Missing Well Logs Prediction Based on Hybrid Kernel Extreme Learning Machine Optimized by Bayesian Optimization

Abstract

Geophysical logging is an essential measurement tool in the oil/gas exploration and development field. In practice, predicting missing well logs is an effective way to reduce the exploration expenses. Because of the complexity and heterogeneity of the reservoir, there must be strong nonlinear correlations between the well logs. To improve the accuracy and stability of the missing well logs prediction, a method based on a Bayesian optimized hybrid kernel extreme learning machine (BO-HKELM) algorithm is proposed. Firstly, the LightGBM algorithm is applied to screen out important features related to the missing well logs and reduce the input dimension of the prediction model. Secondly, the hybrid kernel extreme learning machine (HKELM) algorithm is applied to construct the missing well logs prediction model, and the hyperparameters (C0,C1,d,σ,C) of the model are optimized by the Bayesian algorithm. Finally, the BO-HKELM model is applied to the prediction of the missing well logs in a block of the Ordos Basin in China. The results show that the RMSE, MAE, and R-square predicted by the BO-HKELM model are 0.0767, 0.0613, and 0.9029, respectively. It can be found that the BO-HKELM model has better regression accuracy and generalization ability, and can estimate missing logs more accurately than the traditional machine learning methods, which provides a promised method for missing well logs estimation.