Pattern Recognition for Steam Flooding Field Applications Based on Hierarchical Clustering and Principal Component Analysis.

ABSTRACT: Steam flooding is a complex process that has been considered as an effective enhanced oil recovery technique in both heavy oil and light oil reservoirs. Many studies have been conducted on different sets of steam flooding projects using the conventional data analysis methods, while the implementation of machine learning algorithms to find the hidden patterns is rarely found. In this study, a hierarchical clustering algorithm (HCA) coupled with principal component analysis is used to analyze the steam flooding projects worldwide. The goal of this research is to group similar steam flooding projects into the same cluster so that valuable operational design experiences and production performance from the analogue cases can be referenced for decision-making. Besides, hidden patterns embedded i

SUBMITTER: Zhang N 

PROVIDER: S-EPMC9178724 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature