By: Marco Antonio Guimarães Dias  Internal Consultant by Petrobras, Brazil  Doctoral Candidate by PUC-Rio Visit the first real options website: Real Options in Petroleum: An Overview Real Options Valuation in the New Economy July 4-6, Coral Beach, Cyprus

Presentation Outline u Introduction and overview of real options in upstream petroleum (exploration & production) l Intuition and classical model l Stochastic processes for oil prices u Applications of real options in petroleum l Petrobras research program called “PRAVAP-14 ” Valuation of Development Projects under Uncertainties è Combination of technical and market uncertainties in most cases l Selection of mutually exclusive alternatives for oilfield development under oil price uncertainty l Exploratory investment and information revelation l Investment in information: dynamic value of information l Option to expand the production with optional wells

Managerial View of Real Options (RO) u RO can be viewed as an optimization problem: l Maximize the NPV (typical objective function) subject to: (a) Market uncertainties (eg., oil price); (b) Technical uncertainties (eg., reserve volume); (c) Relevant Options (managerial flexibilities); and (d) Others firms interactions (real options + game theory) u The first paper combining real options and game theory for petroleum applications was Dias (1997) l See the “drilling game” in my website (option-games)

Main Petroleum Real Options and Examples u Option to Expand the Production l Depending of market scenario (oil prices, rig rates) and the petroleum reservoir behavior, new wells can be added to the production system u Option to Delay (Timing Option) l Wait, see, learn, optimize before invest l Oilfield development; wildcat drilling u Abandonment Option l Managers are not obligated to continue a business plan if it becomes unprofitable l Sequential appraisal program can be abandoned earlier if information generated is not favorable

 Undelineated Field: Option to Appraise Appraisal Investment Revised Volume = B’  Developed Reserves: Options to Expand, to Stop Temporally, and to Abandon. E&P as a Sequential Real Options Process  Concession: Option to Drill the Wildcat Exploratory (wildcat) Investment Oil/Gas Success Probability = p Expected Volume of Reserves = B  Delineated Undeveloped Reserves: Option to Develop (What is the best alternative?) Development Investment

Economic Quality of the Developed Reserve u Imagine that you want to buy 100 million barrels of developed oil reserves. l Suppose that the long run oil price is 20 US$/bbl. u How much you shall pay for each barrel of developed reserve? u It depends of many technical and economic factors like: l The reservoir permo-porosity quality (productivity); l Fluids quality (heavy x light oil, etc.); l Country (fiscal regime, politic risk); l Specific reserve location (deepwaters location has higher operational cost than shallow-waters and onshore reserve); l The capital in place (extraction speed and so the present value of revenue depends of number of producing wells); etc.

Economic Quality of the Developed Reserve u The relation between the value for one barrel of (sub-surface) developed reserve v and the (surface) oil price barrel P is named the economic quality of that reserve q (higher q, higher v) u Value of one barrel of reserve = v = q. P l Where q = economic quality of the developed reserve l The value of the developed reserve is v times the reserve size (B) So, let us use the equation for NPV = V  D = q P B  D è D = development cost (investment cost or exercise price of the option)

Intuition (1): Timing Option and Oilfield Value u Assume that simple equation for the oilfield development NPV: NPV = q B P  D = 0.2 x 500 x 18 – 1850 =  50 million $ l Do you sell the oilfield for US$ 3 million? l Suppose the following two-periods problem and uncertainty with only two scenarios at the second period for oil prices P. E[P] = 18 $/bbl NPV(t=0) =  50 million $ E[P + ] = 19  NPV = + 50 million $ E[P  ] = 17  NPV =  150 million $ Rational manager will not exercise this option  Max (NPV , 0) = zero Hence, at t = 1, the project NPV is positive: (50% x 50) + (50% x 0) = + 25 million $ 50% t = 1 t = 0

