Data Analytics Is A Bulwark Against Low Oil Prices

Collecting data from a refinery or oil field is one thing. But using it properly is another

December 19, 2016

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Henry Ford wouldn’t hire an executive who salted his meal without tasting it first—or so the legend goes. True or not, his focus on efficiency helped his motor company perfect its assembly line to produce more with less. Oil and gas producers are now looking for the same level of efficiency as they streamline their business processes in a challenging price environment. Advanced analytics offers several inroads to get more out of existing technology investments and to push back against low commodity prices.

The International Data Corporation (IDC) estimates that the amount of data in the digital universe is doubling every two years. That reality is obvious to anyone who interacts with the volume of data pouring from every piece of equipment in the increasingly digital oil field.
Yet analysis is the key to turning this rapidly and continually accumulating data into valuable insights that can have a quantifiable impact on a company’s bottom line.

A good example of this is the quantity of real-time measurements collected on a refinery floor by data historians. This database of highly detailed process control system data is generally used only to look backwards, analyzing how a pump failed or why productivity declined. Months of specific measurements are stored at high cost while their potential to become a valuable corporate asset is often left unrealized.

Applying both predictive and prescriptive analytics converts this data historian into a forecasting dynamo who can foresee equipment failure and advise how to mitigate against it. Using the data in this new way can send ripples through an organization. The result is unplanned equipment maintenance declines, parts inventory becomes more efficient and management makes more accurate and confident projections based on solid evidence. The effect on the balance sheet is also measurable, to say the least.

Similar examples abound throughout the oil business. One large oil company used advanced analytics to extend the operational lifetime of low-volume, steam-injected wells by optimizing its operations and maintenance schedules. Analytics enabled the company to monitor and adjust its operations 24-7, leading to an improvement in equipment reliability that helped lower costs and maximize production output. The company estimates that its subsequent ability to produce an extra half-barrel per well per day, while reducing its energy footprint, adds hundreds of millions of dollars to its annual revenues.

Applying advanced analytics can also revolutionize oil and gas pipeline forecasting. The pipeline planning process—a yearly regulatory requirement—combines an array of economic assumptions, such as commodity price trends, with large amounts of pipeline data that demonstrate trends in supply and demand. Integrating data from wellheads, meter stations and other sensors is a daunting job, but analytics automates and streamlines the process with startling results.

When these results are compared to executives’ assumptions about the most feasible markets for the pipelines to address, the number of potential markets is narrowed by one-third, as the illustration shows. This speaks volumes about data’s ability to be transformed into actionable information if the right analysis is applied.

Ford, who liked no part of his operations to be wasted, would be pleased.

Ian Jones is Senior Strategist, Energy Risk Management, at SAS

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