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

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 combinin...

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Autores principales:	Ha Quang Man, Doan Huy Hien, Kieu Duy Thong, Bui Viet Dung, Nguyen Minh Hoa, Truong Khac Hoa, Nguyen Van Kieu, Pham Quy Ngoc
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	hydraulic flow units machine learning permeability Nam Con Son Basin Technology T
Acceso en línea:	https://doaj.org/article/0ef2eb6fec304e209b0431b38c7af3cc
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spelling	oai:doaj.org-article:0ef2eb6fec304e209b0431b38c7af3cc2021-11-25T17:28:11ZHydraulic Flow Unit Classification and Prediction Using Machine Learning Techniques: A Case Study from the Nam Con Son Basin, Offshore Vietnam10.3390/en142277141996-1073https://doaj.org/article/0ef2eb6fec304e209b0431b38c7af3cc2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7714https://doaj.org/toc/1996-1073The 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.Ha Quang ManDoan Huy HienKieu Duy ThongBui Viet DungNguyen Minh HoaTruong Khac HoaNguyen Van KieuPham Quy NgocMDPI AGarticlehydraulic flow unitsmachine learningpermeabilityNam Con Son BasinTechnologyTENEnergies, Vol 14, Iss 7714, p 7714 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	hydraulic flow units machine learning permeability Nam Con Son Basin Technology T
spellingShingle	hydraulic flow units machine learning permeability Nam Con Son Basin Technology T Ha Quang Man Doan Huy Hien Kieu Duy Thong Bui Viet Dung Nguyen Minh Hoa Truong Khac Hoa Nguyen Van Kieu Pham Quy Ngoc Hydraulic Flow Unit Classification and Prediction Using Machine Learning Techniques: A Case Study from the Nam Con Son Basin, Offshore Vietnam
description	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.
format	article
author	Ha Quang Man Doan Huy Hien Kieu Duy Thong Bui Viet Dung Nguyen Minh Hoa Truong Khac Hoa Nguyen Van Kieu Pham Quy Ngoc
author_facet	Ha Quang Man Doan Huy Hien Kieu Duy Thong Bui Viet Dung Nguyen Minh Hoa Truong Khac Hoa Nguyen Van Kieu Pham Quy Ngoc
author_sort	Ha Quang Man
title	Hydraulic Flow Unit Classification and Prediction Using Machine Learning Techniques: A Case Study from the Nam Con Son Basin, Offshore Vietnam
title_short	Hydraulic Flow Unit Classification and Prediction Using Machine Learning Techniques: A Case Study from the Nam Con Son Basin, Offshore Vietnam
title_full	Hydraulic Flow Unit Classification and Prediction Using Machine Learning Techniques: A Case Study from the Nam Con Son Basin, Offshore Vietnam
title_fullStr	Hydraulic Flow Unit Classification and Prediction Using Machine Learning Techniques: A Case Study from the Nam Con Son Basin, Offshore Vietnam
title_full_unstemmed	Hydraulic Flow Unit Classification and Prediction Using Machine Learning Techniques: A Case Study from the Nam Con Son Basin, Offshore Vietnam
title_sort	hydraulic flow unit classification and prediction using machine learning techniques: a case study from the nam con son basin, offshore vietnam
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0ef2eb6fec304e209b0431b38c7af3cc
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Hydraulic Flow Unit Classification and Prediction Using Machine Learning Techniques: A Case Study from the Nam Con Son Basin, Offshore Vietnam

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