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Article(id=1149781954129785474, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149781952959574654, articleNumber=null, orderNo=null, doi=10.12404/j.issn.1671-1815.2403089, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1714060800000, receivedDateStr=2024-04-26, revisedDate=1734192000000, revisedDateStr=2024-12-15, acceptedDate=null, acceptedDateStr=null, onlineDate=1752058979779, onlineDateStr=2025-07-09, pubDate=1743091200000, pubDateStr=2025-03-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752058979779, onlineIssueDateStr=2025-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752058979779, creator=13701087609, updateTime=1752058979779, updator=13701087609, issue=Issue{id=1149781952959574654, tenantId=1146029695717560320, journalId=1146123166801305609, year='2025', volume='25', issue='9', pageStart='3529', pageEnd='3967', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752058979501, creator=13701087609, updateTime=1776333392421, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1251596220226027613, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149781952959574654, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1251596220226027614, tenantId=1146029695717560320, journalId=1146123166801305609, issueId=1149781952959574654, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=3584, endPage=3592, ext={EN=ArticleExt(id=1149781954414998147, articleId=1149781954129785474, tenantId=1146029695717560320, journalId=1146123166801305609, language=EN, title=Spatial-temporal Evolution Characteristics of Land Surface Subsidence and Reservoir Parameters Inversion in Sebei Gas Fields, columnId=1156262729351549255, journalTitle=Science Technology and Engineering, columnName=Papers·Astronomy and Geosciences, runingTitle=null, highlight=null, articleAbstract=

Subsidence monitoring and reservoir parameters inversion in gas field can provide important supporting information for safe production protection and mining planning. The SBAS-InSAR method was used to investigate the surface subsidence evolution characteristics of Sebei gas fields from February 2022 to September 2023. Furthermore, the InSAR monitoring results were used as the observation measurements to invert the reservoir center projection coordinates, depth, strike and other parameters of the gas fields through the Prolate spheroid source. The results show that the subsidence funnel occurs in the Tainan gas field, the Sebei No.1 gas field and the Sebei No.2 gas field, and the average annual subsidence rate is -124~-109, -275~-34, and -329~-89 mm/a, respectively. Among them, the Sebei No.1 gas field and the Sebei No.2 gas field show a more significant surface subsidence phenomenon. And the surface of all three gas fields continues to sink rapidly. Further, the reservoir parameters were obtained by inversion of InSAR monitoring results. The results show that there is little difference between the deformation derived by using the optimal parameters and the observed deformation, and the spatial distribution is consistent, which indicates that it is feasible to invert the reservoir parameters of Sebei gas fields based on SBAS-InSAR deformation results.

, correspAuthors=Zong-ren LI, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Yong-lian SHA, Kun ZHANG, Zong-ren LI, Ya-ting WANG, Hui-jun QI, Na ZHANG), CN=ArticleExt(id=1149781996538393094, articleId=1149781954129785474, tenantId=1146029695717560320, journalId=1146123166801305609, language=CN, title=涩北气田地表沉降时空演化特征及储层参数反演, columnId=1156262730077163858, journalTitle=科学技术与工程, columnName=论文·天文学、地球科学, runingTitle=null, highlight=null, articleAbstract=

对气田区进行沉降监测及储层参数反演可为气田安全生产防护、开采规划提供重要支撑信息。以涩北气田区为研究对象,利用SBAS-InSAR方法调查了其于2022年2月—2023年9月期间的地表沉降演化特征,进一步以InSAR监测结果为观测量,通过长椭球模型反演了气田储层中心投影坐标、深度、走向等参数信息。结果表明,涩北气田区中台南气田、涩北1号、2号气田均出现沉降漏斗,年均沉降速率分别为-124~-109、-275~-34、-329~-89 mm/a,其中,涩北1号、2号气田表现出更为显著的地表沉降现象,而且3个气田区地表均在持续快速下沉;进一步以InSAR监测结果为观测量反演得到储层参数,结果显示,利用储层参数正演所得形变与观测形变差异较小,空间分布较为一致,表明基于SBAS-InSAR形变结果反演涩北气田储层参数具有一定的可靠性。

, correspAuthors=李宗仁, authorNote=null, correspAuthorsNote=
* 李宗仁(1987—),男,汉族,青海民和人,硕士,高级工程师。研究方向:资源与环境遥感应用。E-mail:。
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沙永莲(1994—),女,汉族,青海西宁人,硕士,工程师。研究方向:合成孔径雷达干涉测量与应用。E-mail:。

