深水油气管道中水合物堵塞的表征、检测方法与工程解决方案

孟阳 ,  韩冰月 ,  王纪广 ,  储佳伟 ,  姚海元 ,  赵佳飞 ,  张伦祥 ,  李清平 ,  宋永臣

工程(英文) ›› 2025, Vol. 46 ›› Issue (3) : 384 -404.

PDF (8792KB)
工程(英文) ›› 2025, Vol. 46 ›› Issue (3) : 384 -404. DOI: 10.1016/j.eng.2024.10.020
研究论文

深水油气管道中水合物堵塞的表征、检测方法与工程解决方案

作者信息 +

Hydrate Blockage in Subsea Oil/Gas Pipelines: Characterization, Detection, and Engineering Solutions

Author information +
文章历史 +
PDF (9002K)

摘要

随着深水油气资源的规模化开发,天然气水合物对管道多相流系统的安全运行构成了重大挑战。水合物在管道中生成、聚集、沉降,导致管道堵塞,降低输运能力,并造成重大的经济损失和人员伤亡。随着海洋油气勘探逐步向深水/超深水领域延伸,复杂的地质-工程环境进一步加剧了水合物堵塞风险。在水合物堵塞形成后,及时采取适当措施以减轻其危害至关重要,而有效减轻危害的前提是准确检测水合物堵塞的位置、数量并量化堵塞程度。本文系统梳理了水合物生成与堵塞演化过程中的多维度特征参量,包括相变过程的热力学特征演化规律、声波传播特性与阻抗响应规律、介电参数的动态演变特性、工业级监测仪器的实时反馈信号和流体动力学行为的异常波动模式。同时,本文还分析了基于声学、瞬态和光纤的检测方法的原理、局限性和适用性,并列举了现场实验的结果与商业化产品的应用案例。声脉冲反射法和瞬态法精度高、检测范围广,是检测水下管道水合物堵塞的有效方法;基于背压法的早期预警结合声脉冲反射法或瞬变波法的精准定位,可构建高效检测体系。未来研究中,融合水合物生成-堵塞模型与分布式光纤监测技术有望成为新建管道堵塞监测研究的新方向。

Abstract

With the development of offshore oil and gas resources, hydrates pose a significant challenge to flow assurance. Hydrates can form, accumulate, and settle in pipelines, causing blockages, reducing transport capacity, and leading to significant economic losses and fatalities. As oil and gas exploration moves deeper into the ocean, the issue of hydrate blockages has become more severe. It is essential to take adequate measures promptly to mitigate the hazards of hydrate blockages after they form. However, a prerequisite for effective mitigation is accurately detecting the location and amount of hydrate formation. This article summarizes the temperature–pressure, acoustic, electrical, instrumental–response, and flow characteristics of hydrate formation and blocking under various conditions. It also analyzes the principles, limitations, and applicability of various blockage detection methods, including acoustic, transient, and fiber-optic-based methods. Finally, it lists the results of field experiments and commercially used products. Given their advantages of accuracy and a wide detection range, acoustic pulse reflectometry and transient-based methods are considered effective for detecting hydrate blockages in future underwater pipelines. Using strict backpressure warnings combined with accurate detection via acoustic pulse reflectometry or transient-based methods, efficient and timely diagnosis of hydrate blockages can be achieved. The use of a hydrate model combined with fiber optics could prove to be an effective method for detecting blockages in newly laid pipelines in the future.

关键词

油气管道 / 流动保障 / 水合物堵塞检测 / 声学 / 瞬态

Key words

Oil and gas pipeline / Flow assurance / Hydrate blockage detection / Acoustic / Transient

引用本文

引用格式 ▾
孟阳,韩冰月,王纪广,储佳伟,姚海元,赵佳飞,张伦祥,李清平,宋永臣. 深水油气管道中水合物堵塞的表征、检测方法与工程解决方案[J]. 工程(英文), 2025, 46(3): 384-404 DOI:10.1016/j.eng.2024.10.020

登录浏览全文

4963

注册一个新账户 忘记密码

1 引言

天然气水合物(NGHs)是一种在低温高压环境下形成的固态物质,在海水淡化、CO2捕集与封存、气体分离、储能及气体储运等领域已得到广泛研究。然而,水合物会阻碍油气管道的正常运行。随着深水油气勘探的逐步深入,油气集输系统的深度和长度不断增加,这带来了更多挑战[12]。输运距离的增加使得管道内油气温度降低,更易形成水合物[34]。管道中一旦形成水合物,将增大流动阻力、降低输运能力,影响管道运行效率[57]。深水油气管道(尤其是恶劣环境下的大型工程)维护和检修难度极大,这对有效检测管道异常提出了巨大挑战。若水合物堵塞未被及时检测和清除,堵塞范围会持续扩大并最终完全堵塞管道,造成巨大经济损失[810]。与结蜡、结焦和结垢问题相比,水合物堵塞问题更为严重,是流动保障中的主要挑战之一,已引起广泛关注[4,1112]。

工业生产中常使用抑制剂缓解水合物形成[1315]。但热力学抑制剂存在注入量大、盐析沉淀、黏度高等问题,会影响油气的流动性能[1617];防聚剂存在成本高、高含水率下会失效、具有毒性等问题[1819];目前的动力学抑制剂主要对单一气体形成的水合物有效,且在高过冷条件下往往会失效[2021]。另一种水合物堵塞预防方法是预先脱除油气中的水分以防止水合物形成,但脱水设备故障或突发天气状况都可能会导致设备停机,无法达到预期效果[2223]。由此可见,在工程实际应用中难以消除水合物堵塞的风险,管道中总会形成水合物,尤其是在停产期间[24]。水合物在管道中聚集形成堵塞后,管内压力会显著升高,可能导致停产和重大经济损失[2526],处理不当还可能引发事故甚至人员伤亡[2731]。

管道堵塞难以避免,必须及时清除以减少损失[32]。清除前,需掌握准确的堵塞信息,方能制定针对性的清堵方案[24,29]。准确掌握堵塞位置有助于采取精准措施,及早解决堵塞问题,降低经济损失。通常,清堵初期会采用降压法促进水合物分解,但当存在多处堵塞时,即便两侧同时降压,堵塞间的高压也可能导致其高速移动,引发管道破裂事故发生[29,33]。因此,准确掌握堵塞数量对制定针对性方案、提高清堵效率、预防事故和人员伤亡至关重要。综上,获取具体的管道堵塞信息是安全高效清堵的关键,开展堵塞检测研究势在必行。

目前,管道故障检测主要关注泄漏和腐蚀方面,对堵塞检测的关注相对较少。管道堵塞检测的主要研究如下:Yu等[34]分析了声学方法在地下管道泄漏、堵塞等故障检测中的应用;Che等[35]和Brunone等[36]研究了瞬态方法在堵塞检测中的应用(尤其是局部堵塞),但他们的研究主要聚焦在特定检测方法本身;Datta和Sarkar [37]、Wong和McCann [38]综合分析了多种管道故障的检测方法,指出了管道检测的发展方向,但这些研究均缺乏对水合物堵塞检测的针对性研究。

本文总结了水合物形成和堵塞的特性,探讨了声学法、瞬态法、光纤法等多种检测方法,分析了不同方法的原理和应用范围,并总结了部分现场检测试验结果和现有商用产品。第2节介绍水合物形成和堵塞的特性;第3节阐述不同的水合物堵塞检测方法;第4节介绍这些方法的现场应用;第5节对水合物堵塞检测方法的未来发展提出建议。图1展示了本文的结构,着重突出了第2节和第3节的对应关系。

2 水合物的物理化学特性

图2所示,水合物易在有水存在的低温高压环境下生成,通常出现在阀门、倾斜段和低洼处,并可沿管道移动[3940]。水合物颗粒会像蜡/沥青沉积一样在管道中不断堆积,最终造成管道堵塞[24,41]。在停产或重启期间,管道环境尤其有利于水合物的形成,其可在数小时至数十小时内快速形成,导致堵塞[29,42]。

根据输运介质的持液率、空隙率和含水率,油气管道分为油主导、气主导和水主导系统[4344]。如图3 [45]所示,不同系统中水合物的堵塞机理和堵塞特性存在差异。

在油主导系统中,大部分水以乳化液滴形式存在,水合物在液滴与油相的界面处生成,并在液滴内部不断生长[46]。在液桥作用下水合物颗粒聚集,随着聚集持续进行,流动阻力增大,管道压降升高,最终形成堵塞[47]。堵塞初期,堵塞物中含有自由水,结构疏松;随后发生退火现象,水合物堵塞物的结构变得致密[29,48]。

在水主导系统中,大部分油以乳化油滴形式存在[49]。水合物生成后,流速降低导致油水混合物分层,水合物富集于下层流体中,使流体流动性急剧下降并引发堵塞[46]。在水合物生长初期,油滴可能被水合物包裹;但随着水合物持续生长,这些油滴会被挤压排出[5051]。

在气主导的系统中,水合物通常会在管壁上生长;因此,水合物膜会逐渐减小管道的横截面积,而焦耳-汤姆逊效应[52]会进一步促进下游水合物的生长。由于流动剪切力逐渐增大,管道中的水合物从管壁剥落并不断积聚,最终造成管道堵塞[52]。水合物还会在管道低洼积水处形成,并随管道内流体移动,最终导致堵塞[29]。

在堵塞初期,由于水合物块体间的相互黏附作用,堵塞物内部可能不会完全转化为水合物,因而存在自由水或油相,此时水合物堵塞物的孔隙度和渗透率较高[53]。若能在此阶段准确定位堵塞位置并迅速采取有效处理措施,可快速清除水合物堵塞。随着堵塞时间延长,水合物堵塞物会发生退火现象,其孔隙度和渗透率随之降低,堵塞物变得致密,使得后续清除作业更为复杂[29,46]。许多研究者致力于探索水合物形成与堵塞过程的规律及表征方法,以期实现水合物堵塞的快速识别。这些表征手段包括水合物形成过程中的温度、压力、声学信号、电学信号以及特定仪器的检测信号,及这些因素对管道流动的影响。

2.1 热力学特性

水合物形成时消耗气体并释放热量,导致系统压力降低、温度升高。目前针对CH4水合物的研究较多[11],但油气管道中除CH4外,通常还存在C2H6、C3H8等碳氢化合物。纯CH4或纯C2H6气体会形成I型(sI)水合物。但当碳氢化合物混合时,生成的水合物笼状结构可能发生变化,导致相平衡条件更为宽松,从而在管道内更易形成水合物,如图4所示。

CH4水合物的生成是放热过程,会导致局部温度变化。因此,准确计算水合物生成和分解过程的热量变化对计算系统温度至关重要。水合物分解和生成的焓变值通常互为相反数[5455]。水合物分解焓变可通过直接法或间接法获取:直接法包括量热法、压降法和差示扫描量热法(DSC);间接法主要利用克拉佩龙方程或克劳修斯-克拉佩龙方程进行计算。Gupta等[56]采用DSC直接测量水合物分解热,指出在典型温压条件下,sI CH4水合物对温度变化不敏感。许多研究者测得sI CH4水合物分解为水和CH4气体的焓变范围约为51.6~57.7 kJ∙mol-1 [5760]。但当使用1% C2H6和99% CH4生成II型(sII)水合物时,分解焓变增加约30% [60]。不同水合物笼型结构的分解焓变差异显著,使得水合物生成和分解过程中的温度变化幅度也各不相同。此外,流动系统中水合物浆液与管壁的摩擦也可能导致温度升高。

热导率是研究传热特性的关键参数,水合物生成后系统热导率的变化可能阻碍传热。Lingelem等[61]首次提出管道中沉积的水合物会降低壁面热通量。后续研究中,Singh等[62]、Na和Webb [63]、Nicholas等[64]也指出水合物沉积具有与蜡沉积类似的隔热作用。Chong等[65]报道了sI和sII水合物的热导率。油气输运管道中水合物沉积的热导率远低于钢质管道,因此这类沉积物具有隔热作用[66]。

研究水合物的隔热作用需准确测量其热导率。瞬态热线法和瞬态平面热源法是测量水合物热导率的常用方法,但水合物样品的多孔性及其接触热阻可能会对测量结果产生影响[67]。Zhang等[6869]研究了盲管中(指管道中流动极少或无流动的区域)水合物沉积的影响,发现水合物的隔热作用会影响温度分布,使管道内部温度升高。Song等[70]分析了盲管中水合物的热特性,计算了水合物沉积的有效热导率,并确定了纯水合物的热导率。Kumar等[71]发现,随着CH4水合物饱和度增加,多孔介质系统的热扩散率先升高后降低。Li和Liang [72]认为,多孔介质系统的有效热导率主要取决于沉积物的形态:在低水饱和度环境中,水合物将沉积物颗粒黏结,热导率随水合物饱和度增加而升高;在高水饱和度环境中,热导率随水合物饱和度的变化较小。

2.2 声学特性

天然气水合物是固态物质,其声学特性与冰相似。当水与气体形成水合物时,系统中的声速通常会增加,如图5中的两条红色曲线所示。针对沉积物中天然气水合物声速已有大量研究,许多研究人员认为沉积物中的天然气水合物可胶结沉积物颗粒,随着水合物饱和度增加以及介质整体压缩性降低,系统中声波的速度和振幅也会增加[73]。

Hu等[74]测量了多孔介质中NGHs形成和分解过程中的声速,发现在相同饱和度下,分解过程中的纵波和横波速度均高于形成过程中的速度。此外,饱和度低于10%时,纵波和横波速度对饱和度的变化不敏感;但饱和度超过10%时,两者均快速增加,在30%饱和度以下范围尤为明显。Bu等[75]也发现沉积物中的波速会随水合物饱和度增加而增加,且纵波速度的增加速率慢于横波。此外,Bu等[76]研究了沉积物中水合物分解过程的声学响应,发现在分解过程中,随着水合物饱和度降低,纵波和横波速度均下降。

Prasad等[77]称,在砂质多孔介质中,四氢呋喃(THF)水合物饱和度增加时,声波速度也会增加。具体而言,饱和度从15%增加至40%时,速度增加约80%。Duchkov等[78]测量了砂质多孔介质中纵波和横波的传播,得出速度与水合物饱和度呈线性相关的结论。相反,Xing等[79]认为多孔介质中的速度并非与天然气水合物的饱和度呈线性相关:水合物形成初期,水合物与砂粒的结合增加了颗粒间的接触面积,提高了弹性模量,导致速度快速增加;随着水合物饱和度进一步增加,水合物开始在孔隙中形成,使得水合物悬浮在孔隙空间中,不再与砂粒直接接触,而导致声速增加放缓。Bu等[76]得出结论,分解过程中声速随饱和度的变化不均匀:分解初期,与沉积物颗粒直接接触的水合物先分解,导致速度快速下降;分解后期,悬浮在孔隙空间中的水合物分解,速度下降幅度小于初期。

Gimaltdinov等[80]研究了液体中气泡生成CH4水合物过程的声学响应,发现水合物的形成会显著影响频率低于1 kHz波的传播,导致波衰减程度增加约两个数量级。此外,研究者还观察到初始气体含量增加会导致速度降低、反射系数增加。Hu等[81]发现,南海沉积物中的水合物最初悬浮在孔隙空间中,随着饱和度增加,水合物逐渐开始与多孔介质骨架接触。因此,在水合物形成初期,声学衰减随饱和度增加而增加;饱和度高于14%时,声学衰减随饱和度增加而降低。Gubaidullin等[82]研究了含水合物的多孔介质中的波传播,发现水合物饱和度增加会提高介质的声阻抗,使得声波在传播过程中形态保持良好,同时衰减程度也有所降低。在多孔介质中,水合物饱和度增加通常会提高介质的声阻抗,使介质与气相的声阻抗差异更显著,这一特性常用于检测水合物的分布[8384]。

