Article(id=1208356298081546436, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1208356297246879939, articleNumber=null, orderNo=null, doi=10.3981/j.issn.1000-7857.2024.01.00174, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1706112000000, receivedDateStr=2024-01-25, revisedDate=1734537600000, revisedDateStr=2024-12-19, acceptedDate=null, acceptedDateStr=null, onlineDate=1766024191637, onlineDateStr=2025-12-18, pubDate=1756310400000, pubDateStr=2025-08-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1758297600000, onlineIssueDateStr=2025-09-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1766024191637, creator=13701087609, updateTime=1774079905990, updator=sys-migrate, issue=Issue{id=1208356297246879939, tenantId=1146029695717560320, journalId=1146031591421210625, year='2025', volume='43', issue='16', pageStart='1', pageEnd='144', issueExtLink='null', onlineDate='null', pubDate='1756310400000', pubDateStr='2025-08-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1766024191437, creator='13701087609', updateTime=1774330854827, updator='13041195026', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1243196968781070575, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1208356297246879939, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1243196968781070576, tenantId=1146029695717560320, journalId=1146031591421210625, issueId=1208356297246879939, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=128, endPage=135, ext={EN=ArticleExt(id=1208356298383536326, articleId=1208356298081546436, tenantId=1146029695717560320, journalId=1146031591421210625, language=EN, title=Five dimensional seismic prediction method for turbidite based on sedimentary facies model—Taking Binnan-Lijin area as an example, columnId=1150494644690366681, journalTitle=Science & Technology Review, columnName=Papers, runingTitle=null, highlight=null, articleAbstract=

The sedimentary types of turbidite in the Binnan-Lijin area are diverse, making it difficult to predict effective reservoirs. Based on the development location, distance from the source and different flow states of turbidite, the types and sedimentary models of turbidite in the study area were summarized, and the favorable development areas of turbidite were predicted by 5D seismic data. The results indicate that there are four different sedimentary models of turbidite in study area: steep slope-near source-structural slope break type, steep slope-far source-turbidity current type, steep slope-far source-debris flow type and gentle slope-far source-turbidity current type, and there are differences in lithology, logging facies, seismic reflection characteristics and development location. Stacking seismic data along source direction can describe the distribution range of turbidite along the long axis, and stacking data vertically can describe the boundaries of turbidite along the short axis. The sum and product attributes of seismic data stacked in different azimuth can reflect the maximum range of turbidit, and the difference attribute can more clearly reflect the turbidite boundary.

, authors=null, authorsList=Zhiyang GUO, Weizhong ZHANG, Liang QI, Yuntao ZHANG, Zhengchun SHEN, Mingrong WU, Fengqin LOU, authorCompany=null, correspAuthors=null, 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=1208356302472982788, articleId=1208356298081546436, tenantId=1146029695717560320, journalId=1146031591421210625, language=CN, title=基于沉积相模式的浊积岩五维地震预测方法—以滨南-利津地区为例, columnId=1146540929516700224, journalTitle=科技导报, columnName=研究论文, runingTitle=null, highlight=null, articleAbstract=

根据滨南-利津地区浊积岩的发育位置、距离物源远近及流动状态的不同,开展了浊积岩类型划分,建立了浊积岩沉积模式,并利用五维地震数据预测了浊积岩有利发育区。研究结果表明:研究区发育陡坡近源构造坡折型、陡坡远源浊流型、陡坡远源碎屑流型和缓坡远源浊流型4种不同沉积模式的浊积岩,且其岩性、测井相、地震反射特征及发育位置均存在不同。顺物源方向进行地震数据叠加可以更好地描述浊积岩沿长轴方向的展布范围,垂直物源方向可以描述浊积岩短轴方向的边界。不同方位叠加数据体的叠合属性和乘积属性均能反映浊积岩的最大范围;而差值属性能够更清晰反映浊积岩的边界。

, authors=

郭志扬,助理研究员,研究方向为油气藏形成机理与分布规律,电子信箱:

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郭志扬,助理研究员,研究方向为油气藏形成机理与分布规律,电子信箱:

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郭志扬,助理研究员,研究方向为油气藏形成机理与分布规律,电子信箱:

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基于沉积相模式的浊积岩五维地震预测方法—以滨南-利津地区为例
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郭志扬 , 张伟忠 , 亓亮 , 张云涛 , 沈正春 , 吴明荣 , 娄凤芹
科技导报 | 研究论文 2025,43(16): 128-135
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科技导报 |研究论文 2025 , 43 (16) : 128 -135
基于沉积相模式的浊积岩五维地震预测方法—以滨南-利津地区为例
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郭志扬,助理研究员,研究方向为油气藏形成机理与分布规律,电子信箱:

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郭志扬 , 张伟忠, 亓亮, 张云涛, 沈正春, 吴明荣, 娄凤芹
作者信息
  • 中国石油化工集团有限公司胜利油田分公司物探研究院,东营 257022
Five dimensional seismic prediction method for turbidite based on sedimentary facies model—Taking Binnan-Lijin area as an example
Zhiyang GUO , Weizhong ZHANG, Liang QI, Yuntao ZHANG, Zhengchun SHEN, Mingrong WU, Fengqin LOU
Affiliations
  • Geophysical Research Institute, Shengli Oilfield Company, SINOPEC, Dongying 257022, China
出版时间: 2025-08-28 doi: 10.3981/j.issn.1000-7857.2024.01.00174
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根据滨南-利津地区浊积岩的发育位置、距离物源远近及流动状态的不同,开展了浊积岩类型划分,建立了浊积岩沉积模式,并利用五维地震数据预测了浊积岩有利发育区。研究结果表明:研究区发育陡坡近源构造坡折型、陡坡远源浊流型、陡坡远源碎屑流型和缓坡远源浊流型4种不同沉积模式的浊积岩,且其岩性、测井相、地震反射特征及发育位置均存在不同。顺物源方向进行地震数据叠加可以更好地描述浊积岩沿长轴方向的展布范围,垂直物源方向可以描述浊积岩短轴方向的边界。不同方位叠加数据体的叠合属性和乘积属性均能反映浊积岩的最大范围;而差值属性能够更清晰反映浊积岩的边界。

浊积岩  /  沉积模式  /  五维地震数据  /  浊积砂体展布预测

The sedimentary types of turbidite in the Binnan-Lijin area are diverse, making it difficult to predict effective reservoirs. Based on the development location, distance from the source and different flow states of turbidite, the types and sedimentary models of turbidite in the study area were summarized, and the favorable development areas of turbidite were predicted by 5D seismic data. The results indicate that there are four different sedimentary models of turbidite in study area: steep slope-near source-structural slope break type, steep slope-far source-turbidity current type, steep slope-far source-debris flow type and gentle slope-far source-turbidity current type, and there are differences in lithology, logging facies, seismic reflection characteristics and development location. Stacking seismic data along source direction can describe the distribution range of turbidite along the long axis, and stacking data vertically can describe the boundaries of turbidite along the short axis. The sum and product attributes of seismic data stacked in different azimuth can reflect the maximum range of turbidit, and the difference attribute can more clearly reflect the turbidite boundary.