Intuition (2): Timing Option and Waiting Value u Suppose the same case but with a small positive NPV. What is better: develop now or wait and see? NPV = q B P  D = 0.2 x 500 x 18 – 1750 =  50 million $ l Discount rate = 10% E[P] = 18 /bbl NPV(t=0) =  50 million $ E[P + ] = 19  NPV + = million $ E[P  ] = 17  NPV  =  50 million $ Rational manager will not exercise this option  Max (NPV , 0) = zero Hence, at t = 1, the project NPV is: (50% x 150) + (50% x 0) = + 75 million $ The present value is: NPV wait (t=0) = 75/1.1 = 68.2 > 50 50% t = 1 t = 0 Hence is better to wait and see, exercising the option only in favorable scenario

Intuition (3): Deep-in-the-Money Real Option u Suppose the same case but with a higher NPV. l What is better: develop now or wait and see? NPV = q B P  D = 0.25 x 500 x 18 – 1750 =  500 million $ l Discount rate = 10% E[P] = 18 /bbl NPV(t=0) = 500 million $ E[P + ] = 19  NPV = 625 million $ E[P  ] = 17  NPV = 375 million $ Hence, at t = 1, the project NPV is: (50% x 625) + (50% x 375) = 500 million $ The present value is: NPV wait (t=0) = 500/1.1 = < % t = 1 t = 0 Immediate exercise is optimal because this project is deep-in-the-money (high NPV) There is a value between 50 and 500 that value of wait = exercise now (threshold)

Classical Model of Real Options in Petroleum u Paddock & Siegel & Smith wrote a series of papers on valuation of offshore reserves in 80’s (published in 87/88) l It is the best known model for oilfields development decisions l It explores the analogy financial options with real options Financial Option Value Real Option Value of an Undeveloped Reserve (F) Current Stock Price Current Value of Developed Reserve (V) Exercise Price of the Option Investment Cost to Develop the Reserve (D) Time to Expiration of the Option Time to Expiration of the Investment Rights (  ) Paddock, Siegel & Smith’s Real Options Black-Scholes-Merton’s Financial Options Stock Dividend Yield Cash Flow Net of Depletion as Proportion of V (  ) Risk-Free Interest Rate Risk-Free Interest Rate (r) Stock Volatility Volatility of Developed Reserve Value (  )

Equation of the Undeveloped Reserve (F) u Partial (t, V) Differential Equation (PDE) for the option F Managerial Action Is Inserted into the Model u Boundary Conditions: l For V = 0, F (0, t) = 0 For t = T, F (V, T) = max [V  D, 0] = max [NPV, 0] } Conditions at the Point of Optimal Early Investment For V = V*, F (V*, t) = V*  D l “Smooth Pasting”, F V (V*, t) = 1  Parameters: V = value of developed reserve (eg., V = q P B); D = development cost; r = risk-free discount rate;  = dividend yield for V ;  = volatility of V 0.5  2 V 2 F VV + (r  ) V F V  r F =  F t

The Undeveloped Oilfield Value: Real Options and NPV u Assume that V = q B P, so that we can use chart F x V or F x P u Suppose the development break-even (NPV = 0) occurs at US$15/bbl

Threshold Curve: The Optimal Decision Rule u At or above the threshold line, is optimal the immediate development. Below the line: “wait, learn and see”

Stochastic Processes for Oil Prices: GBM u Like Black-Scholes-Merton equation, the classical model of Paddock et al uses the popular Geometric Brownian Motion l Prices have a log-normal distribution in every future time; l Expected curve is a exponential growth (or decline); l The variance grows with the time horizon (unbounded)

u In this process, the price tends to revert towards a long- run average price (or an equilibrium level) P. l Model analogy: spring (reversion force is proportional to the distance between current position and the equilibrium level). l In this case, variance initially grows and stabilize afterwards Mean-Reverting Process

Stochastic Processes Alternatives for Oil Prices u There are many models of stochastic processes for oil prices in real options literature. I classify them into three classes. u The nice properties of Geometric Brownian Motion (few parameters, homogeneity) is a great incentive to use it in real options applications. l Pindyck (1999) wrote: “the GBM assumption is unlikely to lead to large errors in the optimal investment rule”

Nominal Prices for Brent and Similar Oils ( ) u With an adequate long-term scale, we can see that oil prices jumps in both directions, depending of the kind of abnormal news: jumps-up in 1973/4, 1978/9, 1990, 1999; and jumps-down in 1986, 1991, 1997, 2001 Jumps-up Jumps-down

