南海神狐海域天然气水合物微观赋存特征的超分辨率CT图像识别  

Super-resolution CT image recognition of micro-occurrence characteristics of natural gas hydrates from Shenhu area in northern South China Sea

作　　者：李承峰[1,2,3] 叶旺全 陈亮 桂斌[1] 郝锡荦 孙建业 张永超 刘乐乐 陈强[2,3,5] 郑荣儿 LI Chengfeng;YE Wangquan;CHEN Liang;GUI Bin;HAO Xiluo;SUN Jianye;ZHANG Yongchao;LIU Lele;CHEN Qiang;ZHENG Ronger(College of Physics and Optoelectronic Engineering,Faculty of Information Science and Engineering,Ocean University of China,Qingdao 266100,China;Qingdao Institute of Marine Geology,China Geological Survey,Qingdao 266237,China;Laboratory for Marine Mineral Resources,Qingdao Marine Science and Technology Center,Qingdao 266237,China;Zhoushan Field Scientific Observation and Research Station for Marine Geo-hazards,China Geological Survey,Qingdao 266237,China;Technology Innovation Center for Marine Methane Monitoring,Ministry of Natural Resources,Qingdao 266237,China)

机构地区：[1]中国海洋大学信息科学与工程学部,物理与光电工程学院,青岛266100 [2]中国地质调查局青岛海洋地质研究所,自然资源部天然气水合物重点实验室,青岛266237 [3]青岛海洋科技中心海洋矿产资源评价与探测技术功能实验室,青岛266237 [4]中国地质调查局舟山海洋地质灾害野外科学观测研究站,青岛266237 [5]自然资源部海洋甲烷监测工程技术创新中心,青岛266237

出　　处：《海洋地质与第四纪地质》2024年第3期149-159,共11页Marine Geology & Quaternary Geology

基　　金：舟山海洋地质灾害野外科学工作站开放基金“海底含气土体微观结构探测技术与表征方法研究”(ZSORS-22-12);国家自然科学基金项目“CO_(2)置换甲烷水合物前缘演化及其力学特性和置换效率响应(41976205)”,“南海含有孔虫沉积物双重孔隙特征对水合物分解过程中渗透率演化的影响机理”(42006181)。

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。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 hydratecontaining 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 superresolution 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.

关 键 词：天然气水合物储层 微观赋存特征 超分辨率重建 CT图像 

分 类 号：P744[天文地球—海洋科学]