Message Oriented Middleware. The Future of SCADA. Arlen Nipper President and CTO Cirrus Link Solutions

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1 Message Oriented Middleware The Future of SCADA Arlen Nipper President and CTO Cirrus Link Solutions

2 1 3 5 SCADA Host Protocol X Multi-Drop Network 2 4

3 SCADA Protocols & Applications: The Good, The Bad, and The Ugly

4 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

5 Future Apps & Integration Analytics & BIG DATA Electronic Flow Measurement 1 3 ERP Mobile Apps Asset Management & Optimization SCADA Host Protocol X 2 Enterprise (IT) Operations (OT)

6 Message Oriented Middleware: Unlocking the Potential

7 Future Apps & Integration Analytics & BIG DATA SCADA Host #2 SCADA Host #1 1 2 Electronic Flow Measurement ERP Mobile Apps MQTT MOM Broker Asset Management & Optimization MQTT Message Transport n Enterprise (IT) Operations (OT) IT + OT = Agile Enterprise

8 Summary Poll/Response SCADA systems were perfectly viable solutions when first developed 35 years ago. But this is SCADA solutions needs to leverage the very same Message Oriented Middleware technologies that IT solutions use to decouple application to: 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!

9 WHY?

10 Learn from the past Optimize the present Predict the future Extending Oilfield Data Acquisition with Big Data Analytics Mike Brulé PhD, PE 2013 IBM Corporation 10

11 What do we mean by Big Data and Analytics? Big Data - Variety, Velocity, Volume, Veracity Text Image & Video Acoustic Financial Times Series Statistics Mining Predictive Geospatial Page 11

12 Aligning Big Data Analytics with Operational and Business Objectives: Operations Excellence Integrated Operations Operational Integrity Information Challenges Safe and costeffective operations 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 Advanced Control & Automation 12 Predictive Maintenance EPC and Oilfield Services Business Impact Rig uptime and yield Production efficiency and effectiveness Maintenance efficiency Health, Safety & Environment incidents Corporate Performance Production & Process Optimization Drilling & Completions Optimization

13 Industry Differences: OT vs. IT Converged OT + IT environment is necessary to support integrated operations Page 13

14 Real-Time Operational Data System: Enable Real-time Insight From Big Data Production Process Management Operation Performance Monitoring Asset Condition Monitoring Safety Other Apps Collect, process & analyze large volumes of sensor generated data in near real-time Support X higher data rate than general IT middleware via hardware acceleration Real-Time Operational Data System Stream Computing Engine Industry Rules Based Intelligent Data Processors Internet-scale Messaging Engine Real-time Operational Database Predictive analytics Big Data Real-time Integrated, Reliable & Actionable Data

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. 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 Likelihood & Impact Analysis Engine Risk Assessment Engine Total Lifecycle Management Compliance Evaluation Engine ACCESS Capital Budgetin g Stochastic Planning & Budgeting Analysis Long Term Sustainability Planner 15 ADAM Asset Analytics Engine GI S EAM / Maxi mo SCADA / Inspectio ns Compliance Mandates State Federal Agency

16 Current State Reduced Operational Capabilities Limited 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.

17 Stream Computing Illustrated Continuous ingestion Continuous analysis Filter / Sample Transform Annotate Correlate Classify Streams is an ultra-high-efficiency computing engine that avoids landing and lifting data to and from disk (and even in-memory database overhead). 17

18 Big Data & Analytics Platform Combining Data-in-Motion with Data-at-Rest Supply Chain Analytics SOA Web Services Applications Streams Real-Time Analytical Processing and Physics-based Modeling MPP Data Warehouse Cubing Services Production & Financial Planning Operational Data Store Traditional ETL Staging Federated Search Real-Time Message Broker IBM Confidential Hadoop Real-time streaming data MQTT HTTP Semistructured, Unstructured, E&P SoR data sources (Operations, Maintenance, ODSs, etc.)

19 Asset Lifecycle planning enables informed operational and strategic planning Infrastructure Network Relationships Usage / Smart Meters Asset Condition Assessment annual cost repair failure rate replace {Labor, material, service interruptions, } Capital Budget Business Constraints Failure History Environmental Attributes Spatial Coordinates Risk Estimation & Prediction Asset Attributes Failure Impact Replacement Cost Estimation {Labor, routine disruptions, cost, material,.} Maintenance Cost Estimation Backup Assets Decision Support Operational Budget Strategic Plan Operational Plan 19 Periodic inspection Strategic replacement in 2, 5, and 10 years Efficient use of crew and equipment

20 Data Collection Surveillace Predictive Visualization 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 1 2 Generate Predictive Statistical Models Collect & Integrate Data Structured, Unstructured, Streaming 3 Conduct Root Cause Analysis Predictive Asset Optimization Data agnostic User-friendly model creation Interactive dashboards Quickly make decisions 5 4 Display Alerts and Recommend Corrective Actions Act upon Insights Schlumberger OLGA 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 Performance Process Asset Maintenance Integration

21 Another example: Cybersecurity Analytics IT I/S Firewalls Live Packet Capture Processing Element Container Processing Element Container Processing Element Container Processing Element Container Processing Element Container System S Data Fabric Transport Operating System X86 X86 Blade Box X86 Blade X86 Blade X86 FPGA Blade Blade X86 Blade X86 Blade X86 Cell Blade Blade DNS / DHCP / Netflow sources Botnet Behavior modeling External C&C Feeds (live DB queries) Botnet nodes / Malware IP/MAC identifying suspects Remediation Infrastructure / Ticketing Page 21

22 THINK ibm.com/bigdata

23 Additional Information

24 SCADA Current State Asset Managers Data Warehouses Partner Access EFM ETMS ERP RTU RTU RTU Flow Computer Flow Computer Flow Computer PLC PLC P P T L F Poll Response SCADA Host Historian Flow Computer Flow Computer RTU RTU RTU Flow Computer PLC PLC P P T L F Poll Response SCADA Host Historian RTU RTU RTU Flow Computer Flow Computer Flow Computer PLC PLC P P T L F Poll Response SCADA Host Historian. x N OT Domain IT Domain

25 MOM Based SCADA Target Architecture Centralized Event Correlation Mobile Dashboards Centralized Data Warehouse / Historian Enterprise Asset Management SCADA Hosts MQTT Legacy protocol Edge Controller MQTT Push MQTT Push Externalized Web API Services EFM MQTT MOM Broker Flow Computer Push RTU T P P L F MQTT Smart Sensor Network PLC Push IT Domain ERP ETMS Telemetry Hosts MQTT Legacy protocol Edge Controller RTU T P P L F Smart Sensor Network Edge Controller PLC Legacy protocol RTU PLC OT Domain

26 Resources All things MQTT Eclipse Paho Open Messaging for Machine-to-Machine and the Internet of Things OASIS MQTT Technical Committee https://www.oasisopen.org/committees/tc_home.php?wg_abbrev= mqtt MQTT Specification s/library/ws-mqtt/ IBM White Paper: Tapping the Power of Big Data for the Oil and Gas Industry Mike Brule's blog post: Improving Oil and Gas Operations with Big Data and Analytics ibm.co/19ephrs ibm.co/17pt3c0

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