在各类研究系统中,水合物形成会影响声波的传播特性。在管道输运系统,水合物聚集同样对声波传播影响显著。由于水合物堵塞物与周围油气介质的阻抗差异,在界面处会产生明显的反射波和折射波。此外,沉积在管道中的水合物堵塞物的阻尼作用,会削弱声波能量并改变其传播路径,从而影响系统的声学特性,导致频率响应变化。水合物引起的声阻抗不匹配和频率响应变化,正是声学法检测管道水合物堵塞的基础原理。

2.3 电学特性

Du Frane等[85]首次测量了纯CH4水合物的电导率。NGHs的电导率通常比海水低约5个数量级[86],因此常被视为电绝缘体。含NGHs的沉积物具有高电阻率特性,适用于水合物的地质勘探[8788]。Pearson等[89]研究了沉积物中THF水合物对电阻率的影响,发现电阻率随水合物饱和度的增加而升高,在水合物形成过程中,电阻率增加近两个数量级。Lee等[90]提出,THF水合物的形成会影响沉积物中游离水的体积分数,进而影响系统的电阻率。Buffett和Zatsepina [9192]研究了含CO2水合物的沉积物的电阻率,发现电阻率随饱和度增加而升高。Li等[93]对沉积系统(在游离气体和不同盐度水浓度条件下形成了CH4水合物)的电阻率变化进行了原位测量,发现电阻率始终随水合物饱和度的增加而升高。Spangenberg和Kulenkampff [94]测量了全水饱和玻璃珠样品中CH4水合物饱和度增加时的电阻率变化,发现随着水合物饱和度增加,电阻率显著升高;饱和度达到95%时,电阻率约为初始值的52倍。Ren等[73]提出,水合物饱和度的增加会消耗游离水,导致溶液浓度升高降低电阻率;但水合物作为绝缘体同时可能会堵塞多孔介质的孔隙,隔离导电溶液,从而导致电阻率升高。

水合物形成过程中放热会引起温度波动,影响溶液中离子的迁移,进而影响电阻率。温度升高时,离子迁移速率增加,导致电阻率降低[89,9192]。如图5中的黄色曲线所示,水合物生成初期,电阻率随温度升高而降低;随着水合物继续生成,电阻率持续升高。Zhou等[95]测量了CH4水合物形成和分解过程中的温度变化,发现水合物饱和度越高,电阻率越大,同时强调水合物饱和度的变化对电阻率的影响大于温度和压力变化的影响。Li等[96]测量了多孔介质中水合物成核、生长和分解过程的电阻率变化:NGHs成核时,水被消耗,导致溶液中盐浓度升高,电阻率略有降低;水合物生长时,其将系统中的盐溶液隔离,导致电阻率随饱和度增加而升高;水合物分解时,电阻率降低,但其值略高于生成前的值。这些发现证实了电阻率测量可用于检测NGHs的成核和生长。

图6 [97]所示,NGH的介电常数与水、天然气和原油存在显著差异,因此NGH形成过程中介电常数会发生明显变化。Jakobsen等[98]和Jakobsen与Folgero [99]利用介电常数研究了油水乳液中天然气水合物的形成,发现静态介电常数和介电增量可反映水合物的几何结构和演化等信息,同时弛豫时间可提供动力学信息。该研究团队[9899]采用介电谱研究了油包水乳液中天然气水合物的形成动力学,还测量了乳液中NGHs的形成,观察到弛豫时间、静态介电常数和高频介电常数的降低。Haukalid [100]提出,由于水合物的介电常数在千赫兹至兆赫兹范围内变化显著,降低测量频率可获得更好的结果。

2.4 仪器响应特性

X射线计算机断层扫描(CT)、粉末X射线衍射(PXRD)、拉曼光谱法和核磁共振(NMR)光谱法是微观研究中广泛使用的工具,也应用于NGHs领域。X射线CT生成一系列灰度图像,灰度值反映材料对X射线的吸收,通常与材料密度密切相关。水合物的密度通常低于水,因此在水合物研究中,CT图像的灰度值随水合物形成而变化。然后可采用分割技术获得水、气、油、水合物、砂等材料的三维分布。由于水合物与水的密度相近,在CT图像中区分两者有一定难度。增加水合物与溶液的密度差可提高图像分辨率,因此常使用碘化钾等物质提高溶液密度,或使用氩或氪等客体分子生成密度更高的水合物[101103]。该技术的近期进展包括应用随机森林和卷积神经网络等算法处理CT图像[104105]。此外,同步辐射X射线CT可提供更精确的三维图像[106107],这种对内部结构的详细观察无法通过二维图像实现[108]。

PXRD广泛应用于微观研究,当X射线以特定方向射入晶体时,晶体起衍射光栅作用,通过分析衍射X射线的方向和强度,可获得晶体内部的结构信息。拉曼光谱法广泛应用于客体分子与水分子的化学键研究,可提供水合物结构、组成、相变、水合数和占有率等信息[109112]。NMR光谱法可检测原子核在磁场中对电磁波的吸收,能够提供多组分NGHs的结构、组成、相变、水合数和占有率等信息[113115]。

Lu等[116]采用13C NMR法表征从卡斯卡迪亚周边获取的NGHs样品,发现了H型(sH)水合物的存在,证实了其存在于自然界中。Zhang等[117]采用拉曼光谱法对南海NGHs进行原位研究,发现水合物沉积物不止有一种结构。Meng等[118]采用PXRD研究了不同客体分子对水合物微观结构的影响,发现单组分轻质烷烃客体分子形成的水合物,其晶格常数与客体分子的直径呈正相关;而含氧化合物(如CO2和THF)作为客体分子形成的水合物,虽也与分子直径相关,但与晶格参数的关系不明显。由于水合物笼状结构提供的环境不同,客体分子进入笼状结构中时,拉曼信号和NMR信号会发生偏移[119]。通常,与气相相比,水合物中的CH4分子在拉曼光谱中会向低频方向偏移[120],在13C NMR光谱中则向低场方向偏移[121122]。尽管拉曼光谱法主要用于水合物的定性研究,但如果对强度进行校准并准确归属所有峰位,也可对水合物组成进行定量测量[123]。13C NMR光谱中CH4的化学位移具有结构特异性,其强度可进行定量测量,该方法是确定混合气体水合物形成过程中笼状结构占有率的有效手段[119]。

Truong-Lam等[124]采用拉曼光谱法观察CH4水合物的形成和分解过程,发现在水合物形成初期,小型笼状结构的形成速率大于大型笼状结构;而在分解后期,大型笼状结构的坍塌速率却比小型笼状结构大。他们还观察到水合物形成和分解过程中,OH伸缩带的拉曼强度会发生显著变化。多项研究采用拉曼光谱法和13C NMR光谱法已证实,CH4和C2H6混合气体会生成sII水合物,生成水合物的具体结构取决于混合气体的比例[121,125]。Uchida等[123]采用拉曼光谱法研究CH4和C2H6混合气体形成的水合物的晶体结构,根据C—C伸缩峰的强度比计算sI和sII水合物的体积比,并利用相对强度比确定客体分子在水合物笼状结构中的占有率。在sI和sII水合物结构中,C2H6分子单独占据大型笼状结构,CH4分子占据剩余的水合物笼状结构。该研究团队还采用拉曼光谱法和PXRD分析了CH4、C2H6、C3H8和i-C4H10混合气体形成的水合物结构,以及不同客体分子在笼状结构中的占有率。Dec [126]采用13C NMR光谱法研究CH4和C2H6混合气体的水合物形成过程,确定了四个不同阶段:sI和sII水合物同时形成;sI水合物停止形成,sII水合物加速生成;sI水合物开始分解,sII水合物进一步加速形成;最终达到稳定状态。Truong-Lam等[127]采用拉曼光谱法来表征CH4和C3H8混合气体形成的水合物,结果显示sII水合物的小型笼状结构的形成速度比大型笼状结构快,CH4既可占据小型笼状结构,也可占据大型笼状结构,而C3H8只能占据大型笼状结构。水合物分解时,小型笼状结构中的CH4分子比大型笼状结构中的分解更快,而大型笼状结构中的C3H8分解速度要慢于小型笼状结构中CH4的分解速度。Kumar等[119]采用PXRD、NMR和拉曼光谱法表征CH4、C2H6和C3H8混合气体生成的水合物,图像如图7所示。他们采用PXRD和拉曼光谱法确定水合物的组成和笼状结构占有率,并通过NMR数据比较来验证结果。

2.5 流动特性

在流动系统中,水合物颗粒的生成与聚集会影响流动特性,特别是当水合物体积分数较高时,浆体可能表现出复杂的流变特性[128]。掌握水合物浆体的流动特征对于保障油气管道输运安全至关重要。

Shen等[66]称,在高含水率体系中,水合物体积分数较低的浆体表现出类似水的特性,可被视为牛顿流体。然而,大量研究表明,在油主导体系中,浆体黏度随剪切速率增加而降低,表现出剪切稀化特性[128132]。这意味着,一旦停产,在含有水合物的管道中重新启动作业可能存在困难[129]。此外,这种行为会随着水合物体积分数的增加而加剧[128,133]。学界普遍认为,在水合物形成的初期阶段,随着水合物颗粒的形成和聚集,浆液黏度会上升;但当较大体积的水合物分解或重新排列时,浆液黏度则会下降[130]。在相同温度下,水合物的密度低于水,因此,随着水合物形成,浆液密度会降低。然而Shen等[66]指出,水合物形成初期时会产生大量气泡。这些气泡形成的水合物会发生破裂或者增长,导致浆液密度出现显著波动。研究人员还观察到,流速越高,水合物浆液的密度越大。

Wang等[134]研究了CH4水合物浆液的流动行为,发现当水合物体积分数达到30%~40%时,流动压降会发生显著变化。Joshi等[135]对高含水体系中水合物的形成进行了研究,同样发现只有当水合物达到一定浓度时压降才会快速增大,如图8中黄色曲线所示。他们将水合物形成过程划分为三个阶段:第一阶段水合物主要均匀分散于水中,压降无明显变化,对应图中区域I、II;第二阶段当水合物浓度达到一定值时,体系从均质悬浮流转变为非均质悬浮流,压降急剧上升,对应图中区域Ⅲ;第三阶段气体主导流动且含水量低,导致压降显著增大,对应图中区域Ⅳ。Shen等[66]提出水合物形成初期,流动为湍流状态,压降保持相对稳定;当体积分数达15%时,流动由湍流转为层流,压降随之降低;此后流动维持层流状态,压降随体积分数增加而快速增大。研究人员还观察到,流速越高压降越大。然而,Yan等[131]和Tang等[136]认为,如图8绿色曲线所示,水合物形成初期,压降会随水合物生成而增大。此外,Yan等[131]观测到流速会先下降,随后压降出现波动并最终趋于稳定。

水合物颗粒对浆体摩擦系数影响显著。Sinquin等[137]分析了水合物粒径对浆体摩擦系数的影响,发现在油水乳化体系中,浆体摩擦系数随含水量增加而上升[138]。Shi等[139]在研究油水乳液生成水合物时发现,水合物形成会导致压降骤增,且摩擦系数与水合物体积分数呈正比。Rao等[41]指出,需研究水合物聚集分布状态以确定压降。Shi等[139]提出高流速能增强颗粒悬浮效果,减少颗粒与管壁的对冲,从而降低摩擦系数和压降。Ding等[140]认为,水合物在稳定生长一段时间后会导致压降急剧增加;当大块水合物被剪切为小碎片后,压降略有回落,而后续水合物持续沉积将缩小流通面积,致使压降再度升高。Aman等[141]指出,在低流速的气主导体系中,水合物倾向于在管道中呈不均匀分布,导致局部聚集,这可能造成显著的摩擦压降。Fu等[142]指出,在湍流条件下,浆液中的水合物颗粒与管壁的碰撞是产生压降的主要原因之一。此外,水合物的存在所引起的流速变化,以及其与管壁的碰撞和摩擦,可能会产生流动噪声,而这有望用于检测水合物堵塞。

管道中水合物颗粒的形成会导致流动形态改变。Lv等[138]发现曼德汉流型图无法有效预测水合物浆液流型,这证实了水合物的形成确实会影响流动形态。Joshi [143]首次观察到,即使只存在少量水合物也能引发流型转变,促进弹状流形成。Zerpa等[144]建立了纯水中含水合物体系的流动模型,发现水合物可诱发从分层流向弹状流的转变,这与Joshi [143]的实验结果一致。Ding等[145]指出,水合物的存在确实会导致水合物浆液流动状态的变化。他们发现,在较低流速下,水合物可能会促使流动从平滑的分层流转变为弹状流或分层波状流。水合物还能在较低气体流速下,促使弹状流和分层波状流向环状流转变。此外,水合物还会增大从弹状流向泡状流转变的可能性。Liu等[146]指出,水合物的存在使得倾斜管道中的流动状态更容易转变为弹状流,且水合物倾向于在倾斜段与水平段之间的过渡区域积聚。

3 水合物堵塞检测方法

本节基于前文所述的水合物形成和堵塞特征,概述几种常用的水合物堵塞检测方法,并在表1中列出水合物堵塞检测方法与水合物物理化学特征的关系。这些方法可帮助专业人员及时识别和处理水合物堵塞问题,防止经济损失。

3.1 声脉冲反射法

图9为典型的声脉冲反射法示意图。当声脉冲遇到抗阻不匹配界面时,部分声脉冲透射,另一部分则反射回声源[147148]。阻抗变化引起的反射系数表示如下[149]:

R=(z1-z2)2(z1+z2)2
zi=ρici

式中,R为反射系数;zi 为声阻抗(Pa∙s∙m-1);ρi 为密度(kg∙m-3);ci为声波速度(m∙s-1);i取1或2,分别代表反射界面两侧的介质。

当两侧声阻抗差异显著时,会产生较大的声反射系数,这有利于实现准确检测。在天然气管道中,流动介质与水合物堵塞物的密度差异大,导致阻抗差异显著;但在石油管道中,密度差异较小,因此反射系数较低,这可能会影响检测距离和准确性。基于声脉冲反射法原理,Morgan和Crosse [150]成功检测出短距离管道中的堵塞。Amir等[151]采用该方法研究管道堵塞,发现流动横截面收缩会产生正反射,扩张则产生负反射。结合声波在管道中的传播速度和飞行时间(TOF),可获得堵塞位置信息:

x=c*t2

式中,x为测量点到堵塞处的距离(m);c为声波在输运介质中的速度(m∙s-1);t为测量点到堵塞前缘的TOF(s)。堵塞长度也可通过上式确定,此时x为堵塞长度,t为堵塞前缘与后缘之间的TOF,但这仅适用于非完全堵塞情况。

确定位置的关键是确定声波速度和TOF,对于天然气管道而言,声速c可基于理想气体方程计算;对于石油管道而言,c通过体积模量和密度确定。为实现更精准定位,精确估算流体中的声速至关重要。Wang等[152]建议通过测量两个已知点之间的TOF来校准声速。An等[153]采用重采样算法补偿声速变化引起的检测信号漂移。Duan等[154]强调,在时域中分离入射脉冲和反射脉冲对精准确定t值至关重要,这需保证声波发射器、采集装置与障碍物之间有足够间距。

