This paper documents a data-mining study of well, hydraulic fracture treatment, and production parameters for horizontal wells in the north Texas Barnett Shale play. In this study, the authors have analyzed well and production data from more than 13,400 producing Barnett wells. A subsample of over 3,300 horizontal wells was characterized with respect to detailed well architecture data such as drift direction and angle, lateral length, perforations, etc. The study uses Geographical Information System pattern-recognition techniques in conjunction with more traditional statistical techniques to interpret hidden trends in otherwise scattered data sets. This work provides a case study in the practical use of data-mining techniques to address questions of best practices in Shale Gas reservoirs. It is made possible because the availability and quality of public domain well and production data has increased significantly in the past few years. Simple cross plotting of production data against well and treatment variables normally leads to broad scattering of results. This study takes advantage of the largest, richest well and production data set available from the gas shales and identifies key lessons-learned. Relevant trends, such as the impact of toe up versus flat versus toe down, horizontal well length, and drift angle variability on gas production rate are presented. This work is significant in that it shows that the application of practical data-mining methods to a large Shale Gas data set can result in learning key lessons that may not be apparent when working with small data sets. This work is also significant in the use of merged reservoir quality proxies, well architecture data, completion data, and stimulation data, against which production results are placed in geographical perspective for improved interpretation.

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