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IIoT + Predictive Analytics: Solving for Disruption in Oil & Gas and Energy & Utilities

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The electric grid has evolved from linear generation and delivery to a complex mix of renewables, prosumer-generated electricity, and electric vehicles (EVs). Smart meters are generating loads of data. As a result, traditional forecasting models and technologies can no longer adequately predict supply and demand. Extreme weather, an aging infrastructure, and the burgeoning worldwide population are also contributing to increased outage frequency.

In oil and gas, commodity pricing pressures, resulting workforce reductions, and the need to reduce failures, automate workflows, and increase operational efficiencies are driving operators to shift analytics initiatives to advanced data-driven applications to complement physics-based tools.

While sensored equipment and legacy surveillance applications are generating massive amounts of data, just 2% is understood and being leveraged. Operationalizing it along with external datasets enables a shift from time-based to condition-based maintenance, better forecasting and dramatic reductions in unplanned downtime.

The session includes plenty of real-world anecdotes. For example, how an electric power holding company reduced the time it took to investigate energy theft from six months to less than one hour, producing theft leads in minutes and an expected multi-million dollar ROI. How a global offshore contract drilling services provider implemented an open source IIoT solution across its fleet of assets in less than a year, enabling remote monitoring, predictive analytics and maintenance.

Key takeaways:
• How are new processes for data collection, storage and democratization making it accessible and usable at scale?
• Beyond time series data, what other data types are important to assess?
• What advantage are open source technologies providing to enterprises deploying IIoT?
• Why is collaboration important across industrial verticals to increase IIoT open source adoption?

Speaker
Kenneth Smith, General Manager, Energy, Hortonworks

Publicado en: Tecnología
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IIoT + Predictive Analytics: Solving for Disruption in Oil & Gas and Energy & Utilities

