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

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 in steam flooding
applications can be revealed based on data characteristics of each
cluster for different reservoir/fluid conditions. In this research,
principal component analysis is applied to project original data to
a new feature space, which finds two principal components to represent
the eight reservoir/fluid parameters (8D) but still retain about 90%
of the variance. HCA is implemented with the optimized design of five
clusters, Euclidean distance, and Ward’s linkage method. The
results of the hierarchical clustering depict that each cluster detects
a unique range of each property, and the analogue cases present that
fields under similar reservoir/fluid conditions could share similar
operational design and production performance.