Author:
Xiao Feng-feng,Deng Zheng-qiang,Qi Mei,Xie Xian-tao,Jiang Guan-cheng
Publisher
Springer Nature Singapore
Reference27 articles.
1. Schuetter, J., Mishra, S., Zhong, M., et al.: Data analytics for production optimization in unconventional reservoirs. In: Unconventional Resources Technology Conference (2015)
2. Lolon, E., Hamidieh, K., Weijers, L., et al.: Evaluating the relationship between well parameters and production using multivariate statistical models: a middle Bakken and three forks case history (2016)
3. Hegde, C., Wallace, S., Gray, K.: Using trees, bagging, and random forests to predict rate of penetration during drilling. Soc. Pet. Eng. 12(7), 47–55 (2015)
4. Korjani, M., Popa, A., Grijalva, E., et al.: A new approach to reservoir characterization using deep learning neural networks (2016)
5. Wilson, A.: Enhancing well-work efficiency with data mining and predictive analytics. In: Society of Petroleum Engineers (2015)