但实际油气管道内结构复杂,声波信号的传播路径复杂,且声波振幅在传播过程中呈指数衰减,信号易受外部环境噪声干扰。An等人[155]发现,通过调整声波的频率成分,向受噪声污染的管道发射线性调频脉冲,可以检测堵塞。Duan等[154]采用互相关法测量TOF,发现TOF随脉冲频率发生变化,这与脉冲波与堵塞物的相互作用以及热黏性损耗密切相关。当获取的声学检测信号样本数量有限时,选择有效的信号去噪方法从声信号中获取准确的特征信息,对管道故障诊断至关重要[156]。在实际检测过程中,由于入射波和反射波的耦合作用,管道堵塞的测量信号表现出明显的非线性和非平稳性[157158]。

3.2 特征频率偏移法

水合物堵塞会产生额外的声阻抗,导致管道特征频率变化[159]。Domis [160]研究了特征频率与堵塞位置的关系。Wu和Fricke [161]利用特征频率偏移检测管道末端的堵塞,通过测量任意两个相邻点之间的特征频率确定堵塞是否存在,并利用本征频率偏移的幅度确定堵塞率。

Wu和Fricke [162]提出基于傅里叶变换的特征频率偏移分析,该方法可确定管道内多个堵塞的位置和大小,对小型堵塞的检测尤为准确。研究人员指出,由于实际测试中可获取的特征频率数量有限,而低阶特征频率对堵塞检测尤为重要,缺失前五个特征频率会导致无法重建堵塞区域。De Salis和Oldham [163]采用宽带最大长度序列测量法获取管道的特征频率偏移,研究发现该方法比频率扫描测试耗时短,他们还提出了一种无需管道长度即可计算堵塞截面面积的新公式。此外,他们证实了Wu和Fricke [162]的发现,即由于采集方法的限制,当堵塞率超过或接近50%,无法准确重建堵塞。但DeSalis和Oldham [163]认为,低阶特征频率偏移并非必需,所需的最低阶频率偏移取决于堵塞大小。

3.3 振动分析法

根据伯努利原理,管道中形成水合物堵塞时,流通面积减小,导致堵塞初期流速增加、压力降低;堵塞后期则流速降低、压力升高。流速和压力的变化会导致管道振动,因此通过测量管道振动可了解其堵塞情况。Lile等[164165]针对堵塞对圆形管道振动的影响开展了模拟与实验研究,发现振动强度与堵塞大小相关。

3.4 超声导波法

超声导波法中,超声换能器激发超声导波,该导波沿管壁传播并携带管道信息。导波遇到堵塞时,会产生反射和透射信号。在无堵塞管道中,低频率下基本扭转模式T(0,1)是唯一的扭转模式;但存在堵塞时,该模式会发生频散。

Ma等[166]假设含堵塞的管段为双层管,通过测量T(0,1)模式的反射来定位堵塞区域。Simonetti和Cawley [167]指出,双层管的截止频率与堵塞物的剪切波速和厚度有关。他们通过测量反射系数谱的峰值来确定截止频率,然后结合水合物的剪切波速计算堵塞率。研究还发现透射信号可用于堵塞测量,通过水合物的剪切波速和堵塞长度来计算频散曲线,然后将频散曲线与实测值拟合,可获得堵塞率[166,168]。Ma等[168]发现,堵塞物与管道之间的不完全黏结会影响反射系数谱,从而影响堵塞率的计算;当堵塞物厚度沿轴向变化时,反射信号强度降低,透射信号强度增加;当堵塞物不规则时,信号变得复杂,导致无法准确反映堵塞面积。

3.5 瞬态波反射法

基于瞬态波的方法通过开关阀门向管道中引入快速扰动,并利用瞬态波识别异常。瞬态波法也被称为瞬态压力法、水锤波法、水力瞬态法、流体瞬态法和快速瞬态法[35,169]。该方法最初主要应用于水管检测,由于水与油气的某些流体特性相似,瞬态波法被扩展到油气管道检测[170173]。通常认为,检测故障需要产生高幅压力波,但这可能损坏管道[169,174]。为解决这一问题,近期研究提出了一种使用小振幅压力波测试的方法[175]。

当瞬态波在管道内传播时,遇到堵塞会产生反射和透射信号。通过采集管道的压力和波速信息,结合管道结构,即可计算堵塞的位置和长度,实现管道堵塞的快速检测。由于特征波形的传播距离有限,瞬态波法的监测范围通常为几千米[176177]。瞬态波法的激励波形对堵塞检测精度影响显著。Lee等[178]研究了瞬态信号带宽对故障检测的空间分辨率、精度和范围的影响,发现不同激励方法产生的瞬态波具有不同的有效带宽,需根据实际需求选择合适的激励方法。

根据堵塞信号的处理方法,瞬态波法可分为瞬态波反射法和瞬态波频率响应法。瞬态波反射法的原理与声脉冲反射法相似,两者均向管道中引入波,并采用时域方法分析以获取堵塞相关信息。与声脉冲反射法的公式类似,堵塞位置可通过下式获得:

x=a*t2

式中,a为瞬态波在输运介质中的速度(m∙s-1)。

图10 [178]所示,通过比较实际测量信号与无堵塞信号,可识别反射波。若缺乏完整的管道结构数据,可能难以确定反射波的来源,也无法准确判断由此推断的堵塞位置和严重程度,导致潜在的判断模糊风险。此外,在实际应用中,测量信号常受噪声干扰,造成难以确定瞬态波的到达时间[35]。为降低噪声影响,可进行多次重复实验,或采用小波分析、互相关、脉冲响应函数等技术辅助测量TOF [179182]。

对于单堵塞管道系统,反射波振幅越大,堵塞面积越大,因此可基于反射波与入射波的振幅关系来测量堵塞面积。Tian等[183]直接采用反射波与入射波的比值来表征堵塞面积,但其结果误差较大。Meniconi等[175,184]和Adeleke等人[185]分别基于水锤理论和声学原理提出了堵塞面积的检测方法。Chu等[186]提出瞬态波衰减模型,利用三个位置的动压准确检测堵塞。Yu等[187]提出一种基于玻恩近似的近似散射技术,用于重建管道内堵塞剖面,该方法对轻微堵塞效果良好。

管道中的瞬态波受稳态摩擦、非稳态摩擦、管壁黏弹性、非线性效应、振幅衰减和波形畸变等因素影响[35,188]。对于天然气管道,瞬态波衰减模型通常需考虑非线性效应和黏滞耗散[188]。Adeleke等[185]指出,黏滞耗散不影响堵塞长度和位置预测的准确性,但会显著影响堵塞严重程度预测的准确性。

3.6 瞬态频率响应法

管道中的瞬态信号具有周期性重复的双曲线特性,非常适合进行频域分析[189]。Stephens等[190]和Duan等[191]根据堵塞长度相对于管道总长度的大小,将堵塞分为离散型堵塞和扩展型堵塞。如图11 [178]所示,离散型堵塞会导致系统共振频率的振幅变化,但不改变共振频率本身[192193];相反,扩展型堵塞会同时影响系统的共振频率和振幅[194196]。

Sattar等[193]报道,局部堵塞会导致频率响应中奇次谐波的振幅降低、偶次谐波的振幅升高。Mohapatra等[192]研究了带分支的管道系统中的单一堵塞点,通过分析峰值压力频谱的分布确定堵塞的大致位置,利用峰值或谷值的数量确定堵塞位置,峰值压力的平均值可用于估计堵塞大小。Mohapatra和Chaudhry [197]进一步将该方法扩展到检测多个堵塞的应用中,但该方法无法确定每个堵塞的大小。Lee等[198]提出利用频域信息来定位简单管道系统中堵塞并确定了计算堵塞大小的表达式,随后将其扩展到检测多个堵塞的应用中。

Brunone等[199]提到,扩展型堵塞和离散型堵塞对系统频率响应的影响差异显著,适用于离散型堵塞的方法可能不适用于扩展型堵塞。Duan等[191]分析了扩展型堵塞对系统共振频率的影响,采用遗传算法拟合频率响应与解析方程,以此精准确定扩展堵塞的位置、长度和面积。随后,Duan等[194]证实该方法可准确描述扩展型堵塞引起的频率偏移,且获得的堵塞位置和长度信息较堵塞面积信息更准确。Duan等[200]通过波扰动分析解释了扩展型堵塞引起的频率偏移,并采用一阶近似简化了所提出的方法,显著提高了堵塞检测效率。

Louati和Ghidaoui [201202]以及Louati等[203]指出,管道中的堵塞与满足布拉格共振条件的波可发生强烈相互作用。Duan等[204]指出,当前基于瞬态波的频域方法未考虑粗糙堵塞对频率偏移的散射效应,导致在确定粗糙或不规则堵塞的长度和面积时存在显著误差。Che等[205]研究了非均匀堵塞的瞬态响应,发现此类堵塞减弱了高次谐波的频率偏移。Che等[206]从能量角度进一步分析了指数型非均匀堵塞对管道系统共振频率偏移的影响。Meniconi等[195]提出了一种将瞬态波反射法和频率响应法结合在一起的方法,先采用时域方法检测和定位堵塞,再采用频域方法确认堵塞的长度和面积。与单一方法相比,该方法提高了检测精度和计算效率。

3.7 闭路电视(CCTV)法

CCTV法最初是采用CCTV摄像机记录管道内部情况,然后通过分析图像来识别管道缺陷。由于人工图像识别需要高水平专业知识且效率较低,已有研究提出多种缺陷识别算法以提高该方法的准确性和效率[207210]。CCTV法的探头通常需要系绳或电缆进行供电和数据传输[211212],这也限制了其应用范围。为克服这一限制,研究人员开发出了一款电池供电管道机器人,其具有无线数据传输和定位功能。在油气行业,CCTV与清管工具相结合,催生出“智能清管器”[213],这是一种配备摄像机、超声传感器等多种检测设备的机器人,可在不同形状的管道中移动,作业距离可达数英里[214]。特别地,Zhou等[215]设计了一种配备刷子的智能机器人,可检测管壁上的水合物聚集情况并控制解堵剂的释放,实现水合物堵塞的准确识别和清除。

3.8 透射法

透射法为沿管道外部径向发射穿透性射线源(通常为伽马射线、X射线和超声波),通过测量辐射的相对衰减[216217]或到达时间[218]获取堵塞信息。Gouveia等[219]成功采用伽马射线表征输油管道中的物质。Benson和Robins [220]采用断层扫描技术对输油管道进行了多次扫描,其生成的内部密度图像可以用来确定水合物堵塞情况,从而成功定位到了海洋中浮式生产装置与海底管汇之间的堵塞物。Rao等[221]提出先采用伽马射线快速扫描识别可疑区域,再辅以摄影技术确定堵塞情况。Cheng等[222]和Sharma等[223]通过测量射线的透射强度获取管道堵塞信息。Salgado等[224]、Alkabaa等[225]和Askari等[226]将射线检测与人工神经网络相结合,成功检测到管道堵塞,并证明该技术在油气水多相输运系统中的可行性。X射线也可用于扫描输油管道中的堵塞,但穿透力较伽马射线弱[227228]。Harara [229]指出,透射法几乎适用于所有类型的堵塞物检测,但由于辐射强度高,射线检测技术需要疏散检测区域内的所有人员,以确保相关人员免受辐射暴露[230]。图12 [230]显示了模拟伽马射线检测到的管道横截面堵塞与实际结果的对比。

超声检测是一种广泛使用的无损检测方法,但专门针对管道堵塞的超声检测的研究相对有限。Roslee等[231]表明,通过选择合适的频率可避免兰姆波的干扰,使超声波有效应用于钢管的透射检测。Piao等[232233]采用一对置于管道径向两侧的超声传感器,成功检测到了管道中的石蜡堵塞,并称该方法通用性强,对多种类型的堵塞均有效。Li等[218]开发了一种便携式超声波剖面测量装置,用于管道堵塞检测。该装置采用聚焦超声换能器测量管道内部水合物堵塞的剖面,并配备机械轨道系统以实现连续测量,已成功在实验室流动环路中重建水合物堵塞的横截面。

3.9 电学方法

电阻层析成像(ERT)和电容层析成像(ECT)是两种电学层析成像技术,分别测量不同的电学特性。两种技术均采用电极传感器阵列评估管道内部的电学特性,并利用物理模型和图像重建算法推断物质分布。ERT基于电导率差异重建图像,其传感器通常由伸入管道内壁的金属探针组成;相反,ECT利用介电常数差异进行图像重建,其传感器通常由附着在管道外壁的金属板。由于响应速度快且对管道正常运行干扰小,ERT和ECT在油气行业广泛用于测量多相流流型[234238]。近期,ERT已用于研究水合物沉积,可提供水合物分布和时间演化的信息[239241]。尽管关于采用ECT检测水合物的研究有限,但水合物与油或气的介电常数差异显著,表明ECT在水合物堵塞检测中具有相当大的潜力。

利用水合物与油、气、水在介电常数谱上的显著差异,研究人员采用开口同轴探头监测管道壁附近的介电常数[97],成功检测到不足1 mm的水合物沉积。该技术还应用于识别垂直盲管中的水合物沉积。研究者成功识别了水饱和气体系统中液态水凝结和水合物形成的初始阶段,并估算了沉积物的孔隙率和湿度[242]。需注意的是,该方法目前还处于实验室阶段,需要进一步开发和改进。

3.10 背压法

背压法也称为摩擦损失法,需在稳态条件下进行多速率测试,以获取压降与流速的基准曲线。管道运行中若偏离基线,则认为管道存在堵塞或泄漏现象。Scott和Satterwhite [243]以及Scott和Yi [244]采用背压法检测天然气和石油管道中的堵塞情况,并量化了堵塞严重程度的测量误差。但背压法只能检测堵塞的严重程度,无法确定其具体位置。为解决这一问题,Liu和Scott [245]将背压法与瞬态法相结合:先采用背压法检测堵塞区域,然后同时关闭管道两侧的阀门产生瞬态扰动,通过测量沿流向的平均压差计算堵塞位置。关于堵塞的影响,Yang等[246]采用计算流体动力学(CFD)研究了堵塞对管道压降的影响,发现堵塞的长度和面积对压降影响显著,而堵塞位置的影响较小。Ling等[247]开发了一种多速率测试方法,无需管道进出口的压力数据即可确定堵塞的位置和大小。

3.11 光纤法

光纤可通过检测影响其传输光的外部物理因素而发挥传感作用[248]。研究人员目前正开发一系列基于光纤布拉格光栅(FBGs)、布里渊散射、拉曼散射、瑞利散射、萨格纳克效应和马赫-曾德尔干涉的光纤传感器,用于测量压力、温度、热通量、应变和声学特性[249250]。光纤可提供管道内部不同位置的温度、压力和流动特性变化信息,从而有助于分析堵塞情况。

光纤传感器已广泛应用于油气行业[251]。分布式光纤传感技术可沿光纤全长进行管道检测,检测距离达数十千米,使用光纤放大器时检测距离可达300 km [248]。此外,准分布式FBGs光纤传感器可沿光纤按预设距离安装,从而满足水合物堵塞的精确定位要求,同时单根光纤上的多个FBGs传感器可监测数十千米的管道[249,252]。

