Missing data interpolation in well logs based on generative adversarial network and improved krill herd algorithm

Abstract

Accurate logging data is crucial in geology and petroleum engineering for tasks such as geological modelling, reservoir simulation, and decision-making regarding well repair, water injection, and oil recovery. However, logging instrument failure occurs due to complex conditions such as high temperature and pressure, resulting in incomplete data and posing challenges for reservoir evaluation and development. The existing interpolation methods are primarily based on statistical and machine learning methods, lacking deep mining of hidden associations between logging items. Aiming at the problem of incomplete well-logging data, an incomplete well-logging data interpolation method based on a generative adversarial network and an improved krill herd algorithm is proposed. The results show that the proposed method has stable interpolation for well-logging data missing with different missing rates and any missing positions. Compared with other GANs (GAN, WGAN, and WGAN-GP), the RMSE of the proposed method is reduced by 57.63%, and the $R^2$ is increased by 7.94%. The proposed method is compared with statistical methods (averaging and cubic spline interpolation) and machine learning methods (k-nearest neighbor, support vector machine, and random forest). The experimental results show that the proposed model has stable reconstruction performance for logging data with different missing rates and any missing positions.