Article(id=1149738957119861531, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149738954913661267, articleNumber=1003-3033(2024)04-0087-06, orderNo=null, doi=10.16265/j.cnki.issn1003-3033.2024.04.1002, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1697731200000, receivedDateStr=2023-10-20, revisedDate=1705766400000, revisedDateStr=2024-01-21, acceptedDate=null, acceptedDateStr=null, onlineDate=1752048728494, onlineDateStr=2025-07-09, pubDate=1714233600000, pubDateStr=2024-04-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752048728494, onlineIssueDateStr=2025-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752048728494, creator=13701087609, updateTime=1752048728494, updator=13701087609, issue=Issue{id=1149738954913661267, tenantId=1146029695717560320, journalId=1146031787341344770, year='2024', volume='34', issue='4', pageStart='1', pageEnd='252', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752048727968, creator=13701087609, updateTime=1756468927830, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1168278616925286857, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149738954913661267, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1168278616925286858, tenantId=1146029695717560320, journalId=1146031787341344770, issueId=1149738954913661267, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=87, endPage=92, ext={EN=ArticleExt(id=1149738957820310306, articleId=1149738957119861531, tenantId=1146029695717560320, journalId=1146031787341344770, language=EN, title=Dynamic assessment model for seismic resilience of oil depot, columnId=1149733269173878863, journalTitle=China Safety Science Journal, columnName=Safety engineering technology, runingTitle=null, highlight=null, articleAbstract=

To address the safety management issues of oil depots in case of earthquakes,firstly,seismic resilience is defined as the ability of oil depots to maintain their storage functions in terms of resisting earthquake damage,mitigating secondary disasters,adapting to environments after earthquakes,and quickly recovering after earthquakes. Moreover,an assessment model for the seismic resilience of Oil depot is proposed by quantifying the four types of abilities. Secondly,a quantitative assessment of the seismic resilience of the oil depot is realized through a dynamic Monte Carlo method,considering the uncertainty of the resistance and mitigation stages,thereby obtaining the seismic resilience value. Finally,the performance of the proposed model is validated against the oil depot in an earthquake area. The results indicate that the uncertainty of the resilience ability of the oil depot results in a significant fluctuation in resilience values,and the domino effect has a significant influence on the oil depot's resilience and cannot be ignored. Moreover,the farther the earthquake source distance and the lower the fault slip rate lead to higher seismic resilience. The seismic resilience can be improved by staying away from active faults,predicting earthquake hazards,and reducing emergency response time.

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为解决油库面临地震时的安全管理问题,首先,将抗震韧性定义为油库为保持储存功能表现出的抵御地震破坏、减轻地震次生灾害、适应震后环境和震后快速恢复的能力,并量化油库抗震韧性表现出的4种能力,建立油库抗震韧性评估模型;其次,考虑到抵御阶段和减轻阶段的不确定性,基于动态蒙特卡罗法,定量评估油库抗震韧性,并获得油库的抗震韧性值;最后,以某地震区域油库为例,验证模型的有效性。结果表明:油库韧性能力的不确定性导致韧性值出现大幅波动;多米诺效应对油库韧性的影响较大,不可忽略;震源距离越远、断层滑移率越小,抗震韧性越高;远离活动断层、预测地震危害、减少应急响应时间等措施可提高抗震韧性。

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陈超 (1991—),男,四川广安人,博士,教授,主要从事油气及新能源储运系统安全韧性与完整性管理等方面的研究。E-mail:

李长俊 教授

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陈超 (1991—),男,四川广安人,博士,教授,主要从事油气及新能源储运系统安全韧性与完整性管理等方面的研究。E-mail:

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李长俊 教授

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李长俊 教授

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油库抗震韧性动态评估模型
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陈超 1, 2 , 谭心鑫 1 , 王迪 3 , 李长俊 1
中国安全科学学报 | 安全工程技术 2024,34(4): 87-92
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中国安全科学学报 | 安全工程技术 2024, 34(4): 87-92
油库抗震韧性动态评估模型
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陈超1, 2 , 谭心鑫1, 王迪3, 李长俊1
作者信息
  • 1 西南石油大学 石油与天然气工程学院,四川 成都 610500
  • 2 代尔夫特理工大学 安全科学系,荷兰 代尔夫特 2628 BX
  • 3 国家石油天然气管网集团,北京 100028
  • 陈超 (1991—),男,四川广安人,博士,教授,主要从事油气及新能源储运系统安全韧性与完整性管理等方面的研究。E-mail:

    李长俊 教授

Dynamic assessment model for seismic resilience of oil depot
Chao CHEN1, 2 , Xinxin TAN1, Di WANG3, Changjun LI1
Affiliations
  • 1 School of Petroleum Engineering,Southwest Petroleum University,Chengdu 610500,China
  • 2 Safety and Security Science Group,Delft University of Technology,Delft,The Netherlands,2628 BX
  • 3 China Oil & Gas Pipeline Network Group,Beijing 100028,China
出版时间: 2024-04-28 doi: 10.16265/j.cnki.issn1003-3033.2024.04.1002
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为解决油库面临地震时的安全管理问题,首先,将抗震韧性定义为油库为保持储存功能表现出的抵御地震破坏、减轻地震次生灾害、适应震后环境和震后快速恢复的能力,并量化油库抗震韧性表现出的4种能力,建立油库抗震韧性评估模型;其次,考虑到抵御阶段和减轻阶段的不确定性,基于动态蒙特卡罗法,定量评估油库抗震韧性,并获得油库的抗震韧性值;最后,以某地震区域油库为例,验证模型的有效性。结果表明:油库韧性能力的不确定性导致韧性值出现大幅波动;多米诺效应对油库韧性的影响较大,不可忽略;震源距离越远、断层滑移率越小,抗震韧性越高;远离活动断层、预测地震危害、减少应急响应时间等措施可提高抗震韧性。

油库  /  地震  /  安全韧性  /  抗震韧性  /  动态评估  /  评估模型

To address the safety management issues of oil depots in case of earthquakes,firstly,seismic resilience is defined as the ability of oil depots to maintain their storage functions in terms of resisting earthquake damage,mitigating secondary disasters,adapting to environments after earthquakes,and quickly recovering after earthquakes. Moreover,an assessment model for the seismic resilience of Oil depot is proposed by quantifying the four types of abilities. Secondly,a quantitative assessment of the seismic resilience of the oil depot is realized through a dynamic Monte Carlo method,considering the uncertainty of the resistance and mitigation stages,thereby obtaining the seismic resilience value. Finally,the performance of the proposed model is validated against the oil depot in an earthquake area. The results indicate that the uncertainty of the resilience ability of the oil depot results in a significant fluctuation in resilience values,and the domino effect has a significant influence on the oil depot's resilience and cannot be ignored. Moreover,the farther the earthquake source distance and the lower the fault slip rate lead to higher seismic resilience. The seismic resilience can be improved by staying away from active faults,predicting earthquake hazards,and reducing emergency response time.

