Successful implementation of Big Data and Advanced analytics in - - PowerPoint PPT Presentation

This information is confidential and was prepared by Bain & Company solely for the use of our client; it is not to be relied on by any 3rd party without Bain's prior written consent

DRAFT

Successful implementation of Big Data and Advanced analytics in O&G

Oslo, February 10th 2015

This information is confidential and was prepared by Bain & Company solely for the use of our client; it is not to be relied on by any 3rd party without Bain's prior written consent

2

PER Big Data Analytics in Oil and ...

Five fundamental digital trends are disrupting traditional business models

Ubiquitous Instrumentation Ubiquitous Connectivity Unlimited Data Storage High- Performance Computing Intuitive Visualisation

Proliferation of sensors, cameras, microphones, gyroscopes, GPS transponders, etc. High-speed broadband connections and cheap WiFi Cheap and reliable storage solutions Increasingly powerful CPUs, clever networking solutions and advanced algorithms Large displays , 3D technology allow information to be displayed and interpreted intuitively

Rapidly increase in “remote” communications

(Smart) Phones Tablets Desktop PCs

1980 2000 2020 1k 1Bn 1Mn

Moore’s law of doubling computing power bi-annually

bit / s Giga bit/s Mega bit / s Kilo bit / s

2nd 3rd 4th

Range of speed per broadband generation Virtual reality are becoming mainstream

Top: Google glass Bottom: Oculus Rift

Source: Press search, Bain analysis , Intel, Gartner

Global data in zettabytes (1 ZB = 1 Trillion GB)

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PER Big Data Analytics in Oil and ...

Big Data and Advanced analytics build 4 key elements

+ + +

• Large sets of

data (>terabytes)

• Complex, large

number of steps to organize; fuzzy logic

• Incomplete, low

quality and/or hard to sort data

• Need for real-

time results i.e. in minutes Analytical complexity Very large data volume High velocity

• f analytics

Unstructured Data

• Inferring specific

preferences from general habits

• Sensor data
• Machine to

machine logs

• Transaction data
• Language
• Sound/Audio
• Images
• Video
• Social media
• TSA security

check decisions

• Instances of

personalized advertisement

• Offers in a store

Big Data and Advanced analytics

Source: Bain analysis; Industry expert interviews

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PER Big Data Analytics in Oil and ...

Four key application areas are emerging for Advanced analytics in Oil and Gas companies

Remote operations Digital fields Reservoir modelling and seismic imaging Predictive plant and drilling analytics

Ubiquitous Instrumentation Intuitive Visualization Ubiquitous Connectivity

Enabling Technology Trends Oil and Gas applications

Unlimited Data Storage High- Performance Computing Intuitive Visualization

Big Data and Advanced analytics

Big Data/ Advanced analytics

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PER Big Data Analytics in Oil and ...

These applications can have a transformative impact

Source: Bain analysis; Industry expert interviews; MIT Technology Review; SPE Reports; Company presentations and publications

DIGITAL FIELDS

• Downhole

measurement and control systems linked to production model, providing real time

• ptimization

potential

Increased production rate Higher ultimate recovery

PREDICTIVE PLANT / DRILLING ANALYTICS

• Use of predictive

analytics to reduce costs and foresee events (failures) to act before they

• ccur

Less plant outages / production stops Reduced Maintenance costs Increased drilling speed, accuracy, reduced costs

REMOTE OPERATIONS

• Use of telemetry and

sensors to create "control rooms" which monitor activities at multiple remote locations

Additional revenue; increased system throughput Increased safety, less staff

• n site

Reduced operating costs

RESERVOIR MODELLING AND SEISMIC IMAGING

• Increasing insight

into subsurface structures by providing 3D and 4D (time-lapse) displays

Increase in accuracy Growth funnel capacity increase through higher efficiency and quicker screening of low potential prospects

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PER Big Data Analytics in Oil and ...