  1. 1. 1 © Hortonworks Inc. 2011–2018. All rights reserved. Kenneth Smith – General Manager, Energy & Utilities and Oil & Gas Wade Salazar - Senior Solutions Engineer, Hortonworks IIoT & Predictive Analytics: Solving for Disruption in O&G and E&U
  2. 2. 2 © Hortonworks Inc. 2011–2018. All rights reserved. Industrial IoT Market Opportunity Estimates “In other words, the industrial internet will be worth more than twice the consumer internet” https://www.forbes.com/sites/louiscolumbus/2016/11/27/roundup-of- internet-of-things-forecasts-and-market-estimates-2016/#ad68a67292d5
  3. 3. 3 © Hortonworks Inc. 2011–2018. All rights reserved. OT / IT Convergence – Must Occur to Achieve Business Improvement Source: IBM
  4. 4. 4 © Hortonworks Inc. 2011–2018. All rights reserved. Internal Challenges – The Missing Middle Source: Accenture In many companies, a breach— the missing middle—is evident in multiple dimensions: between the data available and disparate systems used; from the lack of end-to-end integration across processes or workflows; and between corporate strategies and analytics efforts at functional and departmental levels. With this gap, energy companies struggle for a complete and timely assessment of the impact of operational decisions on corporate performance. Likewise, corporate entities are unable to factor in day-to-day field operations in their objective setting and planning decisions.
  5. 5. 5 © Hortonworks Inc. 2011–2018. All rights reserved. Why Open Source for IIoT? • Community driven innovation to develop an end-to-end OPEN SOURCE IoT data platform for “industrials” • It’s not just about time-series data; it’s the ability to collect, manage, and analyze all pertinent structured & unstructured data sets related to an industrial asset, operation, process, piece of equipment, etc. in in addition to time-series • Enables OT/IT/ET convergence to build descriptive, predictive, & prescriptive applications • Cost effective storage and parallel processing of large data sets • An open source IIoT platforms allow operators to maintain control over their data and analytics vs. a ”closed” OEM’s IIoT product telling them when their own equipment needs replacing • An open IIoT platform is applicable across all asset intensive industries with “moving metal”; oil & gas, utilities, mining, manufacturing, automotive, transportation, agriculture, etc. • Future Proof - Open source eliminates vendor lock-in and de-risks adoption • “Data is not a competitive advantage. It’s the algorithms you build to analyze your data that will differentiate you from your competitors.”
  6. 6. 6 © Hortonworks Inc. 2011–2018. All rights reserved. Is the Energy Industry Ready to Embrace an Open Model? http://www.lockheedmartin.com/us/news/press-releases/2016/january/160114-mst-us-exxonmobil-awards- lockheed-martin-next-generation-refining-and-chemical-facility-automation-system-contract.html ExxonMobil representatives express frustration when observing step change improvements in adjacent industries enabled by open technologies. Those adjacent industries have deployed significantly higher function software that have lowered lifecycle cost and delivered higher return on investment. The explosive growth of technologies driven by the Internet of Things (IoT) including cloud computing, mobile computing, embedded computing, and consumer electronics makes it obvious that the mainstream industrial automation industry can deliver more value with the adoption of an open, multi-vendor platform approach. http://www.automation.com/automati on-news/article/exxonmobil-to-build- next-generation-multi-vendor- automation-architecture
  7. 7. 7 © Hortonworks Inc. 2011–2018. All rights reserved. Upstream O&G Companies Digital Technology Focus & Investments? Source: https://www.accenture.com/us-en/insight-2017-upstream-oil-gas-digital-trends-survey Source: DZone With a modern IIoT and cloud platform underlying the next generation of applications and analytics, the oil and gas industry can move beyond just doing the same thing faster or cheaper and adopt new levels of productivity. Mostly seeing “bridging the gap” brownfield use-cases
  8. 8. 8 © Hortonworks Inc. 2011–2018. All rights reserved. E&U Industry Data & Analytics Investment by Line of Business While the trend toward more open source technologies as part of a utility’s analytics strategy exists across the board, there are some differences when looking at the use of open source in large utilities (over 1 million customers) versus small utilities (50,000 to 1 million customers). For instance, large utilities are more than three times as likely to depend on Hadoop data storage to a moderate, large or very large extent. SAS Survey: Utility analytics in 2017: Aligning data and analytics with business strategy
  9. 9. 9 © Hortonworks Inc. 2011–2018. All rights reserved. Connected Data Platforms Enables IIoT in Energy & Utilities Source: https://www.cm-collaborative-tech.com/wp-content/uploads/2016/11/Smart-grid-A-1.jpg Predictive MaintenanceFraud DetectionExternal Sources (Weather, Social Media, GPS, etc.) Single View of Customer
  10. 10. 10 © Hortonworks Inc. 2011–2018. All rights reserved. Data Acquisition
  11. 11. 11 © Hortonworks Inc. 2011–2018. All rights reserved. Time Series Data is Emerging as the Fastest Growing Type of Data
  12. 12. 12 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Title Goes HereThe Data Management Challenge for Energy Companies Data is Siloed • Datasets are dispersed and difficult to access • Insights are based sourced from the silos are narrow & incomplete • Historical data (years) is not easily accessible and often takes longer than expected to extract • Data required to solve real problems comes from several different sources (lab systems, product scheduling) and requires significant manual effort to pull the data together Considerable effort to leverage • Lack of connectivity to proper data analytics tools • Python • MATLAB • SAS • R • Inability to find data and a reliance on “tribal knowledge” and previous engineer’s spreadsheets to find tags and queries Data Analytics Missing Lack of Resources Inability to Leverage BOTH OT and IT Data Sources • Time dependence limits how data sets can be processed • Point solutions are almost always closed source, locking users into closed ecosystems • Do not scale with exponential data growth
  13. 13. 13 © Hortonworks Inc. 2011–2018. All rights reserved. Highest Value Data Always on, always connected devices generate a constant stream of data related to the operations of industrial businesses These datasets contain: • What events occurred • Why and event occurred, or not • Quantification of an event’s impact These datasets go by many names: • “SCADA Data” • “Control System Data” • “Historian Data” • “Machine Data” • “Measurement Logs” • “Telemetry” How are my … People? Processes? Equipment? Lots of misnomers
  14. 14. 14 © Hortonworks Inc. 2011–2018. All rights reserved. Stepwise Approach to the Challenge Remote Field or Manufacturing Site RDBMS & EDW Files / Other Unstructured Data Video IoT Gateways WITSML SCADA, DCS, PLC, RTU, Historians Location 1 Data Consumers Data Marts Analytics, Statics & Science Visualization & Dashboards
  15. 15. 15 © Hortonworks Inc. 2011–2018. All rights reserved. Data Lakes Address Part of the Problem Field or Manufacturing Site RDBMS & EDW Files / Other Unstructured Data Video IoT Gateways WITSML SCADA, DCS, PLC, RTU, Historians Location 1 Data Consumers Data Marts Analytics, Statics & Science Visualization & Dashboards
  16. 16. 16 © Hortonworks Inc. 2011–2018. All rights reserved. Open Connected Platform Approach Addresses the End to Challenge Field or Manufacturing Site RDBMS & EDW Files / Other Unstructured Data Video IoT Gateways WITSML SCADA, DCS, PLC, RTU, Historians Location 1 Data Consumers Data Marts Analytics, Statics & Science Visualization & Dashboards
  17. 17. 17 © Hortonworks Inc. 2011–2018. All rights reserved. Instrumentation  Commonly only output is electrical signals  Integration with sensors requires specialized hardware  serial bus, or wireless are increasingly available Challenges in Accessing Data in the ICS Landscape Control Systems  Data is transmitted via proprietary vendor specific protocols  Direct Integration with control systems requires protocol translation/parsing for each platform family Nifi’s is a toolbox of connectors  Ingest text files and interrogate REST APIs using built in connectors  Connect to industry standard protocols like OPC UA with custom processors  Build your own Existing ICS Components PLC, RTU & DCS Open Source Tools Governance &Integration Security Operations Data Access Data Management Process Historians & OPC Servers  Data is typically available via programmatic access such as OPC, API or SQL  There is almost always an option to create text files
  18. 18. 18 © Hortonworks Inc. 2011–2018. All rights reserved. Actual Use Case Results
  19. 19. 19 © Hortonworks Inc. 2011–2018. All rights reserved. Typical Goals for an Industrial Analytics Practice • Data democratization ( broad simple access ) • Event processing – create events or react to variables (e.g. pump overheat, weather, emission) • Forecasting / Prediction - Predict the most likely value • Event Correlation – Measure the coincidence of two things? Measure the likeness of events or periods of time? • Impute missing values - What are the most likely values of missing data? • Anomaly detection – Find “out of normal” events in a series, based on a model of expected behavior
  20. 20. 20 © Hortonworks Inc. 2011–2018. All rights reserved. Time Series Analytics for Power Generation Anomaly Detection  Two week engagement – no direct knowledge of existing systems  Two days were able to isolate problem down from 5000 potential causes to 19 using standard data science algorithms  Company investigated findings and found a valve was installed backwards causing plant to shutdown  Plant failure hasn’t occurred since, saving millions of dollars in unplanned shutdowns  VP of Engineering – “I never thought we would see a solution like this”
  21. 21. 21 © Hortonworks Inc. 2011–2018. All rights reserved. Vertically Integrated Utility’s Data Journey Accelerating Revenue Protection with an Open Analytics Platform  One of the largest electric power holding companies in the US that supplies electricity to approximately 7.4 million customers and operates natural gas distribution services serving more than 1.5 million customers.  Revenue Protection Use Case: Protect revenue from theft, malfunctioning meters, and misconfigured meters.  Why HDP: The only cost effective platform able to do parallel / multi-node analytics on large data sets.  Currently have loaded 200 Billion rows of meter data across 80 nodes of HDP growing to 1.4 Trillion by 2020 from all of their service areas.  Previous energy theft data science process: Predictive model was run on a laptop 1x per week for 10K accounts at a time and produced 100 leads weekly for investigation. At that rate, it would have taken them 6 months to process one state’s data (all states/enterprise data would take much longer)  Current process: Leveraging HDF & HDP to ingest, process, store, and analyze 5 minute meter data from Itron Open Way  Realized business value from the Revenue Protection use-case $17.5M in 2017, goal of $30M for 2018.  Other use-case include predictive equipment maintenance on nuclear power & solar generation, “Next Best Action” program for cross-selling opportunities on goods and service, amongst others.
  22. 22. 22 © Hortonworks Inc. 2011–2018. All rights reserved. Using HDP and HDF for Industrial IoT – Rowan Companies Requirement – A New Business Model: • Fluid and flexible data platforms that can quickly integrate raw data and deliver actionable intelligence to people and processes • Ability to operate when network connectivity with a data center or the shore is intermittent, latent and provide minimal bandwidth • Analysis of large volumes of data and avoid data being stranded and out of reach for analysts and support teams. • Move from an operations posture of reacting and suffering from unnecessary downtime, equipment failures, efficiency losses, and safety risks • Bring the data that increases the collective expertise available to support safer and more efficient operations Solution and Outcomes – New Sources of Value: • HDF aggregates, prioritizes, compresses and encrypts control system data before sending it over a 64 kb/sec satellite link to the data center in real-time • Data from top drives, BOPs and other equipment is in HDP and every data consumer from data scientist to BI users can be serviced from their tool of choice • With predictive analytics and maintenance forecasting, Rowan expects to reduce downtime and alleviate future troubleshooting trips to the rigs. • Rowan will be able to comply with the important BSEE regulations going into effect in 2019.
  23. 23. 23 © Hortonworks Inc. 2011 – 2016. All Rights Reserved Title Goes HereFrom Refinery to Enterprise Level Analytics Problem: Refinery-level analytics sub-optimizes performance  Analytics performed at each refinery in Excel spreadsheets  Missed opportunities for optimization based on larger data sets Solution: Centralize data in Manufacturing Data Lake for analytics  Ingest data from each refinery using with HDF into centralized Data Lake  Initial data set was over 1 million data tags, grew to 6 million  Data Types: Time series, raw materials, quality results, SAP work order data, etc. Benefits: Enterprise level-analytics to optimize performance  ROI Analysis  $106 million in cost savings per year  20X ROI annually Oil Refining Multinational Oil & Gas Company Core Use Cases • Blend Monitoring • Corrosion Prediction • Analyzer Reliability Analytics • Heat Exchanger Performance Analytics • Inferential Models Analytics
  24. 24. 24 © Hortonworks Inc. 2011–2018. All rights reserved. Open source is a way to enable a group of collaborative people to further their individual interests while contributing back to the community for the common good. Open source
  25. 25. 25 © Hortonworks Inc. 2011–2018. All rights reserved. Questions? How can we help you get started?

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