Hydraulic Flow Unit Classification and Prediction Using Machine Learning Techniques: A Case Study from the Nam Con Son Basin, Offshore Vietnam

Ha Quang Man, Doan Huy Hien, Kieu Duy Thong, Bui Viet Dung, Nguyen Minh Hoa, Truong Khac Hoa, Nguyen Van Kieu and Pham Quy Ngoc
Additional contact information
Ha Quang Man: PetroVietnam Exploration Production Corporation, Hanoi 100000, Vietnam
Doan Huy Hien: Vietnam Petroleum Institute, Hanoi 100000, Vietnam
Kieu Duy Thong: Faculty of Oil and Gas, Hanoi University of Mining and Geology, Hanoi 100000, Vietnam
Bui Viet Dung: Vietnam Petroleum Institute, Hanoi 100000, Vietnam
Nguyen Minh Hoa: Faculty of Oil and Gas, Hanoi University of Mining and Geology, Hanoi 100000, Vietnam
Truong Khac Hoa: PetroVietnam Exploration Production Corporation, Hanoi 100000, Vietnam
Nguyen Van Kieu: Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, 30-059 Krakow, Poland
Pham Quy Ngoc: Vietnam Petroleum Institute, Hanoi 100000, Vietnam

Energies, 2021, vol. 14, issue 22, 1-21

Abstract: The test study area is the Miocene reservoir of Nam Con Son Basin, offshore Vietnam. In the study we used unsupervised learning to automatically cluster hydraulic flow units (HU) based on flow zone indicators (FZI) in a core plug dataset. Then we applied supervised learning to predict HU by combining core and well log data. We tested several machine learning algorithms. In the first phase, we derived hydraulic flow unit clustering of porosity and permeability of core data using unsupervised machine learning methods such as Ward’s, K mean, Self-Organize Map (SOM) and Fuzzy C mean (FCM). Then we applied supervised machine learning methods including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Boosted Tree (BT) and Random Forest (RF). We combined both core and log data to predict HU logs for the full well section of the wells without core data. We used four wells with six logs (GR, DT, NPHI, LLD, LSS and RHOB) and 578 cores from the Miocene reservoir to train, validate and test the data. Our goal was to show that the correct combination of cores and well logs data would provide reservoir engineers with a tool for HU classification and estimation of permeability in a continuous geological profile. Our research showed that machine learning effectively boosts the prediction of permeability, reduces uncertainty in reservoir modeling, and improves project economics.

Keywords: hydraulic flow units; machine learning; permeability; Nam Con Son Basin (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/22/7714/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/22/7714/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:22:p:7714-:d:681569

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().