Collation, Analysis of Oil and Gas Production Reports Using Excel, Python and R: A Data Science Approach in Handling Large Data

The ability to have data and manipulate it to uncover meaningful information is a must-have skill in this day and age. In this paper, practical techniques were applied to combine and analyze 65 sets of well test data received from the Field Engineers for a particular well (Well-001). Comparisons were made between manually collating (copy and paste) and analyzing the data and applying Data Science techniques. Analysis was also done after collation of this data. It was on the basis of this review that it was observed that the well had a corroded bean box and that was replaced, while further analysis on the other hand showed that in the future, a Water Shut Off (WSO) and perforation extension opportunity could be carried out to boost and optimize production in this particular well. The emphasis of this paper is not on the analysis of the data but comparing various tools that can be used to combine large data from different excel files and collating them into one sheet for analysis and pointing out how man-hours can be optimized by applying Data Science. Data used in this paper were routine Field reports stored in a file that pertains to a Well in one of the Fields of interest. One of the takeaways from the job done here is that we can achieve more in less time from Data Science tools and codes like R, Python, VBA and also other tools like Power Query and Pivot Tables.