Mean-Reversion + Jumps: Dias & Rocha (98) u We (Dias & Rocha, 1998/9) adapted the jump-diffusion idea of Merton (1976) to the oil prices case, considering: l Normal news cause only marginal adjustment in oil prices, modeled with the continuous-time process of mean-reversion l Abnormal rare news (war, OPEC surprises,...) cause abnormal adjustment (jumps) in petroleum prices, modeled with a discrete- time Poisson process (we allow both jumps-up & jumps-down) u Model has more economic logic (supply x demand) l Normal information causes smoothing changes in oil prices (marginal variations) and means both: è Marginal interaction between production and demand; è Depletion versus new reserves discoveries in non-OPEC countries l Abnormal information means very important news: è In few months, this kind of news causes jumps in the prices, due the expected large variation in either supply or demand

Real Case with Mean-Reversion + Jumps u A similar process of mean-reversion with jumps was used by Dias for the equity design (US$ 200 million) of the Project Finance of Marlim Field (oil prices-linked spread) l Equity investors reward: è Basic interest-rate + spread (linked to oil business risk) l Oil prices-linked: transparent deal (no agency cost) and win-win: è Higher oil prices  higher spread, and vice versa (good for both) u Deal was in December 1998 when oil price was 10 $/bbl l We convince investors that the expected oil prices curve was a fast reversion towards US$ 20/bbl (equilibrium level) l Looking the jumps-up & down, we limit the spread by putting both cap (maximum spread) and floor (to prevent negative spread) l This jumps insight proved be very important: è Few months later the oil prices jumped-up (price doubled by Aug/99) –The cap protected Petrobras from paying a very high spread

PRAVAP-14 and Real Options Projects u PRAVAP-14 is a systemic research program named Valuation of Development Projects under Uncertainties l I coordinate this systemic project by Petrobras/E&P-Corporative u We analyzed different stochastic processes and solution methods l Stochastic processes: geometric Brownian motion; mean- reversion + jumps; three different mean-reversion models l Solution methods: Finite differences; Monte Carlo simulation for American options; and even genetic algorithms è Genetic algorithms are used for optimization: thresholds curves evolution, with the fitness evaluated by Monte Carlo simulation è I call this method of evolutionary real options (see in my website two papers on this subject) u Let us see some real options projects from Pravap-14

E&P Process and Options u Drill the wildcat (pioneer)? Wait and See? u Revelation and technical uncertainty modeling Oil/Gas Success Probability = p Expected Volume of Reserves = B Revised Volume = B’ u Appraisal phase: delineation of reserves u Invest in additional information? u Delineated but Undeveloped Reserves. u Develop? “Wait and See” for better conditions? What is the best alternative? u Developed Reserves. u Expand the production? Stop Temporally? Abandon?

Selection of Alternatives under Uncertainty u In the equation for the developed reserve value V = q P B, the economic quality of reserve (q) gives also an idea of how fast the reserve volume will be produced. u For a given reserve, if we drill more wells the reserve will be depleted faster, increasing the present value of revenues l Higher number of wells  higher q  higher V l However, higher number of wells  higher development cost D  For the equation NPV = q P B  D, there is a trade off between q and D, when selecting the system capacity (number of wells, platform process capacity, pipeline diameter, etc.) For the alternative “j” with n wells, we get NPV j = q j P B  D j

The Best Alternative at Expiration (Now or Never) u The chart below presents the “now-or-never” case for three alternatives. In this case, the NPV rule holds (choose the higher one). l Alternatives: A 1 (D 1, q 1 ); A 2 (D 1, q 1 ); A 3 (D 3, q 3 ), with D 1 < D 2 < D 3 and q 1 < q 2 < q 3 u Hence, the best alternative depends on the oil price P. However, P is uncertain!