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2 Qinghai Remote Sensing Big Data Engineering Technology Research Center, Xining 810012, China
3 The Northern Qinghai-Tibet Plateau Geological Processes and Mineral Resources Laboratory, Xining 810012, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1251249361129259780, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149781954129785474, authorId=1251249360915350242, language=CN, stringName=沙永莲, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, 2, 3, address=1 青海省地质调查院, 西宁 810012
2 青海省遥感大数据工程技术研究中心, 西宁 810012
3 青藏高原北部地质过程与矿产资源重点实验室, 西宁 810012, bio={"content":"

沙永莲(1994—),女,汉族,青海西宁人,硕士,工程师。研究方向:合成孔径雷达干涉测量与应用。E-mail:。

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沙永莲(1994—),女,汉族,青海西宁人,硕士,工程师。研究方向:合成孔径雷达干涉测量与应用。E-mail:。

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tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149781954129785474, language=EN, label=Fig.3, caption=The spatial-temporal baseline combination of SBAS-InSAR, figureFileSmall=Un7TKvAYf1GO01QQD1BMCA==, figureFileBig=qxI+8RE/oJgYFFrYK546xg==, tableContent=null), ArticleFig(id=1251249366950953151, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149781954129785474, language=CN, label=图3, caption=SBAS-InSAR时空基线组合图, figureFileSmall=Un7TKvAYf1GO01QQD1BMCA==, figureFileBig=qxI+8RE/oJgYFFrYK546xg==, tableContent=null), ArticleFig(id=1251249367089365196, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149781954129785474, language=EN, label=Fig.4, caption=Annual average subsidence rate of Sebei gas fields from SBAS-InSAR, figureFileSmall=B0PtZXfj9wJeJQaarc7ZLg==, figureFileBig=KiOguFYL0b6anXVGHjr9wA==, tableContent=null), ArticleFig(id=1251249367177445592, tenantId=1146029695717560320, journalId=1146123166801305609, 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caption=Surface subsidence of Sebei No.1 gas field based on SBAS-InSAR and distribution of buildings, figureFileSmall=Lrgs1ADnQr8hW2wFwNUTYQ==, figureFileBig=MYtWSgSZ+jvRdC6qHWJYvA==, tableContent=null), ArticleFig(id=1251249369194905858, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149781954129785474, language=CN, label=图6, caption=基于SBAS-InSAR的涩北1号气田地表沉降及沉降区建筑分布情况, figureFileSmall=Lrgs1ADnQr8hW2wFwNUTYQ==, figureFileBig=MYtWSgSZ+jvRdC6qHWJYvA==, tableContent=null), ArticleFig(id=1251249369362678030, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149781954129785474, language=EN, label=Fig.7, caption=Surface subsidence of Sebei No.2 gas field based on SBAS-InSAR and distribution of buildings, figureFileSmall=BO5BMHm6gMv+XnkN8Phi6A==, figureFileBig=6EJMZq/SQyOWuWPkAan77g==, tableContent=null), ArticleFig(id=1251249369543033115, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149781954129785474, language=CN, label=图7, caption=基于SBAS-InSAR的涩北2号气田地表沉降及沉降区建筑分布情况, figureFileSmall=BO5BMHm6gMv+XnkN8Phi6A==, figureFileBig=6EJMZq/SQyOWuWPkAan77g==, tableContent=null), ArticleFig(id=1251249369668862239, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149781954129785474, language=EN, label=Fig.8, caption=Schematic diagram of Prolate spheroid source dislocation model, figureFileSmall=pJ4KgtX5dIGXVVj+KxAt3A==, figureFileBig=qFri2BZxF+DthoruHv4p5g==, tableContent=null), ArticleFig(id=1251249369803079978, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149781954129785474, language=CN, label=图8, caption=长椭球模型示意图

O为模型参考原点;M0为椭球源中心;M'0为椭球源中心在地表的投影;b为短半轴;γ=b/a为半轴间无量纲纵横比

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Prolate spheroid source parameters table

