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© Copyr i gh t 2014-15 OSIso f t , LLC.
Presented by
How to achieve
Operational
Intelligence by
becoming a Data-
Driven Organization
Frank Besch, Director of Business Integration, Noble Energy
Rick Howell, Real-time Information Systems Supervisor, Devon Energy
Kelly Kohlleffel, Industry Executive, Hortonworks
Matt Ziegler, Product Manager, OSIsoft
© Copyr i gh t 2014-15 OSIso f t , LLC. 2
83% improved process cycle times
12% less operating expense
6% more profitable
49% had payback in one year or less
54% report ROIs >100%
Sources: Harvard Business Review, Forbes, IDB
© Copyr i gh t 2014-15 OSIso f t , LLC.
Modern Information Architecture
3
3
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Real-time Data isn’t perfect
• Naturally incomplete (delays, shutdowns)
• Not evenly spaced
• Doesn’t look and behave like SQL (RDBMS)
• Subject to errors in measurement
• Varies in fidelity
• Needs Context (Assets, Events)
• Hard to Collect effectively
The Truth about Real-time Data
© Copyr i gh t 2014-15 OSIso f t , LLC.
Decision-Ready Data
5
PI AF
PI Server
FinanceTrading ERP
Central OpsR&D Facilities
Central ITData Science
Single
Source
Other
Data 1
Other
Data 2
Other
Data 3Model
A
Model
B
Raw Data
Conditioned,
Trustworthy, Targeted
Data
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Process Optimization
Quality Improvement
Asset Health & Uptime
Energy Efficiencies
Regulatory Requirements
Safety
Assets Assets Assets Assets Assets Assets
Wherever your data
starts
to Wherever your data
needs to go
Enterprise
Infrastructure
Real-Time Data Infrastructure
© Copyr i gh t 2014-15 OSIso f t , LLC.
Evolving Data Goals
Real-time visibility
Real-time and historical view across all assets
Fleet-wide performance comparison
Prediction and Prevention
HMIPI System via
PI ProcessBook
Big Data?
Monitoring Process Optimization Benchmarking System Optimization
7
© Copyr i gh t 2014-15 OSIso f t , LLC. 8
64% of large enterprises plan to implement a big data project in 2014, but 85% of
the Fortune 500 will be unsuccessful in doing so. These time-consuming data
preparation tasks are largely to blame.
Gartner
Data cleansing and preparation tasks can take 50-80% of the development time
and cost in data warehousing and analytics projects.
poor data quality is the primary reason for 40% of all business initiatives failing to
achieve their targeted benefits.
Harvard Business Review
© Copyr i gh t 2014-15 OSIso f t , LLC.
Project CAST
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All PI System data delivered on your terms, in your language, to
the tools you use, and to the people that can make a difference.
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• Guaranteed Delivery & Storage
• Full Fidelity of Sensor
• Optimized for Real-Time
• Backup/Restore
• HA
• Security
System of Record
Statistical Analytics
Visual Analytics
• Designed to Analyze Large Sets
• Expects that the Data Exists
• Problem Defines Data Shape
• Typically Evenly Spaced in Time
Needs:
Analytics Packages
From Raw Data to Decision Ready Data
“Project CAST”
© Copyr i gh t 2014-15 OSIso f t , LLC.
Anatomy of a Data Publication
11
What our early adopters are saying
input
output
Describes Assets
Time Ranges,
Events, Filters,
Output Style
Many styles, columnar, json, file
Maintain one version of the truth no
matter where the data is used
Captures business rules to make
the data ready for broad
consumption
PI manages it. Great!
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Project CAST Components
“Business Intelligence Accelerator”
Publication Buffer
PI Server
PI System
SAP HANA
PI Integrators for
Oracle
Hadoop
Trustworthy Data
Publications
Coming to the PI System
Managed
by the PI
System
AF
© Copyr i gh t 2014-15 OSIso f t , LLC.
The Minimum Viable Product Process
13
© Copyr i gh t 2014-15 OSIso f t , LLC.
Presented by
Business Driven Data
Rick Howell, Devon Energy
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© Copyr i gh t 2014-15 OSIso f t , LLC.
About Devon Energy
• One of North America’s leading independent producers of natural gas and oil
• Engaged in exploration and production
• Corporate headquarters in Oklahoma City
• More than 5,000 employees
• Member of the S&P 500
• On Fortune magazine’s 100 Best Companies to Work For list each year since
2008.
