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Machine Learning and Big Data Analytics — Are We There Yet?

/Portals/1/Images/IEnergyImages/Publishing/UA_Putting_end_trialerror.jpgAs Dr. Satyam Priyadarshy, Chief Data Scientist at Halliburton, expressed in a recent interview, we still have a way to go; but, E&P companies are slowly beginning to adopt artificial intelligence platforms, like machine learning, to transform our relationship to data analytics. In fact, it’s become a hot topic of discussion…if only we can give up the security blanket of a modular approach to data-driven processes and instead look through an “integrated lens,” as Priyadarshy suggests. He believes that once we can “integrate disparate sets of data from different sources effectively, we can actually create value.”  

Given the complexity of our industry, there are certain obstacles that pose challenges to machine learning implementation. However, this might just be the right solution at the right time; whereby, harnessing its power, we can help our customers improve productivity and optimize drilling programs when we need it the most. The predictive capability alone from big data algorithms has the potential to not only save money for oil and gas companies, but help improve efficiency at the same time. A dream come true? Perhaps, but there is still work to be done in terms of a much-needed paradigm shift that calls for a holistic way of thinking and organizational agility, being adaptive. We will have matured in this area as soon as we can take all the data we collect (in some cases, across thousands of wells), analyze it and create economic value by solving problems that will, potentially, more than pay for the cost of investing in new technology.

Click here to read the full interview

 

 

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