Processing math: 100%

LI Yiming, LIU Dadong, FENG Xia, ZHAO Fuping, CHEN Yi, WANG Yisong, LIN Ruiqin, DU Wei, FAN Qingqing, SONG Yan, JIANG Zhenxue, ZHANG Ziya. 2024. Heterogeneity characteristics and its controlling factors of marine shale reservoirs from the Wufeng−Longmaxi Formation in the Northern Guizhou area[J]. Geology in China, 51(3): 780-798. doi: 10.12029/gc20230920002
Citation: LI Yiming, LIU Dadong, FENG Xia, ZHAO Fuping, CHEN Yi, WANG Yisong, LIN Ruiqin, DU Wei, FAN Qingqing, SONG Yan, JIANG Zhenxue, ZHANG Ziya. 2024. Heterogeneity characteristics and its controlling factors of marine shale reservoirs from the Wufeng−Longmaxi Formation in the Northern Guizhou area[J]. Geology in China, 51(3): 780-798. doi: 10.12029/gc20230920002

Heterogeneity characteristics and its controlling factors of marine shale reservoirs from the Wufeng−Longmaxi Formation in the Northern Guizhou area

    Fund Project: Supported by Guizhou Geological Prospecting Fund Project (No.208−9912−JBN−L1D7), China University of Petroleum (Beijing) Young Top Talents Project (No.2462021BJRC010).
More Information
  • Author Bio: LI Yiming, male, 1999, master candidate, mainly engaged in unconventional oil and gas geology; E-mail: 18872982741@163.com
  • Corresponding author: LIU Dadong, male, 1987, associate professor, mainly engaged in unconventional oil and gas geology; E-mail: liudd@cup.edu.cn
  • This paper is the result of oil and gas exploration engineering.

    Objective

    The heterogeneity is the inherent nature of shale. Study of the microheterogeneity of shale is of great significance for determining the enrichment mechanism of shale gas and the selection of high−quality reservoirs.

    Methods

    This study investigated the marine shale of the Wufeng−Longmaxi Formation in the northern Guizhou area. Through XRD mineralogy, low−temperature N2 adsorption and high−pressure mercury intrusion (HPMI) analyses, we explored the macro and micro heterogeneity characteristics of pore structures of this formation.

    Results

    The Wufeng−Longmaxi Formation shales in the northern Guizhou area are mainly of siliceous lithofacies, followed by mixed lithofacies and clayish lithofacies. The shales of different lithofacies exhibit large differences in total organic carbon (TOC) contents, mineral compositions, and pore structure characteristics. The FHH fractal model of N2 adsorption curves, and the porous fractal theory of HPMI methods, were utilized to calculate the low pressure fractal dimension D1 (0<P/P0<0.45) of N2 adsorption as 2.5351−2.6722, and the high pressure fractal dimension D2 (0.45<P/P0<1) as 2.8311−2.9113. Additionally, the fractal dimension DHg of HPMI was determined to be 2.0904−2.3736, indicating strong heterogeneity within the Wufeng−Longmaxi Formation shale pore structures. A larger fractal dimension corresponds to a more complex pore structure within the shale reservoir and stronger adsorption capacity for shale gas. Furthermore, there are notable differences between various types of pore fractal dimensions and TOC content, mineral composition, pore structure parameters, and other influencing factors. Specifically, it has been found that the fractal dimension DHg exhibits a strong correlation with different mineral contents, suggesting that macropore fractal characteristics are primarily influenced by mineral components. Moreover, there is a clear correlation between fractal dimensions D1 and D2 with TOC content and pore specific surface area parameters within the shale, indicating that micropore and mesopore heterogeneity are mainly influenced by organic carbon contents and development of organic pores.

    Conclusions

    Generally, siliceous shale exhibits relatively high total organic carbon (TOC) contents, high proportions of brittle minerals, and high fractal dimensions. This confirms the siliceous shales are the primary high−quality lithofacies within the Wufeng−Longmaxi Formation in the northern Guizhou area, followed by the mixed lithofacies. Meanwhile, higher organic matter contents indicate not only more favorable conditions for hydrocarbon generation, but also better conditions for shale gas exploration and extraction. Our study offers theoretical and practical support for guiding the optimal selection of favorable reservoirs in marine shales in the northern Guizhou area.