4 水合物堵塞检测方法的工程应用

许多团队已开展水合物堵塞现场检测试验,并推出了众多管道检测商用产品。例如,在英国曼彻斯特大学,Papadopoulou博士带领的团队利用声脉冲反射技术,在大气压力下检测了长度超过0.5 km的直管中的缺陷[157]。随后,他们向管道中注入高压脉冲波进行了多次现场试验[152],在静态条件下成功实现了长度超过1.5 km管道的堵塞检测;测试的工业管道包含阀门、弯头和清管装置、流动条件下的小口径天然气管道以及一条24 km长的海底天然气管道。研究人员发现,随着管道工作压力的增加,检测距离也随之增大。此外,随着管径的增大,脉冲压缩波的衰减减小,从而使检测范围进一步扩大。

意大利佩鲁贾大学Meniconi教授带领的团队采用瞬态波反射法检测了意大利米兰某供水管网的状态[253]。主管道长度超过6 km,直径为800 mm,含多个分支管道。通过停泵产生瞬态波,比较小波变换结果与数值脉冲响应函数,检测到管道中可能存在异常。他们强调需增加测量点数量以提高诊断结果的准确性。此外,他们采用实验室改进的便携式压力波发生器检查了一条直径为约500 mm、长度超过1.3 km的水管,成功定位了一条短支管[184]。另外,佩鲁贾大学的Ferrante教授团队通过消防栓将检测装置与管道连接,采用无刷电机控制阀门精准引入瞬态压力波[254],并利用小波变换和互相关分析精确识别瞬态波的到达时间和振幅。

我国大连理工大学的赵佳飞教授团队也开展了多次现场试验,该团队采用电磁阀快速开关管道释放流体产生负压波,在天津一条长2.5 km、直径为254 mm的管道中进行同时检测两个堵塞的试验,两个堵塞的平均定位误差分别为0.29%和0.65%。

光纤在油气生产和输运中应用广泛,结合水合物生成特性与光纤获取的温度、压力、应变、流量和声学信号,可判断管道的堵塞情况。壳牌集团通过将堵塞产生的噪声与石油生产商提供的分布式声学传感(DAS)数据相关联,成功识别了油井中的结蜡堵塞[255],该集团表示该方法也可用于检测水合物堵塞。

近期,印度辐射与同位素技术委员会采用伽马扫描仪对某炼油厂的管道进行了现场检查[221]。研究者最初通过多次扫描确定大致位置,识别出一个3 m的可疑区域,打开管道后成功定位了卡住的清管器(PIG)。

现有许多公司提供成熟的管道堵塞检测商用产品和服务。例如,Papadopoulou等[157]开发的方法已经由英国iNPIPEP RODUCTS公司商业化,可监测10 km管道内的多个堵塞点,既可用于单堵塞检测,也可用于长期在线监测,定位精度为± 2.5 m。Paradigm Flow Services有限公司开发的Find-Block设备(全通径堵塞定位仪)采用瞬态法定位全通径堵塞,成功在长92 km的管道中识别出卡住的PIG。Tracerco探测仪是一种水下管道外部检测装置,由遥控潜水器(ROVs)部署,可沿管道移动,无需去除管道涂层即可获取密度剖面,从而快速筛查堵塞位置。该设备曾在北海一条直径为150 mm的管道中检测到了全通径堵塞。Tracerco Discovery是一种管道CT扫描仪,通过外部扫描提供水合物堵塞的详细分布。该设备已成功应用于阿拉伯湾和墨西哥湾的一系列管道。TSC Subsea公司的ART vPush系统采用声学技术进行外部管道检测。该系统由ROVs部署,可沿管道顶部行进并检测水合物。该设备曾在西非安哥拉附近1.3 km深的海域,对一段直径为20 mm、长12 km的水下管道进行了33 h的扫描检测。

许多管道机器人已广泛用于管道缺陷检测,这些机器人可在复杂管道中运行,覆盖距离可达数十米。卡内基梅隆大学的Schempf等[211,256]开发了GRISLEE机器人,采用连续油管进行远程操作,在不停产的情况下每天可检查超过600 m的管道。Schempf等[257258]还开发了Explorer机器人系统,该系统是首个部署在地下天然气分配管道中的无线机器人。在多次现场试验中,该机器人成功穿越多个复杂管段,单次运行距离超过900 m。为解决Explorer机器人在通信传输、照明和障碍物处理方面的问题,研究团队开发了第二代Explorer II机器人系统,并成功进行了现场试验[259]。ULC技术公司的M1实时天然气管道在线检测系统已部署在伦敦中压管网的三个地点[260261]。该系统在保持管道承压状态下,成功检测了一条带有多个弯头的677 m大口径天然气管道,显著减少了开挖需求。ROSEN公司从海上平台部署了一种系绳式自推进超声检测工具,用于检查一条长1.3 km的不可清管海底输油管[262],该工具精确控制检测器位置,完成了腐蚀和变形检测。Wellte公司的油井牵引器212设备成功部署到6145 m外的水平油井,并成功进行了数据传输[263]。上海交通大学的Wang等[264]开发了一种新型管道机器人,在山东胜利油田一条长3.5 km的输油管道上进行了现场试验,定位精度满足了现场要求。Li等[265]为中国石油天然气集团公司的标准管道开发了一种管道检测机器人。在现场试验中,该机器人连续运行约31 h,覆盖约70 km,在20 ℃下正常工作,成功检测到管道中小至0.4 mm的缺陷。

5 水合物检测的未来方向

海底油气管道内的多相流动复杂,具有低温高压特点,这使得管道检测极具挑战性。表2 [97,147148,151155,157,159168,170172,175178,183189,191198,200,204205,207210,213214,216,218229,231246,248249,251252]全面分析总结了水合物堵塞检测技术,涵盖其准确性、应用场景、对管道的影响等方面。分析表明,部分方法仍存在局限性:特征频率偏移法、振动分析法、超声导波法和电学方法目前不适用于海底管道的长距离检测,需进一步研究以扩大其检测范围和应用场景。其中,由于声波易传播到周围环境中,影响检测准确性,超声导波技术在海底管道检测中仍具有挑战性。因此,有必要研究环境对波传播的影响,或开发防止超声泄漏的涂层,以提高检测准确性。

目前,大多数声脉冲反射法在低压环境下进行,需进一步研究声波传播特性,确定高压管道中的最大检测距离。低频声波可传播更长距离但检测精度较低,高频声波检测精度较高但传播距离有限,需设计算法解决声波检测距离与精度之间的权衡问题。此外,由于声波功率决定其传播距离,开发适用于工业应用的高功率、耐高压声波发射器至关重要。特征频率偏移法、瞬态波反射法和瞬态频率响应法等均存在检测距离问题,而实现远距离检测需要高功率发射器。

同时,需进一步研究高功率对管道运行的影响,以及所产生波的衰减和畸变问题。具有广阔应用前景的声学法和瞬态波法均依赖于获取完整的管道结构数据,在复杂管网中,监测信号可能异常复杂,需结合模拟仿真消除管道自身信号干扰,提高检测效率。

超声导波法在埋地管道检测中应用较困难,因为声波会传播到周围土壤中,影响检测准确性。需进一步研究周围环境对导波传播的影响,或研发涂层防止超声泄漏,提高检测准确性。采用加速度传感器进行局部管道振动检测的振动分析法,检测距离有限,但可与光纤结合;同样,光纤检测可与管道流动和水合物生成模拟系统相结合,利用光纤获取的分布式测量数据训练模型以预测堵塞。但光纤的主要局限性在于需沿管道预先安装,设备及安装成本较高。

基于传输特性的检测方法适用于高精度局部检测,但难以实现长距离检测。与超声导波技术类似,ROVs可用于管道分段检测。CCTV作为一种传统检测技术,可与智能PIG配合使用,但需研发精确的定位方法以确定其实时位置。背压法可作为一种初始检测方法,用于管道实时监测;当检测到异常信号时,可迅速启动其他检测方法进行验证。通过多种方法相结合,可实现管道堵塞的实时监测与及时、精准诊断,降低误报率,提升检测效率。

6 结论

本文全面综述了油气管道水合物堵塞检测技术,为管道流动保障领域的研究人员和行业专家提供参考。水合物堵塞检测至关重要,因为堵塞初期堵塞物密度会随时间增加,可能导致管道严重损坏;其早期检测和准确诊断是防止运行中断的关键。

本文根据技术原理、使用场景和局限性对多种检测方法进行评估:声脉冲反射法在陆上和海底环境中均能有效进行长距离堵塞检测,但需进一步研究验证其在高压下的性能;特征频率偏移法虽适用于陆上管道,但需要历史数据才能获得准确结果;同样,振动分析法可在陆上应用,但与光纤技术相结合可提高检测精度。

适用于陆上检测的超声导波技术,通过与ROVs结合可拓展至海底应用;瞬态波反射法和瞬态频率响应法因响应迅速、精度高等优势,在陆上和海底管道中均有应用前景,但在天然气管道中的应用研究仍不足;CCTV法和透射法在短距离检测中精确度高,但检测范围有限且成本较高,尤其是在海底环境中;电学方法快速且准确,但目前仅限于陆上应用;背压法成本低、响应快,但缺乏精确定位能力,若与其他方法结合则最适合用于早期预警;光纤法在陆上和海底管道中均可实现实时、高精度检测,但该方法成本较高。

对于陆上管道而言,由于易于操作且运行条件较缓和,上述方法通常均适用;对于海底管道而言,建议采用声脉冲反射法和瞬态波法等远程检测技术,此类方法可从平台或陆上远程识别堵塞,随后采用携带CCTV或透射法的ROVs进行详细检查,以进一步提高精度。

未来研究应解决深水环境带来的独特挑战,这些挑战可能影响检测方法的适用性和稳定性,在复杂深水条件下进行现场验证至关重要。深水管道的超长距离带来了诸多挑战,如信号衰减、环境干扰以及需要大规模设备部署等问题,因此研发出更强大的检测方法来克服这些障碍至关重要。此外,应研发先进的二维和三维截面扫描技术,更准确地确定管道横截面中的水合物分布,为水合物堵塞的高效清除提供技术支持。

今后研究应进一步着力提升检测精度、降低检测阈值,实现在水合物形成初始阶段对堵塞的早期识别,从而为监测与预警系统提供更优支持。此外,整合检测技术以应对管道堵塞、腐蚀和泄漏等多重缺陷,对于保障管道在严苛环境下的安全可靠运行也至关重要。

参考文献

[1]

Zhang J, Li C, Shi L, Xia X, Yang F, Sun G. The formation and aggregation of hydrate in W/O emulsion containing different compositions: a review. Chem Eng J 2022;445:136800. . 10.1016/j.cej.2022.136800

[2]

Ning Y, Yao M, Li Y, Song G, Liu Z, Li Q, et al. Integrated investigation on the nucleation and growing process of hydrate in W/O emulsion containing asphaltene. Chem Eng J 2023;454:140389. . 10.1016/j.cej.2022.140389

[3]

Yu W, Song S, Li Y, Min Y, Huang W, Wen K, et al. Gas supply reliability assessment of natural gas transmission pipeline systems. Energy 2018;162:853‒70. . 10.1016/j.energy.2018.08.039

[4]

Wang T, Sun L, Fan Z, Wei R, Li Q, Yao H, et al. Promoting CH4/CO2 replacement from hydrate with warm brine injection for synergistic energy harvest and carbon sequestration. Chem Eng J 2023;457:141129. . 10.1016/j.cej.2022.141129

[5]

Wang Z, Zhao Y, Zhang J, Wang X, Yu J, Sun B. Quantitatively assessing hydrate-blockage development during deepwater-gas-well testing. SPE J 2018;23(4):1166‒83. . 10.2118/181402-pa

[6]

Lang C, Chen Z, Hassanpouryouzband A, Farahani MV, Zhang L, Zhao J, et al. Spontaneous lifting and self-cleaning of gas hydrate crystals. ACS Nano 2024;18(49):33671‒80. . 10.1021/acsnano.4c12943

[7]

Liu W, Sun Z, Hu J, Chen S, Wu K, Sun Y, et al. Prediction of hydrate formation risk based on temperature-pressure field coupling in the deepwater gas well cleanup process. Energy Fuels 2021;35(3):2024‒32. . 10.1021/acs.energyfuels.0c03417

[8]

Wang F, Ma R, Xiao S, English NJ, He J, Zhang Z. Anti-gas hydrate surfaces: perspectives, progress and prospects. J Mater Chem A 2022;10(2):379‒406. . 10.1039/d1ta08965j

[9]

Farhadian A, Zhao Y, Naeiji P, Rahimi A, Berisha A, Zhang L, et al. Simultaneous inhibition of natural gas hydrate formation and CO2/H2S corrosion for flow assurance inside the oil and gas pipelines. Energy 2023;260:126797. . 10.1016/j.energy.2023.126797

[10]

Zeng X, Feng J, Ke W, Wang J, Zhang S, Xie Y. Film formation kinetics of methane-propane hydrate on gas bubble in MEG and luvicap EG solutions. Appl Energy 2023;330:120301. . 10.1016/j.apenergy.2022.120301

[11]

Sloan ED Jr, Koh CA. Clathrate hydrates of natural gases. 3rd ed. Boca Raton: CRC Press; 2007. . 10.1201/9781420008494

[12]

Both AK, Gao Y, Zeng XC, Cheung CL. Gas hydrates in confined space of nanoporous materials: new frontier in gas storage technology. Nanoscale 2021;13(16):7447‒70. . 10.1039/d1nr00751c

[13]

Perrin A, Musa OM, Steed JW. The chemistry of low dosage clathrate hydrate inhibitors. Chem Soc Rev 2013;42(5):1996‒2015. . 10.1039/c2cs35340g

[14]

Lee D, Jeoung S, Moon HR, Seo Y. Recoverable and recyclable gas hydrate inhibitors based on magnetic nanoparticle-decorated metal-organic frameworks. Chem Eng J 2020;401:126081. . 10.1016/j.cej.2020.126081

[15]

Lee D, Go W, Ko G, Seo Y. Inhibition synergism of glycine (an amino acid) and BMIM BF4 (an ionic liquid) on the growth of CH4 hydrate. Chem Eng J 2020;393:124466. . 10.1016/j.cej.2020.124466

[16]

Anderson BJ, Tester JW, Borghi GP, Trout BL. Properties of inhibitors of methane hydrate formation via molecular dynamics simulations. J Am Chem Soc 2005;127(50):17852‒62. . 10.1021/ja0554965

[17]

Majid AAA, Wu DT, Koh CA. A perspective on rheological studies of gas hydrate slurry properties. Engineering 2018;4(3):321‒9. . 10.1016/j.eng.2018.05.017

[18]

Zhao X, Fang Q, Qiu Z, Mi S, Wang Z, Geng Q, et al. Experimental investigation on hydrate anti-agglomerant for oil-free systems in the production pipe of marine natural gas hydrates. Energy 2022;242:122973. . 10.1016/j.energy.2021.122973

[19]

Xiao P, Li J, Zhang HL, Chen GJ, Sun CY. Study on fluidizing the highly converted methane hydrate for gas storage and transportation. Chem Eng J 2022;427:132047. . 10.1016/j.cej.2021.132047

[20]

Kamal MS, Hussein IA, Sultan AS, von Solms N. Application of various water soluble polymers in gas hydrate inhibition. Renew Sust Energ Rev 2016;60:206‒25. . 10.1016/j.rser.2016.01.092

[21]

Zi M, Wu G, Wang J, Chen D. Investigation of gas hydrate formation and inhibition in oil-water system containing model asphaltene. Chem Eng J 2021;412:128452. . 10.1016/j.cej.2021.128452

[22]

Creek JL. Efficient hydrate plug prevention. Energy Fuels 2012;26(7):4112‒6. . 10.1021/ef300280e

[23]

Kakitani C, Marques DC, Teixeira A, Valim L, Marcelino Neto MA, Sum AK, et al. Experimental characterization of hydrate formation in non-emulsifying systems upon shut-in and restart conditions. Fuel 2022;307:121690. . 10.1016/j.fuel.2021.121690

[24]

Sum AK. Prevention, management, and remediation approaches for gas hydrates in the flow assurance of oil/gas flowlines. In: Proceedings of OTC Brasil; 2013 Oct 29‒31; Rio de Janeiro, Brazil. Richardson: OnePetro; 2013. . 10.4043/24396-ms

[25]

Sloan E, Koh C, Sum A, Ballard A, Shoup G, McMullen N, et al. Hydrates: state of the art inside and outside flowlines. J Pet Technol 2009;61(12):89‒94. . 10.2118/118534-jpt

[26]

Goncalves M, Camargo R, Nieckele AO, Faraco R, Barreto CV, Pires LFG. Hydrate plug movement by one-sided depressurization. In: Proceedings of 31st ASME International Conference on Ocean, Offshore and Arctic Engineering; 2012 Jul 1‒6; Rio de Janeiro, Brazil. New York City: ASME; 2012. . 10.1115/omae2012-83969

[27]

Sloan ED. Fundamental principles and applications of natural gas hydrates. Nature 2003;426(6964):353‒9. . 10.1038/nature02135

[28]

Sloan ED. A changing hydrate paradigm—from apprehension to avoidance to risk management. Fluid Phase Equilib 2005;228:67‒74. . 10.1016/j.fluid.2004.08.009

[29]

Sloan D, Koh CA, Sum AK. Natural gas hydrates in flow assurance. Burlington: Gulf Professional Publishing; 2010.

[30]

Perfeldt CM, Sharifi H, von Solms N, Englezos P. Oil and gas pipelines with hydrophobic surfaces better equipped to deal with gas hydrate flow assurance issues. J Nat Gas Sci Eng 2015;27:852‒61. . 10.1016/j.jngse.2015.09.044

[31]

Chaudhari P, Zerpa LE, Sum AK. A correlation to quantify hydrate plugging risk in oil and gas production pipelines based on hydrate transportability parameters. J Nat Gas Sci Eng 2018;58:152‒61. . 10.1016/j.jngse.2018.08.008

[32]

Wei N, Pei J, Li H, Sun W, Xue J. Application of in-situ heat generation plugging removal agents in removing gas hydrate: a numerical study. Fuel 2022;323:124397. . 10.1016/j.fuel.2022.124397

[33]

Kashou S, Subramanian S, Matthews P, Thummel L, Faucheaux E, Subik D, et al. GOM export gas pipeline, hydrate plug detection and removal. In: Proceedings of Offshore Technology Conference; 2004 May 3‒6; Houston, TX, USA. Richardson: OnePetro; 2004. . 10.4043/16691-ms

[34]

Yu Y, Safari A, Niu X, Drinkwater B, Horoshenkov KV. Acoustic and ultrasonic techniques for defect detection and condition monitoring in water and sewerage pipes: a review. Appl Acoust 2021;183:108282. . 10.1016/j.apacoust.2021.108282

[35]

Che T, Duan H, Lee PJ. Transient wave-based methods for anomaly detection in fluid pipes: a review. Mech Syst Sig Proc 2021;160:107874. . 10.1016/j.ymssp.2021.107874

[36]

Brunone B, Maietta F, Capponi C, Duan HF, Meniconi S. Detection of partial blockages in pressurized pipes by transient tests: a review of the physical experiments. Fluids 2023;8(1):19. . 10.3390/fluids8010019

[37]

Datta S, Sarkar S. A review on different pipeline fault detection methods. J Loss Prev Process Ind 2016;41:97‒106. . 10.1016/j.jlp.2016.03.010

[38]

Wong B, McCann JA. Failure detection methods for pipeline networks: from acoustic sensing to cyber-physical systems. Sensors 2021;21(15):4959. . 10.3390/s21154959

[39]

Wang J, Meng Y, Han B, Liu Z, Zhang L, Yao H, et al. Hydrate blockage in subsea oil/gas flowlines: prediction, prevention, and remediation. Chem Eng J 2023;461:142020. . 10.1016/j.cej.2023.142020

[40]

Sun Z, Shi K, Guan D, Lv X, Wang J, Liu W, et al. Current flow loop equipment and research in hydrate-associated flow assurance. J Nat Gas Sci Eng 2021;96:104276. . 10.1016/j.jngse.2021.104276

[41]

Rao I, Koh CA, Sloan ED, Sum AK. Gas hydrate deposition on a cold surface in water-saturated gas systems. Ind Eng Chem Res 2013;52(18):6262‒9. . 10.1021/ie400493a

[42]

Di Lorenzo M, Aman ZM, Sanchez Soto G, Johns M, Kozielski KA, May EF. Hydrate formation in gas-dominant systems using a single-pass flowloop. Energy Fuels 2014;28(5):3043‒52. . 10.1021/ef500361r

[43]

Charlton TB, Di Lorenzo M, Zerpa LE, Koh CA, Johns ML, May EF, et al. Simulating hydrate growth and transport behavior in gas-dominant flow. Energy Fuels 2018;32(2):1012‒23. . 10.1021/acs.energyfuels.7b02199

[44]

Wang Y, Koh CA, Dapena JA, Zerpa LE. A transient simulation model to predict hydrate formation rate in both oil- and water-dominated systems in pipelines. J Nat Gas Sci Eng 2018;58:126‒34. . 10.1016/j.jngse.2018.08.010

[45]

Turner DJ. Clathrate hydrate formation in water-in-oil dispersions [dissertation]. Golden: Colorado School of Mines; 2005.

[46]

Song G, Li Y, Wang W, Jiang K, Ye X, Zhao P. Investigation of hydrate plugging in natural gas + diesel oil + water systems using a high-pressure flow loop. Chem Eng Sci 2017;158:480‒9. . 10.1016/j.ces.2016.10.045

[47]

Zerpa LE, Salager JL, Koh CA, Sloan ED, Sum AK. Surface chemistry and gas hydrates in flow assurance. Ind Eng Chem Res 2011;50(1):188‒97. . 10.1021/ie100873k

[48]

Fidel-Dufour A, Gruy F, Herri JM. Rheology of methane hydrate slurries during their crystallization in a water in dodecane emulsion under flowing. Chem Eng Sci 2006;61(2):505‒15. . 10.1016/j.ces.2005.07.001

[49]

Melchuna A, Cameirao A, Herri JM, Glenat P. Topological modeling of methane hydrate crystallization from low to high water cut emulsion systems. Fluid Phase Equilib 2016;413:158‒69. . 10.1016/j.fluid.2015.11.023

[50]

Austvik T, Li X, Gjertsen LH. Hydrate plug properties: formation and removal of plugs. Ann N Y Acad Sci 2000;912(1):294‒303. . 10.1111/j.1749-6632.2000.tb06783.x

[51]

Leba H, Cameirao A, Herri JM, Darbouret M, Peytavy JL, Glenat P. Chord length distributions measurements during crystallization and agglomeration of gas hydrate in a water-in-oil emulsion: simulation and experimentation. Chem Eng Sci 2010;65(3):1185‒200. . 10.1016/j.ces.2009.09.074

[52]

Zerpa LE, Aman ZM, Joshi S, Rao I, Sloan ED, Koh C, et al. Predicting hydrate blockages in oil, gas and water-dominated systems. In: Proceedings of Offshore Technology conference; 2012 Apr 30‒May 3; Houston, TX, USA. Richardson: OnePetro; 2012. . 10.4043/23490-ms

[53]

Bassani CL, Melchuna AM, Cameirao A, Herri JM, Morales REM, Sum AK. A multiscale approach for gas hydrates considering structure, agglomeration, and transportability under multiphase flow conditions: i. phenomenological model. Ind Eng Chem Res 2019;58(31):14446‒61. . 10.1021/acs.iecr.9b01841

[54]

Kvamme B. Enthalpies of hydrate formation from hydrate formers dissolved in water. Energies 2019;12(6):1039. . 10.3390/en12061039

[55]

Avlonitis D. An investigation of gas hydrates formation energetics. AlChE J 2005;51(4):1258‒73. . 10.1002/aic.10374

[56]

Gupta A, Lachance J, Sloan Jr ED, Koh CA. Measurements of methane hydrate heat of dissociation using high pressure differential scanning calorimetry. Chem Eng Sci 2008;63(24):5848‒53. . 10.1016/j.ces.2008.09.002

[57]

Lievois JS, Perkins R, Martin RJ, Kobayashi R. Development of an automated, high pressure heat flux calorimeter and its application to measure the heat of dissociation and hydrate numbers of methane hydrate. Fluid Phase Equilib 1990;59(1):73‒97. . 10.1016/0378-3812(90)85147-3

[58]

Kang SP, Lee H, Ryu BJ. Enthalpies of dissociation of clathrate hydrates of carbon dioxide, nitrogen, (carbon dioxide plus nitrogen), and (carbon dioxide plus nitrogen plus tetrahydrofuran). J Chem Thermodyn 2001;33(5):513‒21. . 10.1006/jcht.2000.0765

[59]

Yoon JH, Yamamoto Y, Komai T, Haneda H, Kawamura T. Rigorous approach to the prediction of the heat of dissociation of gas hydrates. Ind Eng Chem Res 2003;42(5):1111‒4. . 10.1021/ie020598e

[60]

Rydzy MB, Schicks JM, Naumann R, Erzinger J. Dissociation enthalpies of synthesized multicomponent gas hydrates with respect to the guest composition and cage occupancy. J Phys Chem B 2007;111(32):9539‒45. . 10.1021/jp0712755

[61]

Lingelem MN, Majeed AI, Stange E. Industrial experience in evaluation of hydrate formation, inhibition, and dissociation in pipeline design and operation. Nat Gas Hydrates 1994;715(1):75‒93. . 10.1111/j.1749-6632.1994.tb38825.x

[62]

Singh P, Venkatesan R, Fogler HS, Nagarajan N. Formation and aging of incipient thin film wax‒oil gels. AlChE J 2000;46(5):1059‒74. . 10.1002/aic.690460517

[63]

Na BC, Webb RL. New model for frost growth rate. Int J Heat Mass Transfer 2004;47(5):925‒36. . 10.1016/j.ijheatmasstransfer.2003.09.001

[64]

Nicholas JW, Koh CA, Sloan ED. A preliminary approach to modeling gas hydrate/ice deposition from dissolved water in a liquid condensate system. AlChE J 2009;55(7):1889‒97. . 10.1002/aic.11921

[65]

Chong ZR, Yang SHB, Babu P, Linga P, Li XS. Review of natural gas hydrates as an energy resource: prospects and challenges. Appl Energy 2016;162:1633‒52. . 10.1016/j.apenergy.2014.12.061

[66]

Shen X, Hou G, Ding J, Zhou X, Tang C, He Y, et al. Flow characteristics of methane hydrate slurry in the transition region in a high-pressure flow loop. J Nat Gas Sci Eng 2018;55:64‒73. . 10.1016/j.jngse.2018.04.023

[67]

Hassanpouryouzband A, Joonaki E, Farahani MV, Takeya S, Ruppel C, Yang J, et al. Gas hydrates in sustainable chemistry. Chem Soc Rev 2020;49(15):5225‒309. . 10.1039/c8cs00989a

[68]

Zhang X, Lee BR, Sa JH, Kinnari KJ, Askvik KM, Li X, et al. Hydrate management in deadlegs: effect of header temperature on hydrate deposition. Energy Fuels 2017;31(11):11802‒10. . 10.1021/acs.energyfuels.7b02095

[69]

Zhang X, Lee BR, Sa JH, Kinnari KJ, Askvik KM, Li X, et al. Hydrate management in deadlegs: effect of wall temperature on hydrate deposition. Energy Fuels 2018;32(3):3254‒62. . 10.1021/acs.energyfuels.7b03962

[70]

Song G, Li Y, Sum AK. Hydrate management in deadlegs: thermal conductivity of hydrate deposits. Energy Fuels 2021;35(4):3112‒8. . 10.1021/acs.energyfuels.0c04141

[71]

Kumar P, Turner D, Sloan ED. Thermal diffusivity measurements of porous methane hydrate and hydrate-sediment mixtures. J Geophys Res Solid Earth 2004;109:B01207. . 10.1029/2003jb002763

[72]

Li D, Liang D. Experimental study on the effective thermal conductivity of methane hydrate-bearing sand. Int J Heat Mass Transfer 2016;92:8‒14. . 10.1016/j.ijheatmasstransfer.2015.08.077

[73]

Ren S, Liu Y, Liu Y, Zhang W. Acoustic velocity and electrical resistance of hydrate bearing sediments. J Pet Sci Eng 2010;70(1‒2):52‒6.

[74]

Hu GW, Ye YG, Zhang J, Liu CL, Diao SB, Wang JS. Acoustic properties of gas hydrate-bearing consolidated sediments and experimental testing of elastic velocity models. J Geophys Res Solid Earth 2010;115:B02102. . 10.1029/2008jb006160

[75]

Bu Q, Hu G, Ye Y, Liu C, Li C, Wang J. Experimental study on 2-D acoustic characteristics and hydrate distribution in sand. Geophys J Int 2017;211(2):990‒1004. . 10.1093/gji/ggx351

[76]

Bu Q, Hu G, Liu C, Xing T, Li C, Meng Q. Acoustic characteristics and micro-distribution prediction during hydrate dissociation in sediments from the South China Sea. J Nat Gas Sci Eng 2019;65:135‒44. . 10.1016/j.jngse.2019.02.010

[77]

Prasad NT, Murthy KNVV, Sandhya CS, Gobichandhru D, Ramesh S, Ramadass GA, et al. Experimental investigations on in-situ properties measurement of gas hydrate systems. In: Proceedings of OCEANS 2022; 2022 Feb 21‒24; Chennai, India. IEEE; 2022. . 10.1109/oceanschennai45887.2022.9775293

[78]

Duchkov AD, Duchkov AA, Permyakov ME, Manakov AY, Golikov NA, Drobchik AN. Acoustic properties of hydrate-bearing sand samples: laboratory measurements (setup, methods, and results). Russ Geol Geophys 2017;58(6):727‒37. . 10.1016/j.rgg.2016.09.029

[79]

Xing L, Zhu T, Niu J, Liu C, Wang B. Development and validation of an acoustic-electrical joint testing system for hydrate-bearing porous media. Adv Mech Eng 2020;12(3):1971‒92. . 10.1177/1687814020908981

[80]

Gimaltdinov IK, Khusainov IG, Khusainova GY, Gimaltdinova AA. Reflection of acoustic waves from a bubble screen in water with hydrate bubbles. IOP Conf Ser Mater Sci Eng 2020;919:062055. . 10.1088/1757-899x/919/6/062055

[81]

Hu G, Ye Y, Zhang J, Liu C, Li Q. Acoustic response of gas hydrate formation in sediments from South China Sea. Mar Pet Geol 2014;52:1‒8. . 10.1016/j.marpetgeo.2014.01.007

[82]

