Oil and gas companies face the challenge of obtaining insights from an enormous amount of data to make better, more informed decisions. To innovate exploration and production, industry stakeholders need to make sense of operational data from the plant floor, supply chains, and connected products.

By applying advanced analytics and artificial intelligence, oil and gas companies can identify trends and predict events throughout processes to quickly respond to disruptions and improve efficiencies.

To push capabilities further, implementing automation and AI helps the oil and gas industry surpass human limitations to enable the type of decision-making that keeps operations running at full speed and optimizes drilling and production. This shift to digitization and use of big data positions your organization to lead the field in shaping the next generation of oil and gas innovations.

Big Data and analytics may be new to some industries, but the oil and gas industry has long dealt with large quantities of data to make technical decisions. In their quest to learn what lies below the surface and how to bring it out, energy companies have, for many years, invested in seismic software, visualization tools and other digital technologies.

Oil producers can capture more detailed data in real-time at reduced costs and from previously inaccessible areas, to improve oilfield and plant performance. For example, they can pair real-time down-hole drilling data with production data of nearby wells to help adapt their drilling strategy, especially in unconventional fields.

Whether it is the improvement of ROI or safety measures, data analytics has a major influence on the industry. Data Analytics in oil & gas is a business that depends significantly on data to operate its processes, which has proved beneficial in several areas of this industry in advanced analytics. The industry's ever-growing reliance on data and the need to move frontiers in the research and production process have given the importance of state-of-the-art analytics in the business.

Using predictive analysis, oil & gas businesses have been able to build simulations that forecast maintenance occurrences. Predictive maintenance lowers the expense of unpredictable reactive and downtime maintenance.

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.