, figureFileSmall=null, figureFileBig=null, tableContent=
参数 含义
X0/m 椭球源中心X坐标
Y0/m 椭球源中心Y坐标
d/m 椭球源中心深度(向下为正)
a/m 长半轴长度
γ 半轴间无量纲纵横比,等于1时为球形
ϕ/(°) 走向,是长半轴相对于北方向的夹角
θ/(°) 倾角,是长半轴相对于水平方向的夹角
P 压力变化/剪切模量(无量纲)
), ArticleFig(id=1251249371048788372, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149781954129785474, language=CN, label=表1, caption=

长椭球模型参数表

, figureFileSmall=null, figureFileBig=null, tableContent=
参数 含义
X0/m 椭球源中心X坐标
Y0/m 椭球源中心Y坐标
d/m 椭球源中心深度(向下为正)
a/m 长半轴长度
γ 半轴间无量纲纵横比,等于1时为球形
ϕ/(°) 走向,是长半轴相对于北方向的夹角
θ/(°) 倾角,是长半轴相对于水平方向的夹角
P 压力变化/剪切模量(无量纲)
), ArticleFig(id=1251249371132674461, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149781954129785474, language=EN, label=Table 2, caption=

Optimal fitting parameters of Tainan gas field

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模型参数 最优估值 模型参数 最优估值
X0/m -8 920.11 γ 0.08
Y0/m 1 487.42 ϕ/(°) 195.46
d/m 1 928.30 θ/(°) -19.95
a/m 49.46 P -537.55
), ArticleFig(id=1251249371262697895, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149781954129785474, language=CN, label=表2, caption=

台南气田储层最优拟合参数

, figureFileSmall=null, figureFileBig=null, tableContent=
模型参数 最优估值 模型参数 最优估值
X0/m -8 920.11 γ 0.08
Y0/m 1 487.42 ϕ/(°) 195.46
d/m 1 928.30 θ/(°) -19.95
a/m 49.46 P -537.55
), ArticleFig(id=1251249371434664361, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149781954129785474, language=EN, label=Table 3, caption=

Optimal fitting parameters of Sebei No.1 gas field

, figureFileSmall=null, figureFileBig=null, tableContent=
模型参数 最优估值 模型参数 最优估值
X0/m -6 059.20 γ 0.04
Y0/m 356.31 ϕ/° 185.28
d/m 2 129.26 θ/° -48.15
a/m 104.73 P -998.76
), ArticleFig(id=1251249371560493488, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149781954129785474, language=CN, label=表3, caption=

涩北1号气田储层最优拟合参数

, figureFileSmall=null, figureFileBig=null, tableContent=
模型参数 最优估值 模型参数 最优估值
X0/m -6 059.20 γ 0.04
Y0/m 356.31 ϕ/° 185.28
d/m 2 129.26 θ/° -48.15
a/m 104.73 P -998.76
), ArticleFig(id=1251249371682128314, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149781954129785474, language=EN, label=Table 4, caption=

Optimal fitting parameters of Sebei No.2 gas field

, figureFileSmall=null, figureFileBig=null, tableContent=
模型参数 最优估值 模型参数 最优估值
X0/m 7 457.00 γ 0.03
Y0/m 650.71 ϕ/(°) 206.19
d/m 2 176.54 θ/(°) -16.51
a/m 146.93 P -790.83
), ArticleFig(id=1251249371837317574, tenantId=1146029695717560320, journalId=1146123166801305609, articleId=1149781954129785474, language=CN, label=表4, caption=

涩北2号气田储层最优拟合参数

, figureFileSmall=null, figureFileBig=null, tableContent=
模型参数 最优估值 模型参数 最优估值
X0/m 7 457.00 γ 0.03
Y0/m 650.71 ϕ/(°) 206.19
d/m 2 176.54 θ/(°) -16.51
a/m 146.93 P -790.83
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涩北气田地表沉降时空演化特征及储层参数反演
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沙永莲 1, 2, 3 , 张焜 1, 2, 3 , 李宗仁 1, 2, 3, * , 王雅婷 4 , 祁慧君 1, 2, 3 , 张娜 1, 2, 3
科学技术与工程 | 论文·天文学、地球科学 2025,25(9): 3584-3592
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科学技术与工程 | 论文·天文学、地球科学 2025, 25(9): 3584-3592
涩北气田地表沉降时空演化特征及储层参数反演
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沙永莲1, 2, 3 , 张焜1, 2, 3, 李宗仁1, 2, 3, * , 王雅婷4, 祁慧君1, 2, 3, 张娜1, 2, 3
作者信息
  • 1 青海省地质调查院, 西宁 810012
  • 2 青海省遥感大数据工程技术研究中心, 西宁 810012
  • 3 青藏高原北部地质过程与矿产资源重点实验室, 西宁 810012
  • 4 31668 部队, 西宁 810012

    • 沙永莲(1994—),女,汉族,青海西宁人,硕士,工程师。研究方向:合成孔径雷达干涉测量与应用。E-mail:。

通讯作者:

* 李宗仁(1987—),男,汉族,青海民和人,硕士,高级工程师。研究方向:资源与环境遥感应用。E-mail:。
Spatial-temporal Evolution Characteristics of Land Surface Subsidence and Reservoir Parameters Inversion in Sebei Gas Fields
Yong-lian SHA1, 2, 3 , Kun ZHANG1, 2, 3, Zong-ren LI1, 2, 3, * , Ya-ting WANG4, Hui-jun QI1, 2, 3, Na ZHANG1, 2, 3
Affiliations
  • 1 Institute of Geological Survey of Qinghai Province, Xining 810012, China
  • 2 Qinghai Remote Sensing Big Data Engineering Technology Research Center, Xining 810012, China
  • 3 The Northern Qinghai-Tibet Plateau Geological Processes and Mineral Resources Laboratory, Xining 810012, China
  • 4 31668 Troops, Xining 810012, China
出版时间: 2025-03-28 doi: 10.12404/j.issn.1671-1815.2403089
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摘要
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对气田区进行沉降监测及储层参数反演可为气田安全生产防护、开采规划提供重要支撑信息。以涩北气田区为研究对象,利用SBAS-InSAR方法调查了其于2022年2月—2023年9月期间的地表沉降演化特征,进一步以InSAR监测结果为观测量,通过长椭球模型反演了气田储层中心投影坐标、深度、走向等参数信息。结果表明,涩北气田区中台南气田、涩北1号、2号气田均出现沉降漏斗,年均沉降速率分别为-124~-109、-275~-34、-329~-89 mm/a,其中,涩北1号、2号气田表现出更为显著的地表沉降现象,而且3个气田区地表均在持续快速下沉;进一步以InSAR监测结果为观测量反演得到储层参数,结果显示,利用储层参数正演所得形变与观测形变差异较小,空间分布较为一致,表明基于SBAS-InSAR形变结果反演涩北气田储层参数具有一定的可靠性。

涩北气田  /  SBAS-InSAR  /  地表沉降  /  参数反演

Subsidence monitoring and reservoir parameters inversion in gas field can provide important supporting information for safe production protection and mining planning. The SBAS-InSAR method was used to investigate the surface subsidence evolution characteristics of Sebei gas fields from February 2022 to September 2023. Furthermore, the InSAR monitoring results were used as the observation measurements to invert the reservoir center projection coordinates, depth, strike and other parameters of the gas fields through the Prolate spheroid source. The results show that the subsidence funnel occurs in the Tainan gas field, the Sebei No.1 gas field and the Sebei No.2 gas field, and the average annual subsidence rate is -124~-109, -275~-34, and -329~-89 mm/a, respectively. Among them, the Sebei No.1 gas field and the Sebei No.2 gas field show a more significant surface subsidence phenomenon. And the surface of all three gas fields continues to sink rapidly. Further, the reservoir parameters were obtained by inversion of InSAR monitoring results. The results show that there is little difference between the deformation derived by using the optimal parameters and the observed deformation, and the spatial distribution is consistent, which indicates that it is feasible to invert the reservoir parameters of Sebei gas fields based on SBAS-InSAR deformation results.

Sebei gas fields  /  SBAS-InSAR  /  surface subsidence  /  parameters inversion
沙永莲, 张焜, 李宗仁, 王雅婷, 祁慧君, 张娜. 涩北气田地表沉降时空演化特征及储层参数反演. 科学技术与工程, 2025 , 25 (9) : 3584 -3592 . DOI: 10.12404/j.issn.1671-1815.2403089
Yong-lian SHA, Kun ZHANG, Zong-ren LI, Ya-ting WANG, Hui-jun QI, Na ZHANG. Spatial-temporal Evolution Characteristics of Land Surface Subsidence and Reservoir Parameters Inversion in Sebei Gas Fields[J]. Science Technology and Engineering, 2025 , 25 (9) : 3584 -3592 . DOI: 10.12404/j.issn.1671-1815.2403089
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涩北气田是中国陆上大气区之一,约占青海气区已探明储量的90%,是保障青藏高原居民用气需求的主要供气基地[1-2]。而气田长期持续性开采会导致地表层破坏、地下压力减弱等,从而引发地表沉降,严重时会对地表建筑及地下设备安全性产生威胁。因此,定期监测气田区地表稳定性并根据监测结果采取防治措施是十分必要的。当前,合成孔径雷达干涉测量(interferometric synthetic aperture radar, InSAR)技术具有全天候、覆盖范围广、空间分辨率高等优势,完全满足气田区地表周期性监测要求,对提取气田区大范围区域性形变具有独特优势,其中,小基线集(small baseline subset, SBAS)InSAR技术利用多个干涉对进行干涉相位建模,可以有效抑制形变提取过程中的误差项,从而获取毫米级精度的地表形变信息,目前也得到了广泛应用。陈志谋等[3]利用SBAS-InSAR技术对国内某油田开采时引起的地表沉降进行监测,综合分析其地表形变速率、范围以及地面形变影响因素。张静等[4]采用SBAS-InSAR技术对地面沉降监测数据进行时序分析,发现累积沉降量和沉降区范围均随着时间不断增大,且地面沉降与地下水开采、油气资源开采、新构造运动等多种因素具有密切关系。龚志强等[5]通过将相干性信息作为权重融入SBAS-InSAR技术获取了辽河三角洲地区2017—2020年地表形变信息。
此外,气田的区域性沉降是储层压强变化、形状变化等在地表的直观体现,反映出地下储层结构发生了一定变化,以遥感监测所得地表形变为约束,利用非线性反演方法,结合相关模型反演气田储层参数,有利于快速掌握地下储层结构,也可为获取开采量信息及下一步开采计划提供参考。自2001年Xu等[6]使用InSAR技术对加利福尼亚州南部Lost Hills油田进行监测,并证明了InSAR监测油田形变进而计算油气产量的可行性后,这方面的研究逐渐受到关注,国内研究起步较晚,近几年才有越来越多的学者研究油气田储层反演。张东晓[7]对新疆克拉玛依油田区采用Mogi点源模型和Okada位错模型分析了油气田区域性形变和油气注采活动的相关性。李春进等[8]以曙光采油厂为研究对象,使用双椭球模型和双Okada模型对油田的储层参数反演展开研究,并对比分析两种双源模型反演结果的可靠性。龚志强等[5]对曙光采油厂使用储层压实沉降模型进行反演,表明该模型能较好地反演油田沉降中心最大沉降量位置。Davis[9]在1988年提出长椭球模型,是对原本用于火山反演的Mogi点源模型的拓展,更有利于解释地下流体变化导致的地表形变,因此,现以涩北气田为研究区,使用可获取毫米级精度的SBAS-InSAR方法,提取地表形变作为反演观测数据,基于非线性贝叶斯反演方法,使用长椭球模型进行气田储层参数的反演,快速获取了涩北气田储层结构相关参数,为涩北气田储层结构的快速掌握提供一定参考意义。
1 研究区域及数据源
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1.1 研究区概况
涩北气田包括涩北1号、2号和台南气田,位于青海省柴达木盆地,由东向西分布于东台吉乃尔盐湖南部,北部为G315国道,其地理位置如图1所示,研究区海拔2 600~2 830 m。根据已有资料显示,涩北气田所在区域为高原干旱荒漠,主要为盐碱地,年均气温3.56 ℃,年降水量30.24 mm,储层岩石以泥质粉砂岩为主,其中,台南气田构造平缓,气藏埋深800~2 000 m[10-11]。
1.2 数据源