  • 中国南方海相页岩气资源丰富,勘探开发潜力巨大。近年来,页岩气产量逐年提高,2022年产量达240亿 m3高之业等,2020龙胜祥等,2021白立勋等,2023舒红林等,2023Wang et al.,2023)。然而,中国南方海相页岩非均质性强,平面上、纵向上页岩气产量差异巨大。前人研究表明,优质页岩储层是页岩富气的关键条件之一,揭示页岩储层发育特征及优质储层发育主控因素对于页岩气有利层段优选具有重要意义(李灿星等,2021屈凯旋和郭少斌,2022郭旭升等,2023)。

    非均质性是页岩储层的固有属性,由于受到构造活动、沉积环境和成岩作用等因素的共同影响,导致地层在沉积相、岩相等宏观属性上,以及储层孔隙结构等微观属性上均表现出较强的非均质性(Loucks et al.,2009安成等,2023a)。四川盆地及周缘五峰组—龙马溪组是目前中国页岩气实现商业开发的最主要层系。勘探进展表明,五峰组—龙马溪组页岩在纵向上和平面上均具有较强的非均质性特征,这对开发过程中钻井地质导向、储量评价及单井实施效果评价工作均带来了较大挑战(郭英海和赵迪斐,2015潘占昆等,2019张鹏等,2020刘冬冬等,2021)。

    黔北地区位于四川盆地东南缘,近年来,发现了安页1井、真页1井等常压页岩气藏,展现出良好的勘探前景。前人对黔北地区五峰组—龙马溪组海相页岩构造演化、沉积环境、保存条件、成藏特征等方面开展了大量研究(Liang et al.,2017Sun et al.,2017连梦利等,2022王奕松等,2023),但针对页岩储层的非均质性研究相对较少。本文基于黔北地区AY−1井、BZ−1井、SD−1井三口井岩心样品,在精细岩相划分的基础上,通过储层微观储集能力及分形特征分析,揭示研究区五峰组—龙马溪组页岩储层的非均质性特征及其主控因素,并优选出黔北地区页岩气开发有利层段。该成果对于四川盆地及周缘海相页岩有利层段优选具有重要指导意义。

    黔北地区位于滇黔北坳陷,区域上属于上扬子板块,地处湘鄂西构造带与川东南构造带之间的武陵褶皱带,毗邻华南造山带,位于上扬子克拉通盆地东南缘(图1)。黔北地区经历了加里东运动、海西运动、印支运动、燕山运动和喜山运动,区域内褶皱和断裂构造极为发育(斯春松等,2016)。黔北地区主要发育NE−SW向和NNE−SSE向断裂,在燕山运动构造改造作用下,形成了一系列NE−SW向分布的宽缓背斜和窄陡向斜,伴生大量滑脱构造,呈现出典型的隔槽式褶皱特征(图1)。

    图 1.  黔北地区地质概况图
    Figure 1.  Schematic geological map of the northern Guizhou area

    晚奥陶世至早志留世时期,受构造挤压作用及海侵事件的影响(常德双等,2021),黔北地区在晚奥陶世形成了一套厚度为几米到数十米不等的富有机质页岩,即五峰组页岩;早志留世,古隆起边缘发育潮坪—浅水陆棚和深水陆棚相,形成了一套下部连续沉积的富有机质页岩,即龙马溪组页岩,以黑灰色硅质、碳质和钙质泥页岩为主,上段沉积一套黄绿色、灰绿色泥岩和粉砂岩。五峰组—龙马溪组页岩是该区域页岩气重点勘探开发层系,在贵州地区主体分布在黔中隆起北缘以及雪峰隆起西缘,埋深为1000~2500 m,厚度在30~120 m。

