Organic Carbon Distribution Characteristics of Qingshankou Shale in Songliao Basin, China
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摘要: 总有机碳(TOC)含量是页岩油气资源量评价的关键参数之一.为定量分析松辽盆地青山口组页岩TOC含量分布特征,预测有利勘探区,以测井资料为基础,首先建立研究区构造模型,应用恢复古埋深法计算镜质体反射率(Ro),通过∆logR方法预测研究区TOC含量,最后利用地质统计学方法建立了研究区页岩TOC三维量化模型.结果表明,纵向上,青一段页岩TOC含量整体分布在0%~4%范围,青二段和青三段TOC含量明显低于青一段.平面上,青一段TOC含量在三肇凹陷南部及朝阳沟阶地中部最高,青二段TOC含量整体低于2%,其在古龙凹陷北部最高,青三段TOC含量普遍低于1.4%.研究成果为松辽盆地页岩油勘探开发有利区选取提供了重要指导和参考.Abstract: Total organic carbon (TOC) content is one of the key parameters to evaluate shale oil and gas resources. In order to quantitatively evaluate the TOC distribution characteristics and predict the sweet spots of shale of Qingshankou Formation in Songliao basin, the three-dimensional (3D) structural model of the Qingshankou shale reservoir was firstly constructed based on the logging data. Then, vitrinite reflectance (Ro) was calculated by restoring the ancient burial depth of reservoir rocks, and the TOC content of the Qingshankou Formation was predicted using the ∆logR method. Finally, the 3D model of TOC content in Qingshankou Formation was built using the geostatistical method. The results show that, vertically, the TOC content of Qing 1 Member is in the range of 0%-4%. However, the TOC content of Qing 2 Member and Qing 3 Member is significantly lower than that of Qing 1 Member. Horizontally, the TOC content of Qing 1 Member in the south of Sanzhao sag and the middle of Chaoyanggou terrace is the highest. The TOC content of Qing 2 Member is lower than 2%, and that in the north part of Gulong sag is the highest. The TOC content of Qing 3 Member is generally lower than 1.4%. This work provides important guidance and reference for selecting favorable shale oil exploration and development areas in Songliao basin.
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Key words:
- Songliao basin /
- Qingshankou Formation /
- shale oil /
- total organic carbon /
- 3D geological modeling /
- petroleum geology
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0. 引言
页岩油气的商业开发从21世纪开始经历了快速高效的发展,以美国为起点,并迅速扩展到加拿大、南美、澳大利亚、中国、欧洲、非洲等地,在全球掀起了页岩油气发展的热潮(邹才能等,2016,2022;Feng et al.,2020;Liu et al.,2020).近年来,针对页岩油气的科学研究在深度和广度上不断拓展,为提高页岩油气开发效率、降低开发成本、控制环境影响等方面积蓄科技力量.当前,我国在松辽盆地、鄂尔多斯盆地、准噶尔盆地及渤海湾盆地等地区都已探明有大规模页岩油资源储量,开发前景明朗(邹才能等,2011;李吉君等,2014;付金华等,2020;周立宏等,2021).松辽盆地是我国重要的含油气盆地,其中白垩系青山口组页岩储层在中央坳陷区内广泛发育,是松辽盆地目前最具潜力的页岩油产层(Liu et al.,2019).
