P H Y S I O - S T A T I S T IC A L P R E D I C T I V E A N A L Y T I C S F O R

F A S T A N D A C C U R A T E V A L U A T IO N O F

O I L & G A S A S S E T S

Production Forecasting: What You Need to Know

Janette Conradson Heidi Anderson Kuzma, PhD CEO/Founder CTO/Founder

Copyright BetaZi, LLC 3/08/2016

Presents:

The Issue

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PRODUCTION FORECASTING: predicting the next 7 years of proved production from existing wells and fields THE BULK OF OIL AND GAS ASSET ECONOMIC EVALUATION IS BASED ON PRODUCTION FORECAST NUMBERS.

— NOT RESERVES: number can never be tested — NOT EXPLORATION

BUT >90% of production forecasts are currently made or adjusted by hand. This makes them:

Slow Expensive Subject to bias

During Due Diligence it is prohibitively expensive and time- consuming to recreate a production forecast from first principles. Instead, all you usually do is spot check an Aries or PhdWin database. In fact, you have no idea how accurate your forecast is and no way of knowing how far off it could be.

Case Study I: $150m Loan

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Sales decline during due diligence

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

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p50 Forecast

BB

L/m

o s

old

Discovered

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Pro

duction (

low

to h

igh)

Vintage of well (old to young)

Moral(s) of the story:

Don’t trust a

database

Well counts shouldn’t

vary by 100%

You need verification

tools that start from

first principles.

The producer was hiding the fact that the field was in severe pressure decline.

Case Study II: $800m loan

Reserves Report (January)

Reserves

Audit (June)

Wells in production

(September)

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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. 1/3 had been shut in by May.

Take-away: $150k was

wasted on

audit.

You need

independent

verification.

Case Study III: Aggressive Type Curves

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10th percentile

50th percentile

90th percentile

Company Type Curve

Data

BB

Ls/m

o

Production from 219 analog 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:

“Analogous” is in

the eyes of the

beholder.

Solutions

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1. Start from first principles.

2. Insist on independent verification.

3. Use statistics.

4. Back-test.

In short: Predictive analytics using physio-statistical models

Specifically: Field Series Expansion Machines

More specifically: BetaZi’s BZ Machine

WE HAVE THE TECHNOLOGY.

Fast enough to meet data room deadlines.

Gives you actionable intelligence.

Production Forecasting 101

Partial shutins

Maintenance

Odd well test

Stimulation or new stage?

Ooops

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Production forecasting is made very difficult by noisy, complicated data.

Human tendency is to overthink the problem.

Standard Workflow

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Decide which data is relevant. Pick a decline curve (exponential, Arps, Duong, etc.) Find the parameters of a curve that fits. Adjust.

(Old School)

The Problem with Old School

PV10 High =

$12.8m

PV50 Medium =

$6.2m

PV90 Low =

$0.7m

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Lots of curves fit the data Every judgment call introduces potential bias It takes a long time (why it’s usually only spot checked) Forecasts are not consistent No rigorous definition of high, medium and low (PRMS)

EUR = 112,959 BBL oil $3.4 m

EUR = 787,822 BBL oil $23.6 m

Multiply by 100 wells -> $2b choice

Someone is making this choice with a box marked EUR showing on the screen.

Pricey Choices

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Instead

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P90

P50

P10

Like letting a million engineers vote.

Find all possible curves capable of explaining the data Let the answers range in complexity Let them deviate from purely physical behavior Run statistics over them to produce percentiles that have real

meaning (90% of the time production will exceed p90) Get a comprehensive answer

Enter the BZ Machine

OUTPUTS: forecast with

calibrated uncertainty

INPUTS: oil, gas, water, monthly

volumes and working days

.

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Automatic production forecast on every well from first principles Not one deterministic answer Computed in 20 – 60 seconds per well Probabilistic means you know the spread of uncertainty Back-tested means you can assess accuracy

THIS IS THE NEW STANDARD.

Analytics vs. Engineering

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ANALYTICS: “Figuring out what’s

there”

ENGINEERING: “Making it

work”

You don’t want to waste your engineers’ time doing rote forecasting.

You don’t want your forecasts to be engineered.

Different problems require different skills. Engineering Problem: Improve the well. Analytics Problem: Tell me what the well is likely to produce based on what it’s done in the past.

