Reliable Forecasts Accurate Decisions

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1 Janette Conradson CEO/Founder Heidi Anderson Kuzma, PhD CTO/Founder Reliable Forecasts Accurate Decisions Copyright BetaZi, LLC 3/08/2016

2 Disruptive Innovation: BetaZi has an unfair advantage All current oil valuations are based on bad math: There has been no change in how wells are valued since The ARPS formula adds errors to forecasts. This is an open secret in industry. Only 200 scientists in the world are trained to do this type of advanced computer modeling: 198 of them are working for Google and Facebook to sell you slippers, we have the other 2. No other company has been able to create what our algorithm can do: We have prior art claims in this arena as we were filing patents before any others were thinking about this type of solution. Our IP is 3 years ahead of everyone else We have flagship contracts that take years to close We are vetted by industry and finance No other program can do forecasts with enough speed and volume to prove they are right or is testable and repeatable The Oil & Gas Industry is slow to adopt new science but they are finally getting here: We are the only company that is ready NOW. 2

3 The Problem PRODUCTION FORECASTING: The process of predicting the future of proved production from existing wells and fields. The bulk of hydrocarbon asset economic evaluation is based on production forecast numbers created by engineering teams. BUT >90% of production forecasts are currently made or adjusted by hand. This makes them: Slow Expensive Potentially subject to bias During Due Diligence it is prohibitively expensive and time-consuming to recreate a production forecast from first principles. Instead, all that is usually done is a spot check of the Aries or PhD-Win database. Using existing methods, you have no idea how accurate any forecast is and, more importantly, no way of knowing how far off it could be.

4 Production Forecasting 101 The goal is to forecast future production from a noisy, complicated curve. Partial shutins Maintenance Stimulation or new stage? Odd well test Ooops 4

5 MORE ASSUMPTIONS MANY Models to Choose From Navier-Stokes: Fluid is continuous and not moving at relativistic velocities Stokes: Stationary, creeping, incompressible in onedimensional, homogenous rock Darcy: Fluid of constant viscosity Arps (exponential and harmonic): Production rate is proportional to decline rate 5

6 Standard Procedure Pick a model > Find a fit > Make a forecast 6

7 Different Possibilities Different curves all fit the model nicely, but... 7

8 Problems with Traditional Forecasting Internal Engineering Forecast Forecast Supplied To Lender EUR = 112,959 BBL oil Lifetime = 16 years EUR = 787,822 BBL oil Lifetime = 386 years Different choices can have radically different outcomes. 8

9 The Solution 1. Start from first principles. 2. Insist on independent verification. 3. Use statistics. 4. Automate the process. 5. Back-test. In short: Predictive analytics using physio-statistical models Specifically: Field Series Expansion Machines More specifically: BetaZi WE HAVE THE TECHNOLOGY. Fast enough to meet data room deadlines. Provides actionable intelligence.

10 All New Science DERIVATIVE TECHNOLOGY All new code. All new approach. No one else is doing this.

11 BetaZi is the Worlds First and Only Physio-Statistical Model Physics is cleverly encoded as solutions to physics flow equations of cascading complexity Flow regime changes Time-dependent permeability Drive mechanisms AI: PREDICTIVE ANALYTICS The algorithm has learned how wells behave AI\AI and this makes it consistently more accurate than humans-- and it keeps on learning.. Samples are proposed and evaluated in light of data (MCMC) Statistical distributions are learned from Big Data which explain deviations from physics. Shut ins Stimulations Bad allocations Bad luck 30 Seconds per well

12 What BetaZi Does A physics-based predictive analytics solution that computes unbiased, accurate, automatic production forecasts with calibrated uncertainty. INPUTS: oil, gas, water, monthly volumes and working days OUTPUTS: forecast with calibrated uncertainty

13 Forecasting BetaZi is: Repeatable Accurate Fast ( ½ a minute per well) Testable Unbiased EUR It is the first fully-automatic, calibrated, probabilistic tool for oil and gas production forecasting. It is the only way to get an accurate assessment of production risk. A Standard forecast or reserves report will only offer up a single point in what might be a very wide, skewed histogram. 13

14 How Internal BetaZi Model Works Generates EVERY potential decline curve for the data by computing a million samples ranging from simple to complicated, using a sophisticated physio-statistical model P10 P50 P90 (Like asking a million petroleum engineers for their estimation)

15 Results = Significant Statistics Samples are then aggregated into true probabilistic percentiles. p1 p10 P50 p99 p90 p90 means that 90% of the time, samples fall above the p90 line

16 Validation Holdback of data for forecast comparison and cross-validation prove results are accurate for producing wells. Holdback Data

17 Quantile-Quantile Plots Q-Q testing ensures validity QQ EUR 1853 Conventional Oil Wells QQ EUR 3163 Bakken Oil Wells A straight horizontal line denotes that 90% of the time, holdback data production exceeds the p90 (85% exceeds the p85, etc.).

