Abstract
Big data analytics and data-driven modeling have become quite the buzzwords in recent years in the context of analyzing the performance of oil and gas reservoirs. Their growing application has been predicated on the potential to usher in exciting new developments related to (1) acquiring and managing data in large volumes, of different varieties, and at high velocities (the 3V problem) and (2) using statistical techniques to “mine” the data and discover hidden patterns of association and relationships in large, complex, multivariate datasets. The terms data mining, statistical learning, knowledge discovery, and data analytics have all been used interchangeably in this context. Essentially, the goal of such an exercise is to extract important patterns and trends and understand “what the data says,” using supervised and/or unsupervised learning.
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