Big Data In Oil & Gas

How data can improve it in almost every area


Oil and gas is a big industry. It is home to some of the world’s largest companies and brings in trillions of dollars every year, but they have a problem.

The problem is simply that with the increase in fossil fuel use over the past 100 years, the supplies that were once abundant are decreasing, meaning that getting to them is harder. They have also seen the market for oil plummet in the last 6 months, meaning that the prices demanded for a barrel is considerably lower than they were a year ago.

One of the major impacts of this has been that oil companies have needed to look at new ideas in order to try and maximize their profits without impacting their bottom lines. It has meant that an industry that employs a reported 9.2 million people worldwide needs to streamline and add agility.

The way that this may be achieved is through the use of Big Data.

Data can have a profound effect on the oil and gas industry in almost everything they do, from the initial exploration for oil to the extraction and supply chain to get it from the ground into a car. It is something that many have realised as a recent survey by Accenture and Microsoft found, when 86% to 90% of oil companies asked said that increasing their analytical, mobile and Internet of Things capabilities would increase the value of their business.

It is therefore not something that has passed by the oil and gas industry, in fact they have coined a new term for the use of data and digital technology: The Digital Oilfield.

The most expensive element in any oil exploration is finding the reserve and then tapping it. Data can help with this as the formation of rocks and natural landscape in the area can be assessed and other potential sites can then be found through the data. It means that rather than needing to scan millions of miles of potential oilfields in order to find a reserve. It has been estimated that drilling a deep water well can cost upwards of $100 million, so rather than drilling in the wrong place, oil companies need to be able to find new sites quickly and efficiently.

It is not simply in the building of these or land based wells that costs money though. Their upkeep can also be incredibly costly, if there is damage to equipment it can often cost the company millions in lost revenue and replacements. Through using relatively simply sensors to monitor the state of machinery and pipelines, maintenance can be done where it is needed the most, or faulty machinery can be turned off before major and expensive damage is done.

Once the oil is out of the ground it then needs to go through a refining process to make it useable. This process can also benefit from the use of data as the amount needed can be carefully assessed rather than simply staying to a pre-defined quota. This can take information from various areas such as use in particular regions or whether more will be needed due to specific weather conditions.

Having the ability to see how much should be produced will mean that there is not over production, which decreases the amount that can be demanded per barrel and therefore less profit for the company and an unnecessary over consumption of the well.

Finally, data can be used to help with the transportation and distribution of oil once it has been refined.

Through data analysis it is possible to see where there is an increased demand for oil or where there is likely to be an increased demand in the future through predictive modelling. It means that rather than needing to quickly react to unforeseen circumstances, oil companies can make sure that the oil is in the right area to make sure there isn’t a shortage and that these areas can be quickly serviced.

Each of these in isolation will make small changes to an oil company, but when you take them all as a package, they can have a considerable impact. As the global oil and gas industry has been affected by the recent reduction in prices, these changes may be coming along sooner than we think. 


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