oil depot  /  earthquake  /  safety resilience  /  seismic resilience  /  dynamic assessment  /  assessment model
陈超, 谭心鑫, 王迪, 李长俊. 油库抗震韧性动态评估模型. 中国安全科学学报, 2024 , 34 (4) : 87 -92 . DOI: 10.16265/j.cnki.issn1003-3033.2024.04.1002
Chao CHEN, Xinxin TAN, Di WANG, Changjun LI. Dynamic assessment model for seismic resilience of oil depot[J]. China Safety Science Journal, 2024 , 34 (4) : 87 -92 . DOI: 10.16265/j.cnki.issn1003-3033.2024.04.1002
油库是储存各种易燃、易爆油品的重要基础设施,一旦发生突发事件导致储罐损坏,极易引发火灾、爆炸等重大事故。油库是由多个储罐构成的储油罐区,单个储罐发生火灾、爆炸等事故容易诱发多米诺效应,导致连锁事故。近年来,随着气候变化、极端天气的加剧,自然灾害频发,不断影响工业基础设施的安全运行。地震作为我国主要的自然灾害,具有预报难度大、破坏性大、影响面广等特点,容易导致储罐破损,并诱发火灾、爆炸等次生灾害,妨碍油库的正常运营和安全管理。目前,油库地震安全防控的研究主要以地震预警[1]和储罐结构抗震设计[2]为主,忽略了地震减灾、应急响应和灾后恢复的重要性。因此,基于安全韧性理论开展油库抗震韧性动态评估研究具有重要意义。
安全韧性研究方面,国内外学者进行了大量理论研究。韧性最早由HOLLING[3]提出,指的是森林周期性成长状态,包括植被增殖、破坏后的骤减以及再生,强调了恢复能力。DOORN等[4]认为韧性是一个“正式”概念,并证明韧性表现为保持或增强系统某些功能的能力。近年来,韧性已成为安全和风险研究的重要概念。AVEN等[5]探究了韧性和风险的关系,并对韧性进行了定性评估,改进韧性管理模式;黄浪等[6]全面分析了韧性理论在安全科学领域的应用现状,指出韧性理论有由静态分析向动态分析的发展趋势;李鑫等[7]运用事故树分析法构建了工人疲劳安全韧性模型,提高了工厂对工人的疲劳管理能力。随着研究的深入,不断有学者将韧性应用到工业领域的安全防控中[8-10]。然而,安全韧性研究主要集中于韧性理论与韧性指标,定量研究较少,罕有安全事故动态演变对韧性影响的研究。
鉴于此,为增强油库抗震韧性,减轻次生灾害的破坏,笔者拟提出一种动态评估模型,量化油库的恢复能力,考虑灾害的演变过程,提出增强韧性的措施,为提高油库的安全管理、增强油气供给韧性提供理论依据。
韧性原理已经被运用于各行各业中,不同领域对韧性的定义存在差异。借鉴美国国家科学院提供的韧性定义[11],油库抗震韧性被定义为油库在地震灾害中为保持其储存性能,表现出抵御地震破坏、减轻地震次生灾害、适应震后环境和震后快速恢复的能力。根据油库抗震韧性的定义,当发生地震灾害时,随着灾害的动态演变,油库可能处于6个阶段,演变过程如图1所示。第1阶段为初始阶段,在发生地震之前油库处于初始状态,储存性能为S0;第2阶段为地震突发阶段,当地震发生时,由于1个或多个储罐受损,油库储存性能立即降低,并导致重大事故;第3阶段为升级阶段,初始的重大事故场景可能会诱发多米诺效应,破坏邻近储罐,进一步降低储存性能,在时间t2时储存性能达到最小值S2;第4阶段为适应阶段,当灾害演变结束后,油库可以调整其操作策略,达到提高储存性能的效果;第5阶段为恢复阶段,工作人员在此阶段采用恢复策略,通过修复或重建受损储罐(t3t4)来恢复储存性能;第6阶段为完全恢复后的阶段。
基于储存性能曲线构建油库抗震韧性模型是建立油库抗震韧性动态评估模型的关键步骤。
油库承担着接收、储存、供给石油产品的重要角色。因此,油库的储存性能可用油库初始阶段的每日油品平均周转量表示,按下式计算:
h Q = i = 1 n h i
式中:hQ为油库平均周转量,m3/d;hi为单种油品平均流量,m3/d;n为油品的种类总数。
另一个储存性能指标是油库库容,油库储罐数量为i,则初始阶段油库总容积为所有储罐容积之和:
V Q = i = 1 m V i
式中:VQ为油库总容积,m3;Vi为单个储罐容积,m3;m为储罐总数。
根据韧性理论[12],量化图1的储存性能曲线,得到抗震韧性:
R = t 1 t 4 S ( t ) d t S ( t 0 ) ( t 4 - t 1 )
式中:R为油库抗震韧性;S(t)为储存性能,m3;S(t0)为初始储存性能,m3;t1t4为韧性过程时间,d。不同事故演化场景的韧性值R可能随式(3)中积分上限(t4)变化而变化[13]。如有M个事故演变场景,t4的最大值为tmax,则韧性表示为:
R = 1 M i = 1 M t 1 t m a x S i ( t ) d t S ( t 0 ) ( t m a x - t 1 )
其中,0<R<1,R越接近1韧性越强。R=1是一种理想状态,在这种情况下,地震不会导致油库储存性能退化,地震对油库的影响被完全吸收。R=0是一种极限状态,在这种情况下,油库被损坏并且无法恢复。大部分演化场景的韧性值位于0~1之间。
1) 抵御能力计算。抵御能力的大小主要取决于地震的强度,可用储罐的易损性表示:
C s = 1 - P f
式中:Cs为单个储罐抵御能力;Pf为单个储罐的失效概率。
地震作用下,多个储罐可能同时受到影响,导致油库的储存性能突然下降,其剩余的储存性能为S1
2) 储罐失效概率计算。地面运动引起的地面强烈震动现象是造成油库储罐破坏的主要原因[14]。峰值地面加速度(Peak Ground Acceleration,PGA)常作为灾害因子,用于分析易损性[15]。为了动态评估储罐的地震易损性,采用Probit概率模型[16]计算地震导致设备失效的概率:
Y = k 1 + k 2 l n ( 100 P G A )
式中:Y为设备失效概率;k1k2为Probit系数;储罐的失效概率Pf可以基于累积标准正态分布的密度函数Φ计算[17]:
P f = Φ ( Y - 5 )
由于抵御能力模型的不确定性,通过Pf随机抽样数确定地震作用下受损的储罐。因此,地震发生后的储存容量按下式计算:
V t 1 = V Q - Δ V e
式中: V t 1为地震发生后油库储存容量,m3Ve为地震中损坏的储存容量,m3
地震灾害导致储罐破损时,可能造成多罐火灾,引发协同效应[18]。因此,储罐接收的热辐射等于不同火灾产生的热辐射之和。在初始阶段,可根据总热辐射计算储罐的失效时间(Time To Failure,TTF)。
T T F k t i = e x p ( c 1 × V k c 2 + c 3 l n ( Q k t i ) + c 4 ) 60
式中: Q k t i为储罐k受到的总热辐射强度; V k c 2为储罐体积,m3;c1c2c3c4为取决于设备类型的常数[12]
储罐遭受的热辐射可能随时间变化,在计算TTF时,应叠加每个周期内储罐接收到的热量,即叠加效应。 t i + 1 T T F k t i + 1可通过ti T T F k t i来更新[18]:
T T F k t i + 1 = Q k t i + 1 Q k t i c 3 × ( T T F k t i - t i + 1 + t i )
当TTF=0时,认为储罐失效,并发生多米诺效应。应急响应对于防止多米诺效应至关重要,其性能主要由应急响应时间(Time Emergency Response,TER)决定。考虑到应急响应的不确定性,通常使用对数正态分布对TER进行建模[12]:
l n T E R ~ N ( μ σ 2 )
式中:μ为均值;σ2为方差。
如果应急响应及时到达,则认为多米诺效应被制止。多米诺效应演变结束时(t2),总储存容量如下:
V t 2 = V t 1 - Δ V u p
式中: V t 2为升级阶段后储存容量,m3Vup为升级阶段中损坏的储罐容量,m3
储备罐可直接增加总储存量,从而增加每日油品流量,计算过程如下:
V t 3 = V t 2 + V a d
f t 3 = V t 2 + V a d V t 2 f t 2
式中: V t 3t3时的总储存量,m3; f t 3为油品的日流量,m3/d;Vad为增加的储存容量,m3。通过适应策略,损失的储存性能可以得到部分恢复。
恢复阶段,所有受损储罐都可以进行重建,储罐恢复时间(Time To Recovery,TTR)是一个定量指标。通常重建储罐需要几个月时间,如果多个储罐损坏,重建顺序也可能影响TTR。恢复能力与施工时间呈负相关,所以,增加投资可以缩短TTR,从而提高储存韧性。恢复完成时间t4表示为:
t 4 = t 3 + T T R
