26
Message Oriented Middleware The Future of SCADA Arlen Nipper President and CTO Cirrus Link Solutions [email protected]
Message Oriented Middleware
The Future of SCADA
Arlen Nipper
President and CTO
Cirrus Link Solutions
SCADA
Host
SCADA
Host
11
Multi-Drop Network
Protocol X
33 55
4422
SCADA Protocols & Applications: The Good, The Bad, and The Ugly
SCADA Protocols & Applications: The Good, The Bad, and The Ugly
Allen-Bradley DF1
Allen-Bradley DH+
Allen-Bradley EN/IP
Amocam
ARCNet
ATS
BITbus
CANbus
CA
CCM2
CDCI
CDCII
Conitel
DeviceNet
Daniel
DL130
DNP 3.0
Elliott
Enron Modbus
F&M
Ferranti MK2A
Galveston-Houston
GPE
GSI
Harris 5000/5500/6000
Hansa S002
HART
Hayes
Honeywell DE
Kodata
L&J
LANDAC
Landis & Gyr
Micromotion Flowscale
MODBUS ASCII
MODBUS RTU
MODBUS Plus
MPS9000
MTS
Omron Host Link
Optomux
PERT 2631
Plessey TC6
RDACSII
REDAC 70H
RNIM
Siemens 3964R
Siemens RK512
SLIP
SNET -I
SNET –II
GE SRTP
TANO Model 10
TANO Model 100
Tejas
Total-Flow
Transit Bus
TRW 9550
Valmet Series 5
Transmitton MT700
TRW S-70
TRW S-703
Varec
Wesdac 4F
WISP
Wireless HART
SCADAHost
SCADAHost
11
Protocol X
22
33
“Operations (OT)”“Enterprise (IT)”
Electronic Flow
Measurement
ERP
Asset Management
& Optimization
Analytics &
BIG DATA
Mobile Apps
Future Apps &
Integration
Message Oriented Middleware: Unlocking the Potential
SCADAHost#2
SCADAHost#2
1122
33
“Operations (OT)”“Enterprise (IT)”
Electronic Flow
Measurement
ERP
Asset Management
& Optimization
Analytics &
BIG DATA
Mobile Apps
Future Apps &
Integration
MQTT
“MOM”
Broker
IT + OT = Agile Enterprise
44
55
nn
SCADAHost#1
SCADAHost#1
MQTT Message Transport
Summary
•Decouple device protocols from applications.
•Dramatically improve critical data update times.
•Reduce network bandwidth consumption.
•Enable “one to many” information exchange.
•“Unlock Intelligence” stranded in field devices!
Poll/Response SCADA systems were perfectly viable solutions when first
developed 35 years ago.
But this is 2013. SCADA solutions needs to leverage the very same Message
Oriented Middleware technologies that IT solutions use to decouple application
to:
WHY?
10© 2013 IBM Corporation
Learn from the past…Optimize the present…
Predict the future…
Extending Oilfield Data Acquisition with Big Data Analytics
Mike Brulé PhD, PE
Page 11
Big Data - Variety, Velocity, Volume, Veracity
Text
Image & Video
Acoustic
Financial
Times Series
Statistics
Mining
Predictive
Geospatial
What do we mean by Big Data and Analytics?
