In the oil industry, big data-enabled solutions have been used to optimize oil-drilling processes to reduce operational costs and maximize profits. However, the use of big-data analytics to discover and identify interrelationships among different types of business data for delivering time-bound decisions has been limited.
Large quantities of data are not new to the oil industry, which may have been analyzed to realize positive results for seismic processing and reservoir modeling. The creation of the Energy Information Administration at the U.S. Department of Energy is a public acknowledgement of the accepted value of data-driven processes in the energy sector. On the other hand, the utilization of commodity hardware and technologies like big data and Hadoop has still not come into focus in the large, oil-industry fraternity.
Importance of data technologies in the oil industry
The oil industry leaders are well aware that ignoring data and data technologies can have disastrous consequences. When hackers damaged an entire network of 30,000 computers belonging to Sauudi Aramcoin, one of the worlds largest oil companies in 2012for the first time, oil industry giants realized the severe risks involved in cybersecurity breaches.
An investigation done by International Institute for Strategic Studies suggested that such attacks could repeat in the oil and gas sectors many times in future. In an increasingly energy-centered, politically turbulent world, the need for next-generation, fully digitized oil and gas sectors with appropriate security and regulatory compliances cannot be overemphasized.
Traditional real-time analytics in oil rigs
The oil companies, for decades, have utilized sensor-aided, data-collection from subsurface wells and surface facilities to provide streaming, real-time data. In the recent times, in addition to sensor data, the data collected from oil rigs may have included high volumes of semi-structured and unstructured datasourced from drilling and production measurements and operational logs. The data may also contain financial results, oil-industry related breaking news, and business-development assets.
Apart from the digital oilfield, vast improvements in sensor technologies, and the easy availability of broadband networks have enabled volume, variety, and velocity in business data. These data can easily add up to petabytes of information with the potential to deliver rare insights through big data analytics.
The current scenario big data repositories
With modern oil drilling and production processes in place, an avalanche of structured (surface and subsurface data, oil-drilling data, oil-production data), semi-structured (data from modeling exercises), and unstructured (drilling specifications, well log, drilling reports, web traffic on oil company sites, and social media) data are routinely generated in the oil business.
What the oil industry needs is the right combination of technology solutionsto move beyond traditional, real-time data monitoring to more agile predictive analytics. In particular, by applying agile methodologies to incoming technical and business data, and then testing those data against complex models in real timeoil companies can derive instantaneous intelligence for business competitiveness. Also, by analyzing vast repositories of competitive intelligence, such as news about mergers, acquisitions, investmentsthe companies can arrive at strategic decisions about their business future.
How big data analytics can help
An average upstream well is reported to be streaming out one terabyte of production data every dayleaving a big challenge for collecting, organizing, and examining that data.
Big data and real-time analytics jointly hold opportunities for establishing processes for more efficient oil production, reducing operational costs, enhancing safety, implementing regulatory compliance, and enhancing strategic decision-making at all stages of the oil business
Big data can certainly lead these oil companies to take the digital oilfields to the next level of business improvement, by integrated operational (sensor-aided) technology with information technology (big data, Hadoop) for enhanced operational and business performance.
Big data benefits to the oil industry
The upstream and midstream segments the oil industry can apply big-data analytics to both structured, operational data and unstructured data in the following ways:
- Big data analytics on news feeds, oil industry reports, or geospatial data could help derive competitive intelligence on bidding prospects.
- Big data analytics can provide instant information on underperforming wells and flag potential problems as they arise. Maximizing production by reducing downtime through preventive maintenance. The oil industry can save millions of dollars.
- Big data analytics techniques like pattern recognition can aid geologists identify potentially productive seismic trace signatures that have been overlooked.
- Big Data and analytics may be applied to real-time, oil drilling data to detect anomalies under certain operational conditions, or to predict the likelihood of drilling success.
- Big data analytics applied to a variety of data at the same timeseismic, drilling, and production data could aid in mapping changes in the reservoir, or for providing decision support in lifting methods.
- Data related to pressure, volume, and temperature (PVT) may be collected and analyzed together and compared with the past history of equipment failure on a compressor, for future maintenance purposes.