    样品的TOC含量采用LECO CS230碳硫分析仪进行分析,在开始测量之前,把样品磨成200目的粉末。用12.5%盐酸溶液去除样品多余的无机碳,溶解后在60~80℃的温度下干燥,随后放入碳硫分析仪中进行充分燃烧(温度>1000℃),分析测量数据,计算获得页岩样品总有机碳含量。在相对恒定的温度(24℃)和湿度(35%)条件下,通过X射线衍射(XRD)对页岩的矿物组成进行了鉴定。

    采用ZWISS 540设备对页岩储层中不同类型的孔隙形态进行了观察。为了获得便于直观观察的光滑表面,使用氩离子抛光机在8 kV电压下轰击页岩样品4 h,然后在6 kV电压下轰击2 h。样品室和工作距离设置为高真空模式和二次电子扫描模式,工作电压为15 kV,可生成理论最大分辨率为3.5 nm的二次图像。

    采用ASAP 2460全自动气体分析仪进行低压气体吸附(LPGA),以N2气体为吸附剂。页岩样品粉末为60~80目,对样品进行72 h风干及脱气站脱气处理后送至分析站进行测量。样品的N2吸附测量选用温度为77.35 K的液氮。操作参数分别为10 s和0.01~0.995176。采用多点Barrent−Joyner−Halenda(BJH)方法计算N2吸附等温线的比表面积(SSA)和孔径分布(PSD)。

    采用Micromeritics AutoPore IV 5920型设备进行实验,样品在80℃环境下干燥24 h后,抽真空至0.01 MPa再进行试验。当压力从0 MPa增加到400 MPa时,汞开始进入页岩样品孔隙内,随着压力的持续增大,在累积进汞量增加的情况下,得到连续的压汞曲线。采用假定界面张力恒定(485 MN/m)和角度(140°)的Washburn方程计算等效孔径。

    选取AY−1井、BZ−1井、SD−1井共40块样品进行TOC含量分析测试,结果表明,黔北地区五峰组—龙马溪组页岩TOC含量为0.29%~6.4%,平均含量为2.93%,其中TOC>3%的页岩占比达50%。根据沥青反射率(Rb)等效关系方程换算得到研究区页岩镜质体反射率(Ro)为2.00%~2.75%,平均等效镜质体反射率为2.34%,表明黔北五峰组—龙马溪组页岩成熟度高,处于主生气阶段。有机质类型为I型和II1型,以I型干酪根为主,具有良好的生烃潜力(高凤琳等,2021李琪琪等,2022)。

    孔隙度和渗透率是表征页岩储层储集与渗流能力的有效参数,利用氦气气测法与压力脉冲衰减法测定龙马溪组页岩孔隙度为0.13%~3.23%,平均孔隙度为0.96%;渗透率为0.0004~0.30 mD,平均渗透率0.021 mD,表现出低孔特低渗的特征,页岩储层物性差(表1)。

    表 1.  黔北五峰组—龙马溪组页岩有机地化与物性参数
    Table 1.  The organic geochemistry and physical property parameters of the Wufeng−Longmaxi Formation shales in the northern Guizhou area

    井号
    有机地化参数 物性参数
    TOC/% Ro 有机质类型 Φ/% K/mD
    AY−1 2.925.974.67 2.002.072.02 I 0.131.460.66 0.00040.30340.0350
    SD−1 0.336.142.33 2.102.432.32 I 0.292.001.05 0.00080.06970.0204
    BZ−1 0.294.951.85 2.512.752.65 I/II1 0.153.231.29 0.00061.69000.2400
      注:
     | Show Table
    DownLoad: CSV

    纵向上,黔北地区五峰组—龙马溪组页岩储层矿物组分变化频繁(图2),具有明显的非均质性;同时储层物性在纵向上也存在一定的非均质性,随深度变化而频繁变化。五峰组及龙马溪组底部页岩TOC含量明显高于顶部,为富有机质页岩层段;底部页岩硅质矿物含量均大于50%,以深水陆棚沉积为主,水体较深,为还原沉积环境,草莓状黄铁矿发育。页岩储层中硅质矿物成因以生物成因与成岩自生为主(连梦利等,2022)。研究区龙马溪组页岩自下至上,岩性由底部的富有机质富硅质页岩过渡为顶部的贫有机质页岩,硅质矿物含量减少;沉积主体由深水陆棚相转变为浅水陆棚相,海平面降低,沉积水体逐渐变浅,沉积环境由还原环境转变为氧化环境,且陆源碎屑输入逐渐增加。

