Forecasting Production Decline Rate in Unconventional Resources by Kriging of Functional Data

The world, in particularly, the United States has seen an explosion in development of unconventional shale resources. In these reservoirs drilling and production occurs at development times orders of magnitude shorter than in conventional resources. As a result, decisions about where to drill and how to complete wells (hydro fracturing) need to be made in almost real-time, rendering the more traditional modelling approaches of geostatistics and flow modelling impractical. In this abstract, we present a novel approach of using the existing production data in a shale play to interpolate production decline rates for newly proposed wells. We develop these methods using novel techniques in statistical modelling based on the kriging of functional data and compare a variety of methods applied to the Barnett shale reservoir. Our Barnett dataset comes form publicly available databases (such as drillinginfo.com), and we considered a period of first 60 months of production in our study. Production profiles and well locations from 456 wells in our dataset were used for training purposes, with an aim to forecast remaining 456 wells that were not used for training (test set)