A good general definition is “Data science is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured.” While techniques and underlying principles of data science have been around for decades through various disciplines such as statistics, computer science, machine learning, and probability theory, it is only recently that data science as a unifying umbrella has received unprecedented attention and popularity, and rightfully so.
Traditionally, the oil and gas industry has been collecting a variety of data, such as production data, log data, geological data, completion data, artificial lift data, maintenance records, and data from permanent down hole sensors.
There are a few universities, startups, and multinational corporations currently trying to apply data science and predictive modeling to several aspects of oilfield management, most making their base in the “Prolific data science” home of Silicon Valley in the San Francisco Bay Area of California.
One of the older and established petroleum data science startups in Silicon Valley is Palantir, which works across many verticals including oil and gas.
The company started as a data science competition company, providing a platform for connecting thousands of data science practitioners to companies through data science competitions.
For young professionals in this industry dealing with data, it is prudent to add a data science skill set, as such skills with enable them to make better, faster decisions, broaden their career choices, and ultimately improve the company’s bottom line.
Never has acquiring such skill sets been easier, with companies like Coursera and Udacity providing online “Nano-degrees” in data science, and prestigious universities like Stanford opening up courses in data science to the masses online.