    图 2.  黔北地区AY−1井五峰组—龙马溪组地层综合柱状图
    Figure 2.  Comprehensive stratigraphic column of the Wufeng Formation−Longmaxi Formation in Well AY−1 from the northern Guizhou area

    岩相是页岩各类非均质性特征的宏观体现,包括岩性、沉积构造、矿物组分和有机碳含量等属性(张鹏等,2020李跃纲等,2022)。不同岩相类型具有不同的勘探开发潜力,目的层岩相研究对寻找页岩气富集有利层段十分重要。前人多以石英+长石、黏土矿物、碳酸盐矿物三种矿物含量,结合总有机碳含量进行岩相划分,该方法可有效识别海相页岩岩相类型(蒋裕强等,2016高凤琳等,2021)。TOC含量与矿物组分是评价页岩储层生烃基础、含气性与页岩脆性的重要指标参数,页岩TOC含量越高、脆性矿物含量越高,生烃能力和可压裂性就越强。

    根据XRD实验数据分析,黔北地区五峰组—龙马溪组页岩矿物组分以石英与黏土矿物为主,其次为长石、碳酸盐矿物及黄铁矿等矿物,石英含量为22.5%~81%,平均含量为49.3%;黏土矿物含量为9.2%~56.9%,平均含量为31.7%;长石含量为3.1%~15.5%,平均含量为9.5%;碳酸盐含量为0.1%~15.8%,平均含量为5.4%;以及黄铁矿含量为0.1%~29%,平均含量为4.6%。黏土矿物类型以伊利石和伊利石/蒙脱石混层为主,相对含量为82%~100%,平均含量为89.1%;并含有少量的绿泥石和高岭石。纵向上,由下至上石英含量逐渐减少,黏土矿物含量逐渐增多,岩性由硅质页岩向混合质页岩及黏土质页岩过渡,富有机质层段集中在五峰组及龙马溪组下段。总体来看,矿物组分及含量存在较大差异,表明黔北地区海相页岩在岩性组合和矿物组分上存在较强的非均质性。

    根据页岩矿物组分,采用硅质矿物含量(石英和长石)、碳酸盐矿物含量(白云石和方解石)、黏土矿物含量三元法(王玉满等,2016),建立页岩岩相划分标准,将黔北地区五峰组—龙马溪组页岩划分为4大岩相类型:硅质页岩SS(硅质矿物含量>50%);黏土质页岩CS(黏土质矿物含量>50%),钙质页岩CAS(碳酸盐矿物含量>50%),混合质页岩MS(硅质、黏土质、钙质矿物含量均<50%)。结合黔北五峰组—龙马溪组页岩总有机碳含量进一步精准划分岩相,根据总有机碳含量的不同,可划分为4类岩相:贫有机质页岩(TOC<1%)、中等有机质页岩(TOC为1%~3%)、富有机质页岩(TOC为3%~5%)、极富有机质页岩(TOC>5%)。

    根据上述岩相划分标准可知,黔北地区五峰组—龙马溪组页岩岩相主要有以下9种类型:贫有机质硅质页岩(LSS)、中等有机质硅质页岩(MSS)、富有机质硅质页岩(RSS)、极富有机质硅质页岩(USS)、贫有机质黏土质页岩(LCS)、富有机质黏土质页岩(RCS)、贫有机质混合质页岩(LMS)、中有机质混合质页岩(MMS)、富有机质混合质页岩(RMS)(图3)。

    图 3.  黔北地区五峰组—龙马溪组页岩岩相划分图
    Figure 3.  Shale lithofacies ternary diagram for the Wufeng−Longmaxi Formation in the northern Guizhou area

