The global energy industry is running out of easy-to-produce oil – the challenges in exploration and production are cutting into the industry’s profits. Combined with the recent nine–month slide in crude prices, these two factors have spurred many conversations between major oil producers and their customers about how to reduce energy costs to support profits.[1]
Costs of extraction are rising. The newly named “data-driven oilfield” is a buzzword that summarizes the attempt to bring down the cost of drilling for oil, the industry’s major expense. Using analytics and automation to optimize production without compromising health, safety, and the environment is an essential part of this effort.
But large reserves—often an effective antidote against low prices—might not be enough of a protection for E&P companies this time around. Analysts’ dire prediction are in the air. So which oil companies will emerge from this latest storm unscathed? And what will they have to do to protect themselves?[2]
Advanced Data and Production Technologies Wring Big Returns from Shale
In spite of gloomy predictions, there is good news in this story. Studies show that a gradual shift to a data- and technology-driven oilfield is expected to produce an additional 125 billion barrels of oil. This is due mainly to two innovative technologies: improved production of oil shale deposits and big data analytics.
The rapid progress in these technologies as well as in sensors, and control systems enable oil and gas companies to produce at maximum efficiency and automate high-cost, dangerous, or error-prone tasks. And lower prices are prompting unprecedented innovation in the oil fields, increasing production per well and slashing costs.
Wrangling Data in Many Forms, from Many Sources
Even as rig counts have fallen, total production has held steady or continued to rise. According to “Shale 2.0,” a May 2015 report by Mark Mills, drilling thousands of wells since the shale revolution began enables producers to apply lessons learned at a much faster rate than earlier.
For example, these days, operators run fiber optic cables down wells to gather data in real time. As a result, they receive vast amounts of data. Thanks to new sensing capabilities, the volume of data produced by a modern unconventional drilling operation is immense—up to one megabyte per foot drilled or between one and 15 terabytes per well.[3]
Surveys of potential conventional drilling sites involve thousands of data points. But with recent development of sensors and real-time data flows, each well could involve more than a million. But what work should this data perform? For example, production forecasting—determining the likely output of a reservoir— is key to determining what resources should be spent on collecting it. It’s just the job for predictive analytics.
Profile of an Effective O&G Analytics Solution
So what might an efficient, effective big data analytics solution look like? Here are some capabilities that enable operators to unlock the value of the information they have gathered and translate it into profit.
- Provide deep understanding of oilfield behavior as it responds to changes in the scale and pace demanded by the business.
- Develop the “big picture”, which connects data analytics elements with business goals and monitors oilfield and production process behavior in detail.
- Model reserves to see how minor tweaks to a certain areas of operations could have big impacts on the productivity or efficiency of another.
- Reuse detailed data across maintenance, logistics, production and reservoir domains.
Next Time: A detailed look at the capabilities that enable big data analytics solutions to make oil and gas operations more efficient.
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Daniel Smith
Director of Data Science and Innovation
About Daniel: Using Business Intelligence platforms to bridge the gap between Advanced Data Analytics and the efficient effective principles of accounting, Daniel applies technology and mathematics to make business faster and smarter.
Daniel has managed solutions for diverse client sectors such as as advertising, military, insurance, and oil & gas. These solutions include Business Intelligence Platform management, online key performance indicator identification and tracking, to full predictive data model construction. Although the analytic solutions are often mathematically complex, Daniel’s presentation and academic background ensures any insights delivered by solutions are relevant and simple to understand.
[1] https://fuelfix.com/blog/2015/05/04/ge-at-otc-big-data-could-help-oil-companies-pump-80-billion-more-barrels-of-oil/#32713101=0
[2] https://www.forbes.com/sites/teradata/2015/02/20/storm-in-a-barrel-data-analytics-and-a-prediction-for-the-oil-industry/
[3] https://www.forbes.com/sites/bernardmarr/2015/05/26/big-data-in-big-oil-how-shell-uses-analytics-to-drive-business-success/
