Is your organization competing on analytics? A roadmap to ... · 4/20/2017 · Enterprise class....

Is your organization competing on analytics?

A roadmap to analytics success

SPE Breakfast Talk, Apr. 20th, 2017

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Let’s start with a story

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Enterprise class. User-friendly. Discovery Analytics.

Pattern of Failure

The impact persists after

the problem is fixed

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Recovery Period (high water cut)

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Investigation: When did failures increase?

New engineer start date

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Algorithm to quantify cost of recovery

Recovery Wedge = the impact

of the recovery period

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Full costs of failure

Combined impact of

Lost Production,

Repair Costs and

Recovery Wedge on

one well in 6 months

is $600,000.

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$12 million annualized benefit (41 wells)

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1) Shift from maximizing production

rates to maximizing net income

2) Data-driven decision = lower

production target

3) Dramatic reduction in unplanned

costs & production recovery impacts

4) Annual net revenue increase of $12

million on 41 wells

For more case study details … Visual Analytic Techniques for Operational Efficiency and Performance Improvements

http://www.verdazo.com/wp-content/uploads/2016/06/VERDAZO-CORS-Presentation-2016-05-31.pdf

Introduction

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Core Themes

1) Why use Visual Analytics?

2) Analytics adoption in O&G

3) Why do so many analytics initiatives fail?

4) Mental Models

5) Roadmap to success

6) Overcoming obstacles

7) Success stories

8) Conclusions

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Part 1

Why use visual analytics?

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Why use Visual Analytics? It’s survived the Hype Cycle

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Visual Data DiscoveryE

xpecta

tions

Time


Why use visual analytics? Get greater insight faster

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Find the anomalies?

drill in and

investigate


Why use visual analytics? Protect against things we can’t control

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Efficiency and optimization protect against things we can’t control… like price.


Why use visual analytics?

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1. Survive & thrive in any price environment

2. Do more with less

3. Make faster better decisions

4. Empower employees to explore & innovate

5. Institute sustainable improvements

Part 2

Analytics adoption in O&G

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Well Lifecycle Cost Breakdown

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Typical well cost breakdowns courtesy of GLJ Petroleum Consultants

Does analytics adoption correlate to cost?

https://www.gljpc.com/


Analytics Adoption & Spend Rate ($/day)

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Does Analytics Adoption Correlate to Spend Rate?

Completion

- Industry adoption = moderate to high

- Data values are discrete & straight-forward

- Analysis: correlations, statistical, regression & AI

Drilling

- Industry adoption = low to moderate

- Data volume is massive & complex

- Analysis: mainly focused on basic KPI’s (+ real-time)

Operations

- Industry adoption = low

- Requires significant data integration

- Analysis: dominated by Excel (often isolated efforts by

individuals)


Operational efficiencies: How big is the prize?

Producing well count and

production started dropping

2014

Downtime-related revenue

potential has been growing

reaching $3 billion in 2016

Why?

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Note: Downtime Revenue Potential excludes NGL’s and shut-in wells

Part 3

Why do so many visual analytics initiatives fail?

(or fall short of expectation)

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Common flaws in failed analytics initiatives

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1. Start down the path without a destination in mind

Easy ≠ Good

Can ≠ Should

2. Distracted by trends (e.g. dashboards)

More ≠ Better

Pretty ≠ Meaningful

Complexity ≠ Improved Understanding

3. Know your audience

Lack of Usability & Accessibility ≠ Adoption

4. Focus on cost, not on value

Lower Costs ≠ Higher Value

Sunk costs is not a good reason to continue


Considerations for Buy vs BIY (Build-It-Yourself)

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• Expertise/Cost

• Resources/Cost

• Risk

• Completeness of Solution

• One-off or Enterprise

• User Adoption

• Adapting vs Customizing

• Time to Value*

Should you try to fit new

technology to legacy

processes?

Beware of the “status-quo”

cognitive bias.

Innovation doesn’t happen

without change.


Be aware of your cognitive biases

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You’re all smart people, you can all envision what a solution looks like

and feels like, but your experience and your core competencies aren’t

centered around visual analytics software… be aware of a common

cognitive bias, “over-confidence”. Who expects to hit a home run at their

very first time at bat?

For example: If you want to drive a car, you go and buy one… you

wouldn’t consider trying to build a car in your garage. Building cars

is not your area of expertise.

BUY vs BIY? sometimes the answer is a combination.