Growth of Advanced Analytics will continue

Increased demand and potential for AA in oil and gas

DRIVERS OF INCREASED NEEDS

Cost reduction to sustain in low margin environment Growing share of maturing fields -need for enhanced recovery Lower drilling cost in unconventionals market Desire for increased exploration certainty

DRIVERS OF INCREASED POSSIBILITIES

Cross leveraging from development in B2C industry Continuously increasing computer power Increased data availability subsurface and surface Continuous development of predictive, optimization, cognitive and artificial intelligence

Source: Bain analysis, industry expert interviews

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PER Big Data Analytics in Oil and ...

However, the Oil and Gas industry has been relatively slow to adopt new technology…

Impact of digital/physical transformation by industry, today and expected until 2020

Source: Bain analysis; Rankings are based on examination of 300+ companies engaged in Digital/physical transformation projects, plus industry interviews

R&D spend typically 5-10% of revenue R&D spend typically 1-5% of revenue

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PER Big Data Analytics in Oil and ...

…our clients cite many reasons

Senior management not bought in WILLINGNESS CAPABILITY Unclear direction for the team Lack of appropriate data in raw form Data architecture limits ability to use islands of great data Insufficient technology skills Lack of sufficient technology tools Insufficient analytical skills Lack of business cases / proof points Misaligned incentives for functional managers

• Org. structure limits collaboration

Culture to enable change

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PER Big Data Analytics in Oil and ...

Boundaries are pushed by service companies and niche IT players

Big 5 NOC’s Mid-tier / independents Service Companies IT vendors

Traditional Basic Advanced Innovative Traditional Basic Advanced Innovative

Niche IT players

Indicative group median

Source: Bain analysis; Industry expert interviews

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PER Big Data Analytics in Oil and ...

8 steps to success

Organize

• Build Future proof IT

architecture, rethinking data management

• Build strategic

partnerships, capturing the best of industry and technology specialists

• Build high performance

team, recruit talent, training 5 6 7

• Drive execution pace to match ambition level

8 Staying abreast in world

• f changing technology

requires constant re- adjustment of the course

• Size the prize, and

locate where in the value chain

• Prioritize

applications strategic importance 1 2

• Organize to capture value, cut

through resistance in Business Units, centralize capabilities, decentralise applications 3

• Foster culture of

innovation and drive change in the business 4

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PER Big Data Analytics in Oil and ...

The size of the price varies per business segment

Exploration Development Production Refining & processing Sales & Marketing

Value driver Potential Time to value delivery

Capacity of growth funnel Increase speed and improve decision making Increase (ultimate) recovery Decrease

• perational

costs Increase certainty

Source: Bain analysis; Industry expert interviews

5 years+ 3-5 years < 1 Year < 1 Year 1-x year 1

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PER Big Data Analytics in Oil and ...

Prioritize analytic and technology applications aligned with company strategy

A D B C

Ability to differentiate Strategic importance Value 2 Fast Follow (Copy) Develop Competitive Advantage Smart follow (Selectively pursue) Do not Prioritise

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PER Big Data Analytics in Oil and ...

• Super-structure
• Shared services
• Operational footprint

Structure

• Decision principles
• Clear decision roles
• Incentives
• Non-negotiables
• Collaboration model
• Expected behaviors
• Governance forums
• Management processes
• Metrics, dashboards

Accountabilities Ways of Working Governance People Process Technology

• Skills, mindsets
• Critical processes
• Architecture &

delivery capability Operating Model

3 The operating model must be adjusted

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PER Big Data Analytics in Oil and ...

O&G generally centralise AA teams

Increasing centralisation of Advanced Analytics

ORGANIC BOTTOM UP CENTRE OF EXCELLENCE TOP DOWN TEAM

• Relies on

‘champions’ within individual business units

• Ideas driven

bottom-up

• Collaborative -

guidance from central team to BU team

• Ideas flow up and

down

• Central team solves

BU analytics problems or other ideas developed independently

BU-LED WITH SUPPORT

• BUs pursue

initiatives

• Central capability

provides assistance BU team

Source: Bain analysis, industry expert interviews

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PER Big Data Analytics in Oil and ...

