The document discusses the role of big data analytics and the Internet of Things in the oil and gas industry, emphasizing the need for innovative solutions due to falling prices and complex supply chains. It highlights the importance of Apache Hadoop for managing vast amounts of data, enabling real-time operations, predictive analytics, and proactive decision-making. Moreover, it showcases various applications of big data technologies in optimizing oil production and improving operational efficiency.

1. THE BIG DATA GUSHER:
Big Data Analytics, the Internet of Things
and the Oil Business
SPONSORED BY

2. InnoTvaHtioAnN: NKe YedOedU A! gain
Jim Crompton
Noah Consulting
SPONSORED BY

3. Innovation Has Always Been Part of the Oil and Gas Industry
SPONSORED BY
• Deepwater
• High Pressure/ High Temperature
• Enhanced Oil Recovery
• Heavy Oil/ Steam Flooding
• Complex wells & real-time drilling
• Imaging beneath salt

4. SPONSORED BY
Shale Gas Extraction

5. U.S. Oil and Gas Production
SPONSORED BY
MBOE/D
25
20
15
10
05
Unconventional Gas
Conventional Gas
Unconventional Oil
Conventional Oil
1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035

6. SPONSORED BY
Price of Oil

7. The Digitization of the Oil Field
Hortonworks & Noah Consulting, LLC copyright 2014
SPONSORED BY

8. But We Can’t Quit Yet
• Falling oil and gas prices threaten margins
• Complex reservoirs require new completion and production techniques
• Complex supply chains require factory-like coordination and management
• Unconventional resources requires a supply chain solution through to the sales
point
SPONSORED BY

9. Become aT HDaAtaN DKri vYenO OUrg! anization
with Hadoop
Don Hilborn
Hortonworks
SPONSORED BY

10. Why is Data the Next Natural Resource of our Century?
“In God we trust, all others
must bring data.”
Dr. W. Edwards Deming
SPONSORED BY

11. Hadoop in Oil and Gas
Production Optimization
• Production parameter optimization is
intelligent management of the
parameters that maximize a well’s
useful life, such as pressures, flow
rates, and thermal characteristics of
injected fluid mixtures.
Real Time Operations
• Join disparate sources of data together
presenting real time and historical
combinations of E&P data at each stage
of the oil and gas production process.
LAS Predictive Analytics
• Leverage the “shovel-ready” nature of
LAS files for predictive analytics across
multiple datasets and the power of
Hadoop for normalization, transformation
and economical storage
Seismic Analytics/Management
• Storing seismic data from multiple
Page 11 © Hortonworks Inc. 2011 – 2014. All Rights Reserved
experiences permits learning in the
aggregate across all of those
experiences.
Enterprise Archive (Unstructured)
• Process unstructured data into an
enterprise archive and blend search with
machine-learning algorithms to discover
value and automatically categorize the
data for eDiscovery and other applications
Other
• Preventative Maintenance
• Condition Monitoring
• Supply Chain and Manufacturing
• Asset Optimization
• Lease Bidding
• QHSE

12. ..Allows a Shift from Reactive to Proactive Interactions
A shift in Production
From speed …to Accelerated Intervention
constraints
A shift in Drilling
From gut feel …to Real Time Automation
A shift in GeoScience
From manual process …to Dynamic Automation
SPONSORED BY
Hadoop allows
organizations to shift
interactions from…
Reactive
Post Transaction
Proactive &
A shift in Retail
From static branding …to Real-time Personalization Prescriptive
A shift in Refining
From break then fix …to repair before break

13. Manage the Modern Volume, Velocity and
Variety of Data Hadoop Value:
SPONSORED BY
Clickstream
Capture and analyze
website visitors’ data
trails and optimize
your website
Sensors
Discover patterns in
data streaming
automatically from
remote sensors and
machines
Server Logs
Research logs to
diagnose process
failures and prevent
security breaches
Sentiment
Understand how
your customers feel
about your brand
and products –
right now
Geographic
Analyze location-based
data to
manage operations
where they occur
Unstructured
Understand patterns
in files across
millions of web
pages, emails, and
documents

14. A Brief History of Apache Hadoop
SPONSORED BY
2013
Focus on INNOVATION
2005: Yahoo! creates team
under E14 to work on
Hadoop
Yahoo! begins to
Operate at scale
Enterprise
Hadoop
Apache Project
Established
Hortonworks
Data Platform
2004 2006 2008 2010 2012

15. A Brief History of Apache Hadoop
Yahoo! begins to
Operate at scale
SPONSORED BY
2013
Focus on INNOVATION
2005: Yahoo! creates team
under E14 to work on
Hadoop
Focus on OPERATIONS
2008: Yahoo team extends focus to
operations to support multiple projects &
growing clusters
Enterprise
Hadoop
Apache Project
Established
Hortonworks
Data Platform
2004 2006 2008 2010 2012

16. A Brief History of Apache Hadoop
Yahoo! begins to
Operate at scale
SPONSORED BY
2013
Focus on INNOVATION
2005: Yahoo! creates team
under E14 to work on
Hadoop
Focus on OPERATIONS
2008: Yahoo team extends focus to
operations to support multiple projects &
growing clusters
Enterprise
Hadoop
Apache Project
Established
Hortonworks
Data Platform
2004 2006 2008 2010 2012
2011: Hortonworks created to focus on STABILITY
“Enterprise Hadoop“. Starts with 24 key
Hadoop engineers from Yahoo

