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.
Recommended Citation
N. Zhang et al., "Pattern Recognition for Steam Flooding Field Applications based on Hierarchical Clustering and Principal Component Analysis," ACS Omega, vol. 7, no. 22, pp. 18804 - 18815, American Chemical Society, Jun 2022.
The definitive version is available at https://doi.org/10.1021/acsomega.2c01693
Department(s)
Geosciences and Geological and Petroleum Engineering
International Standard Serial Number (ISSN)
2470-1343
Document Type
Article - Journal
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2022 American Chemical Society, All rights reserved.
Creative Commons Licensing
This work is licensed under a Creative Commons Attribution 4.0 License.
Publication Date
07 Jun 2022
Comments
This research was supported by the Natural Science Foundation of Shandong Province (grant ZR2021QF076), Project of Shandong Province Higher Educational “Youth Innovation Science and Technology Plan” (grant 2021KJ060), and National Natural Science Foundation of China (grants 52174121 and 71971130).