sigmafine: providing reconciled data to the business

11Confidential and Proprietary © Copyright OSIsoft, Inc. 2007

Sigmafine: Sigmafine: Providing Reconciled Data to the BusinessProviding Reconciled Data to the Business

Tom Hosea

OSIsoft, Houston, TX


A Simple Problem, Complicated by Reality

Production Management/ Loss ControlProduction Management/ Loss Control


The Fog of DataThe 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?


Typical RefineryTypical 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.


What is Data Reconciliation?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 K

ey to P

roducti

on Management


The Issues with Data ValidationThe 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…


SigmafineSigmafine

A product that

enables data reconciliation

and validation

for any industrial process.


Information

Typical Scenario Without ValidationTypical Scenario Without Validation

Some sort of local balance

Some arbitrary and subjective corrections

No agreement on data

Difficult to detect measurement errors

?Fog

Data


Validation with Sigmafine Validation with Sigmafine

A unique balance, valid for the whole operation

Systematic and objective corrections

Agreement on balanced data

Easier to detect measurement problems

InformationData


Data Reconciliation ChallengesData Reconciliation Challenges

RefiningRefining Metals & MiningMetals & Mining ChemicalsChemicals

Many ProductsMany Products X X

Lineup ChangesLineup Changes X

Flows & TransfersFlows & Transfers X X

Model Size (elements)Model Size (elements) 5000 1000 1000

Model ComplexityModel Complexity High High Low

RedundancyRedundancy High Low Low

Analyzer countsAnalyzer counts Low High Medium

Unmeasurable FeedstocksUnmeasurable Feedstocks X

Material Acct per ElementMaterial Acct per Element X

Componenet BalancesComponenet Balances X

Stoichiometric BalanceStoichiometric Balance X


How to solve these problemsUse Sigmafine to…How to solve these problemsUse Sigmafine to…

1. Build and configure a model (once)

2. Run the model using the appropriate analysis rules (frequently)

3. Analyze results (frequently)


Sigmafine ToolsSigmafine 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


Benefits by IndustryBenefits 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


ConclusionsConclusions

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


Good Data for Good Business DecisionsGood 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


Thank YouThank You

Questions?

sigmafine: providing reconciled data to the business

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