turbidite  /  sedimentary model  /  5D seismic data  /  reservoir prediction
郭志扬, 张伟忠, 亓亮, 张云涛, 沈正春, 吴明荣, 娄凤芹. 基于沉积相模式的浊积岩五维地震预测方法—以滨南-利津地区为例. 科技导报, 2025 , 43 (16) : 128 -135 . DOI: 10.3981/j.issn.1000-7857.2024.01.00174
Zhiyang GUO, Weizhong ZHANG, Liang QI, Yuntao ZHANG, Zhengchun SHEN, Mingrong WU, Fengqin LOU. Five dimensional seismic prediction method for turbidite based on sedimentary facies model—Taking Binnan-Lijin area as an example[J]. Science & Technology Review, 2025 , 43 (16) : 128 -135 . DOI: 10.3981/j.issn.1000-7857.2024.01.00174
浊积砂体作为陆相湖盆重要的重力流砂体类型之一,是近几年岩性油气藏勘探的热点。自浊流理论正式建立以来,各国地质学家对浊积岩进行了大量研究,在沉积机理、演化模式、储层特征、预测方法等研究方面获得了重要进展[16]。受古地貌、沉积条件、构造特征等影响,不同地区浊积岩的发育特征和沉积模式存在差异[79]。目前,针对不同类型的浊积岩的预测方法主要有甜心体、叠前和叠后反演、随机森林等方法[1013]。近年来,炮检距向量片(offset vector tile,OVT)域五维地震数据解释技术成为地震勘探的研究热点,为浊积砂体预测提供了新的方法。五维地震数据相比常规共反射点道集(common reflection pint,CRP)道集增加了方位角和偏移距信息,利用OVT道集中不同方位、多个偏移距的地震响应信息差异性能够更好地预测浊积砂体的展布范围[1419]
滨南−利津地区位于济阳坳陷东营凹陷的西部,滨南−利津断裂带的下降盘,北邻陈家庄凸起,西靠滨县凸起,南部为利津洼陷,面积约200 km2。沙四上纯上亚段沉积时期−沙三中沉积时期,研究区在滨县凸起南坡、滨县凸起东坡和利津北带低台阶3个构造带带上广泛发育各种成因的浊积岩油藏,分布范围广、成藏条件好,具有良好的勘探潜力(图 1)。
滨南−利津地区古地形上具有西缓东陡、阶梯式发育的特征,对研究区的沉积具有重要的影响。沙四上亚段沉积时期为盆地裂陷时期,北部边界断裂活动强烈,使剥蚀区与沉积区高差加大,碎屑供给充分;沙三下亚段时期,湖盆加速沉降,湖平面大幅度上升,湖泊进一步扩张。受滨南断层、利津断层和胜北断层的活动速率的影响,沙四上亚段沉积时期分别在滨南地区和利津地区形成了2大不同的沉积体系[2021],滨县凸起存在多个大型古冲沟,自北向南形成了冲积扇—扇三角洲—滩坝沉积体系;利津地区相对较陡,发育近岸水下扇—深水浊积的沉积体系。沙三下亚段时期,碎屑供给能力降低,总体以深湖相沉积为主,自北向南形成了扇三角洲—浊积岩沉积组合。
以滨南−利津地区的构造和沉积特征为背景,根据浊积岩的发育位置、距离物源远近以及流动状态,总结了4种不同的浊积岩沉积模式。
陡坡近源构造坡折型浊积岩的岩性主要为灰色砾状砂岩、含砾砂岩和泥质砂岩,具有泥包砂的特征;沉积物粒度较粗、分选较差,岩心中可见大量粒径约1 cm的砾石,最大者可达3 cm,反映出近源堆积的特征。测井上,自然电位(SP)曲线具有明显的箱形特征,顶底呈现出中—高幅度突变,反映物源供应比较丰富且水体能量比较稳定[22]图 2(a))。地震上,砂体呈现出中强振幅的地震反射特征。这种浊积岩主要发育在断层活动等诱导机制下,扇体滑塌并卸载到断坡坡脚部位(图 2(b)),砂体平面形态多,如利津北带东段沙四上纯下浊积砂体。
陡坡远源浊流型浊积岩的岩性为灰白色含砾砂岩和灰色泥质砂岩,岩心中可见竖立状砾石,反映快速堆积的重力流沉积特征(图 3(a))。测井上。自然伽马(GR)曲线具有齿化箱形特征,浊积砂体呈现中—高幅度隆起,与顶底泥岩突变接触,同时齿化的特征反映了沉积环境、水体能量的快速变化,导致沉积物粒度和分选快速变化;部分浊积砂体的自然伽马曲线表现出指形的特征,厚度较薄,与顶底泥岩突变接触,无明显韵律变化(图 3(b))。地震上,浊积砂体呈现强振幅的连续反射特征。这种类型的浊积岩形成于湖盆陡坡一侧,坡度大,泥、砂、砾组成的重力流沿着水道被输送到深水湖盆中,浊积岩砂体厚度大,多直接前插于湖盆泥岩之中,如利津北带沙三段和滨县凸起东坡沙三段的浊积扇。
陡坡远源碎屑流型浊积岩是滨南地区新发现的浊积岩类型,岩性为灰色或褐灰色细砾岩、含砾细砂岩;岩心上可以观察到泥岩中存在砾石和含砾砂质条带的现象,粒径在大多为2~3 mm,反映出一定的碎屑流特征。测井上,自然伽马曲线具有齿化箱形或者指状尖峰特征,电阻率曲线呈低幅度箱形隆起或者指状尖峰(图 4(a))。地震上,浊积砂体呈现较连续的中强振幅地震反射特征。这种类型的浊积岩形成受构造运动、洪水、火山等事件性因素的影响,砂体发生滑塌形成砂质碎屑流沉积,如滨县凸起东坡沙三下浊积岩(图 4(b))。
缓坡远源浊流型浊积岩的岩性为灰色或褐灰色细砂岩、含砾细砂岩,沉积物粒度较细,分选较好。测井上,自然伽马曲线体现出齿化箱形的特征(图 5(a));同时砂体在地震上呈现连续的强振幅反射特征。这类浊积岩发育在湖盆缓坡的三角洲的前方,坡度小,以滑塌为主,在斜坡上也有一定的滞留沉积,浊积砂体整体呈现宽而薄的席状特点,如滨县凸起南坡沙四上的浊积岩(图 5(b))。
从区域分布特征来看,受到沉积背景的影响,研究区不同的构造带上发育的浊积岩类型也存在差异。利津北带低台阶地区主要发育跟断层活动相关的陡坡近源构造坡折型和陡坡远源浊流型浊积岩;滨县凸起东坡发育与浊流水道相关的陡坡远源浊流型浊积岩和陡坡远源碎屑流型浊积岩;滨县凸起南坡则主要发育缓坡远源浊流型浊积岩。
五维地震预测技术是通过方位角−偏移距的参数优选,获取敏感叠加参数,结合实际工区地质目标的特征,将OVT道集按需叠加,改善部分叠加效果,为后续OVT域地震属性分析提供高质量的叠前地震道集。研究区内现有1块OVT域地震资料,覆盖次数为208次,面元网格为12.5 m×12.5 m,横纵比为0.63,这是本区内第一块宽方位地震采集处理工区,为本次研究提供了资料基础。结合研究区实际资料情况,优选滨县凸起东坡沙三下亚段浊积岩开展OVT域分布范围预测。
受古地形的控制,滨县凸起东坡沙三下亚段发育陡坡远源浊流型浊积岩,且砂体的展布具有一定的方向性,长轴方向为北西−南东向(图 6(a))。选取实际钻遇浊积岩的典型井进行了椭圆拟合,结果表明,浊积岩在五维地震数据中具有明显的各向异性特征,且各向异性的长轴方向代表了浊积岩的沉积方向(图 6(b)~(c))。
利用五维地震数据资料,通过优选优势方位角进行数据部分叠加并提取多个方位地震属性来反映的地质沉积信息并进行浊积砂体展布的精细描述,能够充分发挥多方位地震横向预测能力[19]。选取滨县凸起东坡沙三下所有钻遇浊积岩的井进行椭圆拟合,结果表明研究区浊积岩具有相似的各向异性特征,拟合椭圆的长轴方向为北西—南东向,即顺物源方向。结合拟合模板的方位角信息,优选优势方位角为130~180°。同时,优选130~180°(顺物源方向)、40~90°(垂直物源方向)、85~135°(斜交物源方向)、−5~45°(斜交物源方向)4个不同方位进行了对比,结果表明,顺物源方向进行数据叠加能够更好的描述浊积砂体沿长轴方向的边界,而垂直物源方向进行数据叠加对砂体的横向边界刻画更为明确(图 7)。
偏移距是分区道集优化叠加的最重要参数之一,偏移距优选是根据道集叠加后地质体的成像效果,去除近偏和远偏能量弱、噪声大的道集,优化数据体,能够提高信噪比、突出目的层段反射。滨县凸起东坡OVT域地震数据的偏移距范围为0~4062 m,通过对偏移距的分析,当偏移距小于2400 m时,目的层反射平直段的道集能量强,当偏移距大于2400 m时,道集能量开始逐渐减弱,当偏移距大于3500 m时,道集能量不均衡,信息已被噪音淹没,资料无法使用(图 8)。为了提高信噪比和计算速度,结合椭圆拟合时确定的偏移距范围,最终优选最大偏移距为2200 m。最小偏移距采用倾角计算的方法[23]
$L=H \times \tan \alpha$
式中,L为最小偏移距,H为浊积砂体的垂深,α为浊积砂体的倾角(图 9[23],最终计算出最小偏移距为550 m。
通过对方位角和偏移距的优选,建立了方位角130~180°、偏移距550~2220 m的模板进行数据叠加。对比数据叠加前后的地震剖面,以B681井为例,该井在常规地震资料中表现为强振幅反射,认为是浊积岩的有利发育区;经数据叠加后,该井处的强反射转变为弱反射特征(图 10),与实钻结果对比,该井在沙三下亚段主要钻遇泥岩和油页岩,并未钻遇浊积岩,证明了本次方位角−偏移距优选的正确性。
岩性变化在不同方位角−偏移距的OVT域地震数据体上会有不同的体现,通过对优选出的叠前地震属性体进行适当的数学运算,求取叠合属性、差值属性或乘积属性,可有效凸显岩性边界。利用顺物源方向(130−180−550−2220)的数据体A和垂直物源方向(40−90−550−2220)的数据体B进行联合运算求取差值属性。结果表明,差值属性则能更清晰地反映浊流水道的边界和浊积岩的厚度变化(图 11)。B680、B681等浊积岩钻探失利的井区,本次预测已进行过修正;而B685井区的浊积岩发育范围有所扩大,指出了浊积岩的勘探潜力方向。统计钻遇沙三下的15口井,有13口井的预测结果与钻井结果相吻合,属性吻合率87%,提高了浊积岩砂体的预测精度。B685−X10和B685−X13两口井与预测结果吻合度较差,分析原因认为主要是钻遇浊积岩边部、砂体厚度较薄(图 12),属性预测精确性较低。
1)根据发育位置、距离物源远近以及流动状态的不同,滨南地区发育陡坡近源构造坡折型、陡坡远源浊流型、陡坡远源碎屑流型和缓坡远源浊流型4种不同沉积模式的浊积岩,且其岩性、测井相、地震反射特征及发育位置均存在不同。
2)顺物源方向进行地震数据叠加可以更好地描述浊积岩沿长轴方向的展布范围,垂直物源方向可以描述浊积岩短轴方向的边界。不同方位叠加数据体的叠合属性和乘积属性均能反映浊积岩的最大范围;而差值属性能够更清晰反映浊积岩的边界。
3)通过在滨南地区的实际应用,浊积岩预测吻合率达到87%,提高了预测精度,证实了基于OVT域五维地震数据开展浊积砂体空间展布范围预测的适用性,在浊积岩油气勘探中具有重要的指导意义。

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2025年第43卷第16期
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doi: 10.3981/j.issn.1000-7857.2024.01.00174
  • 接收时间:2024-01-25
  • 首发时间:2025-12-18
  • 出版时间:2025-08-28
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  • 收稿日期:2024-01-25
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    中国石油化工集团有限公司胜利油田分公司物探研究院,东营 257022
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2种不同金属材料的力学参数

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species
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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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