How data science is changing the energy industry

Recent declines in oil prices have hit the world economy hard. Alberta, Canada’s major oil region, has witness 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.

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.

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

Not every energy firm operates at BP’s scale with operations scattered around the world. Fortunately, there are other ways to get started in analytics.

“Before we dive into tools and techniques, it is vital to start with the business problem,” says Francisco Sanchez. Typical business problems in energy include predicting production, improving field efficiency and understanding geological activities. “Large firms such as BP and Halliburton have adopted data science methods already. I see a great opportunity for small companies with less complex data to achieve wins by bringing one or two specialized data scientists on board,” says Sanchez.

“In oil and gas, you have a wide variety of data to work with and it takes time to bring this all together. I have seen projects where some data are in Oracle databases, other databases have drilling data and there are yet other systems for economic and seismic data. Bringing all this data together requires tools such as Hadoop and NoSQL,” Sanchez says.

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“Regarding specific tools, it will depend on the complexity of the problem. If you are working on a problem with over 50 variables, I suggest looking into machine learning tools. Random Forest, produced by Salford Systems, is one option to consider,” he says. For other projects, the data science toolkit includes tools such as R and Python. “Tibco and Tableau are useful visualization tools to present the data,” he add.

Consulting firms and analysts have long added value to the industry through their specialized knowledge – the same holds true for analytics and energy.  Organizing data and presenting it in a useful way is another way to add value with data science.

“In my role as a data analyst, I primarily spend my time visualizing rig performance and drilling performance. I have created data gathering routines that help bring together hundreds of data sources into neat packages for presentation and performance review. My firm then sells this material at above market average rates. The market pays a premium for explanatory data visualizations because many organizations lack in-house capabilities for these activities,” says Graham Eckel, analyst at Precision Drilling in Calgary, Alberta.

“In the energy sector, there are still plenty of data opportunities. It starts with implementing systems and processes for data collection, cleaning and storage. Hiring a data scientist to build the architecture and guide the implementation is one way to start. With that in place, you can start to generate predictive insights,” Eckel says.

The use of data science and analytics is expected to grow in the energy industry. In a low oil price environment, management will seek cost reduction insights from data. During growth periods, data science will guide management decision making with better insights to improve production and adjust to market demand. The continued growth of data science tools and vendors will also support the trend.


Bruce Harpham

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