Applying machine learning for hydraulic flow unit classification and permeability prediction: case study from carbonate reservoir in the Southern part, Song Hong basin

Abstract

Machine learning (ML) is an artificial intelligence (AI) that enables computer systems to classify, cluster, identify, and analyze vast and complex sets of data while eliminating the need for explicit instructions and programming. For decades, machine learning has become helpful for complex reservoir characterization such as carbonate reservoirs. Permeability prediction from well logs is a significant challenge, especially when the core data is rarely available due to its high cost. In this study, we aimed to bridge this gap by demonstrating the practical application of integrating Hydraulic Flow Units (HFU) and machine learning methods. Our goal was to provide a reliable estimation of permeability using core and wireline logging data in the complex Middle Miocene carbonate reservoir of the CX gas field in the southern part of the Song Hong basin. In the first step, due to the reservoir’s heterogeneity, the core plug dataset was classified into 5 HFUs based on the flow zone indicators (FZI) concept from the modified Kozeny-Carman equation using unsupervised machine learning - K-means method. The porosity - permeability for each HFU was defined after HFU clustering. In the second step, we designed three different workflows to predict permeability and HFU using supervised machine learning from a combination of core and log data. These workflows were rigorously test and compared with the core data. The most accurate result was chosen as the base, providing a high confidence level in our predictions’ reliability.