Using ML-Supervised Learnings Based-Algorithms to Create a Relative Permeability Model

Summary Relative permeability analysis in the field begins with compiling Special Core Analysis (SCAL) experimental data on core samples. Conventional methods categorize samples by parameters, including rock quality index (RQI), flow zone indicator (FZI), or Winland R35, based on porosity and permeability. Samples are binned by parameter ranges, and collectively analyzed to derive representative permeability curves. The curves receive endpoint analysis, normalization, and denormalization for different rock type bins as per the previously mentioned parameters. However, this relative permeability analysis is a tedious task and requires significant time. This paper presents a Machine Learning (ML) based approach with a compiled database of laboratory-derived SCAL experiments. Thirty-seven experimental oil-to-water relative permeability datasets were collected, which comprised of around 350 data points specific to sandstone reservoir settings. This approach allows for the prediction of two-phase oil and water relative permeability across a range of datasets derived from specific reservoirs with different pore geometries. The derived curves can be effectively utilized in reservoir simulation exercises. We also compare these proposed curves to those generated using the conventional method of averaging relative permeability curves through a modified Brooks-Corey model.