How Big Data is Transforming the Oil & Gas Industry

oil-rig The broad availability of affordable computing power, massive data storage and low-cost sensors started a revolution in condition-based maintenance in the oil and gas services industry. It is now possible to deploy complex workflows dedicated to collect asset data, detect asset anomalies, and estimate remaining useful life to optimize asset maintenance.  Analytics plays a leading role in all such workflows, leveraging the tremendous amounts of data generated by IoT sensors, maintenance systems and operating systems.

The International Data Corporation (IDC) forecasts worldwide revenues for big data and business analytics (BDA) solutions will reach $260 billion in 2022 with a compound annual growth rate (CAGR) of 11.9% over the 2017-2022 forecast period.  BDA revenues are expected to total $166 billion this year, an increase of 11.7% over 2017.

“[The] primary activities of an oil and gas company, which involves exploration, drilling, production, and maintenance generates large amounts of data. As it is almost impossible to analyze such large volumes of data using traditional data applications, organizations require big data analytics and solutions. With new advances in the sector, big data finds increased application for tracking machinery, equipment, and personnel performance,” said Navin Rajendra, researcher for Technavio, a global technology research and advisory company, which published a market research study identifying the three emerging trends that are expected to propel the global big data in oil and gas sector: (1) adoption of user-friendly predictive modeling; (2) increased demand for advanced analytics; and (3) emergence of cloud computing technology.

According to a new market report published by Transparency Market Research, the “Global Big Data in Oil and Gas market” is expected to reach a value of US$ 10.9 billion by 2026 as a result of digitization across the oil and gas industry. The market is projected to expand at a CAGR of 16.6% during the forecast period from 2018 to 2026.  By 2026, the upstream application segment will account for more than 50% of the global Big Data in Oil and Gas market due to increasing IT spending in the upstream sector of the oil and gas industry. Unstructured data is anticipated to have more than 86% market share by 2026 due to increasing generation of unstructured data from different data sources such as sensor data, well data, oceanographic data, geological data, environmental data, and other data sources. Big Data software such as data analytics, data collection, data discovery and visualization, and data management software holds the major share in the global Big Data in Oil and Gas market with data analytics and data management software driving the growth of this segment.

“It may be overused, but it is accurate to say that data is the new oil — especially in our industry.  It is the most valuable natural resource on earth which we have only just begun to optimize, but industry leaders are making great strides in developing a coherent and comprehensive strategy to leverage the wealth of available data” explains Daniel Viassolo, a Principal Data Scientist for Prognostics & Health Management/Analytics at a large oil and gas services company, “oil and gas services companies have taken their first steps in implementing condition-based maintenance schemes across their product lines. Failures in equipment during field operations heavily contribute to maintenance costs and non-productive time. Service companies quickly realized of the huge benefits health management can have for their bottom lines as well as for their reputations with customers.  The integration of big data and analytics has tremendous potential for the oil and gas services industry.” Daniel Viassolo co-authored 21 US patents, 30+ publications and one book, and is one of the thought leaders propelling the industry-wide push to integrate big data in the oil and gas field.

Image Credit: Daniel Viassolo

Daniel Viassolo most recently was a keynote speaker at the 2018 International Multidisciplinary Modeling & Simulation Multiconference (I3M) that took place in mid-September in Budapest, Hungary.  The I3M Conference brings together researchers, scientists and practitioners, from the Mediterranean, Latin & North Americas, Europe, Asia, Africa and Australia, who are concerned with Modeling and Simulation in industry and academia.

At the Conference, Daniel Viassolo delivered a compelling presentation for attendees describing the typical Analytics project phases: Data Ingestion, Exploration & Transformation, Algorithm Development, and Deployment. Different applications were reported. The first one illustrating an offline health analysis and maintenance prescription for a downhole logging-while-drilling (LWD) tool. The second application demonstrating an approach for automated condition monitoring of subsea blow-out preventers (BOPs).

It is clear that the future of oil and gas exploration and the improved health and maintenance of existing systems will owe a substantial debt to the integration of IoT technology, deep learning, and the advanced processing of massive amounts of data on a monumental scale.

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