    通过氩离子抛光扫描电镜分析,发现黔北地区五峰组—龙马溪组页岩发育的孔隙类型主要有有机质孔、粒间孔、黏土矿物层间孔、粒内孔、微裂缝等(Du et al.,2022)。有机质孔以蜂窝状、带状等不规则形状为主,孔径范围分布在5~200 nm,以孔径小于100 nm的有机质孔为主,少数孔径大于200 nm(图4a)。

    图 4.  黔北地区五峰组—龙马溪组页岩扫描电镜特征
    a—AY−1井,2323.8 m,极富有机质硅质页岩中有机质孔发育特征;b—AY−1井,2323.8 m,极富有机质硅质页岩中溶蚀孔发育特征;c—BZ−1井,1113.0 m,富有机质硅质页岩黏土矿物层间孔发育特征;d—SD−1井,1135.4 m,中等有机质硅质页岩有机质赋存特征;e—SD−1井,1135.4 m,中等有机质硅质页岩黄铁矿晶间孔发育特征;f—BZ−1井,1112.8 m,中等有机质混合质页岩微裂缝发育特征
    Figure 4.  Scanning electron microscope (SEM) characteristics of the Wufeng−Longmaxi Formation shales in the northern Guizhou area
    a−Well AY−1, 2323.8 m, characteristics of organic matter (OM) pores (OM−ultra rich siliceous shale); b−Well AY−1, 2323.8 m, characteristics of dissolution pores (OM−ultra rich siliceous shale); c−Well BZ−1, 1113.0 m, interlayer pores characteristics of clay minerals (OM−rich siliceous shale); d−Well SD−1, 1135.4 m, characteristics of organic matter occurrence (OM−moderate siliceous shale); e−Well SD−1, 1135.4 m, intraparticla pores characteristics of pyrite (OM−moderate siliceous shale); f−Well BZ−1, 1112.8 m, microfracture characteristics (OM−moderate mixed facies shale)

    粒间孔呈多边形或不规则状,孔径最大可达微米级孔隙(图4c)。混合质及黏土质页岩中粒间孔分布较多,硅质页岩中发育相对较少且孔径较小,粒间孔延伸长,具有较强的孔隙连通能力,可在一定程度上增大储层孔隙的连通性。

    同时,五峰组—龙马溪组页岩中发育黄铁矿晶间孔,孔隙孔径以大于100 nm为主,主要发育为宏孔(图4e);其次,以硅质页岩中的碳酸盐矿物和长石等硅酸盐矿物颗粒内溶蚀孔最为发育(图4b),混合质页岩次之。黏土质页岩基本不发育溶蚀孔,相较于粒间孔,页岩储层中的溶蚀孔呈圆形、椭圆形或不规则状,大多以孤立形式存在,孔隙间互不连通。

    在有机质与黏土矿物边界可见微裂缝发育(图4f),其宽度在100 nm以上,微裂缝的发育可使页岩储层中的孔隙连通,形成孔—缝连通网络,为页岩气提供储集空间和渗流通道,在一定程度上提升页岩储层的渗透率。

    选取三口典型井不同岩相页岩共8块样品(表2),通过低温N2吸附及高压压汞分析测试,对目的层储层微观孔隙结构进行定量表征及孔隙结构非均质性评价。N2吸附是表征页岩储层微观孔隙结构的重要方法(李嫣然等,2022)。根据IUPAC对N2吸附−脱附曲线的分类标准,五峰组—龙马溪组页岩N2吸附−脱附曲线整体上呈现反“S”型的曲线特征,曲线滞后环形态接近H3型、H4型(图5a)。当相对压力较小时(P/P0<0.45),页岩吸附曲线与脱附曲线形态一致,此时吸附量随相对压力的变化率较低,未出现滞后现象;当相对压力较大时(P/P0>0.5),吸附曲线迅速上升,该段脱附曲线出现迟滞现象,且迟滞环分布的区间范围大,表明页岩储层具有孔隙大吼道小的特征;也说明页岩储层孔隙结构复杂出现了毛细凝聚现象,表明对应孔径的孔隙发育(张征等,2021),由此可知五峰组—龙马溪组页岩储层孔隙以墨水瓶状或狭缝状孔隙为主。极富有机质/富有机质页岩孔隙更倾向于墨水瓶状,主要来源于有机质孔与粒间孔的贡献;贫有机质页岩则主要是狭缝状或平行板状孔隙,这与黏土矿物层间孔及微裂缝的发育有关。

    表 2.  页岩实验样品基础信息
    Table 2.  Information of analyzed shale samples
    编号井号深度/m层位岩相
    USS−17AY−12323.80龙马溪组极富有机质硅质页岩
    RSS−11BZ−11113.00龙马溪组富有机质硅质页岩
    RMS−1AY−12331.20五峰组富有机质混合质页岩
    MSS−1BZ−11118.25五峰组中等有机质硅质页岩
    MMS−13BZ−11112.80龙马溪组中等有机质混合质页岩
    LSS−15BZ−11112.00龙马溪组贫有机质硅质页岩
    LMS−1SD−11140.80五峰组贫有机质混合质页岩
    LCS−25BZ−11089.75龙马溪组贫有机质黏土质页岩
     | Show Table
    DownLoad: CSV
    图 5.  黔北地区五峰—龙马溪组不同岩相页岩N2吸附孔隙结构定量表征
    a—N2吸附曲线;b—孔径−孔体积分布;c—孔径−比表面积分布
    Figure 5.  Quantitative characterization of N2 adsorption of different lithofacies shales from the Wufeng−Longmaxi Formation in the northern Guizhou area
    a−N2 adsorption curve; b−Distribution of pore size vs. pore volume; c−Distribution of pore size vs. pore specific surface area

    通过N2吸附BJH模型计算孔隙体积及比表面积参数,研究区五峰组—龙马溪组页岩孔体积介于0.0078~0.0184 mL/g,平均为0.014 mL/g;孔隙比表面积介于7.1~25.3 m2/g,平均为16.2 m2/g(图5b、c)。通过孔径与孔体积、比表面积分布图可知,不同岩相页岩中孔发育非均质性强,孔体积与比表面积的变化规律同样符合随有机碳含量与脆性矿物含量的升高而升高的趋势,有机质的沉积与富集有利于有机质孔的发育,而脆性矿物的聚集保证了孔隙的刚性结构不被破坏,进而减小机械压实作用对孔隙空间的压缩。总体上1~10 nm孔径范围内的孔隙最为发育,即五峰组—龙马溪组页岩的孔隙孔体积和比表面积的主要贡献者为微孔和中孔。

    高压压汞分析能提供较大孔隙的分布特征参数(王静等,2021),通过高压压汞进汞−退汞曲线可知,不同页岩岩相类型储层在曲线形态、累积进汞与退汞量上表现出强烈的非均质性,可分为有平台型与无平台型两类。五峰组—龙马溪组页岩储层整体退汞效率高,均在50%以上;排驱压力小,均小于0.5 MPa;最大汞饱和度较低,孔隙喉道单一,与N2吸附分析结果相符。孔隙以狭缝状和墨水瓶状孔隙为主,此类孔隙具有吼道窄、连通性差的特征(图6a)。由于页岩孔隙表面存在一定粗糙度,因此高压压汞曲线在低压区会快速上升。所以,需剔除低压区(<0.1 Ma)的数据以进行校正,该过程称为表面粗糙度校正(Surface Roughness Correction)(Giesche,2006Gao and Hu,2016)。在压力区间位于0.1~50 MPa时,进汞量随压力的增大增速减缓,表明对应孔径的孔隙基本不发育;当进汞压力大于50 MPa时,进汞量变化率明显增大,表明储层孔隙中微孔和中孔较为发育。

    图 6.  黔北地区五峰—龙马溪组不同岩相页岩高压压汞孔隙结构定量表征
    a—高压压汞吸附曲线;b—高压压汞孔径−孔体积分布
    Figure 6.  Quantitative characterization of high−pressure mercury intrusion of different lithofacies shales from the Wufeng−Longmaxi Formation in the northern Guizhou area
    a−HPMI adsorption curve; b−Distribution of pore size vs. pore volume distribution

