Technology for Profitable Tracking and Optimization Rogers
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Each individual business with their own unique assets and supply chain optimizing their decisions according to their incentives, abilities and working culture. The latest developments in Planning (Petro) and AI use cases.
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Technology for Profitable Tracking and Optimization Rogers
- 1. 1 Leading-Edge Technology For Profitable Real-Time Tracking And Optimization. Simon Rogers: KBC (A Yokogawa Company) Nicholas Kenaston: Chevron AFPM OPCAT 2019
- 2. Maintaining the status quo is not acceptable TIGHTENING HEALTH, SAFETY AND ENVIRONMENT REGULATION is forcing facility investment in order to maintain the license to operate TECHNOLOGICAL INNOVATION AND DIGITIZATION is causing disruption and consideration of new operating models CHANGING INDUSTRY DEMOGRAPHICS are presenting leadership, succession and competency challenges, with different worker needs RISE OF SHARE HOLDER ACTIVISM is driving strong appetite for financial stewardship and asset optimization PROLIFERATION OF DATA AND DATA-DRIVEN ORGANIZATIONS is compressing timeframes for decision- making and introducing new digital competitors
- 3. Proprietary Information 3Proprietary Information 3Proprietary Information 3Proprietary Information Squeeze down on the gap between potential and realized margin Create more utility for the end customer Extend problem-solving eco-sphere beyond the plant Outmaneuver competition Manage day-to-day performance safely and reliably Respond rapidly to market swings Operate at true optimum Why Digitalize?
- 4. Proprietary Information 4Proprietary Information 4Proprietary Information 4 Eachindividual businesswiththeirownunique assetsandsupplychainoptimizingtheirdecisions accordingto their incentives, abilities andworkingculture. Window of Optimization HYDROCARBON VALUE CHAIN
- 5. 5Proprietary InformationProprietary Information Does the refinery have the ability to produce the products being demanded? Approximately how much shortfall will need to be factored in? How much surplus is available to sell, and in what time frame? LinearProgramming (LP)tools,suchasPetro, havebeenusedheavily toprovideanswersto:
- 6. Proprietary Information Production Planning Scheduling Supply Planning Investment Planning $$$$ Costof Uncertainty Uncertainty if it was the right crude, or that when processed it delivered the anticipated margin 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
- 7. Proprietary Information Production Planning Scheduling Supply Planning Investment Planning Losses Due to Uncertainty Reduced Backcasting and improved planning technology with Digitalization and the integration of artificial intelligence (AI) 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
- 8. 8Proprietary Information 8 Process and Offsites Control & RTO Reconciliation & Analysis Production Accounting Rigorous Simulation Supply Chain Scheduling Operational 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
- 10. Proprietary Information ApproachandMethods SelectingRefineryOptimizationMethod 10 Solve time Solution convergence Global optimization Accurately models process unit complexity Represents refinery flow Updates easily with model version control Criteria for Success 10
- 11. Proprietary Information ApproachandMethods Convergingon theGlobal Optimum- Quickly 11 Model Type Equations Coefficients Solve Time (Seconds) Single-Period Models 5,000-10,000 100,000-200,000 5 Multi-Refinery Models 10,000-20,000 200,000-400,000 10 Multi-Period Models 25,000-100,000 500,000-1,000,000 60 Petro Model Sizes and Solve Times 11
- 12. Proprietary Information ApproachandMethods MaintainingModel Accuracy 12 Petro Validation Tool Timely refinery optimization supported by confidence in LP Continuous validation of LP accuracy Validation data and SME analysis stored in database Data shared in real-time Monitoring yields, with Petro models as a reference, results in GM improvements for Chevron $13 MM$10 MM$25 MM$78 MM 2015 2016 2017 2018 12
- 13. Proprietary Information ApproachandMethods Model VersionControl andCaseSharing 13 Model and case architecture enables version control and case sharing. Cases or models are searchable and directly downloadable from within the Petro application Model updates automatically pushed to all users - simultaneously 13 Petro Case Sharing in the Cloud
- 14. Proprietary Information PETRODevelopmentand Valueto Chevron 14 Chevron’s PETRO is an industry leading LP modeling platform developed over 35 years through collaboration between software developers and refinery planning engineers. Value is created through use in all refinery optimization work processes including support of capital project evaluation 0 200 400 600 800 1000 1200 1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 PETRO Value Generated Per Year ($ Millions) 1990 Recursion- based models 2007 Terminal- Distribution Models 2014 Code Re-write Petro 2020 Application Database, Case Sharing in the Cloud, and Visualization 1985 PC version released 2000 Windows version released 2002 Multi-period Models 2004 Multi-location 14
- 15. 15Proprietary Information Process and Offsites Control & RTO Reconciliation & Analysis Production Accounting Rigorous Simulation Supply Chain Scheduling Operational Planning Corrected model Corrected data Corrected model Plant Raw data 5 Plant-wide optimization OptimizationAI UseCases 1 Demand and price forecasting 2 Reconciliation and variance analysis 3 Automation 4 Automation of production scheduling 6 Opportunity Engine
- 16. AI study Demand and price forecasting Challenge Feedstock and product prices and product demands are increasingly volatile. Optimization of the value chain with poor price and demand forecasts is sub optimal.
- 17. Solution Use ML algorithms to forecast demand. High performance computing and AI we can optimize and compare multiple scenarios to reduce risk. Find the predictive model that leads to the minimal prediction error such as a neural network (e.g. MONMLP). Provide the model predictions to the planners to improve multi- period optimisation. Predicted Actual
- 18. AI study Automation of LP & Scheduling Model Updates from Rigorous Model Challenge For speed, Planning and Scheduling Models are simplified (often linear) from rigorous models. Linear models result in artificial constraints and are not able to see all opportunities. Maintaining the rigorous models and recalibrating to actual plant performance takes time and expertise.
- 19. Solution Benefits An ML surrogate model that improves the linear models can be generated. This can be done by using synthetic data from the Rigorous Model to train the ML model. Use past model performance and current model deviations and an AI Classifier to find the pattern of deviations that characterize a poor model and trigger and update of the relevant part of the model. Decrease Model Update Times due to better definition of Improvements areas Reduced need for SME participation ML Planning/Scheduling Model that accounts For a larger range of Operating conditions Maximize Model Accuracy by Better Definition Of Re-calibration Needs
- 20. 20Proprietary Information AutomationofLP & SchedulingModelUpdates fromRigorousModel Rigorous Refinery Model Planning /Scheduling Model Monitoring Actual Plant Behavior Lab Data Plant Data Data Clean-Up Select data-set for Recalibration Activate Model Update Signal Historical ML Model Performance Train Source of Inaccuracies Explained AI Classifier Monitoring Recalibration Planning /Scheduling ML Model (*) Train (*) ML structure suitable for client’s needs Historical Rigorous Model Performance Train Rigorous Model Validation
- 21. 21Proprietary Information 21 Summary Optimizing the hydrocarbon value chain is critical to achieving Commercial Excellence in an increasingly VUCA world New technology such as Cloud and AI provides an opportunity to digitally transform the value chain by: • Creating new business models • Expanding the scope of optimization • Improving execution • Increasing agility
- 22. www.kbc.global Simon Rogers Nicholas Kenaston Optimizing an integrated, agile and accurate value chain.