选取25景Sentinel-1A降轨SAR数据对研究区域进行SBAS-InSAR形变监测分析,影像获取时间为2022年2月18日—2023年9月17日,每月至少一景影像。经过裁剪后监测区域面积约为3 543.00 km2,SAR影像空间分辨率约为13.95 m(方位向)×2.33 m(距离向),入射角为33.74°。在干涉处理过程中使用精密轨道数据进行轨道校正,使用高级陆地观测卫星(advanced land observation satellite, ALOS) 12.5 m数字高程模型(digital elevation model, DEM)去除地形相位,并对干涉图进行地理编码。
2 涩北气田形变场提取及分析
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2.1 SBAS-InSAR处理方法
SBAS-InSAR方法通过组合多主影像形成的干涉对,基于短时空基线准则,一般利用最小二乘方法计算地表时序形变和平均速率[12-13]。在一定程度上有效克服了传统差分干涉测量技术(differential InSAR, DInSAR)、基于单主影像的时序InSAR方法中易出现时空失相干的缺陷[14]。该方法利用覆盖同一区域的N+1幅SAR影像,通过设置合适的时空基线阈值组成不同的干涉对,并进行差分干涉处理,干涉图中某像元干涉相位[15-16]可表示为
δϕj=ϕ(tB)-ϕ(tA)≈δ${\varphi }_{j}^{\mathrm{d}\mathrm{e}\mathrm{f}}$+δ${\varphi }_{j}^{\mathrm{t}\mathrm{o}\mathrm{p}\mathrm{o}}$+δ${\varphi }_{j}^{\mathrm{a}\mathrm{t}\mathrm{m}}$+δ${\varphi }_{j}^{\mathrm{n}\mathrm{o}\mathrm{i}\mathrm{s}\mathrm{e}}$
式(1)中:δϕj为像元干涉相位;ϕ(tA)、ϕ(tB)为tA和tB时刻的相位;δ${\varphi }_{j}^{\mathrm{d}\mathrm{e}\mathrm{f}}$为视线向地表形变;δ${\varphi }_{j}^{\mathrm{t}\mathrm{o}\mathrm{p}\mathrm{o}}$为地形相位;δ${\varphi }_{j}^{\mathrm{a}\mathrm{t}\mathrm{m}}$为大气相位;δ${\varphi }_{j}^{\mathrm{n}\mathrm{o}\mathrm{i}\mathrm{s}\mathrm{e}}$为噪声相位。通过奇异值分解法(singular value decomposition, SVD)求解所得干涉图集合,从而获得整个观测期间的地表时间序列形变信息,具体流程如图2所示。在SAR数据处理过程中,临界垂直基线和时间基线分别是130 m和110 d,共组成干涉对66个,图3展示了所有干涉图基线组合情况。
2.2 形变结果分析
由于SAR卫星测视成像,利用SBAS-InSAR测量得到的是沿卫星视线方向(light of sight,LOS)的地表形变。考虑到这里的变形主要表现在垂直向上,为更好地体现气田区变形特征,根据雷达波入射角将LOS向形变转换至垂直向,正值表示抬升,负值表示沉降。图4展示了研究区地表沉降现状,整体来看,涩北气田区地表沉降分布不均匀,台南气田、涩北1号、2号气田区出现明显的沉降区域,并且涩北1号、2号气田地表沉降相对严重,其他区域地表稳定性较好,这也说明气田开采可能是造成这些区域出现快速沉降的主要原因。
2.2.1 台南气田
图5(a)展示了利用SBAS-InSAR测量的台南气田区在2022年2月18日—2023年9月17日期间的年均沉降速率结果,整体年均沉降速率为-124~-109 mm/a。从监测结果来看,台南气田开采区呈现出一个明显的沉降漏斗,即从沉降区边缘至中心A(93.78°E,37.38°N)沉降速率逐渐增加,整个沉降区表现为中心速率大、边缘速率小的“漏斗”状,沉降区东西向约4.50 km,南北向约3.60 km,面积约为13.69 km2,且沉降中心A紧邻地表建筑[图5(b)],200.00 m范围内分布有多处建筑,最近的建筑区仅70.00 m,地表沉降会对建筑安全产生一定的威胁。
为进一步了解台南气田在这段时间的地表沉降演化特征,提取沉降中心A点100 m半径内的特征点沉降时间序列结果[图5(c)],发现2022年2月18日—2023年9月17日期间该区域呈现持续性快速沉降,整体呈线性形变趋势,累计沉降量超过150 mm,该区域地表沉降情况需持续关注。
2.2.2 涩北1号气田
涩北1号气田区年均沉降速率结果如图6(a)所示,整体年均沉降速率为-275~-34 mm/a。从监测结果来看,涩北1号气田区同样呈现出沉降漏斗,沉降区东西向约8.20 km,南北向约5.10 km,沉降区面积约为30.35 km2,沉降中心B(94.30°E,37.30°N) 2.00 km范围内有多处地表建筑群[图6(b)],地表快速沉降会对建筑安全产生一定的威胁。
提取沉降漏斗中心B处100 m范围内的特征点时序曲线,如图6(c)所示,2022年2月18日—2023年9月17日期间涩北1号气田开采区地表沉降严重,从图6中红色拟合曲线趋势可以看出,该区域呈明显线性形变趋势且在持续性快速沉降,累计沉降量超过400 mm,该区域地表沉降现象需要重点关注。
2.2.3 涩北2号气田
涩北2号气田区年均沉降速率结果如图7(a)所示,整体年均沉降速率为-329~-89 mm/a。涩北2号气田沉降漏斗东西向约6.50 km,南北向约5.30 km,沉降区面积约为25.81 km2。提取沉降漏斗中心C(94.14°E,37.36°N)处100 m范围内的特征点时序曲线,如图7(d)所示, 2022年2月18日—2023年9月17日期间涩北2号气田区累计沉降量超过500 mm,地表沉降严重,图7中红色拟合曲线表明该区域呈明显线性形变趋势且在持续性快速沉降。沉降漏斗中心C处3.00 km范围内有多处地表建筑[图7(b)],东南方1.70 km处是一较大的生活区[图7(c)]。沉降区内有大量建筑生活区,在如此快速持续的沉降下,会造成一定的安全隐患,需持续关注该区域地表稳定性。
3 涩北气田储层参数反演
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3.1 反演模型及方法
Davis[9]在1988年对弹性半空间有限维数内任意向的长球形腔体模型的解析计算式进行了推演计算,并提出了长椭球模型。相较于4参数组成的Mogi点源模型(点源中心三维坐标和点源球形半径),该模型由8参数组成(表1),进一步拓展了Mogi点源模型的适用性,其中,走向ϕ为0°/360°时表示北方向,90°为东向,180°为南向,270°为西向;倾角θ为0°表示水平向,90°为垂向[17]。模型示意图如图8所示。
基于长椭球模型,根据模型观测数据要求,以SBAS-InSAR形变信息作为反演的观测数据,以贝叶斯反演方法为理论基础来分析涩北气田储层深度、走向、倾角等参数。贝叶斯反演方法是基于统计理论的随机反演算法,结合马尔科夫链条蒙特卡洛(Markov chain Monte Carlo method,MCMC)采样方法和 Metropolis-Hastings法则计算每个参数概率密度函数的估计值,并获得模型参数的最优解[18-19]。贝叶斯框架可表示为
p(m|d)=$\frac{p\left(d\right|m\left)p\right(m)}{p\left(d\right)}$
式(2)中:p(m|d)为模型参数m={m1,m2,…,mn}的后验概率密度函数;p(d|m)为似然函数,表示在已知观测数据d={d1,d2,…,dn}的情况下随着模型参数m变化的函数;p(m)为m的先验概率密度函数;p(d)为一常量信息。具体贝叶斯反演流程如图9所示。
3.2 反演结果分析
3.2.1 台南气田
基于长椭球模型,以SBAS-InSAR测量的地表平均形变速率为约束,在无形变区选取OA(93.88° E,37.37° N)作为模型参考原点,对台南气田储层参数进行反演,通过后验贝叶斯反演方法获取的台南气田储层最优参数如表2所示。图10分别展示了InSAR观测形变、使用最优参数组合正演所得模拟形变及其与观测形变之间的残差。平面坐标点正负值表示为东正西负,北正南负,都是指相对于坐标原点的距离。从表2来看,台南气田储层中心点投影至地表的平面点M'A位于地表坐标原点OA的西北部,即8 920.11 m西,1 487.42 m北,根据该距离、方向及OA位置推算,可知投影点M'A的地理坐标位置(93.78°E,37.37°N),发现其与沉降中心A(93.78°E,37.38°N)十分接近,较符合开采情况,储层深度约1 928.30 m,倾角约-19.95°,与台南气田气藏埋深800~2 000 m,构造平缓等情况基本相符,储层走向与北方向夹角约195.46°,储层长半轴约49.46 m,半轴间无量纲纵横比为0.08。
由图10可知,从空间分布来看,长椭球模型模拟的形变结果与观测结果空间分布特征十分一致,且InSAR观测结果和模拟值两者的残差基本在零值附近,进一步说明反演所得台南气田储层参数较为可靠。
3.2.2 涩北1号气田
基于长椭球模型,在SBAS-InSAR地表平均形变速率的约束下,以OB(94.36° E,37.29° N)作为参考原点,对涩北1号气田储层参数进行反演,获取的涩北1号气田储层最优参数如表3所示。涩北1号气田储层中心点投影至地表的平面点M'B相对于地表坐标原点OB为 6 059.20 m 西,356.31 m 北,即可知投影点M'B(94.29° E,37.29° N)与沉降中心B(94.30° E,37.30° N)同样十分接近,符合开采情况,储层深度约2 129.26 m,储层走向与北方向夹角约185.28°,储层长半轴约104.73 m,半轴间无量纲纵横比为0.04,相对于水平面倾角约-48.15°,储层倾角较大。
