rs2014 houstonnov hortonworksdevonenergy kellykohlleffelrickhowell...

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 isnt perfect

Naturally incomplete (delays, shutdowns)

Not evenly spaced

Doesnt 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


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 Americas 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 magazines 100 Best Companies to Work For list each year since 2008.

15


Devon TodayDevons 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 opportunitiesTop-tier Eagle Ford developmentStrong Permian Basin positionWorld-class heavy oil projectsUpside potential in emerging plays

Strong liquids-rich gas optionality

EnLink ownership valued at $8 billionAdditional 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 wells 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


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


Get Involved

Demo Pods

See Project CAST and Hadoop in Action

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

Download the Hortonworks Sandbox

33


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