Team-DLY Drilling Report Intel

Monitoring a Drilling Rig activity is one of the most important tasks in Oil & Gas.

In the Drilling business there is one most important document, logging hourly data from the operation. It is called Drilling Report. It is probably the most important document in Oil & Gas Drilling and explorations. It is the most important and closest to reality. It is a very vast source of data until recently not looked at as a source but just an archive of information. we are automating that process to provide detailed information about the Drilling projects. Its use is several layers to be used from Operations Team, Supply chain Team, but most importantly the Sales Team. we can extract market share based on the operations happening with our client. The data is fed in to Business Intelligence (BI) tools for instance Power BI & Tableau to slice and dice the data as needed.

Our Desktop application reads data delivered by the client from a couple of hundreds of rigs operating with several hundred companies. It will generate a CSV file for BI tools to extract business insights.

For example market share and how many technologies are sold to the client can be easily extracted and published to BI tools. This is data extremely valuable because it is data confirmed by the client on which operation happened and which products were provided by which company.

Our project is entirely built using Python libraries. we used the Tkinter package to build our beautiful GUI application. The Tkinter application was then compiled to locally executable application (exe) file using pyinstaller library.

Inside of Tkinter application we have: OS directory to navigate the folders and get each file. Beautiful soup to parse each HTML file and return a soup object. Pandas to build a data frame for each file and append it to the main data frame. Pandas will out put it to .CSV format for analysis.

we had the following out of the main challenges we faced.

Code breaking because of extra files being the directories. we handled it by adding if loops to check for unknown file extensions and skip them.
The application not running if it does not have the right folder input. we handled this by making the application more user-friendly and adding clear instructions.
Learning all the necessary packages for building the app.
Managing time zones for development. we developed the application from three different time zones.
we faced a challenge to out put .xlsx file as an out-put because of "openpyxl" package not able to download.

we created a real-life value-adding product saving more than 24 hrs of highly trained man hours.

and creating value by providing the required detailed data, to make data-driven decisions.

What we learned

Not to take only tutorials for granted, we learned testing each function with actual input and output is as important as designing the code. we have learned managing development and getting everyone up to date is as important as writing code. we have utilized Jira to manage our development and keep track of bugs and monitor progress.