Data Challenges in ML for the Indian Oil and Gas Industry

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Before COVID 19, only 29% of O&G companies, globally, were actively using Predictive Analytics as part of their digital transformation strategy. This is set to change as India and the rest of the world limps out of the economic slump caused by the pandemic. A new normal, requires machine learning in oil and gas industry to optimize exploration and production process and cut costs right through the value chain.

India is the third largest in oil consumption globally and the second largest refiner in South Asia. This makes it imperative that Indian petroleum and alternative energy sectors join other industries that have already seen cost saving through adoption of ML induced efficiencies.

Data, Data everywhere but…

“Data is the new oil. It’s valuable, but if unrefined it cannot really be used.”

The British mathematician, Clive Humby, got it exactly. The petroleum industry has no dearth of data but when it sits in different silos in different formats it becomes the biggest barrier to it being utilized as a single truth.

The challenges in adoption of Machine Learning in the Oil and Gas Industry

1. Closed systems

Much of the data originates from suppliers as well as partner services. These suppliers tend to lock-in their customers by using fully integrated systems that use a proprietary data format that do allow open API access. Sensor based equipment can generate a lot of data but it can often lie unused outside the small window of its localized application. The oil and gas companies are demanding that equipment for future procurement comply with standards-based specification that is secure and interoperable. Big data in oil and gas is the fuel towards numerous applications in upstream,  midstream and downstream operations.