Physics is encoded as solutions to physics flow equations of cascading complexity (Field Series Expansion)

Statistical distributions are learned from Big Data which explain deviations from physics. (Bayesian Network)

Samples are proposed and evaluated in light

of data (MCMC)

Guaranteed physical solution Complexity not assigned apriori Doesn’t get confused by messy data

BZ Machine Schematic

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Flavor for the Maths

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BetaZi pioneers a family of Field Series Expansion Machines: Open Universe Bayesian Network models which solve physical problems using a distribution on the number of terms in an expansion representing the solution to a physical partial differential equation combined with statistical models for the deviation from physics.

Falls under Physio-Statistics.

𝐻𝜓 +𝜕𝜓

𝜕𝑡= 0

Equations of the general form

have solutions that can be expanded as

𝜓 𝑟, 𝑡 = 𝛼0 + 𝛼1𝑒−𝛽1𝑡 + 𝛼2𝑒

−𝛽2𝑡 +⋯

The BZ Machine puts a distribution on how

many terms matter in the expansion as well

as distributions for deviations from pure

diffusion. In the same framework, lots of

equations are solved.

Navier-Stokes

Stokes

Darcy

Arps

Validation

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Holdback blind testing is a rigorous way to ensure accuracy. For purposes of valuation, 2-3-7 year blind tests are more

important than understanding how the science works.

Q-Q Testing

A straight horizontal line denotes that 90% of the time, blind test production exceeds the p90 (85% exceeds the p85, etc.).

Quantile-Quantile testing ensures validity and provides “check engine” light

Area x: 51 wells; 24m holdback

BZ forecasts

slightly wide due to

many wells with <

3 mos history

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Statistical Type Curve Statistical model computed over 20 – 500 wells gives you a statistical type curve. This is a much richer representation of an undrilled well than a single curve. It also gives you a way to assess company type curves for their accuracy.

Price: $35/bbl

Discount: 10%

Estimated Value

p10: $2.8m

p50: $0.9m

p90: $0.3m

Company: $2.5m

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Revisit Type Curve Evaluation Recall: company PUD valuation was twice the valuation arrived at using BZ type curves due to Performance Increase Factors. That valuation represented what

could be achieved, not necessarily what will be.

Why were we so sure that our type curve was more right than the one supplied by the company which fell at a p18?

BZ type curve passed Q-Q blind testing.

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Accurate Forecasts = Accurate Roll-Ups

Ratio of uncertainty to production for a single well is 1:1

Ratio of uncertainty to production for 20 wells is 1:4

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The p90 of a group of wells is not the sum of the individual well p90s. Statistics don’t add.

We use roll-ups to determine if company forecasts are outside

of reasonable bounds.

Return to Case Study I: Roll-Up

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P10 P50 P90

Company Forecast

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Recall: company forecast didn’t match sales. It did, however, eventually lie within bounds of roll-up forecast (p25). This was still not enough to justify economics and loan was scuttled especially in light of pressure depletion.

Quality and Quantity Because of the speed and accuracy of this new technology, it is now possible to have a map of pre-computed forecasts for every well in the USA and Canada available for instant analysis and import to economic modeling programs, along with roll-ups and type curves.

Screenshot of bzAlmanac™ courtesy of BetaZi, LLC

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Massive Well Count Studies

Eagle Ford

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Cleveland

Commissioned by Major producers to compare their production

to that of other operators in an area and as an exploration tool.

Case Study IV: Comparison of Operators

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Operator C

Operator B

Operator A

Incre

ased

pro

duction

Well Count

Operator C has lots of low-producing vertical gas wells. They are not getting nearly the returns on their horizontal wells

that Operators B and C are seeing. We concluded that they are getting hardly any return on laterals! Suggested several operators who were getting lateral return.

Bottom Line

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There are a lot of devils in the details of a production

forecast. Due diligence should start from the beginning, use

statistics and back-testing. This is true independent verification.

New technology exists: exploit it. Pre-computed forecasts on every active well in the USA

with uncertainty are going to change the way deals are done. Adapt now.

Thank You!

Please Come to the Break-Out Session for a Deeper Dive

Janette Conradson, CEO jConradson@betazi.com

530.587.3858

Heidi Anderson Kuzma, PhD CTO/Founder

CORPORATE OFFICE: 816 Congress Avenue

Suite 1200 Austin, TX, 78701

RESEARCH OFFICE:

11209 Brockway Road Suite 302

Truckee, CA 96161

SALES: Houston, Texas. (Coming Q2 2016)

www.betazi.com

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