Big Data in Oil and Gas Financials

As someone who is entering the oil and gas industry after graduation, I have a keen interest in how concepts learned in this class could be applied to my future career. I found this article: http://www.ogfj.com/articles/print/volume-9/issue-07/features/increased-complexity-presents.html detailing how oil companies can potentially use Big Data analytics to manage an ever more complex portfolio of assets and projects. The problem is rooted in the new wave of oil and gas boom, mainly the drilling of shale natural gas or oil which is trapped in harder-to-reach areas of the world such as Canadian sands and extreme deep-water areas of the world. These more difficult projects mean that more time, manpower and capital must be invested in projects in order to see a return on that investment. The volume of data integrated oil companies are processing is so vast that a Chevron executive was quoted as saying that his company manages as much data as Google. Considering that Google processes over 20 petabytes of data a day, that makes the challenge facing the oil majors all the more daunting.

The challenge in the financial sector of the projects is in executives being able to make decisions which are proactive rather than reactive decisions about projects. This means being able to reduce the time between capturing data, analyzing it and making a final decision regarding the data. Making it even more difficult is the multitude of factors which need to be analyzed in making any decisions, including price of oil, expected revenue from production, production decline, and costs of any project. The decision must be made after seeing how all of these different factors interact together and with other factors to calculate expected ROI of any decisions made regarding various projects.

Big data could also be used in predicting the time and cost of projects before they are begun. Because of the extreme investment needed (according to the article, a single offshore oil platforms can cost $1 billion to build and begin operating. This necessitates that they be entirely sure of how much return they will get, and by using Monte Carlo simulation along with data they have generated about a location, they can be much more confident in whether a project will be profitable or not. The predictive analysis also was able to tell the company using these tools where and when problems were most likely to occur in the project timeline so that they could plan ahead and anticipate problems rather than reacting when they happened, thus falling behind the project timeline. In the end, being able to accurately predict project timelines also enabled the company to enhance its reputation as a company committed to excellence in their projects by delivering them on their promised timelines and budgets.