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How data science is changing the energy industry

Bruce Harpham | April 8, 2016
As with many industries, big data science is transforming the energy vertical, providing insights into cost reductions in down markets and allowing oil producers to adjust to market demands in boom times.

Recent declines in oil prices have hit the world economy hard. Alberta, Canada’s major oil region, has witnessed increased unemployment due to declining commodity prices. In January 2016, Saudi Arabia increased the price of gasoline for its citizens by 50 percent given the situation. With major fluctuations in prices and the high cost of energy projects, quality information has never mattered more.

The energy industry uses data science to cut costs, optimize investments and reduce risk. Reducing costs with data science is a popular application in the industry: much work has focused on improving maintenance and equipment monitoring. Optimizing investment decisions takes several forms including better internal resource allocation and assisting investors. Data science also contributes to improving public safety by providing better monitoring and oversight.

Delivering innovation by borrowing ideas from other sectors

Transferring ideas and techniques across industries is a tried-and-true innovation method. “The energy industry has recently started to adopt the survival analysis concept from the medical field,” says Francisco Sanchez, president of Houston Energy Data Science. In medicine, survival analysis is a statistical method to estimate survival rates for patients based on their condition, treatments and related matters. In the oil and gas sector, this concept has been applied to field equipment.

“Survival analysis is used to predict the maintenance requirements for field equipment such as compressors through monitoring and modeling,” Sanchez says. Instead of taking an oil well offline for three days to repair damage from equipment failure, proactive action enabled by data science can reduce downtime to a single day, he says. Saving a day of downtime is valuable. A day’s production at a small site – 1,000 barrels of oil – represents $30,000 of revenue at current prices.

BP leads in data science and analytics

British Petroleum (BP), the U.K.-based energy company, has long been a leader in IT and related disciplines. The company’s drive to invest in this area is driven by several factors. In terms of safety, the company’s 2010 Deepwater Horizon disaster led to $18 billion fine in 2015 and other damage to the environment. Preventing such a disaster through better information is important rationale for the company. In 2013, the company established a Center for High- Performance Computing in Houston, Texas to connect with leading American institutions such as Rice University.

BP’s analytics capabilities

BP’s commitment to improvement through analytics shows an end to end commitment. The process starts with investment in high quality data and monitoring capabilities.

  • Data analytics in the field. The BP Well Advisor provides operational support for oil sites. This information is fed into several dashboards at the production site and at the corporate offices. The Well Advisor is now in use at more than one hundred 100 offshore wells.
  • Improving production. BP builds models and analytics to improve the efficiency of its refineries. This approach optimizes the refinery’s production capabilities. Analytics plays a role in directly improving production.
  • Partnerships and talent. BP’s direct investments in technology are only part of the data story. The company also works closely with IBM to improve its capabilities. BP has also recognized the importance of staff – Charles Cai, head of data science technology at BP, has been recognized as one of the Top 50 U.K. Data Leaders.


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