The Best Alternative Before the Expiration  Imagine that we have  years before the expiration and in addition the long-run oil prices follow the geometric Brownian l We can calculate the option curves for the three alternatives, drawing only the upper real option curve(s) (in this case only A 2 ), see below. The decision rule is: l If P < P* 2, “wait and see” è Alone, A 1 can be even deep-in-the- money, but wait for A 2 is more valuable l If P = P* 2, invest now with A 2 è Wait is not more valuable l If P > P* 2, invest now with the higher NPV alternative (A 2 or A 3 ) è Depending of P, exercise A 2 or A 3 How about the decision rule (threshold) along the time?

Threshold Curves for Three Alternatives u There are regions of wait and see and others that the immediate investment is optimal for each alternative Investments D3 > D2 > D1

E&P Process and Options u Drill the wildcat (pioneer)? Wait and See? u Revelation and technical uncertainty modeling Oil/Gas Success Probability = p Expected Volume of Reserves = B Revised Volume = B’ u Appraisal phase: delineation of reserves u Invest in additional information? u Delineated but Undeveloped Reserves. u Develop? “Wait and See” for better conditions? What is the best alternative? u Developed Reserves. u Expand the production? Stop Temporally? Abandon?

Technical Uncertainty and Risk Reduction u Technical uncertainty decreases when efficient investments in information are performed (learning process). u Suppose a new basin with large geological uncertainty. It is reduced by the exploratory investment of the whole industry l The “cone of uncertainty” idea (Amram & Kulatilaka) is adapted: Risk reduction by the investment in information of all firms in the basin (driver is the investment, not the passage of time directly) Project evaluation with additional information (t = T) Lower Risk Expected Value Current project evaluation (t=0) Higher Risk Expected Value confidence interval Lack of Knowledge Trunk of Cone

Technical Uncertainty and Revelation u But in addition to the risk reduction process, there is another important issue: revision of expectations (revelation process) l The expected value after the investment in information (conditional expectation) can be very different of the initial estimative è Investments in information can reveal good or bad news Value with good revelation Value with bad revelation Current project evaluation (t=0) Investment in Information Project value after investment t = T Value with neutral revelation E[V]

Valuation of Exploratory Prospect u Suppose that the firm has 5 years option to drill the wildcat u Other firm wants to buy the rights of the tract for 3 million $. l Do you sell? How valuable is this prospect? E[B] = 150 million barrels (expected reserve size) E[q] = 20% (expected quality of developed reserve) P(t = 0) = US$ 20/bbl (long-run expected price at t = 0) D(B) = (2. B)  D(E[B]) = 500 million $  NPV = q P B  D = (20% )  500 = MM$ However, there is only 15% chances to find petroleum EMV = Expected Monetary Value =  I W + (CF. NPV)   EMV =  20 + (15%. 100) =  5 million $ 20 million $ (I W = wildcat investment) 15% (CF = chance factor) Dry Hole “Compact Tree” Success Do you sell the prospect rights for US$ 3 million?

Monte Carlo Combination of Uncertainties u Considering that: (a) there are a lot of uncertainties in that low known basin; and (b) many oil companies will drill wildcats in that area in the next 5 years: l The expectations in 5 years almost surely will change and so the prospect value l The revelation distributions and the risk-neutral distribution for oil prices are: Distribution of Expectations (Revelation Distributions)

Real x Risk-Neutral Simulation  The GBM simulation paths: one real (  ) and the other risk- neutral (r  ). In reality r  = , where  is a risk-premium

A Visual Equation for Real Options Prospect Evaluation (in million $) Traditional Value =  5 Options Value (at T) = Options Value (at t=0) = u Today the prospect´s EMV is negative, but there is 5 years for wildcat decision and new scenarios will be revealed by the exploratory investment in that basin. = So, refuse the $ 3 million offer!

E&P Process and Options u Drill the wildcat (pioneer)? Wait and See? u Revelation and technical uncertainty modeling Oil/Gas Success Probability = p Expected Volume of Reserves = B Revised Volume = B’ u Appraisal phase: delineation of reserves u Invest in additional information? u Delineated but Undeveloped Reserves. u Develop? “Wait and See” for better conditions? What is the best alternative? u Developed Reserves. u Expand the production? Stop Temporally? Abandon?

