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Asia Downstream 2019 Simon Rogers

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Optimizing the hydrocarbon value chain means the business of extracting value from each point in the chain from feed to production to client delivery. There are opportunities to leverage recent advances in digital technologies, AI and ML to significantly enhance profitability and allow companies to confidently face the future.

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Asia Downstream 2019 Simon Rogers

  1. 1. 1 Enabling Digital Technologies to Drive Business Excellence Simon Rogers KBC (A Yokogawa Company) Asian Downstream Summit 2019
  2. 2. DIGITALIZATION AIMS FOR A WORLD OF: Proprietary Information 2 No safety incidents Net zero emissions No unplanned outages Nimble response to market changes and plan disturbances A culture of profitability A motivated and informed workforce
  3. 3. Proprietary Information 3Proprietary Information 3Proprietary Information 3 Commercial Excellence DAYS Ahead HOURS Ahead MINUTES Ahead WEEKS Ahead MONTHS Ahead YEARS Ahead SECONDS Ahead NOW THE PAST BACKCASTING EXECUTION SCHEDULING PLANNING HYDROCARBON VALUE CHAIN HYDROCARBONMANAGEMENTTIMEHORIZON Window of Optimization
  4. 4. Proprietary Information Production Planning Scheduling Supply Planning Investment Planning $$$$ Costof Uncertainty DecisionValue Automation MINUTES Ago HOURS Ago DAYS Ago YEARS Ago SECONDS Ahead MINUTES Ahead HOURS Ahead DAYS Ahead MONTHS Ahead MONTHS Ago NOW Decision-Making Time Horizon Decision Impact Time Horizon Planning and Optimization Cycle
  5. 5. Proprietary Information Production Planning Scheduling Supply Planning Investment Planning LossesDue to Uncertainty Reduced DecisionValue Automation Faster Decisions Greater Certainty of outcome Focus on value generation Digitally Wise Planning and Optimization Cycle MINUTES Ago HOURS Ago DAYS Ago YEARS Ago SECONDS Ahead MINUTES Ahead HOURS Ahead DAYS Ahead MONTHS Ahead MONTHS Ago NOW Decision-Making Time Horizon Decision Impact Time Horizon
  6. 6. Proprietary InformationProprietary Information Process and Offsites Control & RTO Reconciliation & Variance Analysis Production Accounting Rigorous Simulation Scheduling Planning Corrected model Corrected data Corrected model Plant Raw data - 1 MONTH - 1 WEEK NOW Today’s Challenges • Limited integration between tools • Linear models have limited validity • Plan does not reflect logistic constraints • Sub optimal schedule • Updating models is time- consuming and SME dependent • Largely heuristic data reconciliation • Data siloed, poor quality • Slow recognition of opportunities to open constraints • Control strategies not updated with schedule changes
  7. 7. Proprietary Information 7 Process and Offsites Control & RTO Reconciliation & Variance Analysis Production Accounting Rigorous Simulation Scheduling Planning Corrected model Corrected data Corrected model Plant Raw data - 1 MONTH - 1 WEEK NOW Digital Future • Data driven, automated identification of optimization using AI • Intelligent, automated work processes • Automated data management and tool integration • Cloud enabled to facilitate; • Scalability • Collaboration • Support • Rapid enhancements • Knowledge management • Integration • Visualization
  8. 8. Proprietary Information Process and Offsites Control & RTO Reconciliation & Variance Analysis Production Accounting Rigorous Simulation Scheduling Planning Corrected model Corrected data Corrected model Plant Raw data Optimization with AI Use Cases Plant-wide optimization Demand and price forecasting Reconciliation and variance analysis Automation of model updates Automation of production scheduling Knowledge Graph
  9. 9. 9Proprietary Information Value Chain Knowledge Graph Global optimization Visualize and manage entire supply chain Connect data silos – Linked Data Add structure and context ML, text mining & NLP Semantic search and AI Automation, classification Reporting, personalization Provide agility Model complexity Business Taxonomy and Ontology – Meta Data Partner 2 Partner 1 Plant 2 Plant 1 Place 2 Place 3 Place 1 Product 3 Product 1 Distance Cost Time Mode Feed Production Cost Product 2 Production Database Simulation Database Scheduling Database Planning Database ERP Database Unstructured Data
