Article(id=1322964765341004273, tenantId=1146029695717560320, journalId=1322258508890079423, issueId=1322964647376191536, articleNumber=null, orderNo=null, doi=10.13809/j.cnki.cn32-1825/te.2025357, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1753718400000, receivedDateStr=2025-07-29, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793348979554, onlineDateStr=2026-10-30, pubDate=1769356800000, pubDateStr=2026-01-26, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793348979554, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793348979554, creator=13701087609, updateTime=1793348979554, updator=13701087609, issue=Issue{id=1322964647376191536, tenantId=1146029695717560320, journalId=1322258508890079423, year='2026', volume='16', issue='1', pageStart='1', pageEnd='224', issueExtLink='null', onlineDate='null', pubDate='1769356800000', pubDateStr='2026-01-26', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793348951428, creator='13701087609', updateTime=1793349012150, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322964902113063080, tenantId=1146029695717560320, journalId=1322258508890079423, issueId=1322964647376191536, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322964902113063081, tenantId=1146029695717560320, journalId=1322258508890079423, issueId=1322964647376191536, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=141, endPage=152, ext={EN=ArticleExt(id=1322964767480099317, articleId=1322964765341004273, tenantId=1146029695717560320, journalId=1322258508890079423, language=EN, title=CO2 storage potential assessment models and their practical progress in oil and gas reservoirs, columnId=1322964766146310643, journalTitle=Petroleum Reservoir Evaluation and Development, columnName=Field Application, runingTitle=null, highlight=

Quantitative calculation of CO2 geological storage potential is a crucial component of preliminary storage site suitability assessment and subsequent quantitative verification. Oil and gas reservoirs are preferred geological formations for CO2 storage, as they combine the economic benefits of enhanced recovery and the environmental benefits of carbon storage. Among them, conventional (depleted) oil and gas reservoirs with well-developed trap structures form the foundation for the development and application of classical carbon storage potential assessment models. However, models that account for different reservoir conditions, different project stages, and fully consider the contributions of various storage mechanisms still require further advancement. This paper systematically reviews the CO2 storage mechanisms and storage potential assessment methods for oil and gas reservoirs, and analyzes and summarizes the development history and application practices of these models. In terms of storage mechanisms, oil reservoirs mainly involve structural, residual, solubility, and mineral trapping, whereas gas reservoirs rely on the synergistic effects of pressure replenishment, competitive adsorption, and gravitational segregation. Twenty mainstream potential assessment models are categorized into four stages based on their fundamental principles: models considering geological reserves and recovery factors, models considering available reservoir pore space for storage, improved and extended models derived from the original models, and models focusing on new perspectives and localized approaches. Current models still primarily emphasize structural and residual trapping, with only a few quantifying solubility and mineralization contributions. Refinement of input parameters and uncertainty quantification have become development trends. Further research is needed on miscibility pressure prediction, solubility calculation, mineralization reaction kinetics, and the spatiotemporal evolution mechanisms of competitive gas adsorption. In the future, integrating geological monitoring data and artificial intelligence technologies and developing lightweight assessment tools will be essential to support site-level engineering decisions and advance the transition of storage potential assessment from theoretical feasibility to engineering implementation.

, articleAbstract=null, authors=JINGWEI LI1, 2, BO PENG1, 2, ZETENG WANG1, 2, XIAOQIAN CHEN1, 2, ZHENGHAO ZHANG1, 2, JINDONG LIU1, 2, SHUANGXING LIU3, XIAOFENG LI4, authorsList=JINGWEI LI, BO PENG, ZETENG WANG, XIAOQIAN CHEN, ZHENGHAO ZHANG, JINDONG LIU, SHUANGXING LIU, XIAOFENG LI, authorCompany=null, correspAuthors=BO PENG, 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, fund=null), CN=ArticleExt(id=1322964774300037634, articleId=1322964765341004273, tenantId=1146029695717560320, journalId=1322258508890079423, language=CN, title=油气藏CO2封存潜力评估模型与实践进展, columnId=1322964768327348727, journalTitle=油气藏评价与开发, columnName=矿场应用, runingTitle=null, highlight=

CO2地质封存潜力的定量计算是前期封存选址适宜性评估和后期量化核查的重要组成部分。油气藏作为封存的优选地质体,兼具提高采收率经济效益与CO2封存环境效益。其中,具有良好圈闭结构的常规(枯竭)油气藏是经典碳封存潜力评估模型发展和应用的基础。而针对不同油藏条件、项目不同阶段场景以及充分考虑各种封存机制贡献的潜力评估模型仍需进一步更新。系统综述了油气藏CO2封存机制及封存潜力的评估方法,并对模型的发展历程与应用实践进行分析总结。在封存机制方面,油藏以构造封存、残余封存、溶解封存和矿化封存为主;气藏则依赖压力补充、竞争吸附和重力分异协同作用。该研究梳理了20种主流潜力评估模型,根据模型基本原理将其划分为考虑地质储量与采收率、考虑封存可用储层孔隙空间、原始模型改进与拓展、新角度与新方法4个阶段。目前,模型仍以构造和残余封存为出发点,仅少数量化溶解与矿化贡献;输入参数精细化与不确定性量化成发展趋势;混相压力预测、溶解度计算、矿化反应动力学和气体竞争吸附的时空间演化机制仍需进一步探究。未来需耦合地质监测数据与人工智能技术并开发轻量化评估工具,支撑场地级工程决策,推动封存潜力评估从“理论可行”迈向“工程落地”。

, articleAbstract=null, authors=李经纬1, 2, 彭勃1, 2, 王泽滕1, 2, 陈晓倩1, 2, 张正昊1, 2, 刘金栋1, 2, 刘双星3, 李晓枫4, authorsList=李经纬, 彭勃, 王泽滕, 陈晓倩, 张正昊, 刘金栋, 刘双星, 李晓枫, authorCompany=null, correspAuthors=彭勃, authorNote=

李经纬(1994—),男,在读博士研究生,主要从事分子模拟及CO2封存潜力评估研究。地址:北京市昌平区府学路18号,邮政编码:102249。E-mail:

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彭勃(1969—),男,博士,教授,主要从事碳捕集、利用与封存和油气田化学与工程研究。地址:北京市昌平区府学路18号,邮政编码:102249。E-mail:
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李经纬(1994—),男,在读博士研究生,主要从事分子模拟及CO2封存潜力评估研究。地址:北京市昌平区府学路18号,邮政编码:102249。E-mail:

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李经纬(1994—),男,在读博士研究生,主要从事分子模拟及CO2封存潜力评估研究。地址:北京市昌平区府学路18号,邮政编码:102249。E-mail:

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Petroleum Reservoir Evaluation and Development, 2025, 15(3): 500-507., articleTitle=Optimization of CO2 water-alternating-gas injection parameters based on an improved hunger game search algorithm, refAbstract=null), Reference(id=1322964796299162299, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, doi=null, pmid=null, pmcid=null, year=2025, volume=53, issue=6, pageStart=130, pageEnd=140, url=null, language=null, rfNumber=69, rfOrder=98, authorNames=吴华, 王小琼, 葛洪魁, journalName=石油机械, refType=null, unstructuredReference=吴华, 王小琼, 葛洪魁, 等. 超临界CO2对页岩断裂裂缝形态的影响[J]. 石油机械, 2025, 53(6): 130-140., articleTitle=超临界CO2对页岩断裂裂缝形态的影响, refAbstract=null), Reference(id=1322964796387242684, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, doi=null, pmid=null, pmcid=null, year=2025, volume=53, issue=6, pageStart=130, pageEnd=140, url=null, language=null, rfNumber=69, rfOrder=99, authorNames=WU Hua, WANG Xiaoqiong, GE Hongkui, journalName=China Petroleum Machinery, refType=null, unstructuredReference=WU Hua, WANG Xiaoqiong, GE Hongkui, et al. Experimental study on the influence of supercritical CO2 on shale fracture morphology[J]. China Petroleum Machinery, 2025, 53(6): 130-140., articleTitle=Experimental study on the influence of supercritical CO2 on shale fracture morphology, refAbstract=null), Reference(id=1322964796496294589, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, doi=null, pmid=null, pmcid=null, year=2018, volume=75, issue=null, pageStart=180, pageEnd=188, url=null, language=null, rfNumber=70, rfOrder=100, authorNames=SANGUINITO S, GOODMAN A L, SAMS J I, journalName=International Journal of Greenhouse Gas Control, refType=null, unstructuredReference=SANGUINITO S, GOODMAN A L, SAMS J I. CO2-screen tool: Application to the oriskany sandstone to estimate prospective CO2 storage resource[J]. International Journal of Greenhouse Gas Control, 2018, 75: 180-188., articleTitle=CO2-screen tool: Application to the oriskany sandstone to estimate prospective CO2 storage resource, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1322964774954349059, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, xref=1, ext=[AuthorCompanyExt(id=1322964774971126276, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, companyId=1322964774954349059, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1 State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing 102249, China), AuthorCompanyExt(id=1322964774987903493, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, companyId=1322964774954349059, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1 中国石油大学(北京)油气资源与工程全国重点实验室,北京   102249)]), AuthorCompany(id=1322964776275554822, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, xref=2, ext=[AuthorCompanyExt(id=1322964776288137735, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, companyId=1322964776275554822, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2 Beijing Key Laboratory for Greenhouse Gas Storage and Enhanced Oil Recovery, China University of Petroleum (Beijing), Beijing 102249, China), AuthorCompanyExt(id=1322964776300720648, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, companyId=1322964776275554822, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2 中国石油大学(北京)温室气体封存与石油开采利用北京市重点实验室,北京   102249)]), AuthorCompany(id=1322964776380412425, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, xref=3, ext=[AuthorCompanyExt(id=1322964776388801034, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, companyId=1322964776380412425, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3 CNPC Research Institute of Safety & Environment Technology, Beijing 102206, China), AuthorCompanyExt(id=1322964776409772555, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, companyId=1322964776380412425, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3 中国石油安全环保技术研究院有限公司,北京   102206)]), AuthorCompany(id=1322964776636264972, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, xref=4, ext=[AuthorCompanyExt(id=1322964776648847885, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, companyId=1322964776636264972, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=4 PipeChina Institute of Science and Technology, Tianjin 300450, China), AuthorCompanyExt(id=1322964776657236494, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, companyId=1322964776636264972, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=4 国家石油天然气管网集团有限公司科学技术研究总院分公司,天津   300450)])], figs=[ArticleFig(id=1322964782055305799, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, language=EN, label=Fig.1, caption=Schematic diagram of development history of storage potential assessment methods for oil and gas reservoirs[6,14,18-38], figureFileSmall=+UHVb86e+wu9ugllZmAMCA==, figureFileBig=0A3A4ICOuXGaNc71PvgMPQ==, tableContent=null), ArticleFig(id=1322964782172746312, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, language=CN, label=图1, caption=油气藏封存潜力评估方法发展历程示意图[6,14,18-38]

注:ECL Tech.为英国能源承包商科技公司;IEAGHG为国际能源署温室气体研究与开发计划机构;USDOE为美国能源部;CGSS为CO2地质封存解决方案;USGS为美国地质调查局。

, figureFileSmall=+UHVb86e+wu9ugllZmAMCA==, figureFileBig=0A3A4ICOuXGaNc71PvgMPQ==, tableContent=null), ArticleFig(id=1322964782382461513, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, language=EN, label=Fig.2, caption=Summary of CO2 storage potential assessment cases in oil and gas reservoirs[20,22,25-33,35-59], figureFileSmall=XKtoChaJg/C21z4PvOW6Nw==, figureFileBig=jfl/GVp2MDx4QDDwt+jTwA==, tableContent=null), ArticleFig(id=1322964782466347594, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, language=CN, label=图2, caption=油气藏CO2封存潜力评估实例汇总[20,22,25-33,35-59], figureFileSmall=XKtoChaJg/C21z4PvOW6Nw==, figureFileBig=jfl/GVp2MDx4QDDwt+jTwA==, tableContent=null), ArticleFig(id=1322964782541845067, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, language=EN, label=Fig.3, caption=Time dependence of contributions by different storage mechanisms, figureFileSmall=wHkiyUxuEr/mR3cJDk1o7A==, figureFileBig=KhtKByuM4d6UdXZRmiXI8A==, tableContent=null), ArticleFig(id=1322964782625731148, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, language=CN, label=图3, caption=封存机制贡献的时间依赖性, figureFileSmall=wHkiyUxuEr/mR3cJDk1o7A==, figureFileBig=KhtKByuM4d6UdXZRmiXI8A==, tableContent=null), ArticleFig(id=1322964782705422925, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, language=EN, label=Fig.4, caption=Parallel coordinate chart for feature classification of CO2 storage potential assessment models[6,14,18-38], figureFileSmall=AhLEqDTrnT/Jan5PVs/aHQ==, figureFileBig=nuH3xpocVSMW0TRSKtbJnA==, tableContent=null), ArticleFig(id=1322964782772531790, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, language=CN, label=图4, caption=CO2封存潜力评估模型特征分类平行坐标[6,14,18-38], figureFileSmall=AhLEqDTrnT/Jan5PVs/aHQ==, figureFileBig=nuH3xpocVSMW0TRSKtbJnA==, tableContent=null), ArticleFig(id=1322964782835446351, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, language=EN, label=Fig.5, caption=Scoring grid diagram of storage potential assessment models, figureFileSmall=Ej30NdWIEiLdjXepY0vzSQ==, figureFileBig=EKhQp0192LUgtJnxBDuiTQ==, tableContent=null), ArticleFig(id=1322964782902555216, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, language=CN, label=图5, caption=封存潜力评估模型赋分网格, figureFileSmall=Ej30NdWIEiLdjXepY0vzSQ==, figureFileBig=EKhQp0192LUgtJnxBDuiTQ==, tableContent=null), ArticleFig(id=1322964782973858385, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, language=EN, label=Table 1, caption=Summary of storage potential assessment models based on material balance principle[18-22], figureFileSmall=null, figureFileBig=null, tableContent=
模型编号计算原理参数含义
1

EXT=5.3%(API≤31)(1.3API-35)%(31<API<41)18.3%(API≥41

驱油情景:MCO2=VOOIPCEXTRCO2

废弃情景:MCO2=VOOIPRFoFVFoρres,CO2

EXT为额外提高采收率比例,%;MCO2为封存量,单位t;VOOIP为原油地质储量,单位m3;C为CO2原油接触比例,取值75%;RCO2为换油率,单位t/t;RFo为废弃油藏的采收率,%;FVFo为原油体积系数,单位地下桶/地面桶;ρres,CO2为储层温压下CO2密度,单位kg/m3
2

MCO2=0.75aVOGIPRfRres,CO2CH4ρstd,CO2

Rres,CO2CH4=2×10-7D2-0.001 5D+4.170 7

VOGIP为原始天然气地质储量,单位m3;a为校正系数,取值1.0;Rf为采收率,%;Rres,CO2CH4为气藏条件CO2/CH4摩尔比;ρstd,CO2为标况CO2密度,单位kg/m3,取值1.98kg/m3;D为气藏埋深,单位m
3

CO2突破:MCO2=ρres,CO2RBTVOOIP/Sh

注入HCPV:MCO2=ρres,CO2RBT+0.6RHCPV-            RBTVOOIP/Sh

RBT为CO2突破时采收率,%;Sh为原油收缩率;RHCPV为注入HCPV时采收率,%
4

WAG:Vnet,CO2=RWAGEWAGVOOIPEα,CO2Bo/Bg

GSGI:Vnet,CO2=RGSGIEGSGIVOOIP0.7Bo/Bg

Vnet,CO2为单位CO2封存量,单位m3;RWAG为水气交替驱目标采收率增量,%;EWAG为WAG实施程度(0~1);Eα,CO2为CO2利用效率(1~2);Bo和Bg分别为石油、天然气形成系数;RGSGI为重力稳定气驱目标采收率增量,%;EGSGI为GSGI实施程度(0~1)
5 MCO2=Vstd,CH4Bfρres,CO2E Vstd,CH4为标况下CH4最终可采储量,单位m3;Bf为储层体积系数,取值0.05;E为封存效率因子,取值0.75
), ArticleFig(id=1322964783057744466, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, language=CN, label=表1, caption=

