Article(id=1146098717997765323, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1146500022830469878, articleNumber=null, orderNo=24, doi=10.3981/j.issn.1000-7857.2023.12.01908, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1702915200000, receivedDateStr=2023-12-19, revisedDate=1705939200000, revisedDateStr=2024-01-23, acceptedDate=null, acceptedDateStr=null, onlineDate=1751180827854, onlineDateStr=2025-06-29, pubDate=1743091200000, pubDateStr=2025-03-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1744992000000, onlineIssueDateStr=2025-04-19, onlineJustAcceptDate=1751285399754, onlineJustAcceptDateStr=2025-06-30, onlineFirstDate=1751180827854, onlineFirstDateStr=2025-06-29, sourceXml=null, magXml=null, createTime=1751180827854, creator=18627231156, updateTime=1774079559703, updator=sys-migrate, issue=Issue{id=1146500022830469878, tenantId=1146029695717560320, journalId=1146031591421210625, year='2025', volume='43', issue='6', pageStart='1', pageEnd='100', issueExtLink='null', onlineDate='null', pubDate='1743091200000', pubDateStr='2025-03-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1751276506383, creator='13701087609', updateTime=1774330962150, updator='13041195026', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1243197418926355120, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1146500022830469878, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1243197418926355121, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1146500022830469878, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=84, endPage=91, ext={EN=ArticleExt(id=1146098718991815386, articleId=1146098717997765323, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Study on the effect of fine particle content on pore structure and stress sensitivity of carbon sequestration reservoir, columnId=1150494644690366681, journalTitle=Science & Technology Review, columnName=Papers, runingTitle=null, highlight=null, articleAbstract=

Carbon dioxide (CO2) geological sequestration is an effective way to control carbon dioxide emission. The fine particle content affects the porosity and permeability of reservoir, thus affecting the efficiency and safety of carbon dioxide injection and sequestration. Based on the H2A sandstone reservoir in Xijiang 23-1 oilfield, the pore structure of sandstone with different fine particle content was analyzed by CT scanning. Stress sensitivity tests of sandstone with different fine particle content were carried out. Stress sensitivity coefficient, permeability damage rate and permeability curvature were used to evaluate the stress sensitivity of sandstone. The results showed that with the increase of fine particle content, the permeability stress sensitivity of sandstone became stronger. The pore structure and permeability stress sensitivity of sandstone were analyzed comprehensively. It was shown that pore volume distribution was the main factor affecting the stress sensitivity of sandstone. In other words, the higher the frequency of small pore, the stronger the permeability stress sensitivity. Finally, based on the double strain Hooke model, a prediction model of porosity and stress of sandstone was proposed to predict the pore evolution of sandstone during the increase of effective stress. Guidance was provided for reservoir porosity and permeability analysis in the process of carbon dioxide geological sequestration.

, authors=null, authorsList=Xin YANG, Mengqiu YAN, Jianan ZHENG, authorCompany=null, correspAuthors=Jianan ZHENG, authorNote=null, correspAuthorsNote=null, copyrightStatement=All rights reserved. Unauthorized reproduction is prohibited., 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=1146098727640469740, articleId=1146098717997765323, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=细粒含量对碳封存储层孔隙结构及应力敏感性影响分析, columnId=1146540929516700224, journalTitle=科技导报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=

以西江23-1油田H2A砂岩储层为背景,分析了不同细粒含量砂岩孔隙结构。开展了不同细粒含量砂岩应力敏感性实验,采用应力敏感性系数、渗透率损害率和渗透率曲率评价了砂岩应力敏感性,结果表明,随着细粒含量的增加,砂岩渗透率应力敏感性增强。综合分析了砂岩孔隙结构及渗透率应力敏感性,表明孔隙体积分布是影响砂岩应力敏感性的主要因素,即小孔频率越高,渗透率应力敏感性越强。提出了砂岩孔隙率-应力预测模型,预测有效应力增加过程中砂岩孔隙演化。

, authors=

杨昕, 博士研究生, 研究方向为二氧化碳地质封存, 电子信箱:

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郑嘉男(通信作者), 副研究员, 研究方向为天然气水合物及二氧化碳地质封存, 电子信箱:
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杨昕, 博士研究生, 研究方向为二氧化碳地质封存, 电子信箱:

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细粒含量对碳封存储层孔隙结构及应力敏感性影响分析
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杨昕 1 , 颜梦秋 2 , 郑嘉男 3, *
科技导报 | 研究论文 2025,43(6): 84-91
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科技导报 |研究论文 2025 , 43 (6) : 84 -91
细粒含量对碳封存储层孔隙结构及应力敏感性影响分析
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杨昕, 博士研究生, 研究方向为二氧化碳地质封存, 电子信箱:

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杨昕1 , 颜梦秋2, 郑嘉男3, *
作者信息
  • 1. 浙江大学建筑工程学院, 杭州 310058
  • 2. 浙江大学海南研究院, 三亚 572025
  • 3. 浙江大学上海高等研究院, 上海 201203
通讯作者:
郑嘉男(通信作者), 副研究员, 研究方向为天然气水合物及二氧化碳地质封存, 电子信箱:
Study on the effect of fine particle content on pore structure and stress sensitivity of carbon sequestration reservoir
Xin YANG1 , Mengqiu YAN2, Jianan ZHENG3, *
Affiliations
  • 1. College of Civil Engineering and Architecture, Zhejiang University, Hangzhou 310058, China
  • 2. Hainan Institute of Zhejiang University, Sanya 572025, China
  • 3. Shanghai institute for advanced study, Zhejiang University, Shanghai 201203, China
出版时间: 2025-03-28 doi: 10.3981/j.issn.1000-7857.2023.12.01908
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以西江23-1油田H2A砂岩储层为背景,分析了不同细粒含量砂岩孔隙结构。开展了不同细粒含量砂岩应力敏感性实验,采用应力敏感性系数、渗透率损害率和渗透率曲率评价了砂岩应力敏感性,结果表明,随着细粒含量的增加,砂岩渗透率应力敏感性增强。综合分析了砂岩孔隙结构及渗透率应力敏感性,表明孔隙体积分布是影响砂岩应力敏感性的主要因素,即小孔频率越高,渗透率应力敏感性越强。提出了砂岩孔隙率-应力预测模型,预测有效应力增加过程中砂岩孔隙演化。

碳封存  /  西江23-1油田H2A层  /  砂岩储层  /  孔隙结构  /  应力敏感性  /  预测模型

Carbon dioxide (CO2) geological sequestration is an effective way to control carbon dioxide emission. The fine particle content affects the porosity and permeability of reservoir, thus affecting the efficiency and safety of carbon dioxide injection and sequestration. Based on the H2A sandstone reservoir in Xijiang 23-1 oilfield, the pore structure of sandstone with different fine particle content was analyzed by CT scanning. Stress sensitivity tests of sandstone with different fine particle content were carried out. Stress sensitivity coefficient, permeability damage rate and permeability curvature were used to evaluate the stress sensitivity of sandstone. The results showed that with the increase of fine particle content, the permeability stress sensitivity of sandstone became stronger. The pore structure and permeability stress sensitivity of sandstone were analyzed comprehensively. It was shown that pore volume distribution was the main factor affecting the stress sensitivity of sandstone. In other words, the higher the frequency of small pore, the stronger the permeability stress sensitivity. Finally, based on the double strain Hooke model, a prediction model of porosity and stress of sandstone was proposed to predict the pore evolution of sandstone during the increase of effective stress. Guidance was provided for reservoir porosity and permeability analysis in the process of carbon dioxide geological sequestration.

