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Big Data & Analytics for Oil & gas industry

Oct 15, 2014 | Oil and Gas | Peeksi Pinus

The terms “big data” and “analytics” are not uncommon in today’s data driven world acquainted with the piled up data from myriad sources. Specifically for oil & gas sector, the major sources include data from equipment monitoring and data maintenance records alongwith untapped sources such as seismic input, weather patterns and social media, etc.  The main objective is to provide integrated insights from the combined disparate sources of data.               Although big data has been mostly associated with people’s buying tendencies, sentiments, behaviors, etc; for oil & gas industry, it is defined in terms of 5 Vs: volume, variety, velocity, veracity and value; a cumulative definition from Meta Group (Gartner), IBM and Oracle. The current barriers to the adoption of big data in oil & gas industry include: lack of technology knowledge/skills, lack of business support, cost associated with technology infrastructure, bandwidth and country specific restrictions for data transaction, etc. Presently, the concept and use of big data in oil & gas industry is still in the experimental stage with key companies such as Shell piloting Hadoop in Amazon Virtual Private Cloud for seismic sensor data, Chevron ‘s proof-of-concept using Hadoop (IBM Big Insights) for seismic data processing, Cloudera seismic Hadoop project combining seismic unix with Apache Hadoop. Presently the use of big data in oil and gas industry is mainly concentrated to improve data quality and data loading to feed existing HPC models, use of big data to run integrated asset models: combination of drilling, seismic and production. However, unconventional resources such as shale gas and tight oil are expected to drive innovation in the use of big data.

 It is a prerequisite for oil and gas industry to tap, utilize and manage the huge cluster of critical and advancing data. For further exploration of big data potentiality, oil & gas companies should leverage existing R&D collaboration centers, formulate big data strategy, conduct gap analysis for big data technology determination and staff requirement. The associated companies should also try to understand the complacency of big data to HPC alongwith the value recognization of untapped data assets in supporting fact based decision making and building use cases for big data. By incorporating all these, the entire oil and gas industry will be benefitted but would require cyber security framework, implementation of better trade secret policies and transparent allocation of data ownership.