A drilling and oil well company located in the Canadian prairies required a process to analyze oil well production data and correlate it to production forecasting and financial information. They wanted to identify their most and least efficient wells and focus their attention on the highest producing wells and regions.
Although their production engineers already used data from multiple data sources such as Oracle, MySQL, and Excel, they spent considerable time extracting and correlating the data instead of analyzing the data. The size of their Excel data extracts was also getting unmanageable for the geographically dispersed users.
During the first phase of the engagement, Imaginet’s BI Architect used an agile approach to identify key business values needed by the analysts.
Imaginet started with a prototype data model to obtain quick and consumable information in Excel. Power Pivot was used to provide an analytical view of compressed info and Power Query was used to clean up the structures view and correlate the data with other sources. The results were then published to Power BI and a consistent view of the most relevant data was obtained so that analysts could begin identifying high producing wells.
Imaginet’s BI Architect continued to refine and enhance the model with the client. New oil production data was coming in daily and it needed to be reviewed early and often. It was determined that the prototype was less viable for long term use because of the large amount of data and time it took to load. Imaginet recommended a traditional warehouse approach using SQL Server Integration Services (SSIS) to perform Extract, Transform, and Load (ETL) operations to populate a Data Warehouse. The SSIS packages could load the warehouse incrementally to significantly reduce the daily load time.
Total Duration: 1 day for prototype /6 weeks and ongoing