Shale companies turn to machines to crunch their drilling data

Before it was cool — a full two decades before Range Resources Corp. began seasoning its corporate presentations with terms like “machine learning” and “neural networks” — West Virginia University professor Shahab Mohaghegh became obsessed with the industry’s big data. He started studying how machines could process data generated during oil and gas production, and learn to spot patterns. Those patterns could form the basis of predictive models that could help operators narrow down where to drill and estimate how much a well will produce. When shale development started to bubble up in the early 2000s, Mr. Mohaghegh figured it would be the perfect candidate for machine learning. It had the right ingredients, including advanced instruments at each well site producing monstrous amounts of data. Shale was also new geology,…


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