15
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Devon TodayDevon’s Core & Emerging Assets
Core
Emerging
Heavy Oil
Rockies Oil
Mississippian-Woodford
Barnett Shale
Permian Basin
Anadarko Basin
Eagle Ford
• Q3 2014 net production: 640 MBOED(1)
• Deep inventory of oil opportunities—Top-tier Eagle Ford development
—Strong Permian Basin position
—World-class heavy oil projects
—Upside potential in emerging plays
• Strong liquids-rich gas optionality
• EnLink ownership valued at ≈$8 billion
—Additional midstream value in Access
and Victoria Express pipelines
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Business Driven Data
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• Executive Buy In
• Devon Enterprise – US and Canada
Drilling
Completions
Production
Facilities
Midstream
• Shape our data culture with tools
• Spotfire
• SAS
• Excel
Rick HowellDevon Energy
Supervisor Real-Time Data
© Copyr i gh t 2014-15 OSIso f t , LLC.
Making Drilling Repeatable
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WellCon Drilling Dashboard
• PI is source of real-time data
• Identify and characterize top
performers
© Copyr i gh t 2014-15 OSIso f t , LLC.
Spotfire, OFM and SAS
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Spotfire Screenshot
• Reservoir characterization
• ESP performance
• Comparison across wells
• Performance metrics
© Copyr i gh t 2014-15 OSIso f t , LLC.
New Tools
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NetworkNOT a traditional Hadoop Top of Rack (ToR) Configuration. Solution leverages
redundant collapsed core model that delivers 40 gigabit aggregate.
Physical Infrastructure
40ea HP DL380 Servers
Each has…
16 CPU Cores
96 Gigs Ram
30 TB Local Storage
(1.2 Petabytes Raw)
10 Gigabit Ethernet
Services Include…
MapReduce
Data Nodes
Hbase
(Every Server)
Apache Solr Search
(4 Server Instances)
Virtual Infrastructure
3ea HP DL580 VMWare
Host. Each has…
32 CPU Cores
512 Gigs Ram
6 TB Local Storage
10 Gigabit Ethernet
Services Include…
Name Nodes
(Primary & Backup)
Hive Catalog
PostGreSQL DB
Zookeeper
DataMeer
Informatica Data
Virtualization
Other Ancillary
Services
Page 21 © Hortonworks Inc. 2011 – 2014. All Rights Reserved
Become a Data Driven Organization with
OSIsoft and Hadoop
Kelly Kohlleffel – Hortonworks – Industry Executive13 November 2014 - OSIsoft Houston Regional Seminar
Hortonworks. We do Hadoop.
Page 22 © Hortonworks Inc. 2011 – 2014. All Rights Reserved
Our Mission:Power your Modern Data Architecture
with HDP and Enterprise Apache Hadoop
Who we are
June 2011: Original 24 architects, developers, operators of Hadoop from Yahoo!
June 2014: An enterprise software company with 520+ Employees
Key Partners
Our model
Innovate and deliver Apache Hadoop as a complete enterprise data platform
completely in the open, backed by a world class support organization
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Page 23 © Hortonworks Inc. 2011 – 2014. All Rights Reserved
Hadoop in Oil and Gas
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.
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.
Seismic Analytics/Management
• Storing seismic data from multiple
experiences permits learning in the
aggregate across all of those
experiences.