CO2 geological sequestration  /  H2A layer of Xijiang 23-1 oilfield  /  sandstone reservoir  /  pore structure  /  stress sensitivity  /  prediction model
杨昕, 颜梦秋, 郑嘉男. 细粒含量对碳封存储层孔隙结构及应力敏感性影响分析. 科技导报, 2025 , 43 (6) : 84 -91 . DOI: 10.3981/j.issn.1000-7857.2023.12.01908
Xin YANG, Mengqiu YAN, Jianan ZHENG. Study on the effect of fine particle content on pore structure and stress sensitivity of carbon sequestration reservoir[J]. Science & Technology Review, 2025 , 43 (6) : 84 -91 . DOI: 10.3981/j.issn.1000-7857.2023.12.01908
碳捕集与封存(CCS)是控制二氧化碳排放,缓解全球变暖问题的有效解决方法[13]。二氧化碳地质封存技术是将二氧化碳注入废弃油气藏、咸水层等深部地下储层[46],以达到永久封存的目的。二氧化碳的注入会改变储层原始应力状态,储层有效应力的变化对储层渗透率、孔隙率等物性参数产生影响[78],即应力敏感性。
渗透率和孔隙率是储层关键参数,直接决定二氧化碳可注入性及封存量[910]。因此,相关研究针对储层渗透率及孔隙率应力敏感性开展了研究[1112]。孙军昌等[13]针对火山岩气藏储层全直径岩心进行渗透率应力敏感实验,结果表明,岩心渗透率减小主要发生在有效应力小于20 MPa范围内,孔隙结构差异是导致岩石渗透率应力敏感的主要原因。Milad等[14]研究了不同孔隙类型碳酸盐岩渗透率及孔隙度应力敏感性,其中致密基质孔隙型岩石应力敏感性最大。刘顺喜等[15]通过覆压孔渗实验,分析了平行层理和垂直层理煤样渗透率应力敏感性,探明了煤层应力敏感性各向异性特征。
目前,中国海上CCS工程主要在珠江口盆地,如中国首个海上二氧化碳封存工程在恩平油田15-1平台建设;中国首个千万吨级二氧化碳封存项目同样计划在珠江口盆地海域开展。珠江口盆地分布有广泛的物性较好的以钙质胶结为主的砂岩储层,如西江23-1油田H2A层方解石胶结含量为11%~15%[16];白云凹陷珠江组钙质胶结含量超过15%[17];文昌A凹陷珠海组钙质胶结含量在0.3%~15.8%[18]。当前,针对以钙质胶结为主的砂岩应力敏感性研究较少。
本研究以西江23-1油田钙质胶结砂岩储层为背景,基于酶诱导碳酸钙沉淀(EICP)技术制作人工砂岩,分析了不同细粒含量对砂岩孔隙结构影响。开展了不同细粒含量砂岩应力敏感性实验,选取应力敏感性系数、渗透率损害率和渗透率曲率3个参数作为砂岩储层渗透率应力敏感性指标。综合孔隙结构分析,提出了影响砂岩应力敏感性的主要因素。最后,提出了砂岩孔隙率−应力预测模型,预测有效应力增加过程中,砂岩孔隙演化规律。
由于方解石胶结砂岩胶结特性较差,结构疏松,现场取样不仅成本高,而且难以大量获得完整试样。因此,本研究以西江油田钙质胶结砂岩储层为背景,采用EICP胶结石英砂的方法制作人工砂岩。
实验用砂采用ISO标准砂,该类型砂99.7% 为石英成分,颗粒比重Gs为2.63。选用标准砂颗粒粒径为0.08~1 mm,将粒径为0.08~0.25 mm定义为细颗粒。按照细颗粒含量占砂颗粒总质量10%、30%、50% 的比例配制样品,不同细粒含量颗粒级配曲线如图 1所示。
实验脲酶采用麦克林脲酶,脲酶活性为200~300 U/mg(1 U为在25℃条件下,1 min内分解尿素(Urea,CO(NH22)量为1 μmol)。胶结液成分为CaCl2、Urea、脱脂奶粉。Urea在脲酶作用下水解为铵根离子和碳酸根离子,碳酸根离子与Ca2+结合生成碳酸钙沉淀,胶结松散砂颗粒。脱脂奶粉的加入为碳酸盐沉淀提供成核点,促进碳酸盐沉淀的形成[19]
实验采用浸泡法[20-21]制作弱胶结人工砂岩,具体步骤如下。(1)清洗调配好的标准砂,将清洗后的标