  10. 10. 10Proprietary Information Opportunity Engine Base Case Profit Opportunities KBC PIP - Market Conditions - Site Operating Conditions Reconciliation and Variance Analysis Scheduling Optimal Targets Expected Benefit Actual Benefit Schedule Constraint Set Historical Information Optimal SolutionPlanning/Scheduling Model Actual Realization Planning Plan Constraint Set Actual Actions Desired Actions Operation Planning Forecasts Programs on > 150 sites Rigorous Site Model to Validate Proposed Improvements Finding improvement opportunities automatically
  11. 11. 11Proprietary Information Plan Schedule Physical Constraint Not Accounted In Planning Model Time Margin The AI Classifier will help to detect bad data and the source of Plan/Schedule Gaps Actual Illustration of Deviation from Plan Simulation
  12. 12. 12Proprietary Information Combining Simulation and Machine Learning First Principles Model Plant Data Inputs Model Outputs Feed quality and flow Product Yields Product Compositions Fuel flow Feed quality and flow Operating parameters Product Yields Product Compositions Energy consumption Catalyst activity Heat exchanger coefficients KPIs KPI analysis and model errors Multiple indices indicating process and model status Profit improvement opportunities Yield Energy Example On-line ML (Multivariate analysis) Process issues and model inaccuracies can be obtained Parameters - Tray Efficiencies - Calibration Factors
  13. 13. 13Proprietary Information Early detection of operational problems Identify ‘potential’ opportunities by monitoring the index in real time Normal Caution Abnorm al Simulation and Measured Data Time Improvement opportunity index
  14. 14. 14Proprietary Information Automation of Production Scheduling using AI Crude Scheduling Refinery Scheduling Blending Scheduling Shipping Scheduling Task updates Nomination updates Violated constraints Deviations from plan Economics Baseline Update Explained AI Classifier Opening inventory discrepancies Inventory and past task updates MCTS, Supervised and Reinforcement Learning
  15. 15. 15Proprietary Information Connectedexperts Customerfacility SME’s (Client / KBC) Shared Digital Twin in the cloud facilitates integrated optimization environment
  16. 16. 16Proprietary Information Digital TransformationRoadmap Guides assessmentand solution development READINESS Data Infrastructure Consumption People SITUATIONAL AWARENESS Hindsight Insight Foresight Oversight DECISION MAKING Forecasting (“What next?”) Prediction (“What-if?”) Optimization (“What’s best?”) OPERATIONAL EXECUTION Best practices Advice-based action Closed loop control Procedural automation Closed loop optimization VALUE SUSTAINMENT Goal monitoring Economic stewardship Knowledge management Management of change
  17. 17. 17Proprietary Information 17 Summary Optimizingthehydrocarbonvaluechainiscritical toachievingCommercialExcellencein anincreasinglyVUCAworld Cloud,BigData,AIandIIoTprovideanopportunityto digitallytransformthevaluechainby: • Creatingnewbusinessmodels • Expandingthescopeofoptimization • Improvingexecution • Increasingagility
  18. 18. www.kbc.global Simon Rogers Simon.Rogers@jp.yokogawa.com Excellence is never an accident. It is always the result of high intention, sincere effort, and intelligent execution; it represents the wise choice of many alternatives - choice, not chance, determines your destiny. Optimizing an integrated, agile and accurate value chain.

Optimizing the hydrocarbon value chain means the business of extracting value from each point in the chain from feed to production to client delivery. There are opportunities to leverage recent advances in digital technologies, AI and ML to significantly enhance profitability and allow companies to confidently face the future.

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