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Spatial variability of tight oil well productivity and the impact of technology

J.B. Montgomery and O’Sullivan, F.M.

Applied Energy, 2017, vol. 195, issue C, 344-355

Abstract: New well productivity levels have increased steadily across the major shale gas and tight oil basins of North America since large-scale development began a decade ago. These gains have come about through a combination of improved well and hydraulic fracturing design, and a greater concentration of drilling activity in higher quality acreage, the so called “sweets spots.” Accurate assessment of the future potential of shale and tight resources depends on properly disentangling the influence of technology from that of well location and the associated geology, but this remains a challenge. This paper describes how regression analysis of the impact of design choices on well productivity can yield highly erroneous estimates if spatial dependence is not controlled for at a sufficiently high resolution. Two regression approaches, the spatial error model and regression-kriging, are advanced as appropriate methods and compared to simpler but widely used regression models with limited spatial fidelity. A case study in which these methods are applied to a large contemporary well dataset from the Williston Basin in North Dakota reveals that only about half of the improvement in well productivity is associated with technology changes, but the simpler regression models substantially overestimate the impact of technology by attributing location-driven improvement to design changes. Because of the widespread reliance on these less spatially resolved regression models, including by the U.S. Energy Information Administration to project shale gas and tight oil resource potential, the overestimate of technology’s role in well productivity has important implications for future resource availability and economics, and the development choices of individual operators.

Keywords: Hydraulic fracturing; Unconventional oil and gas; Bakken and Three Forks Shale; Spatial autocorrelation; Linear regression; Kriging (search for similar items in EconPapers)
Date: 2017
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