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.
Inside the data science toolkit
“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.
“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.
Opportunities for data analytics
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 future for energy data science
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.
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