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,Pham Quy Ngoc +7 more
TLDR
In this paper, the authors used unsupervised learning to automatically cluster hydraulic flow units (HU) based on flow zone indicators (FZI) in a core plug dataset, and then they applied supervised learning to predict HU by combining core and well log data.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.read more
Citations
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Journal ArticleDOI
Robust and Efficient Identification of Hydraulic Flow Units using Differential Evolution Optimization and Two-Stage Clustering Techniques
TL;DR: In this article , the authors proposed robust methods to identify the optimal hydraulic flow units (HFUs) using differential evolution (DE) and two-stage clustering, where the first stage uses OPTICS clustering algorithm to determine the number of HFUs and the second stage generates the desired clusters using the Gaussian mixture algorithm.
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Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units
TL;DR: In this article , the authors used the fuzzy c-means clustering technique to identify rock types in 280 core samples obtained from one of the wells drilled in the Asmari reservoir located in the Mansouri field.
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Experimental Rock Characterisation of Upper Pannonian Sandstones from Szentes Geothermal Field, Hungary
Péter János Koroncz,Zsanett Vizhányó,Márton Pál Farkas,Máté Kuncz,Péter Ács,Gabor Kocsis,Peter Mucsi,Anita Fedorné Szász,F. Fedor,János Kovács +9 more
TL;DR: In this paper , the results imply that fines migration due to formation erosion is one of the key processes that must be better understood and controlled in order to mitigate injectivity issues at the study area.
Posted ContentDOI
Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units
TL;DR: In this paper , the authors used the fuzzy c-means clustering technique to identify rock types in 280 core samples from one of the wells drilled in the Asmari reservoir in the Mansouri field, SW Iran.
Proceedings ArticleDOI
A "data-feature-policy" solution for multiscale geological-geophysical intelligent reservoir characterization
TL;DR: Wenhao Zheng et al. as mentioned in this paper proposed a data-feature-policy solution for multiscale geological-geophysical intelligent reservoir characterization, which has the advantages of data-driven, objectivity, and compatibility.
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