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

Ha Quang Man,Nguyen Van Kieu,Kieu Duy Thong,Truong Khac Hoa,Doan Huy Hien,Nguyen Minh Hoa,Pham Quy Ngoc,Bui Viet Dung

doi:10.3390/en14227714

Ha Quang Man, Nguyen Van Kieu + Show 6 more

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https://doi.org/10.3390/en14227714

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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.

Highlights

One of the most challenging problems in reservoir analysis is predicting reservoir permeability when data is limited
Our study demonstrates that many of these problems can be solved using flow zone indicator (FZI) and hydraulic flow units (HU)
We proposed a novel regression of supervised machine learning (ML) such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), Boosted Tree (BT) and Random Forest (RF)

Summary

Introduction

One of the most challenging problems in reservoir analysis is predicting reservoir permeability when data is limited. We proposed a novel regression of supervised machine learning (ML) such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), Boosted Tree (BT) and Random Forest (RF) These techniques were used to build the predict model of FZI in the zone of interest both for core and log data and application of that model to the uncored section and in the well without core data. By combining supervised and unsupervised machine learning methods and applying this technology to the available core and log data, we were able to significantly improve HU classification and prediction of the reservoir. This novel technique is a significant advance in the state of the art

Geological Setting

Dataset

Data Preparation

Supervised Methods