Predictive Maintenance
Safe and cost-effective operations
Drilling & Completions Optimization
Advanced Control & Automation
Information Challenges
� Operations now capture large volumes and broader
variety of data
� Current analytics capabilities suffer from data lag
and latency
� Subsurface and surface operations have different
industry standard data types and schemas
� Integration of work processes and optimization of
the overall system are difficult
Production & Process OptimizationBusiness Impact
EPC and Oilfield Services
Aligning Big Data Analytics with Operational and Business Objectives:Operations Excellence—Integrated Operations—Operational Integrity
12
� Rig uptime and yield
� Production efficiency and effectiveness
� Maintenance efficiency
� Health, Safety & Environment incidents
� Corporate Performance
Page 13
Industry Differences: OT vs. ITConverged OT + IT environment is necessary to support integrated operations
Real-Time Operational Data System: Enable Real-time Insight From Big Data
Production
Process
Management
Operation
Performance
Monitoring
Safety Other Apps
Internet-scale
Messaging Engine
Real-time
Operational
Database
Stream
Computing
Engine
Real-Time Operational Data System � Predictive analytics
� Big Data
� Real-time
� Integrated, Reliable
& Actionable Data
� Collect, process & analyze large volumes of sensor generated data in near real-time
� Support 10-100X higher data rate than general IT middleware via hardware acceleration
Asset
Condition
Monitoring
Industry Rules Based Intelligent Data Processors
15
Client Problem:� Energy is under intense scrutiny due to major
catastrophic events
� With the growing need for sustainable energy
supply, companies must deal with operational risk
management and changing compliance
requirements
AGA and all of our member companies believe that a
positive safety culture… takes responsibility and
accountability for safety in day-to-day work activities—
this is a priority we all share
– Dave McCurdy, American Gas Association President and CEO.
AGA and all of our member companies believe that a
positive safety culture… takes responsibility and
accountability for safety in day-to-day work activities—
this is a priority we all share
– Dave McCurdy, American Gas Association President and CEO.
Risk Assessment
Engine
Likelihood & Impact
Analysis Engine
Total Lifecycle Management
Compliance Evaluation
Engine
Compliance Mandates
FederalStateAgency
Stochastic Planning & Budgeting Analysis
Long Term Sustainability
Planner
GIS
EAM
/
Maximo
SCADA /
Inspections
ADAM Asset Analytics
Engine
Long Term Sustainability
Planner
ACCESS Capital
Budgetin
g
Solution Approach:� Using Big Data & Analytics, an energy company
can gain deep insights into the sources of risk, the
probability of impact, and the likelihood of
risk occurrences
� A Big Data & Analytics platform supports real-time
descriptive, predictive, and prescriptive analytics
to convert data into actionable insights
Current State – Reduced Operational CapabilitiesLimited Access, Weak Integration, Lacking Real-Time Analytics
• Most pipeline companies use a variety of separate databases and applications for integrity, maintenance, and safety activities
• Some have hardcopy records that were lost, or inaccurately transcribed when they were entered into present-day GIS systems.
• Current asset-based data management practices have critical information gaps that are necessary to validate pipeline safety.
What’s missing is the correlation of data from multiple sources into one actionable platform –to better plan and deploy inspection, detection, and maintenance.
Streams is an ultra-high-efficiency computing engine that avoids landing and lifting data to and from disk (and even “in-memory database” overhead).
Transform
Filter / Sample
Classify
Correlate
Annotate
� Continuous ingestion � Continuous analysis
Stream Computing Illustrated
17
IBM Confidential
SOA Web Services
Supply Chain
Analytics
Streams
Operational Data Store
Real-Time Analytical Processing and Physics-based Modeling
MPP Data
Warehouse
Applications
Production &
Financial
Planning
Traditional
ETLStaging
Semistructured, Unstructured, E&P SoR data sources
(Operations, Maintenance, ODSs, etc.)
MQTT HTTP
Federated
Search
Hadoop
CubingServices
Real-Time Message Broker
Real-time streaming data
Big Data & Analytics Platform Combining
Data-in-Motion with Data-at-Rest
19
Asset Lifecycle planning enables informed operational and strategic planning
Risk
Estimation &
Prediction
Failure
History
Failure
History
Environmental
Attributes
Environmental
Attributes
Spatial
Coordinates
Spatial
CoordinatesAsset
Attributes
Asset
AttributesFailure
Impact
Failure
Impact
Asset Condition
Assessment
Asset Condition
Assessment
Infrastructure
Network
Relationships
Infrastructure
Network
Relationships
Replacement
Cost Estimation
{Labor, material, service
interruptions, …}
Maintenance
Cost Estimation
Backup
Assets
Backup
Assets
{Labor, routine disruptions, cost, material, ….}
Decision
Support
Operational
Budget
Operational
Budget
Capital
Budget
Capital
BudgetBusiness
Constraints
Business
Constraints
Strategic Plan
Operational
Plan
annual cost
failure rate
replacerepair
Periodic inspection Strategic replacement in
2, 5, and 10 years
Efficient use of crew and
equipment
Usage / Smart
Meters
Usage / Smart
Meters
Monitor facilities and operations in environmentally sensitive areas
Business needs:
• Realtime condition monitoring in environmentally sensitive areas
• Analyze high volumes of data-in-motion for real-time monitoring of environmental conditions
• Early detection, e.g., leaks, and quicker response to operational events surrounding offshore installations
Benefits:
• Monitor production operations in highly sensitive environments
• Increased likelihood of gaining regulatory approvals
• Respond promptly to adverse environmental issues like hydrocarbon leakage or chemical imbalances
Asset MaintenanceAsset Performance Process Integration
Collect & Integrate
DataStructured, Unstructured,
Streaming
Generate
Predictive
Statistical Models
Conduct Root
Cause Analysis
Display Alerts and
Recommend Corrective
Actions
Act upon Insights
Predictive
Asset Optimization
• Data agnostic
• User-friendly
model creation
• Interactive
dashboards
• Quickly make
decisions
1
2
3
4
5
Schlumberger OLGA
Data
CollectionSurveillace Predictive Visualization
Another example: Cybersecurity Analytics
� Botnet nodes / Malware
� IP/MAC identifying suspects
X86
Box
X86 Blade Cell
Blade
X86 BladeFPGA
Blade
X86
Blade
X86 Blade X86
Blade
X86 BladeX86
Blade
Operating System
Transport
System S Data Fabric
Processing Element Container
Processing Element Container
Processing Element Container
Processing Element Container
Processing Element Container
Live PacketCapture
� DNS / DHCP / Netflow sources
� Botnet Behavior modeling
� External C&C Feeds (live DB queries)
IT I/S Firewalls
Remediation Infrastructure / Ticketing
Page 21
THINK
ibm.com/bigdata
Additional Information
SCADA Current State
Asset
Managers
Data
Warehouses
Partner
Access
EFM
ETMS
ERP
RTU
RTU
RTU
Flow
Computer
Flow
Computer
Flow
Computer
PLC
PLCP P
TL
F
Poll
Response
SCADA Host
Historian
Flow
ComputerFlow
ComputerRTU
RTU
RTU
Flow
Computer
PLC
PLCP P
TL
F
Poll
Response
SCADA Host
Historian
RTU
RTU
RTU
Flow
Computer
Flow
Computer
Flow
Computer
PLC
PLCP P
TL
F
Poll
Response
SCADA Host
Historian
…. x N“OT”
Domain
“IT”
Domain
“MOM” Based SCADA – Target ArchitectureEnterprise
Asset
Management Centralized
Data Warehouse /
Historian
Mobile
Dashboards
Centralized
Event
Correlation
Externalized
Web API
Services
EFM
ETMS
ERP
SCADA Hosts
Telemetry Hosts
MOM Broker
“OT”
Domain
“IT”
Domain
Smart
SensorNetwork
RTU
RTU PLC
PLC
P P
TL
F
Edge
Controller
Edge
Controller
Edge
Controller
Smart
SensorNetwork
MQTTPush Push
RTUFlow
Computer PLC
P P
TL
F
Legacy
protocol
Legacy
protocol
Legacy
protocol
MQTTMQTT
MQTT
MQTT
MQTTPush Push
Resources
All things MQTT
http://mqtt.org
Eclipse Paho
http://www.eclipse.org/paho/
OASIS MQTT Technical Committee
https://www.oasis-
open.org/committees/tc_home.php?wg_abbrev=mqtt
MQTT Specification
http://www.ibm.com/developerworks/webservices/library/ws-mqtt/
IBM White Paper:
Tapping the Power of Big Data
for the Oil and Gas Industry
ibm.co/19ephrs
Mike Brule's blog post:
Improving Oil and Gas Operations with Big Data and Analytics
ibm.co/17PT3c0
Open Messaging for Machine-to-Machine
and the Internet of Things
