The massive increase in the amount of data collected in the oil and gas industry has led to an urgent need for powerful oilfield production software. This software is the only way to properly deal with the ensuing data management issues and proper analysis of the data, so that the results can be applied to oilfield production issues.
The Four Types of Analytics
In the world of business analytics, there is a commonly agreed-upon categorization of analytics into four types: Descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics. This categorization is now being applied in the oil and gas industry.
As the name suggests, these analytics describe what’s going on in the data. An example is a table showing the average oil production for each well in a region.
These analytics try to explain why a past event occurred based on the data. For example, oil production was significantly lower for a particular well last month, and the reason was that equipment failure at that well disrupted the usual production sequence.
Using sophisticated analytic models such as linear regression and decision trees, predictive analytics tries to predict future performance based on past data. Given geographic factors, equipment age, and other relevant features, these models can predict oil production for each well next month.
Prescriptive analytics seeks to improve or optimize performance based on the data. For example, if a particular well is predicted to have the highest oil production, and that well’s equipment is on a new preventive maintenance procedure, then perhaps the equipment for other wells should be put on the new maintenance procedure too.
These are just some simple examples of how the four types of analytics can be applied to oilfield production. Data scientists and engineers with access to high-quality oilfield production software will be able to develop and model more complicated scenarios.