Super-resolution CT image recognition of micro-occurrence characteristics of natural gas hydrates from Shenhu area in northern South China Sea
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摘要:
南海神狐海域是我国天然气水合物资源勘探开发的主要目标区之一，2017和2020年先后两次现场试验性开采证实了水合物资源的利用前景。目前，对该地区含水合物储层的精细评价还有待进一步提升，水合物在沉积物孔隙空间中的微观赋存形态是其中的重要影响因素。针对水合物微观赋存形态CT图像表征存在的分辨率不足的问题，建立了一种基于自监督学习的数字图像超分辨率重建算法，实现了CT扫描图像空间分辨率的2倍和4倍提升。在此基础上，对南海神狐海域含水合物沉积物孔隙结构演化规律和水合物微观赋存特征进行了形态表征。由于南海沉积物中存在大量有孔虫壳体，水合物主要占据有孔虫壳体内部空间并堵塞了空隙间的连通喉道，显著降低了沉积物的气、水渗透能力；然而，水合物未能全部占据整个孔隙空间，仍然会有少量的气体和水残留，气体则主要分布于水合物颗粒内部，而水则主要分布在水合物颗粒表面，上述实验结果对地震、测井等现场勘探数据解释具有一定的指导意义。
Abstract:The Shenhu area in the South China Sea is one of the main areas rich in natural gas hydrate resources. Two on-site experimental explorations in 2017 and 2022 have confirmed the utilization prospects of hydrate resources. At present, precise evaluation on hydrate-containing reservoirs in the region yet needs further improvement as the microscopic occurrence of hydrates in sediment pore spaces is a key factor. This study addresses the issue of insufficient resolution in CT image representation of micro-occurrence forms of hydrates. A super-resolution reconstruction algorithm based on self-supervised deep-learning was established, in which a 2-fold and 4-fold increase in spatial resolution of CT scanning images were achieved. On this basis, the evolution of pore structure and microscopic occurrence characteristics of hydrates in the Shenhu area of the South China Sea were characterized. Due to the presence of a large number of foraminiferal shells in the sediments of the South China Sea, hydrates occupy mainly the internal space of the foraminiferal shells and block the connecting throats among pores, which significantly reduced the gas and water permeability of sediments. However, hydrates do not fully occupy the entire pore space, and there will still be a small amount of gas and water residue. Gas distributed mainly inside the hydrate particles, while water distributed mainly on the surface of hydrate particles. The above experimental results offered a guidance to the interpretation of on-site exploration data such as earthquakes and logging.
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表 1 超分辨率重建图像的质量评价结果
Table 1. Quality evaluation results of super-resolution reconstructed images
样品类型 超分倍数 PSNR SSIM SMD 南海泥质沉积物 原始 — 1 2.16 2倍 37.75 0.97 4.36 4倍 33.66 0.95 3.79 含水合物沉积物 原始 — 1 2.54 2倍 35.81 0.96 3.75 4倍 31.16 0.92 3.33 
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