    基于高压压汞法获取页岩孔隙结构参数(肖佃师等,2019),获得了不同岩相页岩宏孔的孔体积随孔径的变化率(图6b)。随着孔径的增大,孔隙体积变化率呈逐渐降低的趋势,对应的峰值孔径分别为3 nm和7 nm,孔径分布的连续性较差;而孔径分布大于50 nm的宏孔基本不发育,表明页岩储层孔体积和比表面积主要由微孔和中孔提供。整体上看,黏土质页岩与混合质页岩的宏孔数量较多,而硅质页岩的宏孔发育较少,且随着有机碳的含量增加,宏孔的数量有所增加,可能是由于有机质的富集导致有机质与矿物颗粒边界的边缘缝大量发育。

    分形维数可表征岩石多孔结构和其表面不规则性(Wang and Guo,2021安成等,2023b),前人提出了包括BET模型、FHH模型以及热力学模型等多种方法,其中FHH模型被证明是表征多孔材料孔隙及其表面非均质性最为有效的计算模型(Avnir and Jaroniec,1989Cai et al.,2013Li et al.,2018)。Pfeifer et al.(1989)提出了两种修正后的Frenkel−Halsely−Hill(FHH)理论模型,用于计算样品表面分形维数,公式如下:

    lnV=D3/3ln(lnP0/P)+C (1)
    lnV=D3ln(lnP0/P)+C (2)

    式中,V为在相对压力P/P0(MPa)时N2吸附的气体体积,cm3/g;D为分形维数,C为常数。

    前人针对黔北地区寒武系牛蹄塘组页岩储层开展了N2吸附孔隙分形特征研究,分别采用公式1和公式2进行分形维数计算,结果表明公式1计算所得分形维数D几乎都接近于2,不符合当前对牛蹄塘组页岩孔隙结构特征的认知(唐玄等,2023);而公式2所得分形维数可表征页岩孔隙结构非均质性好坏,评价效果好(赵越等,2022)。由于黔北地区寒武系与奥陶系及志留系沉积环境均为海相沉积,计算公式具有参考性,故本文采用公式2对五峰组—龙马溪组页岩孔隙分形维数进行计算,从而评价页岩储层孔隙发育的非均质性强弱。

    根据FHH模型计算分形维数结果,页岩储层样品N2吸附量与相对压力P/P0的对数数据拟合曲线显示,不同岩相类型页岩在P/P0处于0~0.5与0.5~1.00,表现出不同的吸附特征,具有明显的双重分形特征(图7)。当P/P0介于0~0.5(低压区)时,则把分形维数定为D1,用于表征岩石微孔中分子的吸附与填充能力;当P/P0介于0.5~1.00(高压区)时,则把分形维数定为D2,用于表征储层孔隙空间和孔隙结构的复杂程度。

    图 7.  黔北地区五峰组—龙马溪组不同岩相页岩N2吸附分形维数拟合曲线
    Figure 7.  The fitting curves of N2 adsorption fractal dimensions of different lithofacies shales from the Wufeng−Longmaxi Formation in the northern Guizhou area

    低温N2吸附实验数据及分形维数参数显示,五峰组—龙马溪组页岩样品获得的拟合方程相关系数R2基本都大于0.9,相关性好,表明所得分形维数参数具有可靠性。D1位于2.5351~2.6722,平均值为2.6141;D2介于2.8311~2.9113,平均值为2.8706(表3)。分形维数越接近于3,表明对应岩相储层的孔隙发育越复杂,其非均质性则越强(Yang et al.,2014Wang et al.,2015)。整体上富有机质页岩计算所得分形维数明显大于贫有机质页岩分形维数,结合扫描电镜(图4)有机质孔发育特征分析认为,主要是由于有机碳的沉积富集,导致富有机质页岩有机质孔大量发育;且其孔隙孔径分布范围较小且集中,孔隙呈形态不规则状,孔隙边界粗糙不平,导致页岩孔隙结构复杂多样。但不同有机碳含量范围内的硅质页岩、混合质页岩及黏土质页岩之间孔隙分形维数无明显差异。由此可知,尽管黏土矿物层间孔、黄铁矿晶间孔等宏孔及微裂缝在黏土质页岩中相对发育,但由于其占比较小,故对页岩孔隙结构非均质性程度的贡献较小。