Dynamic Value of Information u Value of Information has been studied by decision analysis theory. I extend this view with real options tools u I call dynamic value of information. Why dynamic? l Because the model takes into account the factor time: è Time to expiration for the rights to commit the development plan; è Time to learn: the learning process takes time to gather and process data, revealing new expectations on technical parameters; and è Continuous-time process for the market uncertainties (oil prices) interacting with the current expectations on technical parameters u How to model the technical uncertainty and its evolution after one or more investment in information? l I use the concept of revelation distribution for technical uncertainty è Revelation distribution is the distribution of conditional expectations è See details in my presentation at the Academic Conference (Saturday) l Let us see the combination of technical and market uncertanties

Combination of Uncertainties in Real Options u The V t /D sample paths are checked with the threshold (V/D)* A Option F(t = 1) = V  D F(t = 0) = = F(t=1) * exp (  r*t) Present Value (t = 0) B F(t = 2) = 0 Expired Worthless V t /D = (q P t B)/D

E&P Process and Options u Drill the wildcat? Wait? Extend? u Revelation and technical uncertainty modeling Oil/Gas Success Probability = p Expected Volume of Reserves = B Revised Volume = B’ u Appraisal phase: delineation of reserves u Technical uncertainty: sequential options u Developed Reserves. u Expand the production? u Stop Temporally? Abandon? u Delineated but Undeveloped Reserves. u Develop? Wait and See? Extend the option? Invest in additional information?

Option to Expand the Production u Analyzing a deepwater project we faced two problems: l Remaining technical uncertainty of reservoirs is still important. è In this case, the best way to solve the uncertainty is not by drilling more appraisal wells. It’s better learn from the initial production profile. l In the preliminary development plan, some wells presented both reservoir risk and small NPV. è Some wells with small positive NPV (are not “deep-in-the-money”) è Depending of the information from the initial production, some wells could be not necessary or could be placed at the wrong location. u Solution: leave these wells as optional wells l Buy flexibility with an additional investment in the production system: platform with capacity to expand (free area and load) l It permits a fast and low cost future integration of these wells è The exercise of the option to drill the additional wells will depend of both market (oil prices, rig costs) and the initial reservoir production response

Oilfield Development with Option to Expand u The timeline below represents a case analyzed in PUC-Rio project, with time to build of 3 years and information revelation with 1 year of accumulated production u The practical “now-or-never” is mainly because in many cases the effect of secondary depletion is relevant l The oil migrates from the original area so that the exercise of the option gradually become less probable (decreasing NPV) è In addition, distant exercise of the option has small present value è Recall the expenses to embed flexibility occur between t = 0 and t = 3

petroleum reservoir (top view) and the grid of wells Secondary Depletion Effect: A Complication u With the main area production, occurs a slow oil migration from the optional wells areas toward the depleted main area optional wells oil migration (secondary depletion) u It is like an additional opportunity cost to delay the exercise of the option to expand. So, the effect of secondary depletion is like the effect of dividend yield

Conclusions u The real options models provide rich framework to consider optimal investment under uncertainty in petroleum, recognizing the managerial flexibilities l Traditional discounted cash flow is very limited and can induce to serious errors in negotiations and decisions u We saw the classical model working with the intuition l We saw different stochastic processes and different models u We got an idea about the real options research at Petrobras and PUC-Rio (PRAVAP-14) l We saw options along all petroleum E&P process l We worked mainly with models combining technical and market uncertainties (Monte Carlo for American options) u Thank you very much for your time

Anexos APPENDIX SUPPORT SLIDES

Managerial View of Real Options (RO) u RO is a modern methodology for economic evaluation of projects and investment decisions under uncertainty l RO approach complements (not substitutes) the corporate tools (yet) l Corporate diffusion of RO takes time, training, and marketing u RO considers the uncertainties and the options (managerial flexibilities), giving two linked answers: l The value of the investment opportunity (value of the option); and l The optimal decision rule (threshold curve)

When Real Options Are Valuable? u Based on the textbook “Real Options” by Copeland & Antikarov l Real options are as valuable as greater are the uncertainties and the flexibility to respond Ability to respond Low High Likelihood of receiving new information LowHigh U n c e r t a i n t y Room for Managerial Flexibility Moderate Flexibility Value Low Flexibility Value High Flexibility Value

