How to achieve Operational becoming a Data- Driven ... · Results and Next Steps Journey to a Data...

© 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


Modern Information Architecture

3

3


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


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


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


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


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

http://www.gartner.com/newsroom/id/2593815


Project CAST

9

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.


• 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”


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!


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


The Minimum Viable Product Process

13


Presented by

Business Driven Data

Rick Howell, Devon Energy

14


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


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

16


Business Driven Data

17

• 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


Making Drilling Repeatable

18

WellCon Drilling Dashboard

• PI is source of real-time data

• Identify and characterize top

performers


Spotfire, OFM and SAS

19

Spotfire Screenshot

• Reservoir characterization

• ESP performance

• Comparison across wells

• Performance metrics


New Tools

20

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

© 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.


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

22


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

23


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

24


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

25


..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

26


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

27

http://hortonworks.com/products/hortonworks-sandbox/

http://hortonworks.com/products/hortonworks-sandbox/


Frank BeschNoble Energy

Director of Business Integration

Increasing Production with Data


Noble Energy – Company Highlights

DJ Basin

Marcellus Shale

Deepwater Gulf of Mexico

West Africa

Israel and Cyprus

Falkland Islands

Northeast Nevada

Levant Basin

29


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

30


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)

31


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

32

http://blog.ginger.io/assets/datascience.jpg

http://blog.ginger.io/assets/datascience.jpg


Get Involved

Demo Pods

• See Project CAST and Hadoop in Action

E-mail us [email protected] (2015 CTP)

Download the Hortonworks Sandbox

33

mailto:[email protected]


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