Application of unlabelled big data and deep semi-supervised learning to significantly improve the logging interpretation accuracy for deep-sea gas hydrate-bearing sediment reservoirs

Abstract

Due to the extremely complex reservoirs and strong heterogeneity, deep-sea gas hydrate logging porosity calculations still have problems, which further leads to insufficient resource calculation accuracy. Logging reservoir evaluation methods based on intelligence may be able to provide more reliable prediction results, especially the logging evaluation model based on deep learning with great potential. This paper proposes a new method to form unlabelled logging big data, and based on this, establishes a semi-supervised deep learning method suitable for deep-sea gas hydrate-bearing sediments porosity calculation, forming a porosity evaluation model. The method of forming logging big data expands 380 original data samples into 2280 labelled samples and 60050 unlabelled samples, which reduces the sampling requirements for deep-sea sediment formations with high sampling costs. The evaluation results show that the model not only obtains better results than other methods in the inspection wells corresponding to the areas where the training wells are located, but also obtains very good results in other wells that are not involved in the modelling. Compared with traditional prediction methods, the average relative error of porosity prediction is less than 4%. As far as we know, this is the first time that deep learning has been successfully applied to deep-sea hydrate sediment reservoir logging evaluation. It provides a new idea for intelligent logging evaluation of deep-sea hydrate sediment reservoirs.