Oil & Gas Valuation Methods with a focus onmonte Carlo Analysis

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1 Oil & Gas Valuation Methods with a focus onmonte Carlo Analysis Presented by: Justin Anderson, MSc., CFA October 4, 202 Calgary

2 Justin Anderson s Bio & Genesis of Xedge Research High School Graduation Programming Diploma Teaching English and learning Russian in Russia BSc Degree in Mechanical Engineering BA Degree in Russian Studies History of Xedge Research MIT:Research used general-equilibrium macroeconomic modelling. Firmed up programming knowledge already acquired at CDI. Thesis and research on the energy industry Thesis title: Impact of CO2 Legislation on Canada and Alberta s Oil Sands. Waybe:Improved programming skill-set necessary for the eventual stream-lining of Xedge Research. McKinsey:Developed basic understanding of running Monte Carlo simulations on exploration portfolios. Significant focus on independent oil & gas companies with Colombian assets - especially Talisman Energy. BMO: Built deeper international oil & gas industry connections and energy market expertise. Xedge:Founded Xedgewith the goal to produce superior technical research with the constraints of rapid coverage capability on many names. Hired by Salman Partners Inc. as an Oil & Gas Research Analyst to produce independent research reports using Xedge-developed methodology Founded waybe.ca while at MIT Founded Xedge Research Masters in Aeronautics with Economics focus Oil & Gas Business Analyst Oil & Gas Investment Banker Oil & Gas Research Analyst 2

3 Expected Time (mins) Presentation Outline Standard Methods to Value Oil & Gas Companies Using Monte Carlo to Value Oil & Gas Companies Sample Valuation of an Oil & Gas Company Q & A 3

4 Primary Valuation Methodologies in Oil & Gas Trading Transaction Share EV* EV / EV / EV / Share EV* EV / EV / EV / Company Price (Smm) Funds Flow Prod. 2P Res. Company Price (Smm) Funds Flow Prod. 2P Res. Multiples Pacific Rubiales Energy $24.68 $8,87.3x $7,042 $6.72 PetroMagdalena (prior to bid) $.2 $263.3x $68,434 $ Gran Tierra Energy $.9 $,299 4.x $6,988 $2.0 PetroMagdalena (bid) $.60 $ x $86,392 $3 Parex Resources $4.8 $60 2.x $8,26 $6.6 C&C Energia $6. $380 2.x $36,6 $20.6 Canacol Energy $0.48 $ x $2,678 $29.6 Petrodorado Energy $0.7 $40 nmf nmf $7.66 PetroNova $0.42 $4 nmf nmf nmf Sintana Energy $0.3 $27 nmf nmf nmf ArPetrol $0.02 $7 nmf $26,728 $0.83 Peer Group Average 3.x $46,63 $32 *EV = Enterprise Value = (Shares Outstanding x Share Price) + Value of Debt Deterministic DCF Peer Group Multiples Transaction Multiples FF Ratio EV / EV / FF Ratio EV / EV / (EV/FF) (3) Prod. 2P Reserves (EV/FF) (3) Prod. 2P Reserves 3.x $46,63 $32 6.4x $86,392 $3 SPE Company Metrics ** SPE Company Metrics Funds Production 2P Reserves Funds Production 2P Reserves Flow ($mm) (bbl/d) (mmbbl) Flow ($mm) (bbl/d) (mmbbl) 400 2, ,000 0 Implied Valuation of SPE Implied Valuation of SPE EV EV EV EV EV EV ($mm) ($mm) ($mm) ($mm) ($mm) ($mm) $,400 $,66 $,600 $2,60 $2,60 $60 Valuation of SPE (based on Trading Multiples) Valuation of SPE (based on Trading Multiples) Current Share Implied EV Implied Market Implied Share Undervauled/ Current Share Implied EV Implied Market Implied Share Undervauled/ Price ($mm) Cap ($mm) Price ($mm) Overvalued Price ($mm) Cap ($mm) Price ($mm) Overvalued $0 $,389 $,289 $4 Undervalued $0 $,790 $,690 $9 Undervalued **90mm Shares Outstanding, $00mm Debt 4

5 Primary Valuation Methodologies in Oil & Gas Inputs Production Economic Annual Realized Sale Operating Taxes Discount (bbl/d) Life (years) Decline (%) Price ($/bbl) Exp. ($/bbl) ($/bbl) Rate (%) Multiples 3, Model Engine Year Year 2 Year 3 Year 4 Year Deterministic DCF NPV-0 ($mm) = CF + CF2 + CF3 + CF4 + CF (+k) (+k)^2 (+k)^3 (+k)^4 (+k)^ Outputs Year Year 2 Year 3 Year 4 Year NPV-0 ($mm) = $8 $37 $73 $94 $ NPV-0 ($mm) = $,26 Valuation of SPE (based on DCF) Current Share Implied EV Implied Market Implied Share Undervauled/ Price ($mm) Cap ($mm) Price ($mm) Overvalued $0 $,26 $,6 $2 Undervalued

