Frac Analysis in VISAGE: How to Refine Your Insights Using Distributions

November 17, 2014 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.

This is a continuation of the last blog Frac Analysis in VISAGE: Using Distributions as an Alternative to Linear Regressions where we demonstrated that cumulative probability distributions have a unique ability to communicate insights that can not be garnered from a linear regression with a weak correlation. We also demonstrated that distributions can sometimes demonstrate a “correlation window”, which is the range of values where the strongest relationship exists between two variables.

Today’s blog is focused on simple (iterative) techniques to further develop insights into optimal ranges for key completion parameters. The techniques include the following steps:

1)     Look at the data from a different perspective to decide what wells to exclude.

2)     Go back to your original chart, now with a constrained well list, and use increasingly smaller bin sizes to hone in on the optimal value.

3)     Repeat as many times as necessary to statistically identify what has optimal impact on production performance.

The last blog ended with this chart which identified the “correlation window”. We’ll use the same set of wells to embark on Step 1 of our analysis.

 Frac-Analysis-Example-2b-wi

Step 1: Look at the data from a different perspective and cull your well list

If we take the same wells and look at the distribution from a Production per $K Completion Cost (or “Bang for Buck” perspective), we can see from the example below that once proppant per stage exceeds 45 tonnes, the cost effectiveness drops below the other distributions. That is, we start to get less production for every dollar spent when proppant per stage exceeds 45 tonnes. Based in this insight we can now exclude any wells with > 45 tonnes of proppant per stage.

Frac-Analysis-Example-3

Step 2: Return to your original chart, now with a constrained well list, and use a smaller bin size

Now our data set is constrained to only wells that have less than 45 tonnes of proppant per stage. If we change the bin size of proppant per stage from 15 down to 5, we can begin to hone in on the best performing data sets.

Frac-Analysis-Example-4

Step 3: Repeat Step 1

Using the smaller 5 tonne bins, we can look at performance in terms of Production per $K Completion Cost. The clear winner is 30 to 35 tonnes. Any completions using greater than 35 tonnes crosses a threshold and becomes less cost effective.

Frac-Analysis-Example-5

 

This process can be used to examine any completion parameter with a reasonably sized data set. It is an excellent technique to test your assumptions and statistically identify what has optimal impact on production performance.

I hope that you found this illustration of distributions for frac analysis to be useful. I welcome your feedback and suggestions for future blogs.  Thanks for reading!

Sources:

Production Data: IHS Information Hub

Frac Data: Well Completions and Frac Database from Canadian Discovery

Visual Analysis: VISAGE

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

Some other blogs you may find of interest:

About VISAGE – visual analytics for the petroleum industry
VISAGE analytics software equips operators and analysts in the petroleum industry to make the most valuable and timely decisions possible. VISAGE brings together public and proprietary oil and gas data from multiple sources for easy to use interactive analysis.