NMR log response prediction from conventional petrophysical logs with XGBoost-PSO framework

Abstract

A combined approach that exploits both the eXtreme Gradient Boosting (XGBoost) method and the Particle Swarm Optimization (PSO) method was used here to predict the nuclear magnetic resonance (NMR) log parameters such as porosity and permeability from field measurements. Data obtained by NMR are extremely critical for modeling the reservoir behavior and are among the most vital inputs for reservoir simulations. Therefore, developing an intelligent framework that can accurately predict NMR logging response by using conventional, low-cost logging data can be a valuable tool for field development. A number of conventional well logs, such as neutron, density, sonic, caliper, gamma-ray, and resistivity logs, recorded from sixteen wells in an offshore oilfield in Persian Gulf were used in the XGBoost-PSO framework to predict NMR log response. The outputs are the free fluid porosity (CMFF), bound fluid porosity (BFV), permeability (KTIM), and total porosity (TCMR). Based on the results, in terms of prediction accuracy, comparing correlation coefficients, the XGBoost-PSO model performs in the range of 88.5–91.4%. Moreover, the application of PSO in hyperparameter optimization of predicted parameters confirmed at least five percent improvement. Overall, we were able to demonstrate that ordinary conventional logging data can be utilized to obtain more advanced NMR data without performing expensive, time-consuming, and high-risk logging operations to decrease operational costs without compromising the benefits.