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 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.

Investigation and Optimization of EOR Screening by Implementing Machine Learning Algorithms

“…Taber et al [6] Creating a Taber table for filtering Al Adasani et al [7] Updating Taber's table Saleh, L et al [9] Application of Box Plots to Polymer Projects Aldhaheri, M et al [10] Designing guidelines on gel processing design parameters AEORS Alvarado et al [12] The earliest proposal for the application of machine learning in EOR screening. Siena et al [13,14] Establishing a Bayesian EOR selection model Zhang et al [15] Analyzing EOR projects using clustering algorithms Khazali et al [17] Using the fuzzy decision tree method to classify the EOR techniques Cheraghi et al [19] Developing neural networks model to predict the category of suitable EOR methods…”

“…Siena et al used the principal component analysis method to reduce the dimension of parameters and established a Bayesian EOR selection model based on reservoir/fluid properties to obtain the target technology for further research to establish a hierarchical clustering model [13,14]. Zhang et al used a hierarchical clustering algorithm combined with principal component analysis to analyze global steamflooding EOR projects [15]. The physicochemical characteristics of the injected reservoir fluid were further correlated with rock characteristics, porosity, and reservoir-specific information, and a Bayesian classifier was used to build the model, achieving 100% accuracy in the validation set [16].…”