Identifying Different Components of Oil and Gas Shale from Low-Field NMR Two-Dimensional Spectra Based on Deep Learning

Abstract

The detection and quantitative analysis of shale components are of great significance for comprehensively understanding the properties of shale, assessing its resource potential and promoting efficient development and utilization of resources. The low-field NMR T1-T2 two-dimensional spectrum can detect shale components non-destructively and effectively. Unfortunately, due to its complexity, the two-dimensional spectral results of low-field NMR are mainly analyzed using manual qualitative analysis, and accurate results of the composition cannot be obtained. Since the information contained in its two-dimensional map is determined by the morphological texture and the position in the map, commonly used image analysis networks cannot adapt. In order to solve these problems, this paper improves a novel Faster Region-based Convolutional Neural Network (Faster-RCNN). Compared with previous models, the improved Faster-RCNN has better image classification and visual key point estimation capabilities. The results show that compared with traditional methods, the deep learning method using this model can directly obtain key information such as kerogen and movable oil and gas content in rocks. The information provided in this study can help complement and improve the development of analytical methods for low-field 2D NMR spectra.