准砂放入105℃烘箱24 h干燥;(2)脲酶溶液活性调至目标值,将干燥的标准砂与脲酶溶液充分混合4 h左右;(3)控制试样干密度为1.53 g/cm3(不同细粒含量试样干密度相同),混合脲酶后的标准砂分层放入模具中,试样尺寸为直径50 mm,高100 mm;(4)配制胶结液,将0.5 mol/L CaCl2、0.5 mol/L Urea 4 g/L脱脂奶粉和超纯水混合均匀;(5)将试样浸泡在胶结液中2 d,取出试样烘干;(6)为提高试样的强度和胶结的均匀性,进行二次胶结,将烘干的试样浸泡脲酶溶液4 h后,重复步骤(4)和(5)。
人工砂岩试样制作完成后,生成的碳酸盐胶结物含量由碳酸盐胶结物质量(人工砂岩干质量减去胶结前干砂质量)与胶结前干砂质量之比来评估。本研究中不同细粒含量人工砂岩碳酸盐胶结物含量相似,均为13.6%~14.0%;不同细粒含量人工砂岩平均孔隙率为23.9%~28.5%;不同细粒含量人工砂岩平均渗透率为335~953 mD。西江23-1油田钙质胶结储层砂岩碳酸盐胶结物为11%~15%,平均孔隙率为22.6%~33.0%,平均渗透率为460~3167 mD。因此,人工砂岩物性特征与西江23-1油田砂岩储层基本一致。
实验系统主要由双缸恒速恒压泵、轴压泵、环压泵、岩心夹持器、采出计量系统和数据采集系统组成(图 2)。双缸恒速恒压泵为流体提供渗流动力,压力范围为0~70 MPa,可实现高速无脉冲,恒速、恒压运行。实验中环压和轴压通过环压泵和轴压泵分别独立控制,压力范围均为0~50 MPa。岩心夹持器适应不同长度试样,中部由聚醚醚酮(PEEK)材料制成,可进行CT扫描。数据采集系统是控制整套系统的核心枢纽,既可以实时采集压力、气体和液体流量等数据,也可以控制恒压恒速泵等泵体的启停、参数设置。
研究首先对不同细粒含量砂岩进行了CT扫描,然后开展了应力敏感性实验,主要步骤如下。(1)将砂岩试样放置在60℃烘箱干燥48 h以上,称量试样干重、直径和长度;(2)将干燥后的砂岩试样负压饱和24 h以上,然后称重,计算初始孔隙率;(3)将饱和后的砂岩试样放入岩心夹持器中,轴压和围压均设置为0.2 MPa,将去离子水以0.5 mL/min速率渗流5 PV(PV为孔隙体积),确保试样完全饱和;(4)逐级增大围压至目标有效应力,目标有效应力分别为1、3、5、8、11 MPa。实验中,有效应力通过Pe =(Pa+2Pc)/3-Pp计算,其中,Pa为轴压,Pc为环压,且Pa=PcPp为孔隙水压力。每级目标有效应力保持1 h以上。
采用NanoVoxel-4000系列高穿透CT系统,X射线源电压为200 kV,电流为230 μA。选用高分辨模式,空间分辨率为10 μm。CT扫描完成后经过数据重构、图像滤波、图像分割等处理步骤得到三维数字图像。随后采用灰度阈值法[22]对砂岩试样孔隙结构进行统计分析。
不同细粒含量砂岩初始孔隙率如图 3所示。随着细粒含量的增加,砂岩初始孔隙率呈线性趋势逐渐降低,10% 细粒含量砂岩初始孔隙率为26.9%~30.1%。图 4为砂岩孔隙结构。其中,灰色为骨架,蓝色为孔隙结构。砂岩存在较多大孔。结合图 3可知,相较于30% 和50% 细粒砂岩,10% 细粒含量砂岩孔隙体积明显较大,孔隙数量较多。
将不同细颗粒含量砂岩孔隙结构按照孔隙体积(0, 2×103μm3)、(2×103μm3, 2×104 μm3)、(2×104 μm3, 2×105μm3)、(2×105μm3, 2×106 μm3)、(2×106 μm3, 2× 107μm3)、(2×107μm3, 2×108 μm3)、(2×108 μm3, 2×109μm3)、(2×109μm3, 2×1010 μm3)范围进行统计分析,其频率分布及三维结构如图 5图 6所示。综合分析图 5图 6可知,孔隙体积在0~2×103μm3,3种细粒含量砂岩频率相似。孔隙体积在2×103~2×106μm3,随着细颗粒含量的增多,频率逐渐增多;且孔隙体积在2×105~2×106μm3,30% 和50% 细粒含量砂岩频率(接近30%)远大于10% 细粒含量砂岩(接近20%)。孔隙体积在2×106~2×107 μm3,30% 细粒含量砂岩频率大于10% 和50% 细粒含量砂岩频率;而孔隙体积在2×107~2×1010μm3,30% 和50% 细粒含量砂岩频率较小(< 5%),远小于10% 细粒含量砂岩。