鉴于韧性模型中存在诸多不确定性,文中采用动态蒙特卡罗算法,动态求解油库抗震韧性[19]
将第2节提出的韧性动态评估模型应用于成品油油库的抗震韧性。该油库由16个储罐(T1—T16)组成,油品全部为汽油,油库的布局如图2所示。油库TER的均值为15 min,方差为5 min。一旦发生事故导致储罐受损,事故调查时间(t2t3)为30 天,为恢复储罐做准备。恢复阶段,依据储罐容积从大到小重建受损储罐。
采用动态蒙特卡罗算法随机动态模拟案例油库的韧性,迭代次数N设置为105次,共形成105个灾害演化场景,每个场景都存在一个韧性值。通过对这些韧性值排序,获得韧性最小值、均值以及最大值。韧性最大值、均值、最小值对应的灾害演化场景(3条曲线)如图3所示。由此得到目标油库抗震韧性R(均值)为0.97、最大值为1、最小值为0.449 1。 油库韧性(0.97)较强,因为油库离地震带的距离较远,地震对油库的冲击较小。韧性的最大值和最小值之间存在明显差距,韧性的随机特性不能忽视。在韧性均值对应灾害场景下,地震导致储罐(T6)立即失效,在随后的升级阶段中,储罐(T5、T7、T9)受损。
1) 多米诺效应对油库韧性的影响。韧性最小值的灾害演化场景分析多米诺效应效果更显著。如果不考虑多米诺效应,韧性最小值将从0.449 1增加到0.784 1。多米诺效应对灾害场景演化的影响如图4所示。演化过程中实线常高于虚线,表明多米诺效应对油库韧性的影响较明显,忽略多米诺效应,油库的韧性可能被高估。
2) 应急响应对油库韧性的影响。应急响应措施可以有效防止多米诺效应,提高油库的安全性。应急响应参数μσ2对油库韧性的影响如图5所示,μ为减轻能力的关键参数,油库韧性随着μ的增加而明显降低,但σ2对油库韧性的影响并不明显。因此,缩短应急响应时间,可有效减小油库储存性能损失,提高油库抗震韧性值。
3) 距离与滑移率对油库抗震韧性的影响。油库和震源的距离D与滑移率δ均对地震灾害发生概率有显著影响,从而间接影响油库的韧性。油库离震源的D越远,δ越小,受地震灾害的影响概率越低,油库抗震韧性越强,如图6所示。
随着油库与震源的D越来越远,δ的减小,油库的抗震韧性值不断增大。因此,油库建设时,选址应远离δ大的活动断层,以增强韧性。
1) 由于韧性的不确定性,不同灾害演化场景对应的韧性值可能出现大幅变化,油库抗震韧性中的不确定性不容忽视。此外,由于油库危险源集中,多米诺效应对油库的抗震韧性影响非常明显,忽略多米诺效应会高估油库抗震韧性。
2) 油库与震源的距离D越远,活动断层的滑移率δ越小,油库的抗震韧性值越高。因此,油库与震源的D以及活动断层的δ,对油库抗震韧性的影响不可忽视。
3) 油库的抗震韧性主要取决于地震的强度和油库抵御地震破坏、减轻地震次生灾害、适应震后环境和震后快速恢复的能力。提高油库的抗震韧性措施包括油库选址时远离活动断层、及时预测地震危害以及减少应急响应的到达时间。
  • 四川省重点研发项目资助(2023YFS0412)
  • 英国劳氏基金资助(Sg3\100021)
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2024年第34卷第4期
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doi: 10.16265/j.cnki.issn1003-3033.2024.04.1002
  • 接收时间:2023-10-20
  • 首发时间:2025-07-09
  • 出版时间:2024-04-28
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  • 收稿日期:2023-10-20
  • 修回日期:2024-01-21
基金
四川省重点研发项目资助(2023YFS0412)
英国劳氏基金资助(Sg3\100021)
作者信息
    1 西南石油大学 石油与天然气工程学院,四川 成都 610500
    2 代尔夫特理工大学 安全科学系,荷兰 代尔夫特 2628 BX
    3 国家石油天然气管网集团,北京 100028
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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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