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
Other• Preventative Maintenance
• Condition Monitoring
• Supply Chain and Manufacturing
• Asset Optimization
• Lease Bidding
• QHSE
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
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Page 24 © Hortonworks Inc. 2011 – 2014. All Rights Reserved
OSIsoft and Hadoop for Oil and GasS
OU
RC
ES
Sensor &
Machine
Logs
SO
UR
CE
S
Unstructured Existing
Systems
Web &
Social
Geolocation
Weather
AN
ALY
ZE
OPERATIONAL USER
AN
ALY
ZE
BUSINESS USER
DATA SCIENTIST
1 ° ° ° ° ° ° °
° ° ° ° ° ° ° °
° °
° °
° ° ° ° °
° ° ° ° °
Script
Pig
SQL
Hive
Java
Scala
Cascading
Stream
Storm
Search
Solr
NoSQL
HBase
Accumulo
HADOOP : HORTONWORKS DATA PLATFORM (HDP)
COMPLEX DATASETS - ENTERPRISE ANALYTICS
In-Memory
Spark
Others
ISV
Engines
YARN: Data Operating System
(Cluster Resource Management)
HDFS (Hadoop Distributed File System)
Tez Slider SliderTez Tez
OSIsoft PI SYSTEM
SYSTEM OF RECORD – REAL TIME ANALYTICS
ASSETS / EVENTS ASSET BASED ANALYTICS
PI Data Archive
Data Publications
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Page 25 © Hortonworks Inc. 2011 – 2014. All Rights Reserved
Hadoop & OSISoft : Enabling Data Driven Innovation
Hadoop &
OSIsoft
Joint Value
Explore Complete Datasets• Leverage Hadoop as a landing pad for all emerging data types and data silos
• Empower Operational Users, Business Users, and Data Scientists
Enable Data Agility – Schema on Demand• Shorten development cycles
• Test 5x – 10x more hypotheses
• Shorten innovation cycles
• Apply to any size dataset
Deliver Data Scale and Variety Economically• Enable exploration of larger datasets
• Preprocess raw data
• Expose unlimited data variety on premise or in the cloud ($250/TB)
Create New Value and Business Innovation• Unlock net new business value within and across emerging data types
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Page 26 © Hortonworks Inc. 2011 – 2014. All Rights Reserved
..allows a shift from reactive to proactive interactions
Hadoop and OSIsoft
allow organizations to
shift interactions from…
ReactivePost Transaction
ProactivePre Decision
…to Real-time PersonalizationFrom static branding
…to repair before breakFrom break then fix
…to Dynamic AutomationFrom manual process
…to Real Time AutomationFrom gut feel
…to Accelerated InterventionFrom speed
constraints
A shift in Production
A shift in Drilling
A shift in GeoScience
A shift in Retail
A shift in Refining
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Page 27 © Hortonworks Inc. 2011 – 2014. All Rights Reserved
Next Steps...
Download the Hortonworks Sandbox
Read the datasheet: Oil & Gas and Hadoophttp://hortonworks.com/blog/modern-oil-gas-architectures-built-hadoop/
Engage the Hortonworks/OSIsoft Joint Account Teamsfor a Business Use Case Workshop
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Page 28 © Hortonworks Inc. 2011 – 2014. All Rights Reserved
Frank BeschNoble Energy
Director of Business Integration
Increasing Production with Data
Page 29 © Hortonworks Inc. 2011 – 2014. All Rights Reserved
Noble Energy – Company Highlights
DJ Basin
Marcellus Shale
Deepwater Gulf of Mexico
West Africa
Israel and Cyprus
Falkland Islands
Northeast Nevada
Levant Basin
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Page 30 © Hortonworks Inc. 2011 – 2014. All Rights Reserved
The ChallengeAn Innovative Approach to Unconventional Resources
Production Growth Drives Margin Growth
• Production growth fuels cash margin growth which drives
long term cash flow
Speed Is Essential
• Working with disparate, complex datasets under a traditional
analysis model limits innovation and does not allow the
speed required for unconventional plays
Data Volume Continues to Grow
• A single well has billions of time series data points and other
key related data sources such as the production systems,
subsurface information, and field information (many times in
unstructured format) make it highly challenging to provide a
consolidated view for analysis
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Page 31 © Hortonworks Inc. 2011 – 2014. All Rights Reserved
Our ApproachData as a Strategic Asset
Gain New Insights Into Production Dynamics
• Across a wide variety of disparate data sources and variables such as
well information systems, SCADA data, sensors, and unstructured data
Build Executive Consensus and Business Sponsorship
• Start with a single business unit – prove the value
• Recognition that data provides sustainable competitive advantage
• Widespread, systemic value creation because data is managed as
professionally as capital or labor
Rely on Trusted Partners to Assist
• Combination of Noble team members along with Hortonworks (Hadoop)
and OSIsoft (PI / CAST)
• High performing operational and data science team (Python)
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Page 32 © Hortonworks Inc. 2011 – 2014. All Rights Reserved
Results and Next StepsJourney to a Data Driven Organization
Operational Value Realized
• Proactive approach to identifying events causing production downtime resulting
in significant savings per day
• Advanced analytics allowed us to move beyond the spreadsheet
On a Path to Strategic and Transformational
• Fostering a data driven culture while realizing value across multiple areas
• Delivering advanced and transformational analytics to each business function
and business unit
• Building out organizational design and capabilities, best practices, and a COE
Enhanced Models and Net New Analytics
• Continuing to add additional datasets to the model for even greater enriched
analysis
• Addressing other areas within the company
• Predictive analytics operational across business processes
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Get Involved
Demo Pods
• See Project CAST and Hadoop in Action
E-mail us cast@osisoft.com (2015 CTP)
Download the Hortonworks Sandbox
33
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