] [email protected] 98 Case study #3: Computation: RO Value of asset = max over policies p (sum over realisations r (probabilityr * sum over times t (asset net cashflow t (p,r) * realisation risk adjustment r,t * time discount factor t))) Same except uniform risk-discounting becomes realisation-dependent risk adjustment © 2007 [email protected] [email protected] [email protected] 99 Case study #3: Policy search Depends on state variables: Prices Tax balances Current operating state (if costs to switching) Generally done within valuation Here an approximation pre-specifies policy that maximises current operating cash-flow Pretax capital costs independent of operating mode Not quite optimal because of operating effects on royalty payout and 41a claim Underestimates value of flexibility © 2007 [email protected] [email protected] [email protected] 100 Case study #3: Policy 0 0 * + # , 1 -./ #) # © 2007 [email protected] [email protected] [email protected] 101 Case study #3: Values DCF gas dual gas dual bit dual choice RO gas dual gas dual bit dual choice no uncert. strong corr. weak corr. no corr. 884 409 630 630 543 13 211 383 536 6 211 426 529 -2 211 462 3473 2693 3249 3249 2991 2135 2710 3217 2969 2112 2710 3328 2944 2086 2709 3422 © 2007 [email protected] [email protected] [email protected] 102 Case study #3: No 41a DCF gas dual gas dual bit dual choice RO gas dual gas dual bit dual choice no unc strong corr weak corr no corr 812 359 571 571 471 -50 148 314 464 -59 148 355 456 -69 148 390 3431 2661 3214 3214 2943 2092 2667 3171 2920 2067 2667 3280 2896 2040 2667 3373 © 2007 [email protected] [email protected] [email protected] 103 Valuation in the petroleum industry Valuation influences: Uncertainty, structure and value estimation A simple demonstration of DCF and RO value mechanics Modelling output and input prices Case study #1: Long-term cash flows at a SAGD project Case study #2: Equity and government cash flows at a coal bed methane project Case study #3: Valuing a dual-fuel boiler at a SAGD project Final comments Final comments Asset valuation methods in the natural resource industries have not incorporated advances in the financial markets – There is some agreement on improving the analysis of effects of dynamic uncertainty with Monte Carlo simulation and decisiontrees – There is not yet an agreement in the industry on whether aggregate risk adjustments (DCF) or source risk adjustments (RO) are better Demonstrated that RO recognizes variations in net cash flow uncertainty across assets while the conventional DCF approach does not – This has important implications for qualified person reports, and the internal analysis of assets with with atypical uncertainties. © 2007 [email protected] [email protected] [email protected] 105 Final comments The ability to manage risk with operating strategies (flexibility) adds value – This is true for both DCF and MBV Tools are being developed to anlyses complex situations with many incertainties and decisions throughout the asset life cycle Work needed on Refining output price models Modelling of input price and technical/geological uncertainty Decision models Methods of presenting multi-dimensional results Creation of commercial-grade software © 2007 [email protected] [email protected] [email protected] 106