Practical implementation of knowledge-based approaches for steam-assisted gravity drainage production analysis

作者：

Highlights：

• Input attributes descriptive of SAGD reservoir heterogeneities are formulated.

• Neural network models are trained using a comprehensive field dataset.

• Uncertainty analysis is performed involving Monte Carlo and bootstrapping methods.

• Sensitivity of model architecture is explored.

• Results demonstrate important potential in facilitating SAGD production analysis.

摘要

•Input attributes descriptive of SAGD reservoir heterogeneities are formulated.•Neural network models are trained using a comprehensive field dataset.•Uncertainty analysis is performed involving Monte Carlo and bootstrapping methods.•Sensitivity of model architecture is explored.•Results demonstrate important potential in facilitating SAGD production analysis.

论文关键词：Statistical analysis,Neural networks,Model uncertainties,Steam-assisted gravity drainage,Petroleum engineering,Data mining,Production forecast

论文评审过程：Available online 29 May 2015, Version of Record 17 June 2015.

论文官网地址：https://doi.org/10.1016/j.eswa.2015.05.047