Application of Geographically Weighted Regression to Model the Effect of Completion Parameters on Oil Production – Case Study on Unconventional Wells

Marshal E Wigwe, Marshall Watson, Alberto Giussani, Ehsaan Nasir, Samuel Dambani

Research output: Contribution to conferencePaper

Abstract

Spatial data exists practically everywhere, including the oil and gas industry. Several factors drive the distribution of the location of oil and gas wells: performance of existing wells, available acreage, need for operators to maintain a certain amount of production and to stay competitive. Some of the important parameters to consider in the design of a completion job for an unconventional oil and gas well are the length of lateral (and by extension perforated interval), number of stages, total pounds of proppants, total volume of fluid pumped, injection pressure and injection rate.<br><br>In big data analytics and building of a regression model to capture the effects of these parameters on oil production, the practice has been to analyze wells in similar formations or similar basins, even when these wells are miles apart. Due to the presence of spatial autocorrelation and non-stationarity in such data, the recommended practice should be to take these spatial dependencies into accou
Original languageEnglish
DOIs
StatePublished - Aug 5 2019

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