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Haskayne School of Business CORS 58th Annual Conference, May 31st, 2016

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Presentation Outline

• Thank you to CORS & Haskayne School of Business
• About Verdazo Analytics Inc. (& a wee bit about me too)
• Outline of presentation

Part 1: Upstream Oil & Gas Industry Part 2: Operations Analytics Part 3: Analytics Journey Part 4: Analysis Challenges

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About Verdazo Analytics Inc.

• Founded 10 years ago as VISAGE (rebranded to VERDAZO in 2016)
• Recognized a need, particularly in operations, for data integration & visualization
• Upstream Oil & Gas focus up to 2016, currently expanding to other industries
• Currently active in >70 companies
• E&P Companies (from start-ups to large North American producers)
• Reserves Evaluators
• Banks/Investment Groups
• Market Research Organizations
• Service Companies

Part 1 Upstream Oil & Gas

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Upstream Oil & Gas

• Huge margins when times are good… not so much now
• Capital intensive ($3.5 million = average horizontal well Drill & Completion cost in

2014) with some wells costing in excess of $20 million

• Completion technologies allow us to get more production more quickly
• Reactive industry, particularly to commodity prices
• Lots of uncertainty… not always well understood or adequately represented in plans
• There’s lots of data
• Still heavily reliant on Excel

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What challenges do Petroleum Producers face?

• Low commodity prices & dramatic price fluctuations
• Wells are expensive to drill
• Well count per Engineer is high (especially after lay-offs)
• Strive for growth with less resources
• Predictable cash flow
• Too many spreadsheets

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Horizontal wells have changed the production landscape

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Horizontal wells have changed the production landscape

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We can’t predict prices, but we can protect against them

Images from VERDAZO Blog: Forward Curves Are a Poor Predictor of Future Spot Prices

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Deep staff cuts: a common approach, but a good one?

Example company:

• spends 65% of G&A on employees (including benefits and bonuses)
• G&A represents 20% of total operating costs
• employees are 13% of total operating costs
• 20% staff reduction = 2.5% reduction of total operating costs (not taking into account

the added costs of severance)

• the impacts to analysis capacity and capability are dramatic and could undermine

their ability to realize operational efficiencies

• targeting operational efficiencies could be more fruitful and could result in

sustainable improvements

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Operational Efficiencies: How big is the prize?

Province Revenue Potential AB $ 2,236,719,763 SK $ 382,188,022 BC $ 162,083,665 MB $ 40,715,664 Total $ 2,821,707,114

Part 2 Operations Analytics

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Types of Analytics

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The process & roles for a successful analytics project

Does this fit operations analytics? It does in well-bounded analytics projects, but…

Source: Five Faces of Analytics presentation by Dark Horse Analytics

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What’s unique about Operations Analytics?

• Significant variability in assets, production technologies, reservoir issues (e.g. CBM,

tight oil, liquids rich gas, water floods…)

• Conditions change over the life cycle of the well (with all wells at different stages)
• Data currency is important (i.e. up-to-date data)
• Team approach (management, engineers, field operators…)
• Multiple engineering disciplines (drilling, completion, facility, reservoir, production)
• Multiple departments (operations, engineering, production accounting, financial

accounting…)

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What’s required for Operations Analytics?

Tool selection is the starting point. The analytics tool needs to:

• support an iterative process of continuous learning, investigation and

collaboration

• enable a narrative … a set of visualizations that tell a story
• be nimble to adapt to evolving needs
• support “Discovery Analytics” workflows

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What is Discovery Analytics?

17

“Discovery Analytics is a sequence of explorations, each predicated

• n the discovery and insight of the last exploration. It’s about a path
• f exploration that can change with each new discovery … it’s not

something that can be anticipated. Some tools let you build an environment to explore data, but only within the bounds of how it was built and limited by the technical and domain expertise of its creator.“

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Key Analytic Needs

Source: What do data analysts need most from their tools?

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The importance of the narrative

Don’t rely on one visualization type, or one performance measure… assemble multiple perspectives that comprise an informative narrative. An illustration of multiple visualization types could include:

1)

Rate vs Time

2)

Cumulative Production vs Time

3)

Rate vs Cumulative Production

4)

Percentile (Cumulative Probability)

5)

Percentile Trendlines

6)

Probit Scale

The following examples are from VERDAZO presentation: Understanding Type Curve Complexities and Analytic Techniques

Each offers an important, and unique, perspective

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The importance of the narrative

An example of three performance measures that tell a different story…

Image from VERDAZO Blog: What production performance measure should I use?

