A nuclear magnetic resonance proxy model for predicting movable fluid of rocks based on adaptive ensemble learning

Abstract

The movable fluid percentage and movable fluid porosity of rocks are important parameters for evaluating the development potential of petroleum reservoirs, which are usually determined by expensive and time-consuming low-field nuclear magnetic resonance (NMR) experiments combined with centrifugation. In this study, an NMR proxy model based on adaptive ensemble learning was proposed to predict the rock movable fluid indexes efficiently and economically. We established adaptive ensemble learning via an opposite political optimizer (AEL-OPO), which adaptively combines 33 base learners through political optimization to increase the prediction accuracy of the NMR proxy model. To improve the generalization ability of the AEL-OPO, opposition-based learning was introduced to improve the global search speed and stability of the political optimizer. Accessible petrophysical parameters, such as rock density, porosity, permeability, average throat radius, and maximum throat radius, were used as a training set, a validation set, and a test set. The prediction results show that our new strategy outperforms the other 33 base learners, with R2 (coefficient of determination) values of 84.64% in movable fluid percentage and 74.09% in movable fluid porosity.