Sigmafine: Providing Reconciled Data to the Business

1. Sigmafine: Providing Reconciled Data to the Business Tom Hosea OSIsoft, Houston, TX

2. Production Management/ Loss Control A Simple Problem, Complicated by Reality

3. The Fog of Data • Typical large petrochemical complex or refinery can be polling 100,000 points, once per minute. • This corresponds to about 200 Gbytes per year. (It will be 200,000 points or more in five years) • Data does not balance • Used (Misused) by multiple groups • Inconsistent and incompatible conclusions • How do you find the data you need? • How do you analyze it?

4. Typical Refinery • A refinery is consistently shows an average mass balance of 97.5%, i.e. the products plus fuel consumed plus known losses is only 97.5% of the crude plus intermediates purchased • Is the refinery paying for crude not received? • Is the refinery not being paid for all products? • Is there theft of product or leakage or evaporation? • Is more fuel being burned than estimated? Flared? • Is there excessive off spec product being recycled? • 2.5% losses on a 200,000 BPD refinery are worth $300,000 per DAY or $110 million per year.

5. What is Data Reconciliation? • A statistical method of resolving detected errors according to pre-specified rules and tolerances • Distributes errors across a system • Reports on and explains errors • Includes: • Data Validation • Systematic Detection of Gross Errors (e.g. missing measurements, mis-specified movement, etc) The Key to Production Management

6. The Issues with Data Validation • Too much data • Thousands of data points • Too many sources • Lab systems, DCS, manual entry • Too many interactions • Transfers, flows, measurements • Not enough time…

7. Sigmafine A product that enables data reconciliation and validation for any industrial process.

8. Typical Scenario Without Validation • Some sort of local balance • Some arbitrary and subjective corrections • No agreement on data • Difficult to detect measurement errors Fog Information Data ?

9. Validation with Sigmafine • A unique balance, valid for the whole operation • Systematic and objective corrections • Agreement on balanced data • Easier to detect measurement problems Information  Data

10. Data Reconciliation Challenges

11. How to solve these problemsUse Sigmafine to… • Build and configure a model (once) • Run the model using the appropriate analysis rules (frequently) • Analyze results (frequently)

12. Sigmafine Tools • Data References • A component that reads, writes and executes calculations • Analysis Rules • Provides model analysis for balances, composition tracking or gross error detection • Data Loader • Imports data elements of many formats to create cases or transfers • Visualization/Analysis tools • ProcessBook, AF Excel Add-in, RtReports

13. Benefits by Industry • Refining • Transfers provide the basis of model receipts, shipments and movements • Automatic Inventory calculations • Composition Tracking of stored products • Refining specific calculations – gross to net conversion • Chemical • Mass and Component balance • Configurable reaction constraints • Meter Compensation – gas and liquid • Inventory calculations • Metals and Mining • Component balance of materials not typically measured • Independent solvability of components • Independent accuracies of measurements • Efficient system management of sparse measurements

14. Conclusions • Sigmafine can be applied to any industry • Validated data is available to make better business decisions • No process model is required to derive value from Sigmafine • The use of data references does not require a model

15. Good Data for Good Business Decisions “You can't manage what you can't control, and you can't control what you don't measure.” Tom DeMarco Sigmafine increases confidence in what you measure and provides estimates of what you don’t measure, helping you to make better business decisions

16. Thank You Questions?