Gubaidullin AA, Boldyreva OY, Dudko DN. Waves in porous media containing gas hydrate. In: Proceedings of 15th All-Russian Seminar on Dynamics of Multiphase Media (DMM); 2017 Oct 3‒5; Novosibirsk, Russia. AIP Publishing; 2017. . 10.1063/1.5027343

[83]

Minshull TA, Singh SC, Westbrook GK. Seismic velocity structure at a gas hydrate reflector, offshore western colombia, from full-wave-form inversion. J Geophys Res Solid Earth 1994;99(B3):4715‒34. . 10.1029/93jb03282

[84]

Chapman R. Fiery ice from the sea: marine gas hydrates. J Acoust Soc Am 2014;136(4):2316. . 10.1121/1.4900393

[85]

Du Frane WL, Stern LA, Weitemeyer KA, Constable S, Pinkston JC, Roberts JJ. Electrical properties of polycrystalline methane hydrate. Geophys Res Lett 2011;38:L09313. . 10.1029/2011gl047243

[86]

Du Frane WL, Stern LA, Constable S, Weitemeyer KA, Smith MM, Roberts JJ. Electrical properties of methane hydrate plus sediment mixtures. J Geophys Res Solid Earth 2015;120(7):4773‒83. . 10.1002/2015jb011940

[87]

Shankar U, Riedel M. Gas hydrate saturation in the Krishna-Godavari basin from P-wave velocity and electrical resistivity logs. Mar Pet Geol 2011;28(10):1768‒78. . 10.1016/j.marpetgeo.2010.09.008

[88]

Lu R, Stern LA, Du Frane WL, Pinkston JC, Roberts JJ, Constable S. The effect of brine on the electrical properties of methane hydrate. J Geophys Res Solid Earth 2019;124(11):10877‒92. . 10.1029/2019jb018364

[89]

Pearson C, Murphy J, Hermes R. Acoustic and resistivity measurements on rock samples containing tetrahydrofuran hydrates—laboratory analogs to natural-gas hydrate deposits. J Geophys Res Solid Earth 1986;91(B14):14132‒8. . 10.1029/jb091ib14p14132

[90]

Lee JY, Santamarina JC, Ruppel C. Parametric study of the physical properties of hydrate-bearing sand, silt, and clay sediments: electromagnetic properties. J Geophys Res Solid Earth 2010;115:B11104. . 10.1029/2009jb006669

[91]

Buffett BA, Zatsepina OY. Formation of gas hydrate from dissolved gas in natural porous media. Mar Geol 2000;164(1‒2):69‒77.

[92]

Zatsepina OY, Buffett BA. Experimental study of the stability of CO2‒hydrate in a porous medium. Fluid Phase Equilib 2001;192(1‒2):85‒102. . 10.1016/s0378-3812(02)00032-8

[93]

Li F, Sun C, Li S, Chen G, Guo X, Yang L, et al. Experimental studies on the evolvement of electrical resistivity during methane hydrate formation in sediments. Energy Fuels 2012;26(10):6210‒7. . 10.1021/ef301257z

[94]

Spangenberg E, Kulenkampff J. Influence of methane hydrate content on electrical sediment properties. Geophys Res Lett 2006;33(24):L24315. . 10.1029/2006gl028188

[95]

Zhou X, Fan S, Liang D, Wang D, Huang N. Use of electrical resistance to detect the formation and decomposition of methane hydrate. J Nat Gas Chem 2007;16(4):399‒403. . 10.1016/s1003-9953(08)60011-0

[96]

Li S, Xia X, Xuan J, Liu Y, Li Q. Resistivity in formation and decomposition of natural gas hydrate in porous medium. Chin J Chem Eng 2010;18(1):39‒42. . 10.1016/s1004-9541(08)60320-1

[97]

Haukalid K, Folgero K. Broad-band permittivity measurements of formation of gas hydrate layers using open-ended coaxial probes. Energy Fuels 2016;30(9):7196‒205. . 10.1021/acs.energyfuels.6b01534

[98]

Jakobsen T, Sjoblom J, Ruoff P. Kinetics of gas hydrate formation in w/o-emulsions the model system trichlorofluoromethane/water/non-ionic surfactant studied by means of dielectric spectroscopy. Colloid Surf A Physicochem Eng Asp 1996;112(1):73‒84. . 10.1016/0927-7757(96)03578-9

[99]

Jakobsen T, Folgero K. Dielectric measurements of gas hydrate formation in water-in-oil emulsions using open-ended coaxial probes. Meas Sci Technol 1997;8(9):1006‒15. . 10.1088/0957-0233/8/9/009

[100]

Haukalid K. Impact of gas hydrate formation/dissociation on water-in-crude oil emulsion properties studied by dielectric measurements. Energy Fuels 2015;29(1):43‒51. . 10.1021/ef502064g

[101]

Chaouachi M, Falenty A, Sell K, Enzmann F, Kersten M, Haberthuer D, et al. Microstructural evolution of gas hydrates in sedimentary matrices observed with synchrotron X-ray computed tomographic microscopy. Geochem Geophys Geosyst 2015;16(6):1711‒22. . 10.1002/2015gc005811

[102]

Lei L, Seol Y, Jarvis K. Pore-scale visualization of methane hydrate-bearing sediments with micro-CT. Geophys Res Lett 2018;45(11):5417‒26. . 10.1029/2018gl078507

[103]

Zhang L, Ge K, Wang J, Zhao J, Song Y. Pore-scale investigation of permeability evolution during hydrate formation using a pore network model based on X-ray CT. Mar Petrol Geol 2020;113:104157. . 10.1016/j.marpetgeo.2019.104157

[104]

Kim S, Lee M, Lee K, Ahn T, Lee J. Data-driven estimation of three-phase saturation during gas hydrate depressurization using CT images. J Pet Sci Eng 2021;205:108916. . 10.1016/j.petrol.2021.108916

[105]

Kim S, Lee K, Lee M, Lee J, Ahn T, Lim JT. Evaluation of saturation changes during gas hydrate dissociation core experiment using deep learning with data augmentation. J Pet Sci Eng 2022;209:109820. . 10.1016/j.petrol.2021.109820

[106]

Le TX, Bornert M, Aimedieu P, Chabot B, King A, Tang AM. An experimental investigation on methane hydrate morphologies and pore habits in sandy sediment using synchrotron X-ray computed tomography. Mar Pet Geol 2020;122:104646. . 10.1016/j.marpetgeo.2020.104646

[107]

Li R, Zhou Y, Zhan W, Yang J. Pore-scale modelling of elastic properties in hydrate-bearing sediments using 4-D synchrotron radiation imaging. Mar Pet Geol 2022;145:105864. . 10.1016/j.marpetgeo.2022.105864

[108]

Zhang L, Wang Y, Lang C, Yang L, Zhao J, Song Y. Crystal growth of CO2‒CH4 hydrate on a solid surface with varying wettability in the presence of PVCap. Cryst Growth Des 2024;24(11):4697‒706. . 10.1021/acs.cgd.4c00304

[109]

Uchida T, Takagi A, Kawabata J, Mae S, Hondoh T. Raman-spectroscopic analyses on the growth-process of CO2 hydrates. Energy Convers Manage 1995;36(6‒9):547‒50.

[110]

Sasaki S, Hori S, Kume T, Shimizu H. Microscopic observation and in situ Raman scattering studies on high-pressure phase transformations of a synthetic nitrogen hydrate. J Chem Phys 2003;118(17):7892‒7. . 10.1063/1.1563600

[111]

Kim DY, Lee H. Spectroscopic identification of the mixed hydrogen and carbon dioxide clathrate hydrate. J Am Chem Soc 2005;127(28):9996‒7. . 10.1021/ja0523183

[112]

Abbasi GR, Arif M, Isah A, Ali M, Mahmoud M, Hoteit H, et al. Gas hydrate characterization in sediments via X-ray microcomputed tomography. Earth Sci Rev 2022;234:104233. . 10.1016/j.earscirev.2022.104233

[113]

Minagawa H, Nishikawa Y, Ikeda I, Miyazaki K, Takahara N, Sakamoto Y, et al. Characterization of sand sediment by pore size distribution and permeability using proton nuclear magnetic resonance measurement. J Geophys Res Solid Earth 2008;113:B07210. . 10.1029/2007jb005403

[114]

Rousina-Webb A, Leek DM, Ripmeester J. Effect of clathrate hydrate formation and decomposition on NMR parameters in THF-D2O solution. J Phys Chem B 2012;116(25):7544‒7. . 10.1021/jp303595y

[115]

Sun H, Chen J, Ji X, Karunakaran G, Chen B, RRanjith PG, et al. Optimizing CO2 hydrate storage: dynamics and stability of hydrate caps in submarine sediments. Appl Energ 2024;376:124309. . 10.1016/j.apenergy.2024.124309

[116]

Lu H, Seo Y, Lee J, Moudrakovski I, Ripmeester JA, Ross Chapman N, et al. Complex gas hydrate from the Cascadia margin. Nature 2007;445(7125):303‒6. . 10.1038/nature05463

[117]

Zhang X, Du Z, Luan Z, Wang X, Xi S, Wang B, et al. In situ Raman detection of gas hydrates exposed on the seafloor of the South China Sea. Geochem Geophys Geosyst 2017;18(10):3700‒13. . 10.1002/2017gc006987

[118]

Meng Q, Liu C, Li C, Hao X, Hu G, Sun J, et al. Effect of common guest molecules on the lattice constants of clathrate hydrates. Acta Phys Chim Sin 2020;36(11):1910010.

[119]

Kumar R, Linga P, Moudrakovski I, Ripmeester JA, Englezos P. Structure and kinetics of gas hydrates from methane/ethane/propane mixtures relevant to the design of natural gas hydrate storage and transport facilities. AlChE J 2008;54(8):2132‒44. . 10.1002/aic.11527

[120]

Sum AK, Burruss RC, Sloan ED. Measurement of clathrate hydrates via Raman spectroscopy. J Phys Chem B 1997;101(38):7371‒7. . 10.1021/jp970768e

[121]

Subramanian S, Kini RA, Dec SF, Sloan ED. Evidence of structure II hydrate formation from methane plus ethane mixtures. Chem Eng Sci 2000;55(11):1981‒99. . 10.1016/s0009-2509(99)00389-9

[122]

Seo YT, Lee H. C-13 NMR analysis and gas uptake measurements of pure and mixed gas hydrates: development of natural gas transport and storage method using gas hydrate. Korean J Chem Eng 2003;20(6):1085‒91. . 10.1007/bf02706941

[123]

Uchida T, Takeya S, Kamata Y, Ikeda IY, Nagao J, Ebinuma T, et al. Spectroscopic observations and thermodynamic calculations on clathrate hydrates of mixed gas containing methane and ethane: determination of structure, composition and cage occupancy. J Phys Chem B 2002;106(48):12426‒31. . 10.1021/jp025884i

[124]

Truong-Lam HS, Cho SJ, Lee JD. Simultaneous in-situ macro and microscopic observation of CH4 hydrate formation/decomposition and solubility behavior using Raman spectroscopy. Appl Energy 2019;255:113834. . 10.1016/j.apenergy.2019.113834

[125]

Dec SF. Clathrate hydrate formation: dependence on aqueous hydration number. J Phys Chem C 2009;113(28):12355‒61. . 10.1021/jp9009977

[126]

Dec SF. Surface transformation of methane-ethane sI and sII clathrate hydrates. J Phys Chem C 2012;116(17):9660‒5. . 10.1021/jp301766y

[127]

Truong-Lam HS, Seo S, Kim S, Seo Y, Lee JD. In situ Raman study of the formation and dissociation kinetics of methane and methane/propane hydrates. Energy Fuels 2020;34(5):6288‒97. . 10.1021/acs.energyfuels.0c00813

[128]

Bbosa B, Ozbayoglu E, Volk M. Experimental investigation of hydrate formation, plugging and flow properties using a high-pressure viscometer with helical impeller. J Pet Explor Prod Technol 2019;9(2):1089‒104. . 10.1007/s13202-018-0524-6

[129]

Camargo R, Palermo T, Sinquin A, Glenat P. Rheological characterization of hydrate suspensions in oil dominated systems. Ann N Y Acad Sci 2000;912:906‒16. . 10.1111/j.1749-6632.2000.tb06844.x

[130]

Peng B, Chen J, Sun C, Dandekar A, Guo S, Liu B, et al. Flow characteristics and morphology of hydrate slurry formed from (natural gas plus diesel oil/condensate oil plus water) system containing anti-agglomerant. Chem Eng Sci 2012;84:333‒44. . 10.1016/j.ces.2012.08.030

[131]

Yan K, Sun C, Chen J, Chen L, Shen D, Liu B, et al. Flow characteristics and rheological properties of natural gas hydrate slurry in the presence of anti-agglomerant in a flow loop apparatus. Chem Eng Sci 2014;106:99‒108. . 10.1016/j.ces.2013.11.015

[132]

Shi B, Chai S, Ding L, Chen Y, Liu Y, Song S, et al. An investigation on gas hydrate formation and slurry viscosity in the presence of wax crystals. AlChE J 2018;64(9):3502‒18. . 10.1002/aic.16192

[133]

Majid AAA, Wu DT, Koh CA. New in situ measurements of the viscosity of gas clathrate hydrate slurries formed from model water-in-oil emulsions. Langmuir 2017;33(42):11436‒45. . 10.1021/acs.langmuir.7b02642

[134]

Wang W, Fan S, Liang D, Yang X. Experimental study on flow characters of CH3CCl2F hydrate slurry. Int J Refrig Rev Int Froid 2008;31(3):371‒8. . 10.1016/j.ijrefrig.2007.09.003

[135]

Joshi SV, Grasso GA, Lafond PG, Rao I, Webb E, Zerpa LE, et al. Experimental flowloop investigations of gas hydrate formation in high water cut systems. Chem Eng Sci 2013;97:198‒209. . 10.1016/j.ces.2013.04.019

[136]

Tang C, Zhao X, Li D, He Y, Shen X, Liang D. Investigation of the flow characteristics of methane hydrate slurries with low flow rates. Energies 2017;10(1):145. . 10.3390/en10010145

[137]

Sinquin A, Palermo I, Peysson Y. Rheological and flow properties of gas hydrate suspensions. Oil Gas Sci Technol 2004;59(1):41‒57. . 10.2516/ogst:2004005

[138]

Lv X, Zuo J, Liu Y, Zhou S, Lu D, Yan K, et al. Experimental study of growth kinetics of CO2 hydrates and multiphase flow properties of slurries in high pressure flow systems. RSC Adv 2019;9(56):32873‒88. . 10.1039/c9ra06445a

[139]

Shi B, Ding L, Liu Y, Yang J, Song S, Wu H, et al. Hydrate slurry flow property in W/O emulsion systems. RSC Adv 2018;8(21):11436‒45. . 10.1039/c7ra13495a

[140]

Ding L, Shi B, Liu Y, Song S, Wang W, Wu H, et al. Rheology of natural gas hydrate slurry: effect of hydrate agglomeration and deposition. Fuel 2019;239:126‒37. . 10.1016/j.fuel.2018.10.110

[141]

Aman ZM, Di Lorenzo M, Kozielski K, Koh CA, Warrier P, Johns ML, et al. Hydrate formation and deposition in a gas-dominant flowloop: initial studies of the effect of velocity and subcooling. J Nat Gas Sci Eng 2016;35:1490‒8. . 10.1016/j.jngse.2016.05.015

[142]

Fu W, Yu J, Xiao Y, Wang C, Huang B, Sun B. A pressure drop prediction model for hydrate slurry based on energy dissipation under turbulent flow condition. Fuel 2022;311:122188. . 10.1016/j.fuel.2021.122188

[143]

Joshi S. Experimental investigation and modeling of gas hydrate formation in high water cut producing oil pipelines [dissertation]. Golden: Colorado School of Mines; 2012.