Part 4

Mental Models(how we think about things)

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Shifts in Mental Models

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1) Earth is flat round (took ~ 1000 years)

2) Sun rotates around Earth Earth rotates around Sun (took ~ 200 years)

3) Disease was an imbalance of the four “humours” (blood, yellow bile, black bile,

and phlegm) germs were the primary cause of disease (took ~ 4 decades)

4) CD collections MP3 players “1000 songs in your pocket” (took 3-4 years)

5) Individual efforts (e.g. Excel) Enterprise Visual Analytics (… feels like forever)


Inertia (sticking points) for new Mental Models in O&G

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1) Margins were too high… that has changed

2) I can get by without it

3) Cost perception/bias

a) Profitability through cost savings (adjust staffing with commodity price)

b) Time isn’t a cost (I’m already paying for that person’s time)

c) Information Technology is a cost, not an investment (compare to D&C tech costs)

d) Intangible benefits don’t justify costs (not connected to a measurable outcome)

e) Ignore many small costs (lack of a holistic view)


1) Tangible Benefits of Visual Analytics

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• Time savings: improved capacity & cycle-time

• Targeted Improvements: target a tangible result (e.g. downtime reduction of __%)

• Future costs: the ability to grow with less resources (i.e. future staff cost savings)

• System savings: complimenting an existing system (e.g. analytics that fill-in

shortcomings of an existing system and save you $$$ and time to switch to another

system)


2) Intangible Benefits of Visual Analytics

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• Better decisions: evidence-based, value-driven decisions

• Improved communication: across all levels & disciplines

• Improved capability: the ability to do things we currently can’t do

• Employee satisfaction, performance, & retention


3) Enterprise & IT Benefits of Visual Analytics

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• Preserve intellectual capital

• Grow intellectual capital through collaboration & sharing of analyses

• Support data governance (centralized data access & business logic)

• Deliver one version of the truth (consistency & reliability of information)

• Provide cross-system integration that broadens use of data


Let’s test your cost perception…. Example 1

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Let’s say your drill and complete costs come in around $5 million per well.

Who thinks they would be successful asking their boss for $25K per well to do

visual analytics? On 16 wells?

• That represents 0.5% of the total cost of the well

• The production improvement required to break even would be 1.4 bbl/day (@ $50/bbl oil) for just

the first year… do you think you could achieve that?

• The improvement over the life of the well (20 yrs) would have to be 0.068 bbl/day

• Does $25K per well sound expensive now?


Let’s test your cost perception…. Example 2

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Let’s say you wanted to train a cognitive computing framework (Watson by

IBM) that could provide better cancer treatment recommendations by

leveraging genetic databases, treatment responses and academic research…

sounds great.

• Let’s say it will cost you $90 million… yikes!

• There are 130,000 cancer patients in the USA each year

• Over 10 years that works out to $70/patient

• Who would hesitate to spend $70 to get better cancer treatment recommendations?

Part 5

Roadmap to success(creating a culture of analytics)

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Analytic Maturity is an Organizational Pursuit

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Analytic Maturity correlates strongly to corporate performance

Analytic Maturity is a measure of

an organization’s:

- Use of data

- Adoption of analytics

- Sophistication of analyses


The Journey Towards Analytic Maturity

1) Eyes on Data (Data Access & Visualization)

2) Identify Diagnostic Measures/Criteria

3) Diagnostic Workflows

4) Pattern Recognition

5) Measurement of Impact

6) Evidence-based Decisions

7) Measurement of Benefit

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Focus on High Value & Low Complexity


A practical approach to Analytic Maturity growth

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1) Target the largest user base

2) Target the largest user need

3) Target a focal point (process)

Complex needs

Potential

Users

Basic needs

Operations


Start with your Asset Review process

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Operations optimization starting point Asset Review

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Intention = calibrate & align operational activities with corporate goals

Reality

• Days of preparation

• Distraction from other responsibilities

• Mountains of paper

Desire = live, repeatable interactive visual analysis with no prep time


Asset Review Integrated Performance Goals

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1) Production Performance (daily surveillance)

reduce downtime impacts on production

identify, prioritize and act quickly

2) Financial Performance (monthly surveillance)

understand & minimize Operating Expenses

ensure Net Operating Income is optimized

3) Performance to Plan (monthly surveillance)

ensure cash flow is available to support upcoming activities

minimize reserve write-downs early


Asset Review Management Direction

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Operations activities are directed by:

1) Clear Corporate Objectives

2) Specific questions

3) Performance Targets

a) Production

b) Financial

c) Plan (budget & forecast)

4) Properly aligned compensation incentives


Asset Review Consistent Outputs (part 1)

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1) Integrated performance goals

2) Consistent format

3) Answer questions*

4) More visuals (tell a story)

5) Less tables (slow to digest)


Asset Review Consistent Outputs (part 2)

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1) Are we on plan? What’s the variance & trajectory?