17. Hadoop Makes Handling Big Data Feasible
Low Cost
Open source software on
commodity hardware
Linear Scale
Infrastructure demands
proportional to data volume
Any Data Type
Structure not required to
store data in Hadoop
SPONSORED BY
Limit risk and allow
experimentation
Grow smoothly with
data needs
Store everything and
gain value over time

18. Hadoop: It’s About Scale & Structure or Lack Thereof
Required on write Required on read
Standards and structured Multiple Structures
processing
Limited, no data processing Processing coupled with data
Structured data types Multi and unstructured
SPONSORED BY
Hadoop
schema
governance
best fit use
Complex ACID Transactions
Operational Data Store
Data Discovery
Processing unstructured data
Interactive Analytics
Traditional
RDBMS SCALE
(storage & processing)
Optimized, reliable transactions Optimized for analytics

19. Hortonworks Process for Enterprise Hadoop
Fixed Issues
Stable Project Releases
Design &
Develop
SPONSORED BY
Upstream Community Projects
Downstream Enterprise Product
Certified at scale using the most advanced
Hadoop test bed on the planet
• 1000’s of production nodes at Yahoo!
• Over 1500 unit & system tests
Integrate
& Test
HDP 2.1
Distribute
Package
& Certify
Release Apache
Hadoop
Test &
Patch
Design & Develop
Virtuous cycle when development & fixed issues done
upstream & stable project releases flow downstream
Apache
Hive
Apache
HBase
Apache
Pig
Apache
Falcon
Apache
Knox
Apache
Storm

20. HDP2 and YARN Enable the Modern Data Architecture
Hortonworks architected and
led development of YARN
Common data set, multiple applications
• Optionally land all data in a single cluster
• Batch, interactive & real-time use cases
• Support multi-tenant access, processing
& segmentation of data
YARN: Architectural center of Hadoop
• Consistent security, governance & operations
• Ecosystem applications certified
by Hortonworks to run natively in Hadoop
Batch Interactive Real-Time
SPONSORED BY
SOURCES
EXISTING
Systems
Clickstream
Web
&Social
HDFS
(Hadoop Distributed File System)
Geoloca9on
Sensor
&
Machine
Server
Logs
Unstructured
DATA SYSTEM APPLICATIONS
Business
Analytics
Custom
Applications
Packaged
Applications
RDBMS EDW MPP YARN: Data Operating System
1 ° ° ° ° ° ° ° ° °
° ° ° ° ° ° ° ° ° N

21. SPONSORED BY
HDP and Platfora
SOURCES
Unstructured Existing
Systems
Web &
Social
Weather
Geolocation
ANALYZE
BUSINESS
USER
DATA
SCIENTIST
Sensor &
Machine
Logs

22. HADOOP : HORTONWORKS DATA PLATFORM (HDP)
COMPLEX DATASETS - ENTERPRISE ANALYTICS
SPONSORED BY
HDP and Platfora
SOURCES
Unstructured Existing
Systems
Web &
Social
Weather
Geolocation
ANALYZE
BUSINESS
USER
DATA
SCIENTIST
Tez
Slider
1 ° ° ° ° ° ° °
° ° ° ° ° ° ° °
° °
° °
° ° ° ° °
° ° ° ° °
Script
Pig
SQL
Hive
Java
Scala
Cascading
Stream
Storm
Search
Solr
NoSQL
HBase
Accumulo
In-Memory
Spark
Others
ISV
Engines
YARN: Data Operating System
(Cluster Resource Management)
HDFS
(Hadoop Distributed File System)
Sensor & Tez
Tez
Slider
Machine
Logs

23. Big DataT HAnAaNlytKic sY fOorU O!i l & Gas
Molly Stamos
Platfora
SPONSORED BY

24. Proven Leader in Big Data Analytics for Hadoop
MOMENTUM OVER THE
PAST 12 MONTHS
PROVEN COMPANY TO WATCH WORLD CLASS CUSTOMERS
SPONSORED BY
April 2014 10 Hot Hadoop
Startups to Watch
Ones to Watch in Big Data
CRN 10 Coolest Big Data
Products of 2013
BACKED BY LEADING INVESTORS
• Launched 9 product versions with feature
innovations
• Grew customers by 4x and employees by 2x

25. SPONSORED BY
Introducing Platfora
MISSION
LEAD THE INDUSTRY TRANSITION FROM BUSINESS
INTELLIGENCE TO BIG DATA ANALYTICS.
#1 Big Data Analytics
platform native on Hadoop
End-to-end platform built
for Multi-Structured Data
Self-service, iterative,
interactive, and fast

26. Data Architect, Data Scientist, Power Analyst Data Scientist, Analysts, Viewers
SPONSORED BY
Platfora Data Workflow
Hadoop
Upload
Files
Data Sources
Other
Datasets Lenses Vizboards
1. Link to the location of the source
files.
3. Analyst selects data fields and
filtering conditions of interest.
Data is materialized into a
Lens
4. Analyst visually explores the
data from the lens in the
vizboards.
2. Create a metadata definition of
how the raw data will be read and
the relationships between the data.

27. SPONSORED BY
Additional Information
•Big Data Analytics Blog: www.Platfora.com/blog
•Big Data Resources: www.Platfora.com/resources
• Big Data Solutions: www.Platfora.com/solutions/

28. THANK YOU!
SPONSORED BY

29. THE BIG DATA GUSHER:
Big Data Analytics, the Internet
of Things and the Oil Business
SPONSORED BY
SPONSORED BY