Author: Nitin Chaudhary

Nitin is a Data Scientist at Ruths.ai working passionately towards helping companies realize maximum potential of their data. He has experience with machine learning problems in clustering, classification and regression applying ensemble and Bayesian approaches with toolsets from R, Python, and Spotfire. He is currently pursuing his PhD in Petroleum Engineering at Texas A&M University, where his research is focused on applications of machine learning algorithms in petroleum engineering workflows. He enjoys cycling, running and overindulging in statistical blogs in his pastime.

Build Type Wells using Selected Wells Method in DCA Wrangler

Reserves evaluators often want to build a Percentile Type Well that represents a certain percentile of the population. It is desired to determine a “P90 Type Well”, “P50 Type Well”, or “P10 Type Well”. When expressed this way evaluator is inherently seeking a type well that results in a percentile EUR. The P90 Type Well will be a representative well where there is a 90% chance that the EUR will be that number or greater. There are two published methods for creating Percentile Type Wells, Time Slice approach and Selected Wells approach.

So, the Percentile Type Wells are expected to provide a forecast that will have an EUR consistent with the target probability. This is not possible with the Time Slice method because that method is based on Initial Productivity (IP) and rates. In other words, Time Slice method makes an implicit assumption of a strong correlation between IP and EUR, whereas in a real-world scenario correlation between IP and EUR has a wide scatter, resulting in a Type Well with an EUR that does not represent the desired percentile. Refer to SPE – 162630 for a more technical discussion on the two methods.

In this blog post we will go through a workflow on how to create Type Wells using the Selected Wells method in DCA Wrangler. We created a template that creates Type Wells using Selected Wells Method, Time Slice Method and using individual well forecasts in the Selected Wells Method.

Following is the workflow for Selected Wells Method:

  1. Select wells in an Area of Interest (AOI)

  2. Create an Auto-Forecast for all the selected wells with desired number of years using DCA wrangler. While doing the Auto Forecast we will use a three-segment approach. The first segment with a constrained b – factor between 1 and 2 (this will take care of the characteristic steep initial decline present in most MFHWs in unconventionals). The second segment with a constrained b – factor between 0 and 1. The third segment for terminal exponential decline.

  3. Generate Well DCA and Well DCA Time results in DCA Wrangler. The Well DCA Time table will have the forecast data for all the wells created using the fitted Arps Model. Remember to refresh these tables every time you change the wells in your AOI.

  4. Next, we will find wells for Target EUR probabilities on an EUR Probit plot generated using all the wells in our AOI. We can enter a threshold value (α) to find wells which have their EUR within the (1 ± α) × EUR at the target probabilities. We can also quickly check the number of wells present within the threshold at each of the target probabilities. Adjust the threshold to get a minimum desired number of wells at each of the target probabilities.

  5. Now we can create Percentile Type Wells for our AOI by running DCA Wrangler in the Type Well mode using the wells we selected in our previous step.

Check out the template and try it with your production data.

Nitin is a Data Scientist at Ruths.ai working passionately towards helping companies realize maximum potential of their data. He has experience with machine learning problems in clustering, classification and regression applying ensemble and Bayesian approaches with toolsets from R, Python, and Spotfire. He is currently pursuing his PhD in Petroleum Engineering at Texas A&M University, where his research is focused on applications of machine learning algorithms in petroleum engineering workflows. He enjoys cycling, running and overindulging in statistical blogs in his pastime.