Why the Shale Oil & Gas Industry is Rapidly Embracing Prescriptive Analytics

M. Drouven, J. Eason
Exenity,
United States

Keywords: shale, oil & gas, analytics, optimization, sustainability

Summary:

Over the past 15 years, the shale oil & gas industry has reshaped the global energy market. The large-scale extraction of hydrocarbons from shale formations has allowed the U.S. to become the world’s leading oil producer. But even though oil and gas output has risen to record highs in recent years, the companies behind the American shale boom are struggling financially. Oil and gas prices continue to plummet as supply is outpacing demand. Investors remain pessimistic about the energy sector’s outlook and many shale companies are under financial pressure as their access to capital constricts. In an effort to combat their economic demise, shale oil & gas companies are rapidly embracing prescriptive analytics. In 2019 the largest natural gas producer in the U.S., EQT, highlighted the deployment of a logistics optimization model that was expected to save the company $25MM-$35MM a year. In its investor presentation the company highlighted the fact that its solution relied on an advanced “mathematical programming algorithm” [1]. Shale producers realize that in order to survive in a sustained low-price environment, they need to make better and faster decisions about their assets and exploration strategies. As a result, companies are increasingly relying on rigorous optimization solutions for decision-support [2]. In this talk we discuss one particular water logistics solution that shows how exactly prescriptive analytics are helping shale companies improve their economics and reduce the environmental footprint of their operations. Water management is one of the most crucial and cost-intensive aspects of shale gas development. Hydraulic fracturing, which enables the extraction of hydrocarbons from shale formations, requires large amounts of water, at times more than a million barrels of water per well. However, a significant portion of the water is recovered once a well is actively producing natural gas. This produced water can be recycled and reused for fracturing new wells in other locations. We describe how rigorous optimization models allow companies to consider all feasible options for satisfying the demand for water at active fracturing sites, while simultaneously managing the supply of produced water from producing wells in the most cost-efficient and sustainable way possible. These specialized models propose precisely how much water should be hauled or piped from a “source” location (e.g. river or lake) to a particular “sink” location (e.g. fracturing or disposal site). To illustrate the benefits of this approach, we present an industrial case study that demonstrates how these water optimization models benefit shale companies. Our analysis shows that by using prescriptive analytics to coordinate water deliveries it is possible to reduce the number of water hauling trucks required for recycling produced water by nearly 25%. We also point out that increasing produced water recycle rates not only reduces costs, it also conserves millions of barrels of freshwater. Our findings suggest that the use of prescriptive analytics for decision-support in the shale oil & gas industry is on the rise – and that companies embracing this technology stand to realize significant cost and environmental benefits.