2) What wells are losing money? Why? Are there identifiable patterns?

3) What are our top performing assets? What’s our strategy to keep them that way?

4) What are our bottom performing assets? What are we doing about it?

5) What’s our shut-in plan if prices drop below ____?

6) What cost/downtime reduction strategies are we exploring? (e.g. change in

chemical treatments, new workover strategy, equipment changes)


Asset Review Use more visuals, less tables

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Are we on plan? What’s the variance & trajectory?

When do I

catch up to my

forecast?


Asset Review: Data quality issues will reveal themselves

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Refine data quality

processes as needed


Enterprise Consideration: Data Governance

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Data governance:

• Use the right data

• Centralize business logic

• Ensure consistency and

reliability of information


Asset Review: holistic view

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Quality

Data

Centralized

Business Logic

Value-based

Decisions

Corporate

Objectives


Why start with Asset Reviews?

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• Make corporate objectives as the driver for operational activities

• Provide a consistent way to communicate concepts visually

• Engage across many disciplines

• A focal point for visual analytics growth across the organization

• Create an impetus for data quality processes

Part 6

Overcoming obstacles

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5) Obstacles to Adopting Analytics

Cost perception/bias

Create tangibility (e.g. targets for improvement which more easily get management buy-in)

Compare to other costs you don’t even think about (e.g. how much do we spend on _____?)

Identify and discuss the sticking points of your organization’s Mental Model

Organizational obstacles

Change management (people & processes are the biggest challenge to adoption and making

new technology effective)

Align analytics initiatives with corporate goals and objectives

Get the right level of executive sponsorship

You need someone to steward the change process

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5) Obstacles to Adopting Analytics

The IT barrier

In many organizations IT is perceived to be an obstacle more than an enabler

Make IT a participant in achieving corporate objectives

Engage them with their language (enterprise solution, data governance, reduced

information delivery costs, maintainability of solution, etc.)

Decision paralysis

Spending more time & resource costs to make the decision than the cost of the decision

Break it down into smaller low-risk items (staged spending plans)

Prototype or pilot solutions with smaller audiences

Get started and build momentum with a series of successes

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Part 7

Success stories(inspiration to get started)

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Success stories

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1. A start-up built a better business plan using visual analytics, attracted investors more

easily and are able to grow with less staff

2. Daily engineer time savings (e.g. 2 hours/day = 25% time savings ~ $45K per engineer)

3. No more stacks of paper replaced with live interactive collaboration ($ 175,000 saved

annually in asset-review process time and shifted focus to more critical tasks)

4. Audit: $200,000 = 3 months of unpaid revenue on a single JV well (while reviewing shut -in

opportunities in a field)

5. Audit: >$100,000 discovered comparing theoretical liquid yield to component sales

volumes in first hour of analysis being set up

6. Optimization: >$100 million NPV improvement in optimized completion design


Case Study: $12 million annualized benefit (41 wells)

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1) Shift from maximizing production

rates to maximizing net income

2) Data-driven decision = lower

production target

3) Dramatic reduction in unplanned

costs & production recovery impacts

4) Annual net revenue increase of $12

million on 41 wells

For more case study details … Visual Analytic Techniques for Operational Efficiency and Performance Improvements

http://www.verdazo.com/wp-content/uploads/2016/06/VERDAZO-CORS-Presentation-2016-05-31.pdf


Case Study: Cleanout Automation

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• ~10,000 CBM wells

• Avg rate = 30 mcf/day

• Too many wells for an engineer to handle

• Creative use of data to develop optimization algorithms

• Automatically sends cleanout request to service company

Part 8

Conclusions

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To successfully create a culture of Visual Analytics…

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1) Consider adapting processes to new technology

2) Get started & build momentum with a series of small successes

3) Target the largest user base & basic needs (Operations)

4) Choose a focal point for growth across the organization (Asset Review)

5) Keep it simple… don’t add complexity unless it is necessary

Thank YouBertrand Groulx

President, Verdazo Analytics

403-561-6786

[email protected]

Check out our blog at verdazo.com

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http://www.verdazo.com/

Is your organization competing on analytics? A roadmap to ... · 4/20/2017 · Enterprise class....

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