18 Accurate Roll-Ups Uncertainty can now be correctly aggregated over groups of wells Risk on a group of wells is normally narrower than for a single well Single well ratio of risk to production 1:1 32 well ration of risk to production 1:4 18

19 The PUD Factor (Proved UnDeveloped = PUD) PUDs are a large part of value and uncertainty in any deal. Most deals claim value in undrilled assets. PUD locations are booked to have values similar to nearby producing wells. Map of undrilled locations Traditional type curve method provides a single curve

20 BetaZi Type Curve The BZ Machine captures the complete statistics for a selected group of wells, generating informed type curves with the full range of uncertainty on undrilled production. The result is a unique new tool to calculate asset value. 20

21 Type Curves May Now Be Tested Using a BetaZi type curve, it is now possible to rapidly determine whether the well population used for a company-generated type curve is based upon truly analogous wells. Results are tested using cross-validation to ensure accuracy and confirm use of the same statistical population. VALIDATING = PASS NOT- VALIDATING = FAIL

22 Sample Applications of BetaZi Science 22

23 Spread of risk on every well North Dakota Basin Study 23

24 pscores Convenient metric for determining where individual wells fall in a population as a function of production. In this case pscores decline as new wells are opened indicating reservoir depressurization. 24

25 Actionable Intelligence Fast Benefit from Lateral lengths study 25

26 Half life of wells 26

27 Verify company statements 27

28 How are companies using this new tool? Perform rapid & reliable risk vs. uncertainty valuation Verify operator type curves Validate deal pricing Visualize lease areas/enhanced investment target id Engineer risk Determine if company type curve provided is correct or seeded with high producing wells to inflate value Run accurate and fast first pass comparisons for valuation of Company vs. SEC Form 10K or posted financial statements Monitor wells that make up the asset base Identify investment opportunities with the best ROI Arbitrage : Identify companies with weakness due to loan value ratios or other metric (take over candidates or restructure) Royalty Analysis: Verify operator production vs. payments 28

29 What BetaZi Does A physics-based predictive analytics solution that computes unbiased, accurate, automatic production forecasts with calibrated uncertainty. INPUTS: oil, gas, water, monthly volumes and working days OUTPUTS: forecast with calibrated uncertainty

30 Thank You! Janette Conradson, CEO Heidi Anderson Kuzma, PhD CTO/Chief Innovation Officer Brockway Road Suite 302 Truckee, CA

31 The following are a few examples of what we are seeing in the O&G finance space. These case studies demonstrate some of the challenges faced in accepting a deal valuation without running a BetaZi analysis to verify production forecast numbers. 31

32 1/1/2 4/1/2 7/1/2 10/1/ 1/1/2 4/1/2 7/1/2 10/1/ 1/1/2 4/1/2 7/1/2 10/1/ 1/1/2 4/1/2 7/1/2 10/1/ 1/1/2 4/1/2 7/1/2 10/1/ 1/1/2 4/1/2 7/1/2 10/1/ 1/1/2 4/1/2 7/1/2 10/1/ 1/1/2 4/1/2 7/1/2 10/1/ 1/1/2 Case Study I: $150MM Deal Company forecasts supplied to lender started at a point that was significantly higher than company sales data. When asked, company provided three more databases. Well counts varied from 108 to 270 PDP wells. During three months of due diligence, sales declined further Company Forecast Sales decline during due diligence p50 Forecast 0 32

33 Production (low to high) Discovered The producer was hiding the fact that the field was in severe pressure decline. Moral(s) of the story: Don t trust a database Well counts shouldn t vary by 100% Demand verification tools that start from first principles. Vintage of well (old to young) This disjunction was revealed in a 24-hour Reality Check analysis.

34 Reserves Report (January) Reserves Audit (June) Case Study II: $800MM DEAL Reserves supplied to lender were made in January. Lender hired auditor in May. Auditor rubber stamped original reserves without changing case name in Aires. Unable to match either report, we discovered that HALF of the wells producing in January were shut in by September. Take-away: $150k was wasted on audit. You need independent verification. Wells in production (September) Who is guarding the guards? 34 This was revealed in 1 day of analysis.

35 BBLs/mo Case Study III: Aggressive Type Curves Production from 219 analogous wells was provided by company to make type curves. Company PUD production forecast was 2x our forecast. Discovered hidden (290%) Performance Increase Factor (PIF) based on 8 wells with long lateral lengths and proposed completion technologies. Moral: Data Company Type Curve Analogous is in the eyes of the beholder. 10 th percentile 50 th percentile 90 th percentile 35 This was revealed in 15 minutes of analysis.