Type Curves Part 6: Operational/Downtime Factors on Idealized Curves

February 1, 2016 by

Editor’s Note: While VISAGE rebranded to VERDAZO in April 2016, we haven’t changed the VISAGE name in our previous blog posts. We’re proud of our decade of work as VISAGE and that lives on within these blogs. Enjoy.

Type Curves have many complexities and can be developed to serve a variety of purposes. As such any decision maker who is using Type Curves (especially Idealized Type-well Curves) as part of any decision making process should be asking:

  • How was this type curve developed? What does it represent?
  • Is it being used to inform economic decisions or development plans?
  • Yes… then has it been scaled to accurately reflect operational realities? (i.e. scaled with an Operational/Downtime Factor)

There are several approaches to calculating an Operational/Downtime Factor for your Idealized Type Curve. Three example approaches include:

1) Downtime Approach

This is = (Hours Producing / Hours Available). While this is an easy calculation to perform can be the least reliable. The main weaknesses are:

  • This is not a “production-weighted” factor (e.g. if more downtime happens in the small producers, then you could be applying too large a factor to your large producers … and vice versa)
  • The downtime percentage can vary year over year … should you apply different factors for each year? No. This would be adding complexity without increasing confidence in the result.

2) Lost Production Approach

Lost Production is an estimate of the production loss that is attributable to downtime. Whether you are using your internal proprietary data, or public data, there are effective algorithms that can help you quickly arrive at a reasonable factor. While this is more reliable that the Downtime Approach, because it is production-weighted, it does require you to have complex algorithms in place. This is better suited as a Production Performance Diagnostic tool.

3) Cumulative Production Approach

This is the simplest and most reliable approach for determining a reasonable Operational/Downtime Factor. It involves selecting a point in time and comparing the Idealized Type Curve’s Cumulative Production with the Actual (Calendar Day) Production Type Curve’s Cumulative Production (as illustrated in the example chart below). The main strengths are:

  • The ease at which this calculation can be done
  • It is inherently production-weighted because it respects the actual cumulative production at a point in time (i.e. a realistic expectation of production)


The biggest challenge with this approach is determining what point in time to use. While there is no correct answer, consider this:

  • Given most of the value of a typical horizontal well occurs in the first 3 to 5 years, there may be little to be gained by including more time than this. Your choice is dependent on the play and expected operational challenges.
  • Too long a time frame can incorporate bias as older and surviving wells begin to dominate the Type curve (this will be discussed in the next blog).


Cumulative production is one of the easiest production-weighted approaches to use and it respects the cumulative production you can realistically expect a well to produce at a particular moment in time. The three most important things to remember when using (and adjusting) Idealized Type Curves for decisions:

  • Calculate and apply a defendable Operational/Downtime Factor (regardless of what method you choose)
  • Use a consistent approach
  • Be sure to communicate to your team or decision maker how the factor was calculated


That concludes part 6 of this series. The remaining topics that you can look forward to include:

  • Survivor Bias
  • Truncation Using Sample Size Cut-off
  • Forecast the Average vs Average the Forecasts
  • Representing Uncertainty
  • Auto-forecast Tools


Production data: IHS Information Hub

Analysis: VISAGE

Thanks for reading. We welcome your questions and suggestions for future blogs.

Some other blogs you may find of interest:

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