6 Expected Time (mins) Presentation Outline Standard Methods to Value Oil & Gas Companies Using Monte Carlo to Value Oil & Gas Companies Sample Valuation of an Oil & Gas Company Q & A 6

7 Stochastic vs. Deterministic DCF Asset Asset 2 Asset 3 Asset Asset 2 Key G&G Data Chance of Success Field Size % Gas Liquids API Key Economic Data 24% 24%xP0 2% 32 o Deterministic DCF Asset 3 Reserves Production profiles 0mmboe Fixed production profile Deterministic DCF Working Interest % Gas Exp. cost Capex Opex Time to develop Fiscal Regime Oil Price 0% 2% $mm F(discovery) $0 per boe 2 years Colombia Medium Oil WTI Futures Cash flows Appraisal and Development wells Asset Asset 2 Fixed cash flows appraisal wells, 20 development wells Asset Asset 2 Stochastic DCF (ie. Monte Carlo) Asset 3 Reserves Stochastic DCF (ie. Monte Carlo) Asset 3 Key Data Same as above except: Chance of Success Production profiles Cash flows Field Size Appraisal and Development wells 7

8 Valuation Methodologies in Oil & Gas Methodology Considerations Applicability to O&G Exploration Most Common Multiples Deterministic DCF Enterprise value (market cap + net debt) is divided by a variety of metrics to compare across companies Possible metrics include reserves, resources (risked and un-risked), production, EBITDA, etc. Assets are valued based on estimated future cash-flows discounted at an appropriate discount rate Model assumptions are deterministic (single-values) as are model outputs (ie. A single NAV or NPV) Pro: Fast and easy Con: Requires comparable companies, relative valuations only (will not indicate if entire sector is over or under-valued). Pro:Reflects fundamental asset value; Useful for diverse companies Con: Requires detailed assumptions to develop reliable cash flow forecasts The diversity of companies in oil and gas exploration limits multiples for valuation. Multiples usually are good st pass indicators but more analysis is needed to improve valuation accuracy. NAV (net asset value) can be derived using multiples or DCF or a mix of both NAV is the valuation method of choice in oil and gas valuations and can be derived using multiples (simple, less refined) or DCF (harder, more refined) or a mix of both Stochastic DCF (ie. Monte Carlo) Assets are valued based on estimated future cash-flows discounted at an appropriate discount rate (same as deterministic DCF) Some model assumptions are probabilistic distributionsrather than single-values. (ie. instead of Resource = 0mmbbl, Resource = values in a log-normal distribution ranging from to 00 mmbbl). Pro:Excels when outcome certainty is low but the possible outcomes are well defined (ie. rolling a 6-sided die, black-jack). Con: Complicated analysis requiring even more detail than deterministic DCF. Also, if poorly defined inputs are used, subject to garbage-in, garbage-out Monte Carlo analysis of exploration portfolios is relatively common within E&P companies for internal portfolio assessments 8

9 Which Methodology is Best? Increasing Understanding of Possible Outcomes Stochastic DCF Well-defined Exploration Assets (prospects) Immature Exploration Assets (leads) Deterministic DCF Multiples Producing Assets (discoveries) Impossible to Value Increasing Outcome Certainty 9

10 Current Methodology Used on the Street? Producing Assets Multiples (ie. NAV) Methodology Deterministic DCF (ie. NAV, NPV) Rationale Less outcome uncertainty in booked reserves and production suggests deterministic DCF is usually the best approach Multiples (ie. NAV) Deterministic DCF (ie. Risked NAV) Deterministic modelling fails to capture the impact of downside risk and upside on valuation Exploration Assets Risked NAV 0

11 Valuing a Single-Attempt Uncertain Outcome Game (one attempt allowed) Adjusting for Risk Risk Neutral ($mm) Risked Averse ($mm) Amount of Downside Risk? Risk Free 00% Chance you get $mm Pmeanof the Game = $mm 2 Coin Toss 0% Chance you get $2mm 0% Chance you get $0 Pmeanof the Game = $mm Unlikely % Chance you get $00mm 99% Chance you get $0 Pmeanof the Game = $mm Price of Risk? Impossible 0.0% Chance you get $0bn 99.99% Chance you get $0 Pmeanof the Game = $mm 0. Valuation Amount of downside riskand the price of riskare the key drivers How to measure the amount of risk? How to measure the price of risk?

