Reasons why data analytics in oil and gas is not creating an impact

There are several reasons why data analytics in oil and gas is not creating an impact. Is not important for the industry? It surely is. Then why it isn’t impacting the industry that’s what we shall investigate in this article.

There is gross mismanagement of data or should we say that we lack the intelligence to manage the data which can do wonders to the industry. Here are the arguments in support of this claim, read below as we take you to a series of questions which we should be asking ourselves before questioning the impact or importance of data analytics:

1. What is the use of data if it isn’t managed properly?
2. Where are you storing your data?
3. What if the data is not accessible?
4. What if it is not being analysed properly?
5. What if there’s a lack of visualisation of the data?
6. Who shall and where should this analysis be communicated?
7. What if it’s not being used for decision making?

These are 7 seven questions which we need to answer.

According to a benchmark analysis by McKinsey & Company in North Sea Offshore there are more than 40,000 of used and unused, allocated and unallocated data tags.

This exhibits that we are not making proper use of the data in hand. Several rigs use sensors to collect a huge chunk of data but what is this data doing for the industry.

The process is complex: We know that oil is technically difficult to find, resources are hard to maintain, and mismanagement can cause serious harm to the environment.

Did you know that the overall operational costs in the upstream sector could be reduced by 3% to 5% if we tend to manage the data properly?

Did you know that overall impact it can have on the industry is that, it can reduce the capital expenditure by 20%?

And, the chief reason we are not able to achieve these numbers is just that the data we collect is not being analysed properly.

There is a serious need to:

• Analyse the data
• Properly manage the data by making clusters of information
• Dedicatedly deploy a team of data scientist
• Building multidisciplinary teams which know which information collected from the data needs to be communicated to which team in the company
• Visualisation of data
• Efficiently use the data in remote locations to reduce downtime and increase operational efficiency
• Data makes sense. And, that sense should be reflected in the decisions which the company makes. So, there’s a need to use data for decision making by the industry leaders

What we have learned, in a way, is how extremely relevant and important data analytics is for the industry. But the serious concern is if we are utilising it our advantage. So, if we think that data analytics is not making an impact, we should be ready to face the 7 questions above.