Data mining and machine learning for identifying sweet spots in shale reservoirs

作者：

Highlights：

• An automatic procedure that can use most of the available information.

• The proposed method can be updated rapidly when new data are available.

• Data can be integrated with different scales and lengths.

• Sweet-spot location can be identified using all available data.

摘要

•An automatic procedure that can use most of the available information.•The proposed method can be updated rapidly when new data are available.•Data can be integrated with different scales and lengths.•Sweet-spot location can be identified using all available data.

论文关键词：Neural networks,Genetic algorithm,Big data,Shale formation,Fracable zones,Brittleness index

论文评审过程：Received 12 February 2017, Revised 20 June 2017, Accepted 11 July 2017, Available online 14 July 2017, Version of Record 26 July 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.07.015