One minute explanation of this post…
So, we are moving the office to Boston and have been incredibly busy the past few weeks – and will probably continue to be busy for a few more. I have been remiss in posting, so I am passing along this Python script to help you calculate all your cumulative production numbers over time periods you specify. The coding is very straight forward and it also shows you a way to reshape a data frame and rename columns. The code, as always, is below and in our Github repository HERE.
Where can you use this?
Calculating cumulative production is a great way to check/back up your decline forecast results, but in our group we mostly use it for machine learning algorithms related to figuring out EURs, predicting production histories, and diagnosing potential production problems.
Some Sundry Data About the Permian in NM
We have been working quite a bit with New Mexico data and thought we would share some statistics with our readers. One piece of code we don’t share is our decline forecasting algorithm. Like every other analytics group out there, ours is the best you will find. Along with that, to answer a common question, “No, you don’t need to use machine learning to build your own.” If you are experienced and have a set procedure that you use, you can build those rules into a function – probably a very large function. The Numpy Python library offers a good deal of data fitting options, and if combined with good logic and some ingenuity using other freely available libraries, you can create some incredibly powerful software.
The hexbin graphs below show the distributions of oil, gas, and water declines vs. b-values by formation in Eddy and Lea counties, NM. The well set reflects horizontals drilled in the last 5 years with at least 12 months of consistent production data. In terms of b-values, we try to stay in line with optimistic auditor calls and max out at 1.6.