Also consider:

• Payout
• NPV
• Completion cost
• Operations implications
• Etc.

Measure against what’s important to you!

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Narrative Example: 1) Rate vs Time

Strength: good for early production comparative analysis. Weakness: not as good for longer term production comparative analysis.

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Narrative Example: 2) Cumulative Production vs Time

Weakness: not as good for early production comparative analysis. Strength: very good for longer term comparative

• analysis. Also useful for quick payout analysis.

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Narrative Example: 3) Rate vs Cumulative Production

Strength: provides a visual trajectory towards Estimated Ultimate Recoverable (EUR). Weakness: does not effectively communicate the time it takes to achieve a level of cumulative production.

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Narrative Example: 4) Percentile (Cumulative Probability)

Strength: communicating statistical variability of a dataset. Weakness: it only represents a single moment in time.

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Narrative Example: 5) Percentile Trendlines

Percentile Trendline = extrapolated percentile of a collection of wells for each period in time. Strength: provides a meaningful comparative context to assess performance.

Image from VERDAZO Blog: So what is the problem with production type curves?

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Narrative Example: 6) Probit Scale (Cumulative Probability)

Weakness: it only represents a single moment in time. Strengths: 1) the shape can help determine if the results trend towards a lognormal or normal distribution 2) a “Probit Best Fit” regression can provide a variety of statistical insights including a measure of uncertainty (P10/P90 Ratio)

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Enhance the narrative with normalization

Comparative analysis using normalization is an effective means to put performance into a meaningful context. Types of data normalization include:

• Time normalization
• Time alignment to a common starting point (e.g. first production, peak rate). Lets you compare behavior from that

common starting point.

• Dimensional Normalization
• Establish a meaningful comparative context (e.g. production/100m completed length lets you compare wells of

different length and quantify production gains as wells get longer)

• Fractional Normalization
• Used to characterize temporal behavior relative to a timed-benchmark (e.g. Production rate as a percent of peak used

to characterize decline behavior)

See SPE Presentation Understanding Type Curve Complexities and Analytic Techniques for more details.

Part 3 The Analytics Journey

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Operations Performance Triad

Production

Cash flow & Delivery obligations

Financial Plan

Profitability Predicated on Capital Optimization Corporate value (reserves)

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Analytics Are Important to Cash Flow

Production Performance (daily surveillance)

 reduce downtime impacts on production  identify, prioritize and act quickly

Financial Performance (monthly surveillance)

 understand & minimize Operating Expenses  ensure Net Operating Income is optimized

Performance to Plan (constant surveillance)

 ensure cash flow is available to support upcoming activities  minimize reserve write-downs early

Production Financial Plan

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Typical Operations Analytics Journey

1) Eyes on Data (Data Access & Visualization) 2) Development of Diagnostic Measures 3) Diagnostic Workflows 4) Pattern Recognition 5) Measurement of Impact 6) Evidence-based Decision 7) Measurement of Benefit

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1) Eyes on Data

1.

Eyes on Data

2.

Development of Diagnostic Measures

3.

Diagnostic Workflows

4.

Pattern Recognition

5.

Measurement of Impact

6.

Evidence-based Decision

7.

Measurement of Benefit

Data Access and Visualization

• Improves resource utilization **
• Inherent data quality improvements

 more eyes on data, stronger reliance on good data

• Identify additional data capture

needs

• Identify data integration
• pportunities

** Production engineers that rely on Excel for analyses typically spend 4 to 6 hours a day gathering data and manipulating spreadsheets.

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2) Development of a Diagnostic Measure

1.

Eyes on Data

2.

Development of Diagnostic Measures

3.

Diagnostic Workflows

4.

Pattern Recognition

5.

Measurement of Impact

6.

Evidence-based Decision

7.

Measurement of Benefit

When existing data isn’t adequate to identify and prioritize issues or opportunities… get creative. Develop algorithms to create measures that:

• Quantify impacts
• Indicate/predict undesirable impacts
• Etc.

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2) Development of a Diagnostic Measure

Not all downtime is created equal Quantify production impacts of downtime

Images from VERDAZO Blog: Lost Productio duction in n VISAGE: AGE: Not All l Downt ntime ime is Created ated Equ qual al

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3) Diagnostic Workflow … tools for the journey

1.