Estimating the Underlying Asset Value u How to estimate the value of underlying asset V? l Transactions in the developed reserves market (USA) è v = value of one barrel of developed reserve (stochastic); è V = v B where B is the reserve volume (number of barrels); è v is ~ proportional to petroleum prices P, that is, v = q P ; è For q = 1/3 we have the one-third rule of thumb (average value in USA); – So, Paddock et al. used the concept of economic quality (q) –This is a business view on reserve value (reserves market oriented view) l Discounted cash flow (DCF) estimate of V, that is:  NPV = V  D  V = NPV + D è For fiscal regime of concessions the chart NPV x P is a straight line. Let us assume that V is proportional to P è Again is used the concept of quality of reserve, but calculated from a DCF spreadsheet, largely used in oil companies.

Estimating the Model Parameters  If V = k P, we have  V =  P and  V =  P (D&P p.178. Why?) Risk-neutral Geometric Brownian: dV = (r  V ) V dt +  V V dz u Volatility of long-term oil prices (~ 20% p.a.) l For development decisions the value of the benefit is linked to the long-term oil prices, not the (more volatile) spot prices l A good market proxy is the longest maturity contract in futures markets with liquidity (Nymex 18th month; Brent 12th month) u Dividend yield (or long-term convenience yield) ~ 6% p.a. l Paddock & Siegel & Smith: equation using cash-flows If V = k P, we can estimate  from oil prices futures market  Pickles & Smith’s Rule (1993): r =  (in the long-run) l “We suggest that option valuations use, initially, the ‘normal’ value of net convenience yield, which seems to equal approximately the risk-free nominal interest rate”

NYMEX-WTI Oil Prices: Spot x Futures u Note that the spot prices reach more extreme values and have more ‘nervous’ movements (more volatile) than the long-term futures prices

Mean-Reversion + Jump: the Sample Paths  100 sample paths for mean-reversion + jumps ( =  jump each 5  years)

Technical Uncertainty in New Basins u The number of possible scenarios to be revealed (new expectations) is proportional to the cumulative investment in information l Information can be costly (our investment) or free, from the other firms investment (free-rider) in this under-explored basin u The arrival of information process leverage the option value of a tract. Investment in information (wildcat drilling, etc.) Investment in information (costly and free-rider) Today technical and economic valuation t = 0 t = 1 Possible scenarios after the information arrived during the first year of option term t = T Possible scenarios after the information arrived during the option lease term Revelation Distribution

Simulation Issues u The differences between the oil prices and revelation processes are: l Oil price (like other market uncertainties) evolves continually along the time and it is non-controllable by oil companies (non-OPEC) l Revelation distributions occur as result of events (investment in information) in discrete points along the time è In many cases (appraisal phase) only our investment in information is relevant and it is totally controllable by us (activated by management) P E[B] Inv u Let us consider that the exercise price of the option (development cost D) is function of B. So, D changes just at the information revelation on B. l In order to calculate only one development threshold we work with the normalized threshold (V/D)* that doesn´t change in the simulation

Modeling the Option to Expand u Define the quantity of wells “deep-in-the-money” to start the basic investment in development u Define the maximum number of optional wells u Define the timing (accumulated production) that reservoir information will be revealed and the revelation distributions u Define for each revealed scenario the marginal production of each optional well as function of time. l Consider the secondary depletion if we wait after learn about reservoir u Add market uncertainty (stochastic process for oil prices) u Combine uncertainties using Monte Carlo simulation u Use an optimization method to consider the earlier exercise of the option to drill the wells, and calculate option value l Monte Carlo for American options is a growing research area

Bayesian Drilling Game (Dias, 1997) u Oil exploration: with two or few oil companies exploring a basin, can be important to consider the waiting game of drilling u Two companies X and Y with neighbor tracts and correlated oil prospects: drilling reveal information l If Y drills and the oilfield is discovered, the success probability for X’s prospect increases dramatically. If Y drilling gets a dry hole, this information is also valuable for X. l In this case the effect of the competitor presence is to increase the value of waiting to invest Company X tract Company Y tract