When you start searching the internet for oil and gas data, the first thing you will probably come across are sites for expensive data services. For small consultant groups and companies, this can be quite a dent in a budget. Why should you pay for data that is easily available when that money can be spent on buying interests or actual operating expenses? A lot of what you are looking for can be acquired for free or at a really low price. In this post we will give you the links to some of these sites and show you the way to get at some data that is very useful right now.
State Sites
- California – Great amount of data stored in Access databases. They have a search capability for one-off wells, but we think what you really want is found HERE. Unfortunately, you will have to combine the databases via a SQL query, but that is a small amount of effort when it comes to the fact that everything the state has is free. In a future post we will show you how to take data from SQL and work with it, much faster, in Python. Of note for California, watch when wells switch from injection to production and vice versa in the database production table (due to steam floods).
- Pennsylvania – Not bad, the state finally started keeping track of production by well, monthly, in April of 2015. This is where you can collect all sorts of PA data, but, more specifically, production in an Excel format HERE. Be aware of the awful data quality prior to the aforementioned time. Select the data you want and download it, en masse.
- Ohio – Ugh, Ohio. Quarterly data for unconventionals, but at least it is allocated by well. Many of the older wells are single values by year. A way we think about how to get the unconventional quarterly to a monthly format is to look at investor presentations for the operators you can find and see who chokes their wells for a flat initial monthly profile (number of months) and use that with general b values and declines from older wells to calculate the breakdown. There is also a well database setup available – the intent is good, the execution is horrible. The database tool uses an old Access database version and their own queries throw errors if you have a more recent version of the program. You can still get at the data, though. Open it ignoring the errors and copy it into a usable format. This is an automation project we will most likely do in the near future.
- North Dakota – $175. $175 and you are able to get all of the state data for an entire year. Way to go ND. You probably pay more on a monthly basis for some Adobe products.
- Montana – Not much going on oil and gas wise in the state, except it being the western edge of the Williston. The search is HERE.
- Louisiana – The opposite of ND – charges a ridiculous amount for one state in terms of a mass download. The well search is HERE.
- Texas and Oklahoma – This is 4 or 5 posts in itself. The Texas Railroad Commission allocates by lease and the search functionality is very unfriendly, though you can get some decent data from University Lands for a large number of wells. You can buy the state data and write an allocation program yourself, too – and the pricing from the state isn’t terrible. In terms of Oklahoma… Well, there is some data that is easily pulled, but you realize what you are in for when their front page says, “Oil production has not been tracked by the Commission for several years, and is not available from our database. ” Because, you know, why bother keeping track of oil? It is possible to get this data and make it work, but, again, it is a little more involved than just a few posts.
New Mexico Data
So we said at the beginning of the article that we would show you how to go about getting data for an area that is popular right now. New Mexico’s Eddy and Lea counties are in the northwest portion of the Permian Basin – a large amount of the Delaware basin. We are going to show you where to get all of it at once (and we mean ALL of the wells…in the state), how to view it, and how to work with it by giving you a small example that will pull all of the effective lateral lengths with their corresponding APIs and some pointers on how to clean the data before using it. Also, keep in mind, NM updates their production quarterly.
Where to Find the New Mexico Data
To start, if you are looking for just a single well or maybe a few wells, you should use their well search . Otherwise, to download all of it, go HERE and follow the link to the FTP server. From there, select the OCD Data folder. The OCD_DataDictionary.xls file will really help with trying to figure out what data you would want from the proper file. The T_WC_VOL (production) and OCDExport (other general data) will be the zip files that contain the bulk of the information. There is a lot of good data in most of the FTP folders, but the two files mentioned are going to be key.
The Not-So-Easy Part
All of the files you will download, minus the data dictionary, are XML files. There are many other formats I prefer over XML, and I like to joke that it is “literally the least you could do” in getting data out there in a usable form. If you have an XML reader, great. If not, you will probably try to open it with some other program – perhaps using notepad, some browser, or any application that converts something to text. Seeing as how a majority of these files are larger than 1 GB in size, you may be waiting a while for your request to complete – if your computer just doesn’t crash trying to handle it. There is an incredibly easy way to view, parse, and use these files in Python.
Program Description
The program requires 3 libraries: Pandas, Numpy, and ElementTree. If you go on to cleaning the data, you will probably also want to import Matplotlib.pyplot to build some histograms to see where data has to be culled or find outliers. After the import, it only takes 2 lines of script to view some top portion of the file and get an idea of how it is structured. You can replace the ‘xxx’ portion of the xmlf.read(xxx) statement to view as many characters as you want until you have a good understanding of the patterns. The two pickle statements that are commented out below the main data acquisition loop allow you to save or read the final file from the loop. I usually keep those two lines in a program if something takes a bit of time to run so I have that portion saved and don’t have to rerun that part. By the time the looping is done, you will have data for 122,470 wells in terms of IDs and perf footage values. After that, the program combines the identification values to get full API numbers and subtracts the perf values to get effective lateral lengths. The full program can be found HERE.
Tips
I would suggest that if you use this, you do some cleaning on the data. This is just a basic example to show how to get the data and give some pointers on what to watch out for. Speaking of pointers, when you sort the data by the perf locations and distances, you will find that either companies are drilling 99,999′, 9,999′, and 9′ wells incredibly often or that they are able to drill in negative feet. Different people will clean the data in different ways. Most up for debate would be what a user would call a cut off for the minimum and maximum lengths of a lateral in this part of the world. A good way to tie all of this together is to use this data extract script to get more qualitative well data and figure out which wells are horizontals in the first place, where you have duplicate wells and take the greater/lesser of the two values, and if that general well data you just pulled to clean this well data is, itself, clean. Data set secret: NM has a lot of wells in their database tagged as verticals that suspiciously end with a number and the letter ‘H’ and were drilled in the last 3-5 years, so heads up.