Eyes on Data

2.

Development of Diagnostic Measures

3.

Diagnostic Workflows

4.

Pattern Recognition

5.

Measurement of Impact

6.

Evidence-based Decision

7.

Measurement of Benefit Production Performance (daily surveillance)

 reduce downtime impacts on production  identify, prioritize and act quickly

Financial Performance (monthly surveillance)

 understand & minimize Operating Expenses  ensure Net Operating Income is optimized

Performance to Plan (constant surveillance)

 ensure cash flow is available to support planned activities  minimize reserve write-downs early

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3) Diagnostic Workflow Structure

1) Identify & Prioritize

 Categorize to help you understand the opportunities  Focus on the opportunities with the biggest impact

2) Inform & Assess

 What is the cause? …. Can I do anything about this?

3) Investigate

 Support any decision/actions with the necessary detail

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Diagnostic Workflow: Production Performance

Diagnostic Measure How important is the well

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Diagnostic Workflow : Production Performance

Categorize to help understand, identify and prioritize

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Diagnostic Workflow : Production Performance

Inform and assess

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Diagnostic Workflow : Production Performance

Investigate

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Diagnostic Workflow : Financial Performance

Categorization would help… Profitability Contribution to Net Income

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Diagnostic Workflow : Financial Performance

Identify and prioritize

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Diagnostic Workflow : Financial Performance

Inform and assess

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Diagnostic Workflow : Financial Performance

Investigate

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Diagnostic Workflow : Performance to Plan

Identify and prioritize Degree of Variance Magnitude of Variance

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Diagnostic Workflow : Performance to Plan

Inform and assess

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Diagnostic Workflow : Performance to Plan

Investigate

(leveraging tools from other diagnostic workflows)

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4) Pattern Recognition

1.

Eyes on Data

2.

Development of Diagnostic Measures

3.

Diagnostic Workflows

4.

Pattern Recognition

5.

Measurement of Impact

6.

Evidence-based Decision

7.

Measurement of Benefit

Repetition of the diagnostic workflow structure can lead to identifiable patterns. (e.g. rod failure pattern, well servicing is the key driver of unprofitable wells)

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5) Measurement of Impact

1.

Eyes on Data

2.

Development of Diagnostic Measures

3.

Diagnostic Workflows

4.

Pattern Recognition

5.

Measurement of Impact

6.

Evidence-based Decision

7.

Measurement of Benefit

If I don’t measure the impact, in terms

• f dollars, how can I know how much

I’m willing to spend to try to find a solution?

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6) Evidence Based Decision

1.

Eyes on Data

2.

Development of Diagnostic Measures

3.

Diagnostic Workflows

4.

Pattern Recognition

5.

Measurement of Impact

6.

Evidence-based Decision

7.

Measurement of Benefit

A decision should be based on real data and with supporting evidence presented in a compelling narrative. The alternative  trust your gut (?)

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7) Measurement of Benefit

1.

Eyes on Data

2.

Development of Diagnostic Measures

3.

Diagnostic Workflows

4.

Pattern Recognition

5.

Measurement of Impact

6.

Evidence-based Decision

7.

Measurement of Benefit

How do you know you were successful? Would you do it again? Could you do it differently to improve the benefit? Measure the benefit!

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Case Study: Diagnostic used to identify this well

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Case Study: Identifiable Pattern of Failure

The impact persists after the problem is fixed

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Case Study: Investigate causation

New engineer start date

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Case Study: Understand Recovery Time (water cut)

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Case Study: New diagnostic measure

Recovery Wedge = the impact

• f the recovery period

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Case Study: Measure Impact (1 well)

Combined impact of Lost Production, Recovery Wedge and Workover Costs on

• ne well in 6 months

is $600,000.

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Case Study: Measure Benefit (41 wells)

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Case Study: Measure Financial Benefit (41 wells)

Part 4 Analysis Challenges

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Analysis Challenges

1.

Data Quality

2.

Data Granularity

3.

Missing Historical Data

4.

Accounting Practices

5.

Personnel Changes

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1) Data Quality

Challenge:

1.

Bad data

2.

Integration issues:

• Broken links
• ID changes, not updated (e.g. HZ wells)
• Duplicate IDs
• Different Working Interest in different systems
• Different hierarchy levels in different systems

Solution: More eyes on data inherently helps improve data quality. Use reports, algorithms and notifications to identify issues as they happen and make data health part of your culture. Client quote:

“Data quality is like cleaning a toilet … if it hasn’t been done for a long time it’s a miserable job, but once it’s been cleaned it’s easy to maintain”

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2) Data Granularity

Challenge:

1.

Plan at field level, capture results at well level  Were all wells executed according to plan?

2.

Unitized wells: production is recorded at well level, while costs and revenue are rolled into a single cost center  Which wells are not profitable?

Solution:

1.

Think ahead … plan at the same level of granularity that you want to track performance.

2.

Be innovative … sometimes it’s better to be vaguely correct than precisely wrong. For example:

a)

We identified that well servicing was the biggest cost & grabbed that from Wellview

b)

We used the realized price of the unit (from Qbyte) against production (from Avocet) to estimate revenue

c)

We calculated a Net Revenue, after well servicing costs, and quickly identified individual wells that were costing more than they were making.

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3) Missing Historical Data

Challenge: acquired a well without any production history. The ability to see the production history adds valuable context to production

• ptimization.

Solution: integrate data from two data sources into a seamless array.

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4.1) Accounting Practices (Latency of Data)

Challenge: Latency of data (cost accruals result in a 3+ month delay in ability to measure financial performance of individual wells). Dramatic shifts in commodity prices can have a massive impact. Solution: A set of algorithms that:

• Use historical operation costs (from accounting system) as a proxy for current costs (fixed monthly

costs and variable costs associated to gas, oil, fluid and water)

• Apply cost structure to current production rates (from field data capture system)
• Input commodity prices (oil, gas, and NGL)
• Calculate an estimated Net Operating Income for each well (i.e. is a well making money right now?)
• Input different prices to look at profitability scenarios

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4.2a) Accounting Practices (BOE Conversion Factor)

Challenge: BOE conversion 6:1  commodity prices are not based

• n heat energy, so why should

the conversion factor be? *2015 average oil:gas price ratio was 25:1

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4.2b) Accounting Practices (BOE Conversion Factor)

An operations decision using 6:1 could be very different than using 25:1 price-based BOE conversion factor

Note: 6:1 is the number of mcf of gas that have the same heat energy as a barrel of oil (it’s actually 5.4 to 6.1 to 1 depending on product grades).

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4.2c) Accounting Practices (BOE Conversion Factor)

Solution: Consider indicators that are independent of BOE conversion factors like Netback as Percent

• f Revenue.

**Operations should use the conversion factors that fully inform decisions and provide the best actionable insights.

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4.2d) Accounting Practices (BOE Conversion Factor)

Investors need to beware of cost indicators that use a 6:1 BOE conversion factor. Gas weighted companies can show overly favourable results.

Chart description Red dots: the published supply costs using 6:1 BOE conversion. Blue diamonds: what a company spends to make $50 in oil + gas (not including NGLs) using 25:1 Black squares: percent difference in results relative to 6:1 based supply costs.

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5) Personnel Changes

Challenge: If intellectual capital resides in spreadsheets and stand alone tools, then when people leave the company so does their know- how. Solution: Preserve intellectual capital and build a sustainable analytic maturity model using enterprise tools that manage analytic capabilities centrally and cultivate shared learning.

Analytic maturity correlates strongly to corporate performance.

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Summary

Part 1: Upstream Oil & Gas Industry

… is a reactive industry ripe with uncertainty and opportunities for operational efficiencies.

Part 2: Operations Analytics

… the variety and variability in technologies and production conditions necessitates a nimble, evolving toolkit with discovery workflow capabilities.

Part 3: Analytics Journey

… operations analytics isn’t about a destination, it’s about a journey of sequential

• explorations. Diagnostic workflows serve as a foundation for pattern recognition and value

driven decisions.

Part 4: Analysis Challenges

… there are many challenges to creating and sustaining effective operations analytics. Data quality, integration and creatively are critical to delivering value-driven insights.

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Conclusions

• Operations Analytics is about journey, not a destination. It needs “Discovery

Analytics” to help build informative narratives.

• Having the ability to evolve and adapt is critical to successful adoption and

sustainability.

• Operations should have the latitude to use its own metrics, that are inconsistent

with standard accounting practices, to better inform decisions that can positively impact the bottom line.

• Analytics is a craft where the technical married to the creative can yield valuable

insights.

Thank You

Bertrand Groulx President 403-561-6786 bertrand@verdazo.com Check out our blog at verdazo.com