Description

Designed for you, if you are...

• A professional tasked with metering and measuring oil, gas and water, in teams such as operations, technical services, safety, geology, reservoir engineering and oil & gas export

How we build your confidence

• Highly interactive course led by an instructor with extensive experience in reconciling flow readings and training engineers in the fundamentals of this application
• Practical examples will be presented and adequate exposure will be provided to the participants through exercises

The benefits from attending

• The basics of Data Reconciliation (DR)
• The application of principles of Data Reconciliation to any set of metered and reconcilable data
• Rectifying metered readings through Data Reconciliation
• The essence and importance of Data Reconciliation in metering
• Common types of errors in metering
• Weeding out irrelevant data
• The technique of error minimisation and reconciliation of data

Topics

• What is Data Reconciliation and its significance?
• Types of errors in industrial metering
• Bell Curve and Probability Distribution Function
• Z Score for filtering out outliers from data
• IQR (Inter Quartile Range) method for weeding out the outliers
• Box and Whisker representation
• Manifestation of the Box & Whisker representation on Bell Curve
• Relation between Z Score and IQR method
• Advanced techniques for weeding out the outliers
• The weighted least squares method for minimisation
• Local vs. the global minima, acceptance criterion
• Effect of standard deviation on reconciliation process
• Concept of redundancy of flow metering
• Minimisation of gross errors
• Calculable and non-calculable systems
• Gas compression process
• Flow metered data of gas compressors - reconciling within (no custody transfer)
• Real-life data of flowmeters - custody transfer

• Exercise 1: Filtering out outliers on a Data set with Z Score
• Exercise 2: Filtering out outliers on a Data set with even number of elements-IQR method
• Exercise 3: Filtering out outliers on a Data set with odd number of elements - IQR method
• Exercise 4: Draw the Box & Whisker representation for Exercise 2 & 3
• Exercise 5: Minimise the problem subject to the constraints using Z Score
• Exercise 6: Minimise the problem subject to constraints using the IQR method
• Exercise 7: Minimise the problem with a different standard deviation based on IQR method and Z score
• Exercise 8: Reconcile flow metered data using Z Score for outliers
• Exercise 9: Reconcile flow metered data using IQR method for outliers
• Exercise 10: Reconcile flow metered data using Z Score for outliers
• Exercise 11: Reconcile flow metered data using IQR method for outliers