不同细粒含量砂岩应力敏感性如图 7所示。由图 7(a)可知,随着细粒含量的增加,砂岩初始渗透率降低,10% 细粒含量砂岩渗透率在953~1027 mD。由图 7(b)可知,不同细粒含量砂岩渗透率变化随有效应力变化趋势相似。有效应力为1~5 MPa,渗透率迅速降低;随后有效应力在5~11 MPa,渗透率呈线性下降。随着细粒含量的增加,渗透率降低程度越大,50% 细粒含量砂岩在有效应力为11 MPa条件下渗透率降低约为50%。
采用幂函数对不同细粒含量砂岩渗透率随有效应力降低规律进行拟合
$\frac{k}{k_0}=p_{\mathrm{e}}^{\mathrm{c}}$
式中,k表示已知有效应力下渗透率,mD;k0表示初始渗透率,mD;pe表示有效应力,MPa;c表示拟合常数,10%、30%、50% 细粒含量值分别为-0.20、-0.24、-0.30。
为了能较为全面地评价不同细粒含量砂岩渗透率应力敏感性,根据相关学者研究[11-12, 23],选取应力敏感性系数、渗透率损害率、渗透率曲率3个指标进行评价。有效应力改变导致砂岩储层渗透率发生变化,采用应力敏感性系数评价砂岩储层应力敏感性。应力敏感性系数[23]计算公式为
$S_{\mathrm{s}}=\frac{1-\left(\frac{k}{k_0}\right)^{1 / 3}}{\lg \frac{p_{\mathrm{e}}}{p_0}}$
式中,Ss表示应力敏感性系数;p0表示参考有效应力,MPa。
根据式(2),渗透率比的1/3次幂与有效应力比的对数呈线性关系,拟合后方程斜率即为应力敏感性系数,不同细粒含量砂岩应力敏感性系数评价如图 8所示。由图 8可知,10%、30%、50% 细粒含量砂岩应力敏感性系数分别为0.14、0.17、0.21。随着细粒含量的增加,砂岩应力敏感性系数越大,应力敏感程度越强。
渗透率损害率是评价储层渗透率应力敏感性的重要参数[24],表示储层渗透率随有效应力增加而降低的程度。渗透率损害率越大,表明储层渗透率受有效应力损害程度越大,应力敏感程度越高。计算公式为
$k_{\mathrm{s}}=\frac{k_0-k_i}{k_0} \times 100 \%$
式中,ks表示渗透率损害率;ki表示i级有效应力下渗透率,mD。
图 9为不同细粒含量砂岩渗透率损害率。由图 9可知,不同细粒含量砂岩渗透率损害率变化趋势相似,随着有效应力的增加呈幂函数关系增大。渗透率损害率随着细粒含量的增加而增大。
渗透率曲率表征渗透率随有效应力变化速率[15]。渗透率曲率越大,渗透率随有效应力变化越快,应力敏感性越强。渗透率曲率计算公式为
$k_{\mathrm{c}}=\frac{\left|k^{\prime \prime}\right|}{\left(1+k^{\prime 2}\right)^{3 / 2}} \times 100 \%$
式中,kc表示渗透率曲率,%;kk′′分别表示渗透率对有效应力的一阶、二阶导数。
将式(1)代入后,渗透率曲率为
$k_{\mathrm{c}}=\frac{\left|k_0 \mathrm{c}(\mathrm{c}-1) p_{\mathrm{e}}^{\mathrm{c}-2}\right|}{\left[1+\left(k_0 \mathrm{c} p_{\mathrm{e}}^{\mathrm{c}-1}\right)^2\right]^{3 / 2}}$
不同细粒含量砂岩渗透率曲率如图 10所示。由 图 10可知,不同细粒含量砂岩渗透率曲率随有效应力增加而降低。在低有效应力下(2~5 MPa),渗透率曲率迅速降低,且渗透率曲率随细粒含量的增加而增加,即细粒含量越高,渗透率随有效应力降低越快;在高有效应力下(>5 MPa),渗透率曲率基本不变,不同细粒含量砂岩渗透率曲率相似,即渗透率随有效应力降低速率基本一致。
通过应力敏感性系数、渗透率损害率、渗透率曲率3个评价指标可得:随着细粒含量的增加,砂岩应力敏感性增强。综合小节2.1孔隙结构分析可知,随着细粒含量的增加,砂岩小孔数量增多,大孔数量减少,导致砂岩应力敏感性越强。因此,对于碳酸盐胶结砂岩,孔隙体积分布是影响渗透率应力敏感性的主要因素(小孔频率越高,渗透率应力敏感性越强)。
图 11为不同细粒含量弱胶结砂岩孔隙度-应力演化。由图 11可知,细粒含量越高,同一有效应力下孔隙率降低越多。不同细粒含量弱胶结砂岩孔隙率-应力演化规律相似。在初始应力(0~5 MPa)区间,岩石内部孔隙压缩,孔隙率迅速降低。随着应力的增加(> 5 MPa),岩石骨架承受应力,孔隙率随有效应力的增加呈线性趋势降低。
Liu等[25-26]认为岩体是非均匀的,将岩体分为概念上的“软体”和“硬体”。“软体”被认为是岩体中的孔、裂隙,“硬体”被认为是岩体骨架部分。“软体”能够承受较大的变形,遵循基于自然应变的胡克定律来描述;“硬体”仅能承受较小的变形,遵循基于工程应变的胡克定律描述。因此,对于岩体孔隙
$V^{\mathrm{p}}=V_{\mathrm{e}}^{\mathrm{p}}+V_{\mathrm{t}}$
式中,VP表示岩体孔隙体积;Vep表示“硬体”部分孔隙体积;Vt表示“软体”体积。其中,“软体”部分遵循自然应变胡克定律
$V_{\mathrm{t}}=V_{0, \mathrm{t}} \exp \left(-\frac{\sigma}{K_{\mathrm{t}}}\right)$
式中,V0, t表示初始“软体”体积;σ表示应力;Kt表示“软体”弹性模量。
“硬体”部分遵循工程应变胡克定律
$V_{\mathrm{e}}^{\mathrm{p}}=V_{0, \mathrm{e}}\left(1-\frac{\sigma}{K_{\mathrm{e}}}\right)$
式中,Ke表示“硬体”弹性模量;V0, e表示初始“硬体”体积。
$\mathrm{d} \phi=\frac{\mathrm{d} V^{\mathrm{p}}}{V_0}=\frac{\mathrm{d} V_{\mathrm{e}}^{\mathrm{p}}+\mathrm{d} V_{\mathrm{t}}}{V_0}$
式中,ϕ为岩体孔隙率;V0为岩体体积。
将式(7)、(8)代入式(9)
$\mathrm{d} \phi=-\phi_{\mathrm{e}} C_{\mathrm{e}} \mathrm{~d} \sigma-\frac{\gamma_{\mathrm{t}}}{K_{\mathrm{t}}} \exp \left(-\frac{\sigma}{K_{\mathrm{t}}}\right) \mathrm{d} \sigma$
$\gamma_{\mathrm{t}}=\frac{V_{0, \mathrm{t}}}{V_0}$
$\phi_{\mathrm{e}}=\phi_0-\gamma_{\mathrm{t}}$
式中,Ce为压缩系数;ϕe为“硬体”部分孔隙率;ϕ0为岩体初始孔隙率;γt为“软体”体积占岩体比例。
$\phi=\phi_{\mathrm{e}}\left(1-C_{\mathrm{e}} \sigma\right)+\gamma_{\mathrm{t}} \exp \left(-\frac{\sigma}{K_{\mathrm{t}}}\right)$
为分析比较不同细粒含量孔隙率随有效应力变化,将孔隙率归一化:
$\frac{\phi}{\phi_0}=\frac{\phi_{\mathrm{e}}}{\phi_0}\left(1-C_{\mathrm{e}} \sigma\right)+\frac{\gamma_{\mathrm{t}}}{\phi_0} \exp \left(-\frac{\sigma}{K_{\mathrm{t}}}\right)$
$\alpha=\alpha_{\mathrm{e}}\left(1-C_{\mathrm{e}} \sigma\right)+\left(1-\alpha_{\mathrm{e}}\right) \exp \left(-\frac{\sigma}{K_{\mathrm{t}}}\right)$
$\alpha=\frac{\phi}{\phi_0}$
$\alpha_{\mathrm{e}}=\frac{\phi_{\mathrm{e}}}{\phi_0}$
式(14)、(15)中,未知参数分别为αeCeKt
基于图 11中的实验数据,通过拟合的方法反分析出上述3个未知参数。由图 12可知,3个未知数据与细粒含量均呈指数关系,其公式为
$\alpha_{\mathrm{e}}\left(F_{\mathrm{c}}\right)=0.018 \exp \left(-F_{\mathrm{c}} / 19.7\right)+0.97$
$C_{\mathrm{e}}\left(F_{\mathrm{c}}\right)=0.0001 \exp \left(F_{\mathrm{c}} / 14.6\right)+0.001$
$K_{\mathrm{t}}\left(F_{\mathrm{c}}\right)=0.027 \exp \left(F_{\mathrm{c}} / 16.25\right)+1.65$
式中,Fc表示细颗粒含量,%。
将式(18)~(20)代入式(15)得到弱胶结砂岩孔隙率-应力预测模型,如式(21)。模型预测值如图 11所示,模型能够较好地预测弱胶结砂岩孔隙率−应力演化。
$\alpha=\alpha_{\mathrm{e}}\left(F_{\mathrm{c}}\right) \cdot\left(1-C_{\mathrm{e}}\left(F_{\mathrm{c}}\right) \cdot \sigma\right)+\left(1-\alpha_{\mathrm{e}}\left(F_{\mathrm{c}}\right)\right) \cdot \exp \left(-\frac{\sigma}{K_{\mathrm{t}}\left(F_{\mathrm{c}}\right)}\right)$
CO2注入储层过程中,会改变储层应力状态。综合以上分析可知,随着有效应力的增加,储层渗透率和孔隙率降低,从而影响CO2注入速率及封存量。储层细粒含量的不同,渗透率与孔隙率应力敏感性变化规律不同,因此,通过对不同细粒含量砂岩渗透率应力敏感性变化规律及孔隙率预测模型的研究,对CO2地质封存注入压力条件的设置与封存总量的评估具有重要意义。
1)随着细粒含量的增加,砂岩孔隙体积逐渐降低。孔隙体积为0~2×103μm3,3种细粒含量砂岩孔隙体积频率接近。孔隙体积为2×103~2×106μm3,随着细颗粒含量的增多,频率逐渐升高。孔隙体积为2×106~ 2×107μm3,30% 细粒含量频率最大。孔隙体积为2×107~2×1010μm3,30% 和50% 细粒含量频率远小于10% 细粒含量。
2)有效应力为1~5 MPa,渗透率迅速降低;有效应力为5~11 MPa,渗透率呈线性下降。随着细粒含量的增加,应力敏感性系数及渗透率损害率增大。在低有效应力下(1~5 MPa),渗透率曲率随细粒含量的增加而增加;在高有效应力下(> 5 MPa),不同细粒含量砂岩渗透率曲率基本不变。
3)孔隙体积分布是影响渗透率应力敏感性的主要因素,即小孔频率越高,渗透率应力敏感性越强。细粒含量越高,同一有效应力下孔隙率降低越多。在初始应力(0~5 MPa)区间,岩石内部孔隙压缩,孔隙率迅速降低。随着应力的增加(> 5 MPa),孔隙率随有效应力的增加呈线性趋势降低。
  • 国家自然科学基金资助项目(52306205)
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2025年第43卷第6期
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doi: 10.3981/j.issn.1000-7857.2023.12.01908
  • 接收时间:2023-12-19
  • 首发时间:2025-06-29
  • 出版时间:2025-03-28
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  • 收稿日期:2023-12-19
  • 修回日期:2024-01-23
基金
国家自然科学基金资助项目(52306205)
作者信息
    1. 浙江大学建筑工程学院, 杭州 310058
    2. 浙江大学海南研究院, 三亚 572025
    3. 浙江大学上海高等研究院, 上海 201203

通讯作者:

郑嘉男(通信作者), 副研究员, 研究方向为天然气水合物及二氧化碳地质封存, 电子信箱:
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

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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