基于物质平衡法的封存潜力评估模型汇总[18-22]

, figureFileSmall=null, figureFileBig=null, tableContent=
模型编号计算原理参数含义
1

EXT=5.3%(API≤31)(1.3API-35)%(31<API<41)18.3%(API≥41

驱油情景:MCO2=VOOIPCEXTRCO2

废弃情景:MCO2=VOOIPRFoFVFoρres,CO2

EXT为额外提高采收率比例,%;MCO2为封存量,单位t;VOOIP为原油地质储量,单位m3;C为CO2原油接触比例,取值75%;RCO2为换油率,单位t/t;RFo为废弃油藏的采收率,%;FVFo为原油体积系数,单位地下桶/地面桶;ρres,CO2为储层温压下CO2密度,单位kg/m3
2

MCO2=0.75aVOGIPRfRres,CO2CH4ρstd,CO2

Rres,CO2CH4=2×10-7D2-0.001 5D+4.170 7

VOGIP为原始天然气地质储量,单位m3;a为校正系数,取值1.0;Rf为采收率,%;Rres,CO2CH4为气藏条件CO2/CH4摩尔比;ρstd,CO2为标况CO2密度,单位kg/m3,取值1.98kg/m3;D为气藏埋深,单位m
3

CO2突破:MCO2=ρres,CO2RBTVOOIP/Sh

注入HCPV:MCO2=ρres,CO2RBT+0.6RHCPV-            RBTVOOIP/Sh

RBT为CO2突破时采收率,%;Sh为原油收缩率;RHCPV为注入HCPV时采收率,%
4

WAG:Vnet,CO2=RWAGEWAGVOOIPEα,CO2Bo/Bg

GSGI:Vnet,CO2=RGSGIEGSGIVOOIP0.7Bo/Bg

Vnet,CO2为单位CO2封存量,单位m3;RWAG为水气交替驱目标采收率增量,%;EWAG为WAG实施程度(0~1);Eα,CO2为CO2利用效率(1~2);Bo和Bg分别为石油、天然气形成系数;RGSGI为重力稳定气驱目标采收率增量,%;EGSGI为GSGI实施程度(0~1)
5 MCO2=Vstd,CH4Bfρres,CO2E Vstd,CH4为标况下CH4最终可采储量,单位m3;Bf为储层体积系数,取值0.05;E为封存效率因子,取值0.75
), ArticleFig(id=1322964783145824851, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, language=EN, label=Table 2, caption=Summary of sequestration potential assessment models based on effective volume principle[6,23-26], figureFileSmall=null, figureFileBig=null, tableContent=
模型编号计算原理参数含义
6

气藏:MCO2=ρres,CO2Rf1-FIGVOGIP[psZrTr/prZsTs]

油藏:MCO2=ρres,CO2(RfVOOIP/Bf-Viw+Vpw)

MCO2=ρres,CO2[RfAhφ1-Sw-Viw+Vpw]

Me,CO2=CmCbChCwCaMCO2=CeMCO2

FIG为注入气体体积分数; p为压力,单位Pa;Z为气体压缩系数;T为温度,单位℃;下标r为储层条件;下标s为地表条件;Bf为储层体积系数;Viw和Vpw分别为注入和采出水体积,单位m3;A为储层面积,单位m2;h为储层厚度,单位m;φ为储层孔隙度,%;Sw为束缚水饱和度;Me,CO2是有效封存量,单位t;Cm、Cb、Ch、Cw、Ca和Ce分别为流动性、浮力、非均质性、含水饱和度、邻近含水层强度和有效容量系数
7 GCO2=Ahnφe1-SwiBfρstd,CO2E GCO2为封存量,单位t;A为储层面积,单位m2;hn为平均厚度,单位m;φe为有效孔隙度,%;Swi为初始含水饱和度
8

BPV=KRES=(KOIL+KNGL)/FOIL+KGAS/FGAS

BSR=BPVBSEρres,CO2

VSF=ASVTPI φPI

RPV=VSF-BPV

RSR=RPV RSE RPV ρres,CO2

TSR=BSR+R1,SR+R2,SR+R3,SR

BPV为已知石油采收量,单位m3;KRES为据储层条件校正体积值,单位m3;KOIL为据储层条件校正的已知油气采收量体积值,单位m3;KNGL为已知天然气液相采收量,单位m3;FOIL为石油和天然气液相地层体积系数;KGAS为已知气相采收量,单位m3;FGAS为气相馏分地层体积系数;BSR为浮力封存量,单位t;BSE为浮力封存效率,%;VSF为SF(储层)的孔隙体积,单位m3;ASV为面积,单位m2; TPI为SF中孔隙度大于等于8%的储层厚度,单位m;φPI为多孔层平均孔隙度,%;RPV为可用于残余捕获孔隙体积,单位m3;RSR为残余封存量,单位t;RSE为残余捕获效率因子;TSR为整个SF技术可行封存量,单位t;R1,SR、R2,SR和R3,SR分别为1类、2类和3类区域的残余捕获封存量,单位t
9 MCO2=VRφSgρres,CO2E VR为总储层体积,单位m3;Sg为残余气饱和度,%
), ArticleFig(id=1322964783233905236, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, language=CN, label=表2, caption=

基于有效容积法的封存潜力评估模型汇总[6,23-26]

, figureFileSmall=null, figureFileBig=null, tableContent=
模型编号计算原理参数含义
6

气藏:MCO2=ρres,CO2Rf1-FIGVOGIP[psZrTr/prZsTs]

油藏:MCO2=ρres,CO2(RfVOOIP/Bf-Viw+Vpw)

MCO2=ρres,CO2[RfAhφ1-Sw-Viw+Vpw]

Me,CO2=CmCbChCwCaMCO2=CeMCO2

FIG为注入气体体积分数; p为压力,单位Pa;Z为气体压缩系数;T为温度,单位℃;下标r为储层条件;下标s为地表条件;Bf为储层体积系数;Viw和Vpw分别为注入和采出水体积,单位m3;A为储层面积,单位m2;h为储层厚度,单位m;φ为储层孔隙度,%;Sw为束缚水饱和度;Me,CO2是有效封存量,单位t;Cm、Cb、Ch、Cw、Ca和Ce分别为流动性、浮力、非均质性、含水饱和度、邻近含水层强度和有效容量系数
7 GCO2=Ahnφe1-SwiBfρstd,CO2E GCO2为封存量,单位t;A为储层面积,单位m2;hn为平均厚度,单位m;φe为有效孔隙度,%;Swi为初始含水饱和度
8

BPV=KRES=(KOIL+KNGL)/FOIL+KGAS/FGAS

BSR=BPVBSEρres,CO2

VSF=ASVTPI φPI

RPV=VSF-BPV

RSR=RPV RSE RPV ρres,CO2

TSR=BSR+R1,SR+R2,SR+R3,SR

BPV为已知石油采收量,单位m3;KRES为据储层条件校正体积值,单位m3;KOIL为据储层条件校正的已知油气采收量体积值,单位m3;KNGL为已知天然气液相采收量,单位m3;FOIL为石油和天然气液相地层体积系数;KGAS为已知气相采收量,单位m3;FGAS为气相馏分地层体积系数;BSR为浮力封存量,单位t;BSE为浮力封存效率,%;VSF为SF(储层)的孔隙体积,单位m3;ASV为面积,单位m2; TPI为SF中孔隙度大于等于8%的储层厚度,单位m;φPI为多孔层平均孔隙度,%;RPV为可用于残余捕获孔隙体积,单位m3;RSR为残余封存量,单位t;RSE为残余捕获效率因子;TSR为整个SF技术可行封存量,单位t;R1,SR、R2,SR和R3,SR分别为1类、2类和3类区域的残余捕获封存量,单位t
9 MCO2=VRφSgρres,CO2E VR为总储层体积,单位m3;Sg为残余气饱和度,%
), ArticleFig(id=1322964783317791317, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, language=EN, label=Table 3, caption=Summary of modified and extended models based on effective volume and material balance principles[14,27-34], figureFileSmall=null, figureFileBig=null, tableContent=
模型编号计算原理参数含义
10

Mt=ρres,CO2109[ERbAhφ1-Swi-Viw+Vpw+

SW,CO2(AhφSwi+Viw-Vpw)+SO,CO21-ERbAhφ1-Swi]

Mt=ρres,CO2109[0.4ERb+0.6ERhAhφ1-Swi-Viw+Vpw+

SW,CO2(AhφSwi+Viw-Vpw)+SO,CO21-0.4ERb-0.6ERhAhφ1-Swi]

Mt为CO2理论封存量,单位106 t;ERb为CO2突破前原油采收率,%;ERh为注入某体积CO2时原油采收率,%;Viw为注水量,单位m3;Vpw为产水量,单位m3;SW,CO2为CO2水中溶解系数;SO,CO2为CO2油中溶解系数
11

Me,CO2=ρres,CO2Ahφ1-SwiSCO2

SCO2=Ce[1-SwiRf+SwiRw]+EfSpw1-RwSW,CO2+Ef(1-Spw)1-RwSO,CO2

Me,CO2为有效封存量,单位106 t;SCO2为综合封存系数;Rw为水采收率,%;Ef为CO2波及效率,%;Spw为当前含水饱和度
12 MCO2=(∑i=1nAihiφi)1-SwBfρres,CO2ES Ai为单一网格面积,单位ft2;hi为对应网格厚度,单位ft;φi为该网格孔隙度,%;i为网格号,i=1,2,3,…,n;ES为包含储层非均质性、CO2浮力和波及效率的总封存有效系数
13 MCO2=VPore1 000[Sgi(Reu0+∆ReuCO2)ρres,CO2+EfSwdSW,CO2] VPore为气藏孔隙体积,单位m3;Sgi为原始含气饱和度;Reu0为天然气藏衰竭开采标定采收率,%;∆ReuCO2为CO2驱提高天然气采收率,%;Ef为注入CO2的波及系数;Swd为最终含水饱和度
14

M1=RuVOOIPBfCaq(ρres,CO2/ρstd,CO2)

M2=VOOIPBf1-RuEfSO,CO2(ρres,CO2/ρstd,CO2)

M3=MwEfSW,CO2(ρres,CO2/ρstd,CO2)

M1为采出油原占据体积用于封存容量,单位m3;Ru为最终采收率,%;Caq是咸水入侵效率因子(强侵入0.5,弱侵入0.97);M2为残余油中溶解CO2量,单位m3;M3为地层水中溶解CO2量,单位m3;Mw为储层含水量,单位m3
15

Mco2,oil=(VOOIP/ρres,CO2)Bf1-RfCco2,oilmco2

Mco2,w=(VOOIP/ρres,CO2)Bf/SoiSwρwwf,co2/107

Mco2,m=∑j=1n(3.153 6rjmco2×10-5)t

Mco2,oil为CO2在残余油中的溶解封存量,单位Mt;Cco2,oil为平衡状态CO2在油中摩尔溶解度,单位mol/L;mco2为CO2分子量44 g/mol;Mco2,w为CO2地层水中溶解封存量,单位Mt;Soi为初始含油饱和度;ρw为饱和CO2后地层水密度,单位kg/m3;wf,co2为饱和地层水中CO2质量分数;Mco2,m为CO2矿化封存量,单位Mt;j为矿化反应矿物种类的编号,j=1,2,3,…,n;rj为第j种能与CO2发生反应的矿物的溶解速度,单位mol/s;t为时间,单位a
16

Me,CO2=0.85Vstd,gRres,CO2CH4ρstd,CO2+MCO2rj+MCO2kh

MCO2rj=AhφSwρH2OCkhXCO2/103

MCO2kh=0.85Vstd,gRres,CO2CH4ρstd,CO2Kkh

Vstd,g为标况下最终可采天然气体积,单位109 m3;MCO2rj为CO2水中溶解封存量,单位t;MCO2kh为矿化封存量,单位t;ρH2O为地层水密度,单位kg/m3;Ckh为矿化度修正系数;XCO2为地层水被CO2饱和时的CO2占地层水的平均质量分数;Kkh为矿化封存系数,取值0.015
), ArticleFig(id=1322964783418454614, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, language=CN, label=表3, caption=

基于有效容积理论和物质平衡理论的改进和拓展模型汇总[14,27-34]

, figureFileSmall=null, figureFileBig=null, tableContent=
模型编号计算原理参数含义
10

Mt=ρres,CO2109[ERbAhφ1-Swi-Viw+Vpw+

SW,CO2(AhφSwi+Viw-Vpw)+SO,CO21-ERbAhφ1-Swi]

Mt=ρres,CO2109[0.4ERb+0.6ERhAhφ1-Swi-Viw+Vpw+

SW,CO2(AhφSwi+Viw-Vpw)+SO,CO21-0.4ERb-0.6ERhAhφ1-Swi]

Mt为CO2理论封存量,单位106 t;ERb为CO2突破前原油采收率,%;ERh为注入某体积CO2时原油采收率,%;Viw为注水量,单位m3;Vpw为产水量,单位m3;SW,CO2为CO2水中溶解系数;SO,CO2为CO2油中溶解系数
11

Me,CO2=ρres,CO2Ahφ1-SwiSCO2

SCO2=Ce[1-SwiRf+SwiRw]+EfSpw1-RwSW,CO2+Ef(1-Spw)1-RwSO,CO2

Me,CO2为有效封存量,单位106 t;SCO2为综合封存系数;Rw为水采收率,%;Ef为CO2波及效率,%;Spw为当前含水饱和度
12 MCO2=(∑i=1nAihiφi)1-SwBfρres,CO2ES Ai为单一网格面积,单位ft2;hi为对应网格厚度,单位ft;φi为该网格孔隙度,%;i为网格号,i=1,2,3,…,n;ES为包含储层非均质性、CO2浮力和波及效率的总封存有效系数
13 MCO2=VPore1 000[Sgi(Reu0+∆ReuCO2)ρres,CO2+EfSwdSW,CO2] VPore为气藏孔隙体积,单位m3;Sgi为原始含气饱和度;Reu0为天然气藏衰竭开采标定采收率,%;∆ReuCO2为CO2驱提高天然气采收率,%;Ef为注入CO2的波及系数;Swd为最终含水饱和度
14

M1=RuVOOIPBfCaq(ρres,CO2/ρstd,CO2)

M2=VOOIPBf1-RuEfSO,CO2(ρres,CO2/ρstd,CO2)

M3=MwEfSW,CO2(ρres,CO2/ρstd,CO2)

M1为采出油原占据体积用于封存容量,单位m3;Ru为最终采收率,%;Caq是咸水入侵效率因子(强侵入0.5,弱侵入0.97);M2为残余油中溶解CO2量,单位m3;M3为地层水中溶解CO2量,单位m3;Mw为储层含水量,单位m3
15

Mco2,oil=(VOOIP/ρres,CO2)Bf1-RfCco2,oilmco2

Mco2,w=(VOOIP/ρres,CO2)Bf/SoiSwρwwf,co2/107

Mco2,m=∑j=1n(3.153 6rjmco2×10-5)t

Mco2,oil为CO2在残余油中的溶解封存量,单位Mt;Cco2,oil为平衡状态CO2在油中摩尔溶解度,单位mol/L;mco2为CO2分子量44 g/mol;Mco2,w为CO2地层水中溶解封存量,单位Mt;Soi为初始含油饱和度;ρw为饱和CO2后地层水密度,单位kg/m3;wf,co2为饱和地层水中CO2质量分数;Mco2,m为CO2矿化封存量,单位Mt;j为矿化反应矿物种类的编号,j=1,2,3,…,n;rj为第j种能与CO2发生反应的矿物的溶解速度,单位mol/s;t为时间,单位a
16

Me,CO2=0.85Vstd,gRres,CO2CH4ρstd,CO2+MCO2rj+MCO2kh

MCO2rj=AhφSwρH2OCkhXCO2/103

MCO2kh=0.85Vstd,gRres,CO2CH4ρstd,CO2Kkh

Vstd,g为标况下最终可采天然气体积,单位109 m3;MCO2rj为CO2水中溶解封存量,单位t;MCO2kh为矿化封存量,单位t;ρH2O为地层水密度,单位kg/m3;Ckh为矿化度修正系数;XCO2为地层水被CO2饱和时的CO2占地层水的平均质量分数;Kkh为矿化封存系数,取值0.015
), ArticleFig(id=1322964785121342039, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, language=EN, label=Table 4, caption=Summary of storage potential assessment models considering new classification and calculation principles[35-38], figureFileSmall=null, figureFileBig=null, tableContent=
模型计算原理参数含义
17 MHCPV=Vndep+∫ndepndep+ΔnGiV¯xdndep MHCPV为注入目标HCPV的CO2时封存量,单位ft3;Vndep为根据气液平衡理论计算的虚构封存体积,单位ft3;ndep为枯竭期油气摩尔数,单位mol;ΔnGi为注入含杂质CO2摩尔数,单位mol;V¯x为广义偏摩尔体积,单位ft3/lb-mol
18

pc=ΔρgH

pt=γcosθ/R

pchc=ΔρhcgHhc

HCO2=pchcMΔρCO2g

pc为毛细压力,单位Pa;Δρ为润湿相和非润湿相间的密度差,单位kg/m3;g为重力,单位m/s2;H为非润湿相柱的高度,单位m;Pt为临界压力,单位Pa;γ为侵入相的界面张力,单位N/m;θ为润湿角,单位(°);R为孔喉半径,单位m;pchc为地层水和油气之间的毛细压力,单位Pa;Δρhc为地层水和油气之间的密度差,单位kg/m3;Hhc为油气相柱高,单位m;HCO2为CO2的可持续柱高,单位m;M为压力转换乘数; ΔρCO2为CO2密度差,单位kg/m3
19 Mtb,CO2=∑k=1nQog,k[Sc,o-ρg,surf1 000(GOR-Rsi)] Mtb,CO2为同步封存量,单位104 t;k为以年为单位的项目评价时间序号,k=1,2,3,…,n;Qog,k为第k年的气驱产量,单位t/a;Sc,o为CO2换油率,单位t/t;ρg,surf为注入气地面密度,单位 kg/m3,取值1.9 kg/m3;GOR为CO2驱生产气油比,单位m3/m3;Rsi为原始溶解气油比,单位m3/m3
20

Msum=Mprod,CO2+Mres,CO2+Msub,CO2

Mprod,CO2=CpVprod,Pρposs,CO2

Mres,CO2=CrVres,P(1-Pinit/Poss)ρposs,CO2

Msub,CO2=CsVsub,P(1-Pinit/Poss)ρposs,CO2

Msum为总封存量,是生产封存量Mprod,CO2、残余封存量Mres,CO2和亚经济封存量Msub,CO2加和,单位t;Cp、Cr和Cs分别为产出气、残余气和亚经济气藏注入效率,%;Vprod,P、Vres,P和Vsub,P分别为储层条件下产出气、剩余气和亚经济区天然气资源所占据孔隙体积,单位m3;Pinit为气藏初始压力,单位Pa;Poss为气藏最佳安全封存压力,单位Pa;ρposs,CO2为储层温度和最佳安全封存压力下CO2密度,单位kg/m3
), ArticleFig(id=1322964785234588248, tenantId=1146029695717560320, journalId=1322258508890079423, articleId=1322964765341004273, language=CN, label=表4, caption=

考虑新分类和新计算原理的封存潜力评估模型汇总[35-38]

, figureFileSmall=null, figureFileBig=null, tableContent=
模型计算原理参数含义
17 MHCPV=Vndep+∫ndepndep+ΔnGiV¯xdndep MHCPV为注入目标HCPV的CO2时封存量,单位ft3;Vndep为根据气液平衡理论计算的虚构封存体积,单位ft3;ndep为枯竭期油气摩尔数,单位mol;ΔnGi为注入含杂质CO2摩尔数,单位mol;V¯x为广义偏摩尔体积,单位ft3/lb-mol
18

pc=ΔρgH

pt=γcosθ/R

pchc=ΔρhcgHhc

HCO2=pchcMΔρCO2g

pc为毛细压力,单位Pa;Δρ为润湿相和非润湿相间的密度差,单位kg/m3;g为重力,单位m/s2;H为非润湿相柱的高度,单位m;Pt为临界压力,单位Pa;γ为侵入相的界面张力,单位N/m;θ为润湿角,单位(°);R为孔喉半径,单位m;pchc为地层水和油气之间的毛细压力,单位Pa;Δρhc为地层水和油气之间的密度差,单位kg/m3;Hhc为油气相柱高,单位m;HCO2为CO2的可持续柱高,单位m;M为压力转换乘数; ΔρCO2为CO2密度差,单位kg/m3
19 Mtb,CO2=∑k=1nQog,k[Sc,o-ρg,surf1 000(GOR-Rsi)] Mtb,CO2为同步封存量,单位104 t;k为以年为单位的项目评价时间序号,k=1,2,3,…,n;Qog,k为第k年的气驱产量,单位t/a;Sc,o为CO2换油率,单位t/t;ρg,surf为注入气地面密度,单位 kg/m3,取值1.9 kg/m3;GOR为CO2驱生产气油比,单位m3/m3;Rsi为原始溶解气油比,单位m3/m3
20

Msum=Mprod,CO2+Mres,CO2+Msub,CO2

Mprod,CO2=CpVprod,Pρposs,CO2

Mres,CO2=CrVres,P(1-Pinit/Poss)ρposs,CO2

Msub,CO2=CsVsub,P(1-Pinit/Poss)ρposs,CO2

Msum为总封存量,是生产封存量Mprod,CO2、残余封存量Mres,CO2和亚经济封存量Msub,CO2加和,单位t;Cp、Cr和Cs分别为产出气、残余气和亚经济气藏注入效率,%;Vprod,P、Vres,P和Vsub,P分别为储层条件下产出气、剩余气和亚经济区天然气资源所占据孔隙体积,单位m3;Pinit为气藏初始压力,单位Pa;Poss为气藏最佳安全封存压力,单位Pa;ρposs,CO2为储层温度和最佳安全封存压力下CO2密度,单位kg/m3
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油气藏CO2封存潜力评估模型与实践进展
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李经纬 1, 2 , 彭勃 1, 2 , 王泽滕 1, 2 , 陈晓倩 1, 2 , 张正昊 1, 2 , 刘金栋 1, 2 , 刘双星 3 , 李晓枫 4
油气藏评价与开发 | 矿场应用 2026,16(1): 141-152
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油气藏评价与开发 |矿场应用 2026 , 16 (1) : 141 -152
油气藏CO2封存潜力评估模型与实践进展
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李经纬(1994—),男,在读博士研究生,主要从事分子模拟及CO2封存潜力评估研究。地址:北京市昌平区府学路18号,邮政编码:102249。E-mail:

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李经纬(1994—),男,在读博士研究生,主要从事分子模拟及CO2封存潜力评估研究。地址:北京市昌平区府学路18号,邮政编码:102249。E-mail:

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李经纬1, 2 , 彭勃1, 2, 王泽滕1, 2, 陈晓倩1, 2, 张正昊1, 2, 刘金栋1, 2, 刘双星3, 李晓枫4
作者信息
  • 1 中国石油大学(北京)油气资源与工程全国重点实验室,北京   102249
  • 2 中国石油大学(北京)温室气体封存与石油开采利用北京市重点实验室,北京   102249
  • 3 中国石油安全环保技术研究院有限公司,北京   102206
  • 4 国家石油天然气管网集团有限公司科学技术研究总院分公司,天津   300450
通讯作者:
彭勃(1969—),男,博士,教授,主要从事碳捕集、利用与封存和油气田化学与工程研究。地址:北京市昌平区府学路18号,邮政编码:102249。E-mail:
作者简介:

李经纬(1994—),男,在读博士研究生,主要从事分子模拟及CO2封存潜力评估研究。地址:北京市昌平区府学路18号,邮政编码:102249。E-mail:

CO2 storage potential assessment models and their practical progress in oil and gas reservoirs
JINGWEI LI1, 2 , BO PENG1, 2, ZETENG WANG1, 2, XIAOQIAN CHEN1, 2, ZHENGHAO ZHANG1, 2, JINDONG LIU1, 2, SHUANGXING LIU3, XIAOFENG LI4
Affiliations
  • 1 State Key Laboratory of Petroleum Resources and Engineering, China University of Petroleum (Beijing), Beijing 102249, China
  • 2 Beijing Key Laboratory for Greenhouse Gas Storage and Enhanced Oil Recovery, China University of Petroleum (Beijing), Beijing 102249, China
  • 3 CNPC Research Institute of Safety & Environment Technology, Beijing 102206, China
  • 4 PipeChina Institute of Science and Technology, Tianjin 300450, China
出版时间: 2026-01-26 doi: 10.13809/j.cnki.cn32-1825/te.2025357
文章导航
收藏切换
枯竭油气藏  /  CO2-EOR(CO2提高石油采收率)  /  CO2-EGR(CO2提高天然气采收率)  /  CO2地质封存  /  潜力评估
depleted oil and gas reservoirs  /  CO2-EOR (CO2-enhanced oil recovery)  /  CO2-EGR (CO2-enhanced gas recovery)  /  CO2 geological storage  /  potential assessment
李经纬, 彭勃, 王泽滕, 陈晓倩, 张正昊, 刘金栋, 刘双星, 李晓枫. 油气藏CO2封存潜力评估模型与实践进展. 油气藏评价与开发, 2026 , 16 (1) : 141 -152 . DOI: 10.13809/j.cnki.cn32-1825/te.2025357
JINGWEI LI, BO PENG, ZETENG WANG, XIAOQIAN CHEN, ZHENGHAO ZHANG, JINDONG LIU, SHUANGXING LIU, XIAOFENG LI. CO2 storage potential assessment models and their practical progress in oil and gas reservoirs[J]. Petroleum Reservoir Evaluation and Development, 2026 , 16 (1) : 141 -152 . DOI: 10.13809/j.cnki.cn32-1825/te.2025357
CCUS(碳捕集、利用与封存)技术是指将大型排放源或空气中CO2捕集后,进行利用或封存的全链条技术。IPCC(联合国政府间气候变化专门委员会)第六次评估报告指出,CCUS是消除刚性碳排放的关键工具,是保证1.5 ℃温控目标实现的必要手段[1]。IEA(国际能源署)指出,2023年全球能源相关碳排放达377×108 t并再创新高,而现有的CCUS项目规模有限,全球年封存量仅4 500×104 t[2]。对中国而言,CCUS是实现化石能源大规模低碳化利用的唯一技术,是中国碳中和技术体系不可或缺的重要组成[3]。近期发布的《绿色金融支持项目目录(2025年版)》首次将CCUS技术路径以完整链条形式纳入,标志着中国CCUS发展进入新阶段[4]。
CO2地质利用与封存主要包含油气藏封存、煤层气开采、咸水层封存和玄武岩矿化[5]。其中,油气藏封存因兼具增采经济价值和碳封存环境效益而受到广泛关注。CSLF(碳收集领导人论坛)指出,CO2地质利用与封存项目部署的最大障碍之一是缺乏科学统一的封存潜力评估方法[6]。现有封存潜力评估模型综述通常只介绍了传统的封存模型[7-12]。该研究梳理了自1999年以来适用于油气藏的封存潜力评估方法,通过介绍发展历程,分析模型间联系和差异并系统总结评估案例,便于查找合适的评估方法,并为新模型的设计提供思路。
①构造封存(含水动力封存):CO2在浮力作用下向上运移并在密封层下方汇聚形成构造封存,由背斜、断层或地层尖灭形成的圈闭构造能够确保CO2气顶长期稳定封存工作;②残余封存(束缚空间封存):CO2在毛细管力作用下留存在孔隙中形成束缚封存,束缚CO2能够与残余油、地层水以及岩石长期接触从而促进CO2溶解和矿化;③溶解封存:CO2与残余油和地层水反复接触并溶解至饱和,部分CO2随地层流体被采出,剩余的CO2则保持溶解态封存在地下;④矿化封存:CO2溶于地层水后形成碳酸并与储层岩石发生一系列矿化反应,造成矿物溶蚀和沉淀并影响孔隙度和渗透率,除玄武岩外其他矿化反应均十分缓慢,但矿化对永久封存贡献最大[13]。
①补充气藏能量:枯竭气藏经衰竭开发后地层能量显著下降,注入CO2可迅速提升储层压力并发挥三重作用,使气藏恢复至开采压力从而提高采收率并延长开采寿命,有效抑制边底水入侵从而降低气藏含水率,同时降低开采成本,迅速占据已开采天然气原始孔隙空间实现早期束缚封存。②竞争吸附:进入储层孔隙的CO2能够与岩石壁面吸附的残余CH4产生竞争吸附。CO2分子尺寸更小,分子量更大,因而在色散作用下发生物理吸附,同时解吸的CH4转变为自由态被采出。③重力分异:储层条件下CO2呈近似液相的超临界态,其密度大于CH4并且在重力作用下沉于气藏底部,当选择“低注高采”模式时可分隔顶部天然气和底部水层,同时降低开采气的含水量并延缓CO2突破,进一步提高构造封存CO2稳定性[14]。
目前,有关CO2油气藏封存的综述更关注封存机制认识、实验和模拟方法创新以及国内外项目进展[15-17]。自STEVENS等[18]提出油气藏封存潜力评估方法以来,陆续涌现出各种评估模型。模型按其基本原理可分为物质平衡法、有效容积法、溶解机制法和其他方法[7]。在此基础上结合模型的关键参数和提出年份将其发展历程划分为4个阶段(图1)。
如图1和表1所示,最早一批模型均采用物质平衡法,即将采出油气资源换算为等量CO2作为理论封存量。STEVENS等[18]通过调研美国7个二叠纪盆地的驱油项目提出了根据原油API度(美国石油学会制订的用以表示石油及石油产品密度的一种量度)预测封存量的模型1。该模型适用于油藏驱油和废弃情景,并假设开采后亏空体积可被CO2完全取代,无水侵且储层压力恢复至初始压力时停注。此后,GESTCO(欧洲地质封存项目)计划补充了项目由驱油结束转为废弃油藏纯CO2封存后的潜力计算公式[19]。早期,气藏封存量同样依据可采天然气体积换算,模型2假设埋深介于900~3 500 m,单个区块封存量大于100×104 t[18]。2002年,SHAW等[20]认为适于封存的油藏应满足以下条件:原油API度介于27~48;储层温度介于31~120 ℃。储层压力不小于最小混相压力(MMP)的0.95倍,剩余油饱和度不低于25%以保证驱油经济性(模型3)。该研究的不足之处在于没有考虑小规模封存以及深部储层封存带来的高昂成本,此外,在计算MMP时未考虑杂质气体对临界条件的影响。2005年ECL Tech.指出不同于水气交替注入(WAG),重力稳定气驱(GSGI)模式的封存可能与储层孔隙体积相关,而不是提高采收率(模型4)[21]。这一认知为后来基于封存可用孔隙空间的模型设计提供了参考。2009年,国际能源署温室气体研究与开发计划机构[22]开发了利用气藏产能估算封存潜力的方法(模型5),假设枯竭气藏可用CO2重新填充至初始压力,模型5的封存效率因子与模型2同取0.75。
如图1和表2所示,与物质平衡法依赖采收率不同,研究人员认识到如何界定CO2可用储层孔隙空间是进一步提高评估精度的关键。2008年,建立技术小组提出了基于储层孔隙空间的评估方法,并建立了有效封存量计算模型(模型6)[6]。该模型通过引入有效容量系数反映水侵、储层非均质性、CO2重力分异等导致实际封存量的削减程度。同期,美国能源部在《美国与加拿大碳封存图集》中也提出了类似的方法USDOE法(模型7),该方法不考虑CO2油水中溶解以及CO2注入滞后效应[23],当封存效率因子取相同数值且不考虑注采水时2个模型基本等价。在此期间澳大利亚也开展了CCUS先导示范,其中,昆士兰碳地质封存倡议组织开发了CO2地质封存解决方案(模型8)[24-25]。该方法认为迁移辅助封存捕获(MAS)是CO2地质封存的主要机制。在圈闭中只有紧贴密封层下方的薄层会受残余气体饱和度(RGS)影响。模型认为CO2羽流迁移触及的储层厚度一般不超过25 m。RGS与孔隙度、分选性和沉积物尺寸呈负相关,但与胶结作用和黏土含量呈正相关。准确的RGS需根据岩心样品测算,但对于区域评估RGS常保守取值0.1。由于封存效率因子的取值非常保守(0.10%~0.15%),因此基于该模型的评估结果要低一到两个数量级。2013年,美国地质调查局为封存可用空间提出了更严苛的定义USGS法(模型9)[26]。该模型使用存储评估单元(SAU)即“由多孔流动储层和边界区域封闭层组成的可统计岩石体积”。储层中只统计密封层下方区域,因此沉积盆地被细分为一系列SAU。USGS法主要考虑浮力俘获和残余俘获机制。
前2个阶段模型主要以构造和束缚封存作为评价CO2封存量的主体,2010年后许多学者开始思考其他封存机制的贡献并不断改进核心参数的获取方法(表3)。
一些学者在有效容积法的基础上进行改进。如沈平平等[27]在USDOE法的基础上考虑到注入和采出水问题以及CO2在油藏流体中的溶解建立了模型10。2012年,ZHAO等[28]根据MMP与储层压力的关系判断是否适宜混相驱且采用管流模拟确定核心参数(模型11)。2015年,ZHAO等[29]进一步更新了此方法,MMP计算从传统经验公式预测优化为混合单元网格法。
除补充其它封存机制的贡献外,ZHONG等[30]对封存可用孔隙空间开展了精细化描述(模型12)。研究利用人工神经网络将岩心尺度的孔隙度和渗透率等数据等放大到井尺度,并基于随机高斯函数和成熟变差函数生成三维模型。THANH等[31]使用高斯模拟和协同克里金方法,结合方位角和倾角参数生成三维静态地质模型。此外,还通过动态模拟进行了钻杆测试匹配,以验证孔隙度和渗透率的准确性。2025年,廖广志等[14]在考虑CO2可用孔隙空间的基础上提出了简化版气藏封存潜力评价方法(模型13)。该方法只考虑了CO2在地层水中的溶解,应结合其他模型使用。
另一些学者从物质平衡法出发对模型进行改进。ZHANG等[32]创新地引入水侵系数来体现临近咸水层入侵导致的封存能力削弱(模型14)。2018年,DING等[33]结合化学反应动力学、泊肃叶方程和毛细管流模型推导了矿物溶解速率方程并据此建立了CO2矿化封存公式(模型15)。2021年,唐良睿等[34]将模型2的CO2和CH4摩尔比更新为基于彭-罗宾逊状态方程的气体偏差因子比(模型16)。研究将气藏中CO2实际利用孔隙空间的占比由75%调整为85%,此外,还考虑了矿化反应和CO2在水中的溶解作用。模型计算结果对比数值模拟误差仅为3.06%,从而验证了其准确性。
除对早期模型进行精细描述和拓展外,一些学者开始尝试考虑其他原理或重新划分封存空间(表4)。
2012年,VALBUENA等[35]发现来自燃煤电厂烟气捕集碳源中的N2和CO可能影响封存效率。因此提出了基于热力学状态函数和广义偏摩尔体积(PMV)的模型17。模型创新地将注入流体简化为“伪组分”,从而实现快速处理CO2-N2-CO混合注气。研究认为封存依赖CO2与油气间的超临界混合,短期内CO2在地层水中的溶解率低于1%,因此可忽略。封存量与注入速率、井口数量等无明确关系,仅取决于压力差、流体组成和温度等参数。此外,烟气杂质会降低封存效率,需权衡分离成本与封存效益。
第二种创新为根据CO2柱高判断封存潜力(模型18)。2017年,HEINEMANN等[36]认为当两相体系的毛细压力超过上覆地层阈值压力时会发生毛细泄漏。泄漏将导致CO2柱高降低,直至二者再次相等。若持续注入非润湿流体,则注入和泄漏间将建立动态平衡。
2019年,王高峰等[37]认为实际封存量可分为同步封存量和油藏废弃后的深度封存量,假设项目处于注、采、驱、埋4个过程同步发生,通过求取项目历年产油量,与换油率做乘积,并扣除产出气中的CO2,即可得到历年同步封存量(模型19)。该方法的核心参数为气驱产量、生产气油比和CO2换油率。当年气驱产量由气驱增产倍数乘以基于注气前一年水驱产量指数递减后计算的同期水驱产量进行计算。生产气油比则分为见气前、见气后至气窜前、气窜后3个阶段计算。CO2换油率被定义为生产每吨原油需注入的CO2量,主要受干层吸气系数和裂缝疏导系数影响。
2020年,阿尔伯塔油砂开采作业产生了超7 000×104 t碳排放,占当年加拿大总排放的10%。该省东北部有大量浅层枯竭气藏(埋深介于150~800 m,储层温度介于5~35 ℃),浅层气藏中低密度的CO2往往带来较低封存效率和较高泄漏风险。但CHEN等[38]认为气藏埋深不应成为判断其是否适合封存的决定因素。新模型20将封存可用空间分为三部分:由可采气占据的空间;由不可采即残余压缩气占据的空间;储层邻近层系的亚经济含气层空间。采用油砂热采最大操作压力(MOP)作为最大CO2注入压力和CO2最佳安全封存压力。当MOP高于初始储层压力时,总封存量等于生产封存量、残余封存量和亚经济封存量之和。
为进一步分析不同模型的异同和适用情景,特汇总整理了近40项潜力评估实例研究[20,22,25-33,35-59]。如图2所示,通过实例解读得到封存潜力层次、模型适用场景、封存机制贡献、时间尺度和储层特征对潜力评估影响的4点认识。
桑树勋等[60]指出封存适宜性评价与封存容量评估均属于地质封存潜力及其评价体系框架,二者在对应层级上应建立相互匹配关系。其中,CSLF将地质封存适宜性评价划分为国家、盆地、区域、目标区和封存地点5个层级;中国地质调查局则将其划分为区域、盆地、目标区/靶区、场地和灌注级[61-63]。与之对应的是封存容量可分为理论、有效、实际和匹配4个层级。李阳等[64]则认为含油气盆地碳封存潜力应根据区域尺寸划分为盆地级(项目集群化)、坳陷级(项目规模化)、区带级(项目大型化)和圈闭级(单体项目);且潜力分级为理论、工程和经济封存量3个层级。绝大部分模型仅能落实到有效封存潜力,这是模型为提高普适性所做的妥协。实际应用中对不同细分类型的储层应考虑差异化效率因子,同时引入能够反映储层典型特征的额外约束条件。
早期模型仅依据物质平衡理论和有效容积理论计算目标区域的静态封存总量,因此并无特殊适用场景。后部分模型尝试分开讨论注气开采情景和视为废弃储层纯CO2封存情景(图2)。然而,直接废弃进行纯封存情景是否能够比注气开采获得更高封存量仍然存在争议。模型4认为GSGI模式能够比WAG注入更有效地封存CO2。而模型3、模型10和模型15则考虑了CO2突破前后情景的封存潜力差异。不同之处在于模型3实例中封存量会随注入PV(孔隙体积)增大而大幅增大,而模型15实例则发现封存量在CO2突破后仅有小幅增长。此外,模型19虽不能用于讨论废弃情景,但可考察开采周期内每年的动态封存量。实例分析表明:15 a评价期内CO2封存率为64.8%,CO2换油率为3.76 t/t(每吨CO2注入油藏后可换得的原油量),与同类油藏CO2驱现场试验结果吻合,因此该模型是唯一评估层级属实际封存量的模型[37]。模型17从注气组成角度出发提出了烟气捕集碳源含N2和CO杂质场景。评估实例显示杂质气体因压缩因子更高占据体积更大而大幅削弱了CO2封存潜力。此外,该模型在处理复杂组分的同时相比于数值模拟结果的最大偏差仅为3.6 %且处理速度极快[35]。
由于模型常依据封存机制分项计算,因此其结果能够反映不同封存机制的贡献占比。模型9实例中残余封存量远大于浮力俘获封存量(超过60倍)。模型10实例中溶解封存量与自由空间封存量(构造+残余封存)处于同一数量级,其中,油中溶解封存量远大于水中溶解。对矿化封存而言,模型15实例分析认为HB油田CO2残余油中溶解封存282×104 t;水中溶解封存48×104 t;矿化封存1 820×104 t,长期看矿化封存规模远高于溶解封存[33]。而模型16实例中即便以封存100 a计算,矿化占封存量的占比仍然很小,矿化系数(Kkh)仅为0.015[34]。
随着CCUS项目的推进,不同封存机制的贡献会随着CO2突破、注气和关井等时间节点而变化。IPCC指出,残余、溶解以及矿化封存量通常在前期缓慢增长,尤其是矿化封存更是以百年为时间单位进行测量(图3a);而停注后残余、溶解和矿化封存能够显著提高封存安全性(图3b)[65]。延长石油CCUS项目为低渗致密砂岩油藏中的CO2封存机制演化规律提供了更多细节[66-69]。如图3c所示,项目初期(0~<10 a),CO2以构造和溶解封存为主;中期(10~100 a)随着原油开采直到关井,构造和溶解封存量减少,残余封存量增大;开发周期结束后(>100 a),圈闭结构和束缚孔隙中的CO2与地层水长期接触,逐渐转化为溶解封存并最终转变为矿化封存。
除适用场景和时间尺度,储层特征也会影响封存潜力。如模型11根据MMP与注气安全压力间的关系将油藏划分为适宜混相驱和适宜非混相驱两类。多个油田的评估实例表明:混相驱增采率通常大于10%,优质储层最高可达35%,平均封存系数介于0.25%~0.40%;而非混相驱的增采幅度通常小于10.0%,甚至下探至6.5%,平均封存系数介于0.15%~0.30%[28-29,54-56]。此外,ZHAO等[57]利用该模型分析了中国3种典型油藏:长庆油田长6(低渗油藏)、吉林油田黑59(高含水油藏)和新疆油田克上组(强非均质性油藏),发现额外增采分别为16.7%、14.2%和10.6%,封存系数分别为0.45%、0.33%和0.21%。由此推测储层强非均质性对封存潜力的削弱可能明显大于含水量和渗透率。此外,若目标储层有临近含水层,则建议选择模型14通过调整水侵因子来反映地层水的侵入程度及其对封存潜力的影响。
综合模型发展历程和潜力评估实例,将模型从基本原理、封存机制、储层类型和潜力分级4个角度进行分类(图4)。其中,物质平衡法和有效容积法均有10个模型考虑,仅有2个模型引入了矿化速率原理。封存机制中考虑了溶解和矿化封存的高精度模型15可与其它专门讨论自由空间封存机制的模型联合使用。而气藏评价模型16同时涵盖了4种封存机制,单独使用即可满足需求。适用储层类型中模型6、模型7、模型9、模型17和模型18可同时用于评估油藏和气藏封存潜力。对潜力分级而言,大多数模型仍只能用于评估理论封存量,仅模型19能够一定程度上体现实际封存潜力。
提出4个一级指标和细分12个二级指标,通过赋分对比分析20个模型的异同点(图5)。每考虑1个封存机制多0.25分,考虑动态封存赋1分,使用非均质空间描述赋1分。所需参数越少且参数获取越容易赋分越高,引入封存效率因子赋1分。输出特性中与其他方法评估对比赋0.5分,有场地数据验证赋1分。不确定性量化中提供取值范围和均值赋0.5分,有置信区间赋1分,有敏感性分析赋1分。实用程度中计算效率以秒计赋1分,以天计赋0分。理论、有效、实际和匹配封存潜力分别赋0.25、0.50、0.75和1.00分。模型以公式展示赋0分,有表格或脚本赋0.5分,集成为软件赋1分。
①模型特征:仅模型15和模型19考虑了封存量随时间的演化规律,模型9和模型12考虑了封存空间的非均质化。②输入需求:为获得更精确的评估结果,新模型会引入更多参数,同时也加大了部分参数的获取难度。为适应不同条件油气藏的评估,模型构建中普遍引入封存效率因子来实现差异化评估。③输出特性:早期模型缺乏准确性和不确定性量化,新模型则考虑了储层分布的不连续性。尤其是面对中国原油品味偏差,储层非均质性强等特征,在模型设计时应充分考虑数据的置信区间。④实用程度:模型12需根据岩心数据外推至井组和整个区块并建立精细三维地质模型因此评估耗时较长。目前,仅有模型3提供了交互式表格评估,而美国能源部已将模型7和非常规潜力评估方法集成为软件[70]。
油气藏CO2封存潜力评估发展至今,已形成多机制融合、多尺度协同的评估体系。
1) 在封存机制上,从早期依赖单一自由空间封存的模型,逐步纳入溶解封存,并探索矿化封存贡献,揭示多机制协同对长期封存安全性的作用。
2) 在评估精度层面,努力实现空间描述精细化,从静态储量换算发展为非均质三维地质建模。同时注重动态过程耦合,尝试整合注入—采出、相态演化及CO2矿化反应动力学,显著提升预测可靠性。
3) 从适用性拓展角度看,模型逐步兼容浅层枯竭气藏、超低渗储层等复杂地质条件并尝试考虑含杂气源等工程约束,支撑场地筛选与经济性优化。
然而,当前评估模型仍存在不足,未来应建立矿物溶解/沉淀动力学数据库,进一步研究CO2-CH4竞争吸附、超临界态相变等关键过程的时空演化规律。同时,将地质监测数据与人工智能新技术相结合,尝试开发轻量化评估软件,进一步集成参数敏感性分析和经济性评价模块,支撑场地级快速决策。最终实现封存潜力评估从“理论可行”迈向“工程落地”,为中国碳中和目标提供油气行业解决方案。

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2026年第16卷第1期
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doi: 10.13809/j.cnki.cn32-1825/te.2025357
  • 接收时间:2025-07-29
  • 首发时间:2026-10-30
  • 出版时间:2026-01-26
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  • 收稿日期:2025-07-29
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    1 中国石油大学(北京)油气资源与工程全国重点实验室,北京   102249
    2 中国石油大学(北京)温室气体封存与石油开采利用北京市重点实验室,北京   102249
    3 中国石油安全环保技术研究院有限公司,北京   102206
    4 国家石油天然气管网集团有限公司科学技术研究总院分公司,天津   300450

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彭勃(1969—),男,博士,教授,主要从事碳捕集、利用与封存和油气田化学与工程研究。地址:北京市昌平区府学路18号,邮政编码:102249。E-mail:
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2种不同金属材料的力学参数
科
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属数
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genus
种数
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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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