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

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.

Artificial Intelligence Methods in Hydraulic System Design

“…The maximum absolute errors of void fraction and temperature were 0.1 and 0.03 K with an estimation speed-up of the order of 10 4 . A case study related to hydraulic flow unit classification and prediction using machine learning techniques was carried out by Man [41]. Multiple AI algorithms were used in the research, including both unsupervised and supervised machine learning methods for hydraulic flow unit clustering and the prediction of hydraulic parameters.…”

Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units

“…In 2020, rock type and hydraulic flow units were used as a successful tool for reservoir characterization of the Bentiu-Abu Gabra sequence, Muglad basin, SW Sudan (El Sawy et al, 2020;Shalaby, 2021;Shoghi et al, 2020;Wu et al, 2020). Machine learning is effectively used by Man et al (2021) to boost the prediction of permeability and reduces uncertainty in reservoir modeling. Recently, a variety of conventional methods and machine learning algorithms were investigated in determining hydraulic flow units (HFUs), and the performance of each method was evaluated (Fernandes et al, 2023a;Kianoush et al, 2022bKianoush et al, , 2023cKianoush et al, 2023a;Masroor et al, 2023;mohammadinia et al, 2023;Shi et al, 2023;Yu et al, 2023).…”

Determining the petrophysical rock types utilizing the Fuzzy C-means Clustering technique and the concept of hydraulic flow units

“…In 2020, rock type and hydraulic flow units were used as a successful tool for reservoir characterization of the Bentiu-Abu Gabra sequence, Muglad basin, SW Sudan (El Sawy et al, 2020;Shalaby, 2021;Shoghi et al, 2020;Wu et al, 2020). Machine learning is effectively used by Man et al (2021) to boost the prediction of permeability and reduces uncertainty in reservoir modeling. Recently, a variety of conventional methods and machine learning algorithms were investigated in determining hydraulic flow units (HFUs), and the performance of each method was evaluated (Fernandes et al, 2023a;Forbes Inskip et al, 2020;Kianoush et al, 2022bKianoush et al, , 2023cKianoush et al, 2023a;Masroor et al, 2023;mohammadinia et al, 2023;Shi et al, 2023;Yu et al, 2023).…”