12 Example of Risked NAV vs. Monte Carlo? Imagine this game:a box with bills in it. Each bill has a dollar figure the amount you receive if you select that bill. Assume you know the distribution of bills in the box (normal; mean = $,000; standard deviation = $00). You must pay $X to play the game. After paying $X, flip a coin,if heads, pick a random bill out of the box and receive the value listed. If tails, pick nothing. How much should you pay to play? $27 $346 $,283 $,43 $428 $94 $930 $,82 $720 $2 $,032 $,034 $483 $,0 $,092 $,329 $967 $87 $443 $,83 $97 $74 $767 $,277 $947 $646 $78 $,077 $,37 $,4 $,376 $68 $2,246 $899 $880 $769 $4 $ $39 $29 $,069 $79 $943 $894 $,326 $,008 $,003 $,223 $,83 $,27 $,740 $276 $,222 $3 $,22 $973 $,820 $407 $,283 $98 $768 $,69 $,30 $,482 $,38 $97 $624 $93 $,0 $629 $,222 $,46 $,097 $683 $,39 $280 $,26 $,374 $2,60 $,002 $,08 $,46 $739 $643 $,76 $39 $824 $36 $,902 $472 $90 $,3 $,324 $729 $809 $2 $,69 $29 $,76 $,067 Method : Risked NAV (Deterministic) Method 2: Monte Carlo (Stochastic) Unrisked Pmean = $,000 POS = 0% Value of Game = $00 Pmean = $499 (00,000 iterations) Downside Risk Adjust= -$7 (using downside risk adjustment derivation) Value of Game = $324 Risk Neutral Risk Averse Oil & Gas Exploration Application The box represents the inherent uncertainty in predicted hydrocarbon accumulations Coin toss represents the chance of success 2

13 Expected Time (mins) Presentation Outline Standard Methods to Value Oil & Gas Companies Using Monte Carlo to Value Oil & Gas Companies Sample Valuation of an Oil & Gas Company Q & A 3

14 Assembling the Prospect Portfolio Leads Prospects Drill-ready prospects Leads, prospects and drill-ready prospects data are gathered through public information, field analogs and other estimates Leadsare qualitativelyassessed. Prospectsare generally dependent on other drill-ready discoveries and funding likelihood. Drill-ready prospectsare included in the analysis. Lease Inventory Prospect identification Prospect delivery Drill-ready prospects First oil Impossible to Value Stochastic DCF (Monte Carlo) Deterministic DCF 4

15 $ per FD share Asset Value ($ per FD share) $40.00 $3.00 $30.00 $2.00 $20.00 $.00 $0.00 A B C D $80.00 $70.00 $60.00 $0.00 $40.00 $30.00 $20.00 $0.00 $0.00 $.00 $0.00 Rubiales & Piriri $.0 Quifa SW $29.2 $4.84 Company Base Quifa Norte $4.6 $0.0 Sabanero $.42 Exploration Portfolio A CPE-6 2 La Creciente $2.22 $0.07 $0.8 $0.40 $0.22 $2.3 $4.9 $.6 Abanico $2.9 $0. $37.38 $ Qualitative Adjustment* Abanico Norte Guaduas Current Price Dilution** Target NAV Other 4 Other Assets G&A Net Debt Tax Credits* $2.37 $29.2 Company Base Deterministic DCF applied separately to each asset and G&A estimates Balance sheet generally used to value other assets Debt minus working capital Tax credits come from a DCF on tax reduction from unallocated G&A (if offsetting income is available), interest on corporate debt and tax pools.. See Appendix for detailed derivation Sample Valuation of Pacific Rubiales Energy B A C D 2 Frequency -$.90 $0.0 $2.0 $4.0 $6.0 $8.0 $0.0 $2.0 $4.0 $6.0 Exploration Portfolio Valuation Exploration Portfolio Risk and Reward ($ per FD share) Best Case*** Worst Case* Exploration Portfolio - Pmean Value [A] $7.83 over Mid Case** over Mid Case** Downside Risk Reduction [B] $2.4 2b Exploration Portfolio Valuation [A- B] $.42 Reserve Additions 33% 26% Exploration Portfolio NAV 348% 24% Worst Case* $0.33 Mid Case** $4.96 Best Case*** $7.26 *Worst case scenario where only 0% of simulated results are worse **Mid case scenario where 0% of simulated results are better ***Best case scenario where only 0% of simulated results are better 4 3 Monte Carlo Simulation is run on the company s prospect inventory 2a Upside (7-00%) Likely (2-7%) Downside (0-2%) Uses the treasury method to determine incremental dilution beyond the current trading price if the pre-dilution target NAV is reached. Qualitative adjustments to account for intangible valuations such as a management premium/discount, expected M&A, etc. Benefits of Methodology Matches the proper valuation tool to each asset based on the nature of the asset being valued Systematic and transparent approach to valuation which allows readers to understand exactly where the target price comes from

16 Expected Time (mins) Presentation Outline Standard Methods to Value Oil & Gas Companies Using Monte Carlo to Value Oil & Gas Companies Sample Valuation of an Oil & Gas Company Q & A Presented by: Justin Anderson, MSc., CFA More Questions? Please reach out: [email protected] Phone: