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Visual Analytic Techniques for Operational
Efficiency & Performance Improvements
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 presentationPart 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
on the discovery and insight of the last exploration. It’s about a path
of 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
FinancialPlan
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
FinancialPlan
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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
opportunities
** Production engineers that rely on Excel for analyses typically spend 4 to 6 hours a day gathering data and manipulating sp readsheets.
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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 Production in VISAGE: Not All Downtime is Created Equal
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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
Dia
gn
osti
c M
ea
su
re
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…P
rofi
tab
ilit
y
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
De
gre
e o
f V
ari
an
ce
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
of 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
of the recovery period
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Case Study: Measure Impact (1 well)
Combined impact of
Lost Production,
Recovery Wedge and
Workover Costs on
one well in 6 months
is $600,000.
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Case Study: Measure Benefit (41 wells)
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Case Study: Measure Fianncial 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:
o Broken links
o ID changes, not updated (e.g. HZ wells)
o Duplicate IDs
o Different Working Interest in different systems
o 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
optimization.
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
on 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
of 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
Check out our blog at verdazo.com