总有机碳(TOC)含量是衡量烃源岩有机质丰度和生烃潜力的重要参数,TOC含量的准确预测对于页岩油气资源评价及地质甜点区预测具有重要意义(柳波等,2018;黄文彪等,2021).目前国内外常用于确定TOC含量的方法有:(1)岩心实验测试法,即通过有机地球化学分析得到岩心的TOC含量(Tourtelot,1964).利用岩心测试得到的TOC含量数据准确性高,但是其代表的范围小,取心和实验过程长且花费昂贵,难以对大范围区域的TOC含量做出准确有效的评价(Handhal et al.,2020);(2)测井数据解释法,即挖掘测井数据和TOC含量之间的线性/非线性关系,实现测井尺度的TOC含量预测(邹长春等,2018;Li et al.,2021).基于测井数据预测的TOC含量结果在有测井数据的井段是有效的,同时需要实测岩心数据进行标定;(3)地震数据分析法,即建立地震岩石物理属性与TOC含量的定量关系,利用此关系在有地震资料的区域实现TOC含量预测,但是受限于地震分辨率低,该方法的精度较差(侯华星等,2016).综上所述,利用上述任何单一的方法来进行盆地尺度TOC含量的预测及其分布规律的研究都具有一定局限性,因此,多源异构数据融合技术是解决该类问题的关键,也是必然趋势.多项研究表明,多种测井数据与TOC含量具有一定的相关性(朱光有等,2003;李子梁等,2021).Passey et al.(1990)在阿尔奇公式基础上提出了广泛适用于碳酸盐岩、碎屑岩烃源岩的∆logR法来预测TOC含量.王祥等(2020)在前人基础上提出了考虑密度因素的广义∆logR法,对渤中凹陷西南部烃源岩TOC含量进行了预测,取得了很好的效果.王贵文等(2002)搭建D⁃BP神经网络,获得了塔里木盆地台盆区寒武‒奥陶系21口井碳酸盐烃源岩TOC含量.Mahmoud et al.(2021)以自适应神经模糊推理系统(ANFIS)、支持向量回归(SVR)、功能神经网络(FNN)和随机森林(RFs)四种机器学习算法仅从铀、钍、钾的体伽马射线和光谱伽马射线测井曲线来预测TOC含量.徐思煌和朱义清(2010)利用多元统计的方法在惠州凹陷文昌组烃源岩8口井上建立了声波时差、密度测井、中子测井等测井曲线与TOC含量的回归关系,成功预测了研究区TOC含量.
松辽盆地青山口组页岩油具有优越的地质条件和巨量的资源基础,勘探开发前景广阔,是大庆油田增储上产的重要的接替领域(李士超等,2017;王玉华等,2020;王岩等,2021).为实现松辽盆地青山口组中央坳陷区页岩油储层TOC含量预测,在有限的TOC含量实测数据、较完整的测井数据情况下,本文综合声波时差、密度、电阻率等测井数据,采用恢复古埋深法建立了研究区岩心镜质体反射率(Ro)与埋藏深度之间(Depth)的线性关系,再通过∆logR方法得到青山口组井段的TOC含量,之后利用地质统计学方法建立了TOC含量三维属性模型,最终实现整个研究区TOC含量的定量解释,为松辽盆地青山口组地质甜点区预测提供量化依据.
1. 研究区概况
1.1 地质概况
松辽盆地是中国东部叠置于古生代基底上的大型中新生代沉积盆地,大致经历了热隆张裂(T⁃J3)、裂陷(J3⁃K1)、坳陷(K1⁃K2)和萎缩褶皱(K2⁃Q)四个阶段,具有明显的下断上坳的双重结构及高地温高热流等特征,其中松辽盆地中央坳陷区主要包括古龙凹陷、大庆长垣、三肇凹陷等9个构造区(图 1)(陈发景等,1992;迟元林等,2002;赵忠华等,2018).松辽盆地的含页岩油地层主要有嫩江组、青山口组等层位,其中青山口组沉积于盆地坳陷时期,泥页岩形成于受海侵影响的深水‒较深水湖相还原环境,沉积厚度大、分布范围广,发育有大量钙质、砂质薄夹层,是目前松辽盆地最有潜力的页岩油层系(雷振宇等,2012;曾维主等,2021).
1.2 数据资料
本次研究区主要为松辽盆地中央坳陷区(图 1a),目标层位是青山口组,以页岩为主,共收集了地震解释的青山口组顶与底构造层面数据2套、井位资料1 370余口,分析处理了748口井的测井资料,包括自然电位、自然伽马、密度、中子孔隙度、声波时差、电阻率等,以及661口井的录井资料.综合利用以上资料建立三维构造模型,在此基础上建立工区面积达3.396×1010 m2的青山口组三维TOC含量属性模型.
本文中使用的测井数据工区范围大、时间跨度长,部分井进行了多次测井,因此需要对数据进行标准化.(1)首先,对松辽盆地已收集的大量测井资料进行整理,获取有效的青山口组测井信息;(2)其次,进行全盆地范围的测井数据(主要包括自然伽马GR、密度RHOB、声波时差AC、深侧向电阻率LLD曲线)标准化处理,即通过匹配测井曲线的趋势特征进行单一测井类型不同深度数据的校正,标准化后的代表性井位测井曲线特征如图 2所示.
2. 基于ΔlogR法的TOC含量计算
2.1 ΔlogR方法
TOC含量预测的方法较多,但是以Passey et al.(1990)开发的∆logR方法应用最广.因此本文主要采用ΔlogR法,从常见测井曲线出发,预测松辽盆地中央坳陷区青山口组TOC含量.ΔlogR方法可用任何一种孔隙度测井曲线声波时差AC、密度RHOB、中子孔隙度NPHI以及电阻率曲线深侧向或深感应计算.
$$ \mathrm{TOC}=a \times\left(\Delta \log R \times 10^{0.297-0.168 \text { 8LOM }}\right)+b, $$ (1) $$ \begin{aligned} \Delta \log R= & \log _{10}(\mathrm{RESD} / \mathrm{RESDBase})+ \\ & c \times\left(\log _{10}(\mathrm{PHI})-\log _{10}(\text { Base })\right) \end{aligned}, $$ (2) 其中,R为电阻率数值,PHI为孔隙度测井声波时差AC、密度RHOB、中子孔隙度NPHI之一;Base为对应孔隙度测井的基线;RESD为深电阻率测井深侧向LLD或深感应ILD之一;RESDBase为对应电阻率测井基线;a、b、c为常数.
2.2 镜质体反射率及有机质成熟度计算
松辽盆地青山口组以泥岩为主,其他岩性主要为各种砂质、钙质夹层等,导热性相似,可以假设研究区内地热梯度一致.总体上来讲,Ro与深度成线性正相关关系,不同井位Ro与深度关系的斜率基本相同,但是截距差别较大(图 3).因此,本文首先筛选Ro数据点多于5个的28口井的所有数据,并建立Ro与埋深的线性关系继而确定斜率,基于此斜率,计算各井中Ro与埋深线性关系的截距,此截距与地层剥蚀和抬升密切相关,因此须结合已经解释的剥蚀厚度数据来间接计算地层古埋深,由此得到研究区范围内各井不同深度的Ro.
假设在地层抬升/剥蚀前,Ro与埋深之间为线性关系Ro=d×Depth+e,其中d和e为常数.针对松辽盆地,将Ro与埋深的线性关系改写为:
$$ \begin{aligned} & R_{\mathrm{o}}=\frac{1}{1\;147.78}(M D+A A)-\frac{965.38}{1\;147.78}= \\ & \frac{(M D+A A)}{1\;147.78}+\frac{A A-965.38-E T}{1\;147.78} \end{aligned} $$ (3) 其中,MD为现今埋深,AA为埋深调整量,ET为剥蚀厚度.截距与剥蚀厚度的关系为ET=AA-965.38-1 147.78e.根据有机质成熟度(LOM)与Ro对应关系图(图 4),得到该研究区各井的LOM值.
图 4 镜质体反射率Ro与有机质成熟度LOM关系图(据Crain, 2010改)Fig. 4. Relationship between vitrinite reflectance (Ro) and level of maturity (LOM)2.3 TOC含量的确定
本文先用多元非线性回归方法来确定研究区上述公式中的常系数a、b、d、e,再对公式(2)涉及的各个参数进行分析,优选出最佳的孔隙度、电阻率测井数据,以∆logR和岩心TOC含量的回归曲线的相关性系数R2值为标准,筛选最佳组合.通过系统的对比分析,发现在该研究区声波时差AC与深侧向电阻率LLD的组合且系数c为0.02时效果最好.LOM通常针对研究区不同区域热演化程度不同选择平均值对TOC进行预测,但是研究研究范围大、埋深跨度大,针对不同区域、不同深度选择不同的LOM值比较繁琐且不准确.因此本文以实验测得的Ro为基础,根据LOM与Ro对应关系,得到研究区内单井的Ro与∆logR数据,进而计算得到TOC含量(图 5).
3. TOC三维地质模型建立
3.1 三维构造模型建立
本文首先建立了研究区三维构造模型,然后在此基础上实现TOC含量三维属性建模.基于研究区381口井的自然伽马曲线和深侧向电阻率或深感应电阻率曲线识别地层分层标志,进行井间对比并建立地层格架.以地震解释的构造图为软约束,控制大尺度构造特征(主要为地层倾角),并以381口井的分层数据为控制点,建立各层构造图.在建立三维构造模型的过程中,首先确定三维构造模型范围,将研究区在平面上以400 m×400 m的网格进行划分,同时将网格方向设置为平行于和垂直于古龙凹陷主轴方向;纵向上以各层构造面以及分层数据建立3个地层,然后将3个地层进行细分,在纵向上将青山口组共划分为400层,每层厚度在0.2∼2.0 m,平均值约为1 m.最终,结合地震数据和测井数据利用Petrel软件建立的三维构造模型如图 6所示.
3.2 基于地质统计学方法的三维TOC属性建模
本文以实验测得的Ro为基础,利用恢复古埋深的方法得到全区域单井上的Ro值,根据式(2)和LOM与Ro对应关系,得到研究区的∆logR数据,最后使用式(1)计算得到TOC含量.以此为输入数据,在空间插值中选用序贯高斯模拟(Sequential Gaussian Simulation)算法,建立TOC含量三维属性模型.在三维属性建模过程中,受限于空间上测井数据的不连续性,井间属性具有一定的不确定性,因此需要利用空间插值算法对井间TOC含量进行插值,本文采用的序贯高斯模拟方法在进行空间插值时,可以控制全局估值误差,该方法对于全局属性参数的不确定性控制比确定性插值算法有较好的容错能力.图 7为松辽盆地中央坳陷区青山口组TOC含量三维属性模型,为直观展示其空间分布特征,选取了大庆长垣横、纵向截面,三肇凹陷截面与古龙凹陷截面等4个代表性剖面.可以看出,青山口组TOC含量整体分布在0%~4%范围内,纵向上,从青三段顶面至青一段底面,TOC含量自上而下逐渐增大.由于青山口组大量发育薄夹层,因此TOC含量纵向分布也受其控制,呈较为明显的层状分布特征.
松辽盆地中央坳陷区青山口组各段TOC含量平面分布如图 8所示,其中青一段TOC含量明显高于青二段和青三段,普遍分布在2%~3.6%的范围内,平面上三肇凹陷南部及朝阳沟阶地中部TOC含量显著高于其他部位.平面上,青二段TOC含量整体低于2%,其中古龙凹陷TOC含量较高,三肇凹陷TOC含量较低(< 1.4%).青三段TOC含量最低,普遍低于1.4%,特别是在三肇凹陷中部,TOC含量低于0.4%.青山口组TOC含量在纵向上和平面上均表现出较强的差异分布特征.
4. 结论与认识
针对松辽盆地中央坳陷区青山口组页岩油储层,本文基于岩心实测TOC含量数据和大量测井数据,利用恢复古埋深技术和ΔlogR法,对目的层系的烃源岩总有机碳含量和成熟度进行计算,建立了松辽盆地中央坳陷区青山口组页岩储层的三维TOC含量属性模型.研究结果表明松辽盆地中央坳陷区青山口组TOC含量有如下分布特征:
(1)纵向上,TOC含量整体处于0%~4%范围内,但青一段TOC含量整体高于3%,明显高于青二段和青三段的TOC含量,因此,青一段的页岩油资源潜力最大.
(2)平面上,青一段页岩在三肇凹陷南部、朝阳沟阶地中部的TOC含量最高,青二段TOC含量整体虽低于2%,但其在古龙凹陷北部出现高值.
(3)预测三肇凹陷南部和朝阳沟阶地中部均为未来松辽盆地页岩油勘探开发的有利区,青二段古龙凹陷有待进一步研究.
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图 1 松辽盆地平面图(a)与地层层序图(b)
a.红色线区域为主要研究区;b.青山口组为主要研究层位;据高翔等,2017;李敏等,2019;蒙启安等,2021;Ye et al.,2022修改
Fig. 1. Map (a) and stratigraphic sequence (b) of Songliao basin
图 4 镜质体反射率Ro与有机质成熟度LOM关系图(据Crain, 2010改)
Fig. 4. Relationship between vitrinite reflectance (Ro) and level of maturity (LOM)
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