Type Curves Part 5: Condensing Time (Idealized Type Curves)
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.
A technique that is often used to compare wells or to augment the production decline profile of a well is “condensing time”. While this technique has its merits, it should be used with caution… it results in an “Idealized Type Curve” that may significantly over-represent the production (and value) you can expect from a well. You run the risk of falling short of your production targets if you don’t take into account realistic downtime expectations (I will discuss this in detail next week). This should give an even greater impetus for decision makers to ask, “How did you develop your type curve?”.
The two most common techniques used to condense time include: 1) Removing Time Periods with no (or low) production; and 2) measuring production in the context of “Cumulative Producing Time”.
Removing Time Periods
By removing time periods with no production you are aligning producing months across the data set. While this is a good practice on Rate vs Cumulative Production charts (which do not effectively communicate time anyway) it comes with some cautions on Rate vs Time charts. Long periods without production can result in flush production spikes on gas wells (see example below) or periods where the well is taking time to recover from a downtime event (e.g. in the case of a water flood).
A second approach is to remove time periods which have production rates below a specified threshold. This is attempting to only include “representative” production periods. This introduces a strong bias of what a “representative” production period is, without letting the data statistically demonstrate that for you.
Cumulative Producing Time
This approach essentially ignores all downtime and shows production relative to the cumulative producing hours of a well. The exclusion of downtime has the potential to portray wells as being quite similar, while in reality they demonstrate very different production performance. This is the same technique used in IP90 production measures (see the following blog for more details How useful are IP30, IP60, IP90 … initial production measures?). In the example below the first chart shows how two wells appear to have the same production profiles using condensed (producing) time, while the second chart shows how in actual elapsed time these same wells yield dramatically different results.
Important Questions Decision Makers Should Ask
Given the possible issues demonstrated in the examples above (when using Idealized Type Curves), it is increasingly important that decision makers ask these questions:
- 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?
Next week we will discuss techniques to apply operational/downtime factors to idealized type curves to better reflect expected outcomes.
That concludes part 5 of this series. The remaining topics that you can look forward to include:
- Operational/Downtime Factors on Idealized Curves
- Survivor Bias
- Truncation Using Sample Size Cut-off
- Forecast the Average vs Average the Forecasts
- Representing Uncertainty
Production data: IHS Information Hub
Thanks for reading. We welcome your questions and suggestions for future blogs.
Some other blogs you may find of interest:
- Type Curves Part 1: Definitions and Chart Types
- Type Curves Part 2: Analogue Selection
- Type Curves Part 3: Normalization
- Type Curves Part 4: Calendar Day vs Producing Day
- What production performance measure should I use?
- Frac Analysis in VISAGE: How to Refine Your Insights Using Distributions
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