[144]

Zerpa LE, Rao I, Aman ZM, Danielson TJ, Koh CA, Sloan ED, et al. Multiphase flow modeling of gas hydrates with a simple hydrodynamic slug flow model. Chem Eng Sci 2013;99:298‒304. . 10.1016/j.ces.2013.06.016

[145]

Ding L, Shi B, Lv X, Liu Y, Wu H, Wang W, et al. Investigation of natural gas hydrate slurry flow properties and flow patterns using a high pressure flow loop. Chem Eng Sci 2016;146:199‒206. . 10.1016/j.ces.2016.02.040

[146]

Liu Z, Liu Z, Wang J, Yang M, Zhao J, Song Y. Hydrate blockage observation and removal using depressurization in a fully visual flow loop. Fuel 2021;294:120588. . 10.1016/j.fuel.2021.120588

[147]

Albors GO, Kyle AM, Wodicka GR, Juan EJ. Computer simulation tool for predicting sound propagation in air-filled tubes with acoustic impedance discontinuities. In: Proceedings of 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2007 Aug 22‒26; Lyon, France. IEEE; 2007. . 10.1109/iembs.2007.4352761

[148]

Silva LL, Monteiro PCC, Vidal JLA, Netto TA. Acoustic reflectometry for blockage detection in pipeline. In: Proceedings of 33rd International Conference on Ocean, Offshore and Arctic Engineering; 2013 Jun 8‒13; San Francisco, CA, USA. IEEE; 2013. . 10.4043/24294-ms

[149]

Kinsler LE, Frey AR, Coppens AB, Sanders JV. Fundamentals of acoustics. Hoboken: John Wiley & Sons; 2000.

[150]

Morgan ES, Crosse PAE. The acoustic ranger, a new instrument for tube and pipe inspection. NDT Int 1978;11(4):179‒83. . 10.1016/0308-9126(78)90052-4

[151]

Amir N, Barzelay O, Yefet A, Pechter T. Condenser tube examination using acoustic pulse reflectometry. J Eng Gas Turbines Power Trans ASME 2010;132(1):014501. . 10.1115/1.3125302

[152]

Wang X, Lewis KM, Papadopoulou KA, Lennox B, Turner JT. Detection of hydrate and other blockages in gas pipelines using acoustic reflectometry. Proc Inst Mech Eng Part C J Eng Mech Eng Sci 2012;226(C7):1800‒10. . 10.1177/0954406211431029

[153]

An Y, Wang XC, Yue B, Qu ZG, Wu LQ, Chu RH. Compensation of sound velocity variation based on resampling algorithm for natural gas pipeline safety monitoring. Measurement 2019;148:106942. . 10.1016/j.measurement.2019.106942

[154]

Duan W, Kirby R, Prisutova J, Horoshenkov KV. On the use of power reflection ratio and phase change to determine the geometry of a blockage in a pipe. Appl Acoust 2015;87:190‒7. . 10.1016/j.apacoust.2014.07.002

[155]

An Y, Wang X, Yue B, Jin S, Wu L, Qu Z. A novel method for natural gas pipeline safety online monitoring based on acoustic pulse compression. Proc Saf Environ Protect 2019;130:174‒81. . 10.1016/j.psep.2019.08.008

[156]

Zhao Y, Feng Z, Zhu X. Condition identification of buried drainage pipeline based on CEEMDAN-DE and time-frequency images recognition. In: Proceedings of the 32nd 2020 Chinese Control and Decision Conference (CCDC 2020); 2020 Aug 22‒24; Hefei, China. IEEE; 2020. . 10.1109/ccdc49329.2020.9164553

[157]

Papadopoulou KA, Shamout MN, Lennox B, Mackay D, Taylor AR, Turner JT, et al. An evaluation of acoustic reflectometry for leakage and blockage detection. Proc Inst Mech Eng Part C J Eng Mech Eng Sci 2008;222(6):959‒66. . 10.1243/09544062jmes873

[158]

Zeng W, Zecchin AC, Gong J, Lambert MF, Simpson AR, Cazzolato BS. Inverse wave reflectometry method for hydraulic transient-based pipeline condition assessment. J Hydraul Eng 2020;146(8):04020056. . 10.1061/(asce)hy.1943-7900.0001785

[159]

Domis MA. Acoustic resonances as a means of blockage detection in sodium-cooled fast-reactors. Nucl Eng Des 1979;54(1):125‒47. . 10.1016/0029-5493(79)90080-3

[160]

Domis MA. Frequency dependence of acoustic resonances on blockage position in a fast reactor subassembly wrapper. J Sound Vib 1980;72(4):443‒50. . 10.1016/0022-460x(80)90356-9

[161]

Wu QL, Fricke F. Estimation of blockage dimensions in a duct using measured eigenfrequency shifts. J Sound Vib 1989;133(2):289‒301. . 10.1016/0022-460x(89)90927-9

[162]

Wu QL, Fricke F. Determination of blocking locations and cross-sectional area in a duct by eigenfrequency shifts. J Acoust Soc Am 1990;87(1):67‒75. . 10.1121/1.398914

[163]

De Salis MHF, Oldham DJ. A rapid technique to determine the internal area function of finite-length ducts using maximum length sequence analysis. J Acoust Soc Am 2000;108(1):44‒52. . 10.1121/1.429528

[164]

Lile NLT, Hadi H, Roslan MR. Vibration analysis of blocked circular pipe flow. Appl Mech Mater 2012;165:197‒201. . 10.4028/www.scientific.net/amm.165.197

[165]

Lile NLT, Hasnul MJ, Siregar RA, Leong JC. Effect of blockage size on pipe vibration. Adv Mat Res 2012;626:993‒6. . 10.4028/www.scientific.net/amr.626.993

[166]

Ma J, Simonetti F, Lowe MJS. Scattering of the fundamental torsional mode by an axisymmetric layer inside a pipe. J Acoust Soc Am 2006;120(4): 1871‒80. . 10.1121/1.2336750

[167]

Simonetti F, Cawley P. A guided wave technique for the characterization of highly attenuative viscoelastic materials. J Acoust Soc Am 2003;114(1):158‒65. . 10.1121/1.1575749

[168]

Ma J, Lowe MJS, Simonetti F. Feasibility study of sludge and blockage detection inside pipes using guided torsional waves. Meas Sci Technol 2007;18(8):2629‒41. . 10.1088/0957-0233/18/8/039

[169]

Wylie EB, Streeter VL, Suo L. Fluid transients in systems. Englewood Cliffs: Prentice Hall; 1993.

[170]

Gato LMC, Henriques JCC. Dynamic behaviour of high-pressure natural-gas flow in pipelines. Int J Heat Fluid Flow 2005;26(5):817‒25. . 10.1016/j.ijheatfluidflow.2005.03.011

[171]

Yuan Z, Deng Z, Jiang M, Xie Y, Wu Y. A modeling and analytical solution for transient flow in natural gas pipelines with extended partial blockage. J Nat Gas Sci Eng 2015;22:141‒9. . 10.1016/j.jngse.2014.11.029

[172]

Alghlam AS, Stevanovic VD, Elgazdori EA, Banjac M. Numerical simulation of natural gas pipeline transients. J Energy Resour Technol 2019;141(10):JERT-18-1862. . 10.1115/1.4043436

[173]

Hinderdael M, Jardon Z, Guillaume P. An analytical amplitude model for negative pressure waves in gaseous media. Mech Syst Sig Proc 2020;144:106800. . 10.1016/j.ymssp.2020.106800

[174]

Ghidaoui MS, Ming Z, McInnis DA, Axworthy DH. A review of water hammer theory and practice. Appl Mech Rev 2005;58(1):49‒76. . 10.1115/1.1828050

[175]

Meniconi S, Brunone B, Ferrante M, Massari C. Small amplitude sharp pressure waves to diagnose pipe systems. Water Resour Manage 2011;25(1):79‒96. . 10.1007/s11269-010-9688-7

[176]

Ferrante M, Brunone B, Meniconi S. Leak detection in branched pipe systems coupling wavelet analysis and a Lagrangian model. J Water Supply Res Technol 2009;58(2):95‒106. . 10.2166/aqua.2009.022

[177]

Haghighi A, Covas D, Ramos H. Direct backward transient analysis for leak detection in pressurized pipelines: from theory to real application. J Water Supply Res Technol 2012;61(3):189‒200. . 10.2166/aqua.2012.032

[178]

Lee PJ, Duan HF, Tuck J, Ghidaoui M. Numerical and experimental study on the effect of signal bandwidth on pipe assessment using fluid transients. J Hydraul Eng 2015;141(2):04014074. . 10.1061/(asce)hy.1943-7900.0000961

[179]

Beck SBM, Curren MD, Sims ND, Stanway R. Pipeline network features and leak detection by cross-correlation analysis of reflected waves. J Hydraul Eng 2005;131(8):715‒23. . 10.1061/(asce)0733-9429(2005)131:8(715)

[180]

Ferrante M, Brunone B, Meniconi S. Wavelets for the analysis of transient pressure signals for leak detection. J Hydraul Eng 2007;133(11):1274‒82. . 10.1061/(asce)0733-9429(2007)133:11(1274)

[181]

Meniconi S, Brunone B, Ferrante M. In-line pipe device checking by short-period analysis of transient tests. J Hydraul Eng 2011;137(7):713‒22. . 10.1061/(asce)hy.1943-7900.0000309

[182]

Wang X, Lin J, Ghidaoui MS. Usage and effect of multiple transient tests for pipeline leak detection. J Water Resour Plann Manage 2020;146(11):06020011. . 10.1061/(asce)wr.1943-5452.0001284

[183]

Tian Y, Zhao X, Tian D, Wu R, Tang H. Dynamic detection of the multiple hydrate blockages in natural gas pipeline using mass pulse at the inlet. Appl Mech Mater 2014;490:490‒7. . 10.4028/www.scientific.net/amm.490-491.490

[184]

Meniconi S, Brunone B, Frisinghelli M, Mazzetti E, Larentis M, Costisella C. Safe transients for pipe survey in a real transmission main by means of a portable device: the case study of the Trento (I) supply system. Proc Eng 2017;186:228‒35. . 10.1016/j.proeng.2017.03.232

[185]

Adeleke N, Ityokumbul MT, Adewumi M. Blockage detection and characterization in natural gas pipelines by transient pressure-wave reflection analysis. SPE J 2013;18(2):355‒65. . 10.2118/160926-pa

[186]

Chu J, Liu Y, Lv X, Li Q, Dong H, Song Y, et al. Experimental investigation on blockage predictions in gas pipelines using the pressure pulse wave method. Energy 2021;230:120897. . 10.1016/j.energy.2021.120897

[187]

Yu W, Wen K, Min Y, He L, Huang W, Gong J. A methodology to quantify the gas supply capacity of natural gas transmission pipeline system using reliability theory. Reliab Eng Syst Saf 2018;175:128‒41. . 10.1016/j.ress.2018.03.007

[188]

Zhao J, Lang C, Chu J, Yang L, Zhang L. Flow assurance of hydrate risk in natural gas/oil transportation: state-of-the-art and future challenges. J Phys Chem C 2023;127(28):13439‒50. . 10.1021/acs.jpcc.3c02134

[189]

Zanganeh R, Jabbari E, Shabakhty N, Keramat A. The first half-period of pressure heads for frequency response extraction and analysis of waterhammer with fluid-structure interaction. European Journal of Mechanics B Fluids 2023;101:89‒105. . 10.1016/j.euromechflu.2023.05.003

[190]

Stephens M, Lambert M, Simpson A, Nixon J, Vitkovsky J. Water pipeline condition assessment using transient response analysis. In: Proceedings of New Zealand Water and Wastes Association Conference; 2005; Auckland, New Zealand. The University of Adelaide; 2005.

[191]

Duan H, Lee PJ, Ghidaoui MS, Tung YK. Extended blockage detection in pipelines by using the system frequency response analysis. J Water Resour Plann Manage 2012;138(1):55‒62. . 10.1061/(asce)wr.1943-5452.0000145

[192]

Mohapatra PK, Chaudhry MH, Kassem A, Moloo J. Detection of partial blockages in a branched piping system by the frequency response method. J Fluids Eng Trans 2006;128(5):1106‒14. . 10.1115/1.2238880

[193]

Sattar AM, Chaudhry MH, Kassem AA. Partial blockage detection in pipelines by frequency response method. J Hydraul Eng 2008;134(1):76‒89. . 10.1061/(asce)0733-9429(2008)134:1(76)

[194]

Duan H, Lee PJ, Kashima A, Lu J, Ghidaoui MS, Tung YK. Extended blockage detection in pipes using the system frequency response: analytical analysis and experimental verification. J Hydraul Eng 2013;139(7):763‒71. . 10.1061/(asce)hy.1943-7900.0000736

[195]

Meniconi S, Duan HF, Lee PJ, Brunone B, Ghidaoui MS, Ferrante M. Experimental investigation of coupled frequency and time-domain transient test-based techniques for partial blockage detection in pipelines. J Hydraul Eng 2013;139(10):1033‒40. . 10.1061/(asce)hy.1943-7900.0000768

[196]

Louati M, Ghidaoui MS, Meniconi S, Brunone B. Bragg-type resonance in blocked pipe system and its effect on the eigenfrequency shift. J Hydraul Eng 2018;144(1):04017056. . 10.1061/(asce)hy.1943-7900.0001383

[197]

Mohapatra PK, Chaudhry MH. Frequency responses of single and multiple partial pipeline blockages. J Hydraul Res 2011;49(2):263‒6. . 10.1080/00221686.2010.544887

[198]

Lee PJ, Vitkovsky JP, Lambert MF, Simpson AR, Liggett JA. Discrete blockage detection in pipelines using the frequency response diagram: numerical study. J Hydraul Eng 2008;134(5):658‒63. . 10.1061/(asce)0733-9429(2008)134:5(658)

[199]

Brunone B, Ferrante M, Meniconi S. Discussion of “detection of partial blockage in single pipelines” by PK Mohapatra, MH Chaudhry, AA Kassem, and. J. Moloo. J Hydraul Eng 2008;134(6):872‒4. . 10.1061/(asce)0733-9429(2008)134:6(872)

[200]

Duan HF, Lee PJ, Ghidaoui MS, Tuck J. Transient wave-blockage interaction and extended blockage detection in elastic water pipelines. J Fluids Struct 2014;46:2‒16. . 10.1016/j.jfluidstructs.2013.12.002

[201]

Louati M, Ghidaoui MS. Eigenfrequency shift mechanism due to variation in the cross sectional area of a conduit. J Hydraul Res 2017;55(6):829‒46. . 10.1080/00221686.2017.1394373

[202]

Louati M, Ghidaoui MS. Eigenfrequency shift mechanism due to an interior blockage in a pipe. J Hydraul Eng 2018;144(1):04017055. . 10.1061/(asce)hy.1943-7900.0001380

[203]

Louati M, Meniconi S, Ghidaoui MS, Brunone B. Experimental study of the eigenfrequency shift mechanism in a blocked pipe system. J Hydraul Eng 2017;143(10):04017044. . 10.1061/(asce)hy.1943-7900.0001347

[204]

