Big Data Analytics has steadily gained momentum in the upstream E&P industry. Much of the attention has been on advancing data-driven methods including empirical statistical and stochastic approaches, and especially artificial neural networks (ANN). The focus has been on the particular analytics method used rather than to the management, governance, and refinement of the data used in models. Studies conducted through the SPE and by global E&P companies have validated that data management is a major problem in the oil & gas industry. They have clearly established that over half the engineer's and geoscientist's time is spent just looking for data and assembling it before multidisciplinary analysis is even begun (Brulé et al. 2009). Because Big Data Analytics encompasses the four V's of data: Volume, Velocity, Variety, and Veracity, the complexity of managing the data has increased substantially and will become even more of a deterrent to performing analytics. The strategy for collecting, streaming, storing, transporting, cleansing, and securing the data has become just as important as the analytic methods. Promising Big Data management and governance concepts continue to evolve. Among the newest is the “Data Lake,” a massively scalable “landing zone” for semistructured and unstructured data of any type, format, or schema, implemented through Hadoop, other NoSQL, and SQL technologies. This paper will explore the Data Lake for E&P and how its implementation and refinement into an E&P Data Reservoir can be achieved by combining Big Data and industry data standards and other petrotechnical technologies.

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