Permeability prediction of isolated channel sands using machine learning.

Abstract Permeability prediction has long been one of the most important and difficult tasks of reservoir characterization, especially for tight sands with strong heterogeneity. A unified model is important for the un-cored wells and can reduce a lot of work in practice. In this case, we are going to establish a field-scale unified permeability model for the tight gas sands of the Middle Jurassic Shaximiao Formation in the Western Sichuan Basin, China. The tight gas sands are isolated channel sands with strong heterogeneity. In addition to the porosity, well logs are also selected as the input feature to help to derive the non-linear relations. We choose simple linear regression (SLR) using porosity, multiple linear regression (MLR), multiple-layer perceptron (MLR) and support vector regression (SVR) using multiple input features as our machine learning methods for this task. For the non-linear methods, MLP and SVR, we present how to decide their key parameters based on the theoretical analysis and experiments to keep their generalization capability. Finally, the results show that the non-linear MLP and SVR outperform the linear SLR and MLR for the unified permeability model. The blind well average performance of MLP and SVR improves about 50% on R 2 and decrease about 30% on MARE than MLR. Highlights • Field-scale unified permeability models are established for all the isolated channel sands with strong heterogeneity. • Proper key parameter values of MLP and SVR are chosen for permeability prediction. • The non-linear MLP and SVR outperform the linear SLR and MLR. • Multiple input features help the derivation of the non-linear relations. [ABSTRACT FROM AUTHOR]