Duan HF, Lee PJ, Che TC, Ghidaoui MS, Karney BW, Kolyshkin AA. The influence of non-uniform blockages on transient wave behavior and blockage detection in pressurized water pipelines. J Hydro Environ Res 2017;17:1‒7. . 10.1016/j.jher.2017.08.002

[205]

Che TC, Duan HF, Lee PJ, Pan B, Ghidaoui MS. Frequency responses for pressurized water pipelines containing blockages with linearly varying diameters. J Hydraul Eng 2018;144(8):04018054. . 10.1061/(asce)hy.1943-7900.0001499

[206]

Che TC, Duan HF, Pan B, Lee PJ, Ghidaoui MS. Energy analysis of the resonant frequency shift pattern induced by nonuniform blockages in pressurized water pipes. J Hydraul Eng 2019;145(7):04019027. . 10.1061/(asce)hy.1943-7900.0001607

[207]

Yang MD, Su TC, Pan NF, Yang YF. Systematic image quality assessment for sewer inspection. Expert Syst Appl 2011;38(3):1766‒76. . 10.1016/j.eswa.2010.07.103

[208]

Su TC, Yang MD, Wu TC, Lin JY. Morphological segmentation based on edge detection for sewer pipe defects on CCTV images. Expert Syst Appl 2011;38(10):13094‒114. . 10.1016/j.eswa.2011.04.116

[209]

Halfawy MR, Hengmeechai J. Automated defect detection in sewer closed circuit television images using histograms of oriented gradients and support vector machine. Autom Constr 2014;38:1‒13. . 10.1016/j.autcon.2013.10.012

[210]

Zhou Q, Situ Z, Teng S, Chen G. Convolutional neural networks-based model for automated sewer defects detection and classification. J Water Resour Plann Manage 2021;147(7):04021036. . 10.1061/(asce)wr.1943-5452.0001394

[211]

Schempf H, Mutschler E, Goltsberg V, Chemel B. Robotic repair system for live distribution gasmains. Field and Service Robotics Conference, FSR. . 10.1109/robot.1997.620015

[212]

Shi Y, Hao L, Cai M, Wang Y, Yao J, Li R, et al. High-precision diameter detector and three-dimensional reconstruction method for oil and gas pipelines. J Pet Sci Eng 2018;165:842‒9. . 10.1016/j.petrol.2018.02.070

[213]

Karkoub M, Bouhali O, Sheharyar A. Gas pipeline inspection using autonomous robots with omni-directional cameras. IEEE Sens J 2021;21(14):15544‒53. . 10.1109/jsen.2020.3043277

[214]

Burkett S, Schempf H. Wireless self-powered visual and NDE robotic inspection system for live gas distribution mains. Report. Pittsburgh: Carnegie Mellon University; 2006. . 10.2172/876616

[215]

Zhou S, Yu H, Ren Z, Ma Q, Du H, Dong L, inventors; Wang M, assignee. An intelligent pipe cleaner for natural gas pipelines. China patent CN202110624300.7. 2021.

[216]

Kim J, Jung S, Moon J, Cho G. A feasibility study on gamma-ray tomography by Monte Carlo simulation for development of portable tomographic system. Appl Radiat Isot 2012;70(2):404‒14. . 10.1016/j.apradiso.2011.09.019

[217]

Alnaimat F, Ziauddin M. Wax deposition and prediction in petroleum pipelines. J Pet Sci Eng 2020;184:106385. . 10.1016/j.petrol.2019.106385

[218]

Li X, Liu Y, Liu Z, Chu J, Song Y, Yu T, et al. A hydrate blockage detection apparatus for gas pipeline using ultrasonic focused transducer and its application on a flow loop. Energy Sci Eng 2020;8(5):1770‒80. . 10.1002/ese3.631

[219]

Gouveia MAG, Lopes RT, de Jesus EFO, Camerini CS. Materials characterization in petroleum pipeline using Compton scattering technique. Nucl Instrum Methods Phys Res Sect A 2003;505(1‒2):540‒3.

[220]

Benson D, Robins L. Nonintrusive pipeline-inspection techniques for accurate measurement of hydrates and waxes within operational pipelines. In: Proceedings of SPE Offshore Europe Oil and Gas Conference and Exhibition; 2007 Sep 4‒7; Scotland, UK. Richardson: OnePetro; 2007. . 10.2118/108361-ms

[221]

Rao P, Yelgaonkar V, Tiwari C, Dhakar V, Panicker M. Locating block caused by stuck PIGs in multi product pipeline using combination of radioisotope techniques. Appl Radiat Isot 2023;193:110660. . 10.1016/j.apradiso.2023.110660

[222]

Cheng C, Jia W, Hei D, Geng S, Wang H, Xing L. Determination of thickness of wax deposition in oil pipelines using gamma-ray transmission method. Nucl Sci Tech 2018;29(8):109. . 10.1007/s41365-018-0447-4

[223]

Sharma A, Sandhu B, Singh B. Incoherent scattering of gamma photons for non-destructive tomographic inspection of pipeline. Appl Radiat Isot 2010;68(12):2181‒8. . 10.1016/j.apradiso.2010.05.007

[224]

Salgado WL, RSdFDam, Teixeira TP, Conti C, Salgado C. Application of artificial intelligence in scale thickness prediction on offshore petroleum using a gamma-ray densitometer. Radiat Phys Chem 2020;168:108549. . 10.1016/j.radphyschem.2019.108549

[225]

Alkabaa AS, Nazemi E, Taylan O, Kalmoun EM. Application of artificial intelligence and gamma attenuation techniques for predicting gas-oil-water volume fraction in annular regime of three-phase flow independent of oil pipeline’s scale layer. Mathematics 2021;9(13):1460. . 10.3390/math9131460

[226]

Askari M, Taheri A, Kochakpour J, Sasanpour MT. An intelligent gamma-ray technique for determining wax thickness in pipelines. Appl Radiat Isot 2021;172:109667. . 10.1016/j.apradiso.2021.109667

[227]

Candeias JP, de Oliveira DF, dos Anjos MJ, Lopes RT. Scale analysis using X-ray microfluorescence and computed radiography. Radiat Phys Chem 2014;95:408‒11. . 10.1016/j.radphyschem.2013.03.007

[228]

Oliveira DF, Santos RS, Machado AS, Silva ASS, Anjos MJ, Lopes RT. Characterization of scale deposition in oil pipelines through X-ray microfluorescence and X-ray microtomography. Appl Radiat Isot 2019;151:247‒55. . 10.1016/j.apradiso.2019.06.019

[229]

Harara W. Deposit thickness measurement in pipes by tangential radiography using gamma ray sources. Russ J Nondestr Test 2008;44(11):796‒802. . 10.1134/s1061830908110090

[230]

Oliveira DF, Nascimento JR, Marinho CA, Lopes RT. Gamma transmission system for detection of scale in oil exploration pipelines. Nucl Instrum Methods Phys Res Sect A 2015;784:616‒20. . 10.1016/j.nima.2014.11.030

[231]

Roslee MN, Muji SZM, Pusppanathan J, Shaib MFA. A pilot study on pipeline wall inspection technology tomography. In: Proceedings of the 11th National Technical Seminar on Unmanned System Technology; 2019 Dec 2‒3; Perth, Malaysia. Singapore: Springer; 2021. . 10.1007/978-981-15-5281-6_16

[232]

Piao C, Kim SH, Lee JK, Choi WG, Kim YY. Non-invasive ultrasonic inspection of sludge accumulation in a pipe. Ultrasonics. 2022;119:106602. . 10.1016/j.ultras.2021.106602

[233]

Piao C, Lee J, Kim SH, Kim YY. Ultrasonic inspection of sludge accumulated in plastic pipes using meta-slab mode-converting wedge transducers. NDT E Int 2024;142:103020. . 10.1016/j.ndteint.2023.103020

[234]

Maung CO, Kawashima D, Oshima H, Tanaka Y, Yamane Y, Takei M. Particle volume flow rate measurement by combination of dual electrical capacitance tomography sensor and plug flow shape model. Powder Technol 2020;364:310‒20. . 10.1016/j.powtec.2020.01.084

[235]

Zhang L, Wang Z, Wu S. Gas-liquid flow behavior analysis based on phase-amplitude coupling and ERT. IEEE Trans Instrum Meas 2022;71:1‒10. . 10.1109/tim.2022.3160553

[236]

Jing N, Li M, Liu L, Shen Y, Yang P, Qin X. Visualization detection of solid-liquid two-phase flow infilling pipeline by electrical capacitance tomography technology. Comput Model Eng Sci 2022;131(1):465‒76. . 10.32604/cmes.2022.018965

[237]

Xu Y, Pu H, Li Y, Wang H. Flow pattern identification for gas-oil two-phase flow based on a virtual capacitance tomography sensor and numerical simulation. Flow Meas Instrum 2023;92:102376. . 10.1016/j.flowmeasinst.2023.102376

[238]

Zhao Y, Yue S, Zhang Y, Wang H. Flow velocity computation using a single ERT sensor. Flow Meas Instrum 2023;93:102433. . 10.1016/j.flowmeasinst.2023.102433

[239]

Li Y, Wu N, Liu C, Chen Q, Ning F, Wang S, et al. Hydrate formation and distribution within unconsolidated sediment: insights from laboratory electrical resistivity tomography. Acta Oceanolog Sin 2022;41(9):127‒36. . 10.1007/s13131-021-1972-2

[240]

Liu Y, Zou C, Chen Q, Zhao J, Li Y, Sun J, et al. Cross-hole electrical resistivity tomography as an aid in monitoring marine gas hydrate reservoirs for gas recovery: an experimental simulation study. Geophys J Int 2023;233(1):195‒210. . 10.1093/gji/ggac454

[241]

Liu Y, Chen Q, Li S, Wang X, Zhao J, Zou C. Characterizing spatial distribution of ice and methane hydrates in sediments using cross-hole electrical resistivity tomography. Gas Sci Eng 2024;128:205378. . 10.1016/j.jgsce.2024.205378

[242]

Sa JH, Lee BR, Zhang X, Folgero K, Haukalid K, Kocbach J, et al. Hydrate management in deadlegs: detection of hydrate deposition using permittivity probe. Energy Fuels 2018;32(2):1693‒702. . 10.1021/acs.energyfuels.7b03963

[243]

Scott SL, Satterwhite LA. Evaluation of the backpressure technique for blockage detection in gas flowlines. J Energy Resour Technol 1998;120(1):27‒31. . 10.1115/1.2795005

[244]

Scott SL, Yi J. Flow testing methods to detect and characterize partial blockages in looped subsea flowlines. J Energy Resour Technol 1999;121(3):154‒60. . 10.1115/1.2795975

[245]

Liu L, Scott SL. Development of a type curve to locate partial blockages in gas flowlines. In: Proceedings of SPE Annual Technical Conference and Exhibition; 2000 Oct 1‒4; Dallas, TX, USA. Richardson: OnePetro; 2000. . 10.2118/63187-ms

[246]

Yang L, Fu H, Liang H, Wang Y, Han G, Ling K. Detection of pipeline blockage using lab experiment and computational fluid dynamic simulation. J Pet Sci Eng 2019;183:106421. . 10.1016/j.petrol.2019.106421

[247]

Ling K, Wu X, Shen Z. A new method to detect partial blockage in gas pipelines. Oil Gas Facil 2016;5(5):SPE-174751-PA. . 10.2118/174751-pa

[248]

Inaudi D, Glisic B. Long-range pipeline monitoring by distributed fiber optic sensing. In: Proceedings of 2006 International Pipeline Conference; 2006 Sep 25‒29; Calgary, AB, Canada. ASME; 2006. . 10.1115/ipc2006-10287

[249]

Brower DV, Prescott CN, Zhang J, Howerter C, Rafferty D. Real-time flow assurance monitoring with non-intrusive fiber optic technology. In: Proceedings of Offshore Technology Conference; 2005 May 2‒5; Houston, TX, USA. Richardson: OnePetro; 2005. . 10.4043/17376-ms

[250]

Li L, Pan R, Zhang W, Li H. Overview of fiber optic pipeline monitoring sensors. Appl Mech Mater 2013;246‒247:872‒6.

[251]

Ashry I, Mao Y, Wang B, Hveding F, Bukhamsin AY, Ng TK, et al. A review of distributed fiber-optic sensing in the oil and gas industry. J Lightwave Technol 2022;40(5):1407‒31. . 10.1109/jlt.2021.3135653

[252]

Berthold JW. Detection of flowline blockage using bragg grating sensors. In: Proceedings of Fiber Optic and Laser Sensors and Applications; Including Distributed and Multiplexed Fiber Optic Sensors VII; 1999 Feb 3; Boston, MA, USA. SPIE; 1999.

[253]

Meniconi S, Brunone B, Ferrante M, Capponi C, Carrettini CA, Chiesa C, et al. Anomaly pre-localization in distribution-transmission mains by pump trip: preliminary field tests in the Milan pipe system. J Hydroinf 2015;17(3):377‒89. . 10.2166/hydro.2014.038

[254]

Ferrante M, Bartocci D, Busti B, Fracchia S, Gentile MT, Marelli F, et al. Diagnosis of water distribution systems through transient tests: the pilot study of Milan. J Water Resour Plann Manage 2022;148(6):05022004. . 10.1061/(asce)wr.1943-5452.0001565

[255]

Panhuis P, den Boer H, van Der Horst J, Paleja R, Randell D, Joinson D, et al. Flow monitoring and production profiling using DAS. In: Proceedings of SPE Annual Technical Conference and Exhibition; 2014 Oct 27‒29; Amsterdam, Netherlands. Richardson: OnePetro; 2014. . 10.2118/170917-ms

[256]

Schempf H, Mutschler E, Goltsberg V, Crowley W. GRISLEE: Gasmain repair and inspection system for live entry environments. Int J Rob Res 2003;22(7‒8):603‒16.

[257]

Schempf H, Vradis G. Explorer: untethered real-time gas main assessment robot system. Report. San Francisco: Academia; 2003. . 10.22260/isarc2003/0074

[258]

Schempf H, D’Zurko D. Long-range untethered real-time live gas main robotic inspection system. Report. Washington, DC: US Department of Energy; 2004. . 10.2172/892746

[259]

Schempf H, Mutschler E, Gavaert A, Skoptsov G, Crowley W. Visual and nondestructive evaluation inspection of live gas mains using the explorerTM family of pipe robots. J Field Rob 2010;27(3):217‒49. . 10.1002/rob.20330

[260]

Verifying tee installations—solution for unlocatable PE gas main features. Report. Hauppauge: ULC Technologies; 2024.

[261]

Enablement works on London medium pressure gas network. Report. Hauppauge: ULC Technologies; 2024.

[262]

Rosen. Corrosion and crack detection in an offshore riser and flowline. Report. Obere Spichermatt: Rosen group; 2024.

[263]

PLT logging while tractoring with Well Tractor® 212 tandem. Report. Gydevang: Welltec; 2024.

[264]

Wang Z, Cao Q, Luan N, Zhang L. Development of an autonomous in-pipe robot for offshore pipeline maintenance. Industrial Robot 2010;37(2):177‒84. . 10.1108/01439911011018957

[265]

Li H, Li R, Zhang J, Zhang P. Development of a pipeline inspection robot for the standard oil pipeline of China National Petroleum Corporation. Appl Sci Basel 2020;10(8):2853. . 10.3390/app10082853

AI Summary AI Mindmap
PDF (8792KB)

5413

访问

0

被引

详细

导航
相关文章

AI思维导图

/