Developing grid-based smart proxy model to evaluate various water flooding injection scenarios. (25th July 2020)

• Record Type: Journal Article
• Authors: Haghshenas, Yousof; Emami Niri, Mohammad; Amini, Shahram; Amiri Kolajoobi, Rasool
• Is Part Of: Petroleum science and technology. Volume 38:Number 17(2020)
• Page Start: 870
• Page End: 881
• Publication Date: 2020-07-25
• Subjects: data mining -- data-driven modeling -- machine learning -- proxy model -- water flooding; Liquid fuels -- Periodicals; Petroleum -- Periodicals; 665.505
• DOI: 10.1080/10916466.2020.1796703
• Languages: English
• ISSNs: 1091-6466

Abstract

Abstract: This study aims to develop a grid-based smart proxy model (G-SPM) for water flooding enhancement under different injection/production scenarios. The contribution of this work is combining experimental design and data mining techniques to prepare a high-quality database and introducing a hyper-feature to incorporate flow physics into database. The proxy was constructed by training a sequential neural network model. Validating G-SPM on three blind cases showed that oil saturation in grid blocks is accurately predicted in shorter time compared to numerical simulator. This study also revealed that data from one previous time step is enough to be used in G-SPM training.