This document provides an overview of Harris Corporation's work in developing digital oilfield technologies using big data analytics. It discusses Harris' background in energy solutions and big data, how "big" oilfield data can be, and a prototype system developed by Harris to parse, store, visualize and analyze oilfield operational data using Splunk. The prototype demonstrated 3D and 5D visualization of distributed temperature sensor data across multiple wells over time. An upcoming project in Alberta, Canada will further test these digital oilfield technologies.

1. | 1NON-EXPORT CONTROLLED INFORMATION SplunkLIve Orlando 1/8/2015harris.com
SplunkLive Orlando
Mike Sklar Ph.D.
A Splunk Adventure
Into the Digital Oilfield
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2. | 2NON-EXPORT CONTROLLED INFORMATION SplunkLIve Orlando 1/8/2015
Who is the Harris Corporation
Harris in the Oil & Gas Business –Energy Solutions
Harris Big Data/Analytics Background
Digital Oilfields
– How “Big” is “Big”
Why Splunk
Digital Oilfield Exploration Prototype System
Prototype Demo
Alberta CCEMC Demonstration – Coming in Early 2015
Conclusions
Thanks & Helpful Links
Overview

3. | 3NON-EXPORT CONTROLLED INFORMATION SplunkLIve Orlando 1/8/2015
Defense National
Intelligence
Civilian
Government
International
Government
Public Safety
& Professional
Communications
Energy &
Maritime
Healthcare IT Services
Harris Corporation
Leading provider of innovative solutions in
government and commercial markets
• $5 billion in fiscal 2014 revenue
• Global reach in more than 125 countries
• 14,000 employees, including 6,000 engineers and scientists

4. | 4NON-EXPORT CONTROLLED INFORMATION SplunkLIve Orlando 1/8/2015
Since 2008 Harris has been developing Radio Frequency antenna
systems for heating reservoir fluids
Our HeatWave™ antennas and ESEIEH™ process unlock billions
of barrels of Heavy Oil in an efficient , environmentally safe
manner
Nearing full-scale system readiness (FY2015-16)
Harris RF Energy Overview

5. | 5NON-EXPORT CONTROLLED INFORMATION SplunkLIve Orlando 1/8/2015
How does ESEIEH Work?
(Patented Process)

6. | 6NON-EXPORT CONTROLLED INFORMATION SplunkLIve Orlando 1/8/2015
ACTIONABLE
INTELLIGENCE
Securely transforming data into intelligence
Web PortalStreaming
Rapid Data
Ingest
Analytics
Consulting
Customization
Services
• New insights
• Faster decision making
• Increased revenue
• Increased cost savings
• Optimized operations
CUSTOMER
DATA
DOMAIN
DATA
GOVERNMENT
DATA
OPEN SOURCE
DATA
GEOSPATIAL
INFORMATION
Correlation Trending
Big Data Storage/Indexing
Management
Predictive Analytics
Visualization
Query
Forensics
Support
Services
Harris Analytics Engine
Harris “Dream” Big Data & Analytics Services

7. | 7NON-EXPORT CONTROLLED INFORMATION SplunkLIve Orlando 1/8/2015
Oil & Gas Big Data Problems
DIGITAL OIL FIELDS
IMAGE COURTESY OF :
• Connect/digitize all surface & in-situ devices
and sensors to a central facility and the
enterprise
• Provide complete local/global awareness of
the field/asset
• Enhances safety & improves performance
OTHER E&P ACTIVITIES
• Drilling & Completions
• Massive data sets
• Managed in Real Time.
• Operational errors not safe
• Optimal monitoring can
lower drill times and cost
•Seismic Surveying
• Analog and Dist. Fiber
• TB of data /survey
• Required legally in many
cases

8. | 8NON-EXPORT CONTROLLED INFORMATION SplunkLIve Orlando 1/8/2015
Big Data Visualization & Analytics Services
Typical Oilfield Production
ACTIONABLE
INTELLIGENCE
Web PortalStreaming
Rapid Data
Ingest
Analytics
Consulting
Customization
Services
Correlation Trending
Big Data Storage/Indexing
Management
Predictive Analytics
Visualization
Query
Forensics
Support
Services
Harris Analytics Engine
3D Well Temp (time, temp,
location
5D Field Viz
Simulation
Match/Accuracy
Optimal
Decisions
Predicted Field
NPV
Harris value added:
1) Maximum Field Performance
2) Maximum oil recovery
3) Easy expert collaboration during
challenges
User Manipulates in RT
In-Situ Sensors
•Distributed temp
•Formation pressures
•Component temps
XMTR Data
•Power delivered
•Power Quality
Surface Equipment
•Flow Rates
•Component temps
•Fluid/gas pressures

9. | 9NON-EXPORT CONTROLLED INFORMATION SplunkLIve Orlando 1/8/2015
• Digital Oilfield Data typically reasonably sized
• Example Conoco Phillips Surmont Oil Sands Field in Alberta, CA
– Entire site has 60 production wells, 56 steam wells, 20 obs. Wells
– Data comes from Distributed temp sensing fibers on each well,
thermocouples, pressure sensors and surface equipment
– Total of ~ 50,000 data points (typically sampled at 1/minute)
– Total bytes /day = 71.7Mb
– Total bytes /month = 2.1Gb
• Drilling Automation /Offshore Platforms
– Massive data sets Gb/day
• Seismic data collection
– Tb/month/survey
How “Big” is “Big”
Taken from:
(available at www.aer.ca )