Foster a culture of innovation to drive change

4

INNOVATIVE ENVIRONMENT BUSINESS APPLICATIONS Applications to reach maturity before applied in business context Nurturing environment where multiple experiments deliver some breakthroughs Regular business environment where results are judged on value and efficiency

• Applying strict business trade-offs

will limit the ability to stimulate innovation

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PER Big Data Analytics in Oil and ...

Legacy IT organizations struggle with achieving full potential

TYPICAL POINT OF DEPARTURE SUCCESS FACTORS

• Facilitate reporting, enable financial

decisions, or guide operational decisions

• Data captured and used in one

field/well is not relevant elsewhere

• Data architecture set up to address

large volumes of predictable, structured, and relatively clean data

• Data not tied together through an

effective integration layer

• Real-time decision making; enable

continuous learning

• Data captured in one field/well used

to improve outcomes in similar

• perating areas
• Architecture is set up to address large

volumes of structured, unstructured and real-time data

• Data from all sources tied together

by enterprise-wide data integration layer

Source: Bain analysis, industry expert interviews

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PER Big Data Analytics in Oil and ...

A broad set of players involved

IT Companies Operators focusing on “field of the future” and improved reservoir management and modelling Research institutions are focusing on the long-term applications of AA, such as integrated oil field and AI Small technology start- ups are developing unique applications to specific problems faced along the value chain Leverage knowledge from outside by tailoring existing products and services to O&G Service companies developing AA tools that reduce costs or enhance production

6

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PER Big Data Analytics in Oil and ...

Analytic talent and the optimal talent mix will be key

BUSINESS

(10-12, ~15%)

ENGINEERING

(30-35, ~45%)

DATA

(20-25, ~30%)

LEGAL

(5-10, ~10%)

Source: Bain Big Data Diagnostic Survey, N = 409

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Critical Big Data talent

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PER Big Data Analytics in Oil and ...

Choose your battles – “Leadership” is not always the best strategy

Researches and develops new practices Quickly adopts select industry practices Adopts widely used industry practices

8

LEADER SMART-FOLLOWER LATE-MAJORITY

Strategic objective Capability Resources Flexibility Operating model Risk Deciding factors

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PER Big Data Analytics in Oil and ...

Choose your implementation speed based on ambition level and capabilities

SLOW AND STEADY MEASURED GROWTH FULL THROTTLE

“Somewhere in the group” “Leading in the climb” “Pioneering”

8

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PER Big Data Analytics in Oil and ...

Big Data is not a fad…

• ~2X more likely to be in the top quartile of

financial performance within their industry

• ~3X more likely to execute decisions as intended
• ~5X more likely to make decisions “much faster”

1 2 3

Source: Bain Survey, N=409

Companies with the best Big Data and Advanced analytics capabilities are…

…However, only 4% of companies are actually executing well on their Big Data strategy

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PER Big Data Analytics in Oil and ...

In summary - E&P companies can get ahead in the game

TYPICAL PITFALLS KEY SUCCESS FACTORS

• Assume technology leadership is the

right strategy

• Choose your battles - Allocate scarce

resources selectively, choosing where to lead and where to follow

• Follow the money rather than

technological fascination – understand where the value comes from

• Invest in Talent - Invest in recruiting

the best talent – look outside the industry

• Foster a culture of innovation - this

should be isolated from the rest of the

• rganisation
• Implement the technologically most

interesting applications

• Build and hold on to legacy IT

architecture

• Resource a dedicated team with

internally recruited staff

• Ensure culture assimilation

throughout the entire company

• Integrate - Rethink data management,

value is created by connecting rather than separating, build an integrated architecture

Source: Bain analysis, expert interviews