图11分别展示了InSAR观测形变、使用最优参数组合正演所得模拟形变及其与观测形变之间的残差。InSAR观测结果与长椭球模型模拟的形变结果空间分布较为一致,二者残差主要位于北部沉降区边缘,沉降区整体残差在零值附近,反演所得涩北1号气田储层参数是较为可靠的。
3.2.3 涩北2号气田
基于长椭球模型,以SBAS-InSAR测量的地表平均形变速率为约束,在无形变区选取OC(94.05° E,37.35° N)作为模型参考原点,对涩北2号气田储层参数进行反演,通过后验贝叶斯反演方法获取的涩北2号气田储层最优参数如表4所示。涩北2号气田储层中心点投影点M'C在参考原点OC的东北部,即为7 457.00 m东,650.71 m北,即可知涩北2号气田储层中心点在地表的投影点M'C(94.13° E,37.36° N)与沉降中心C(94.14° E,37.36° N)也是十分接近,符合开采情况,储层深度约2 176.54 m,储层走向与北方向夹角约206.19°,储层长半轴约146.93 m,半轴间无量纲纵横比为0.03,倾角约-16.51°,较为平缓。
图12分别展示了InSAR观测形变、使用最优参数组合正演所得模拟形变及其与观测形变之间的残差。从空间分布来看,长椭球模型模拟的形变结果与观测结果空间分布特征较为一致,残差主要分布于沉降区东部及南部边缘区,残差较小,说明反演所得台南气田储层参数较为可靠。
4 结论
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以2022年2月18日—2023年9月17日间的25景Sentinel-1A降轨SAR数据为数据源,利用SBAS时序InSAR方法对涩北气田区进行了地表沉降监测,并以监测结果作为反演观测量,基于长椭球模型为对涩北气田区进行储层参数反演,得出结论如下。
(1)台南气田、涩北1号、2号气田均出现显著的沉降漏斗,其沉降趋势呈现为持续快速线性沉降,并且涩北1号、2号气田地表沉降相对更加严重。其中,台南气田整体年均沉降速率为-124~-109 mm/a,累计沉降量超过150 mm;涩北1号气田区年均沉降速率为-275~-34 mm/a,累计沉降量超过400 mm;涩北2号气田区年均沉降速率为-329~-89 mm/a,累计沉降量超过500 mm。
(2)通过长椭球模型反演了台南气田、涩北1号、2号气田储层中心投影坐标、深度、走向等相关参数信息,并将最优参数正演所得形变与观测形变比较后,发现二者残差较小,空间分布较为一致,表明了基于SBAS时序InSAR观测形变,结合长椭球模型反演涩北气田储层参数具有一定的可行性。
基金
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  • 青海省地矿局计划(2024-24-4)
文献
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2025年第25卷第9期
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doi: 10.12404/j.issn.1671-1815.2403089
  • 接收时间：2024-04-26
  • 首发时间：2025-07-09
  • 出版时间：2025-03-28
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  • 收稿日期：2024-04-26
  • 修回日期：2024-12-15
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青海省地矿局计划(2024-24-4)
作者信息
    1 青海省地质调查院, 西宁 810012
    2 青海省遥感大数据工程技术研究中心, 西宁 810012
    3 青藏高原北部地质过程与矿产资源重点实验室, 西宁 810012
    4 31668 部队, 西宁 810012

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* 李宗仁(1987—),男,汉族,青海民和人,硕士,高级工程师。研究方向:资源与环境遥感应用。E-mail:。
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2种不同金属材料的力学参数
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Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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