10. | 10NON-EXPORT CONTROLLED INFORMATION SplunkLIve Orlando 1/8/2015
Click to edit master title style Click to edit master title style
Why Splunk
Comparing With Conventional Data Systems
SPLUNK
 Cloud Served reduces customer
HW needs/capital
 No special training on wide range
of custom SCADA/
database tools
 OPEX cost is similar
CAPEX drastically lower
 All data across field enterprise
available for analysis
TRADITIONAL
 Wide range of highly specialized
HW/SW platforms
 Well entrenched in Oil & Gas. All
DOF’s use conv SCADA boxes
– Of course vendors want/provide
higher level data services
 Even have web hosted GUI’s and
open Javascript interfaces
 Small set of standard historians
already in use
 Ability to scale, visualize and see
the larger “enterprise” picture is
lacking
http://www.osisoft.com/
http://www.rockwellautomation.com/rockwell
software/factorytalk/overview.page

11. | 11NON-EXPORT CONTROLLED INFORMATION SplunkLIve Orlando 1/8/2015
Senior Design project sponsored by Harris
Two Semester 32 week effort
Objective
– Develop a flexible, scalable software system to
parse, store, and visualize a wide array of data
from oil and gas operations.
– Critical abilities include monitoring system health
and changing trends of operational data.
– Must allow for remote user collaboration with no
special HW/SW or installations
– Eventually will evolve to field optimization engine
and predictor
Digital Oil Field eXploration Prototype
TEAM
Derek Brown
Manny Momot
Matteo Zancanella
David Califf
Neeti Pathak
Kyle Grosjean
Advisor: Professor Entezari

12. | 12NON-EXPORT CONTROLLED INFORMATION SplunkLIve Orlando 1/8/2015
Software Architecture

13. | 13NON-EXPORT CONTROLLED INFORMATION SplunkLIve Orlando 1/8/2015
 The system currently handles two types of files discrete and distributed
files (see illustration below)
 Configuration files create/define input types for different data:
– inputs.conf points Splunk to the data location for indexing and monitors for changes.
– props.conf defines the source type for both. Lots of tweaks necessary for extraction.
– fields.conf parses variables at index time rather than at search time.
– transform.conf transforms the discrete into an easier to use form before search time.
Uses FORMAT to label values with variable names
 Searches are then designed in regex to retrieve data in convenient format
SPLUNK INDEX _i
Under The Hood – Data Input
Harris Control File - RF
Harris Control File - ENV
Distributed Temp
SPLUNK INDEX_k

14. | 14NON-EXPORT CONTROLLED INFORMATION SplunkLIve Orlando 1/8/2015
What is 3D and 5D Visualization
In an Oil Field
3D visualization of Distributed Temp data
Features 360 degree rotation, zoom in/out,
and panning
Temp(C)
Distributed Temp Data in 3d5D Distributed Temp Data
5D visualization of Distributed Temp data
Across multiple wells animated in time. Features 360
degree rotation, zoom in/out,
and panning Dimensions
Well location (X,Y,Z)
Temp
Time (animated)
1 2 3
4
5
Dimensions
Well position (X)
Temp
Time (animated)
1
2
3

15. | 15NON-EXPORT CONTROLLED INFORMATION SplunkLIve Orlando 1/8/2015
DOFEX Demonstration
Vertical instrument strings
Horizontal and vertical strings
43.5 m
Transducer and casing instrumentation
X
Y
Z
A1
ANT CL
X
X
Z
1.5m
3m
4.75m
6.75m
-1.75m
-3.75m
13.5m13.5m13.5m

16. | 16NON-EXPORT CONTROLLED INFORMATION SplunkLIve Orlando 1/8/2015
$44M+ flagship project with partners in Alberta
ESEIEH CCEMC Project Background
Effective Solvent Extraction Incorporating
Electromagnetic Heating
• $44M+ project developing key technologies
needed for a reliable in situ RF heating system
• Key CAPEX savings: no steam plant
• Key OPEX savings: solvents use reduces
energy requirements
Solvent + RF Advantage
Low Temperature
Reduces
GHG
Reduced CO2
Emission
Penalties
Reduces
Fuel Costs
Reduced
OPEX
No Steam
No Water
Treatment
Reduced
CAPEX/OPEX
No Steam
Plant
Reduced
CAPEX
ESEIEH Status
Phase 1: January 2012 – successful completion
of 12.5m horizontal heating experiment at
Suncor mine.
Phase 2: CURRENT – begin testing 160m deep
100m lateral horizontal RF heater and solvent
injector
Canadian Partners

17. | 17NON-EXPORT CONTROLLED INFORMATION SplunkLIve Orlando 1/8/2015
 The Splunk tool provides framework for rapid development
 Open architecture (Python, JavaScript etc.) provides endless
possibilities
– Rich visualization possible in this case
– Advanced analytic platform calls
Combining the power of Splunk index and search with new ways
to view data leads to intelligence and answers!
Massive fast growing opportunities to access Oil & Gas field data
at a large scale
•Rich set of Oil & Gas SCADA Inputs
•Oil & Gas standard data input (WITSML, PRODML http://www.energistics.org)
•See OSISoft “Cloud Connect” beta product (www.osisoft.com )
•Kepware (www.kepware.com ) interfaces to nearly every SCADA system in
existence
•Now have Specialized forwarded and plug in for Splunk
(http://info.kepware.com/idf-for-splunk)
Conclusions
Set Splunk Free!

18. | 18NON-EXPORT CONTROLLED INFORMATION SplunkLIve Orlando 1/8/2015
For more info regarding Harris RF Energy see:
To Contact the Author
– Mike Sklar
Senior Engineer Energy Systems
Harris Corp.
msklar@harris.com
321-729-2442
Special thanks to these folks at Splunk:
– Nate McKervey - Brian Gilmore
– Gilberto Castillo - Melissa Nealon
– Chris Hill
Contacts and Links
http://www.harris.com/rfheating