    表 3.  黔北地区五峰组—龙马溪组不同岩相页岩N2吸附分形维数
    Table 3.  N2 adsorption fractal dimension of different lithofacies shales from the Wufeng−Longmaxi Formation in the northern Guizhou area


    岩相
    不同孔径范围累积
    孔体积/(cm3/g)
    不同孔径范围累积
    比表面积/(m2/g)
    P/P0<0.5 P/P0>0.5
    0~2 nm 2~50 nm >50 nm 0~2 nm 2~50 nm >50 nm 拟合系数R2 分形维数D1 拟合系数R2 分形维数D2
    USS−17 0.0052 0.0170 0.0184 16.134 25.261 25.323 0.9931 2.6722 0.8987 2.9113
    RSS−11 0.0050 0.1070 0.0191 15.888 24.910 24.995 0.9944 2.6488 0.9243 2.8884
    RMS−1 0.0046 0.0146 0.0167 14.285 21.834 21.920 0.9936 2.6478 0.9482 2.8892
    MSS−1 0.0031 0.0115 0.0133 9.4016 16.064 16.136 0.9981 2.6101 0.9523 2.8864
    MMS−13 0.0016 0.0078 0.0095 4.7206 9.2929 9.3620 0.9988 2.6104 0.9640 2.8622
    LSS−15 0.0012 0.0063 0.0078 3.3643 7.0746 7.1357 0.9992 2.5351 0.9705 2.8422
    LMS−1 0.0015 0.0083 0.0106 4.2739 8.9615 9.0539 0.9995 2.5922 0.9761 2.8311
    LCS−25 0.0034 0.0151 0.0181 10.196 18.794 18.920 0.9991 2.5968 0.9717 2.8539
     | Show Table
    DownLoad: CSV

    根据毛细管力数学几何模型结合分形维数理论,带入拉普拉斯(Laplace)计算方程,推导高压压汞进汞饱和度分形维数计算公式(赖锦等,2013):

    N(r)=VHg/(πr2l)rD (3)
    SHg=αPc(2D) (4)
    lgSHg=(2D)lgα(2D)lgPc (5)

    将Laplace方程带入公式3中得到公式4,再对公式4两边同时取对数得到公式5,通过lgSHg与lgPc交会图建立二者之间的拟合曲线,获得线性拟合方程斜率参数即可求得分形维数DHg

    式中,N(r)为分形样品所容纳的特定目标数量,r为毛细管对应的孔喉半径,l为毛细管长度,SHg为进汞饱和度,Pc为任意孔径对应的毛细管压力,VHg为通过半径r的累积进汞体积,α为常数。

    在压汞相对压力较低时,汞先进入样品微裂缝及表面孔隙空间,此时,对应的压汞数据不能反映页岩储层内部的孔隙结构特征;同时,由于高压压汞对微孔的测定精度不够,在进汞高压阶段,页岩样品可能会发生形变、破碎,甚至出现液体与样品固体边界性质改变的现象,进而影响测试结果的准确性。故本文高压压汞计算的分形维数只用于表征宏孔(>50 nm)孔隙的非均质性(图8)。利用汞饱和度法拟合的分形维数参数能在较大程度上反映岩石内部孔隙发育的复杂程度、连通性及孔隙结构的非均质性特征,实现对储层孔隙结构的分类与评价。

    图 8.  黔北地区五峰组—龙马溪组不同岩相页岩高压压汞分形维数拟合曲线
    Figure 8.  Fitting curves of high−pressure mercury intrusion fractal dimensions of different lithofacies shales from the Wufeng−Longmaxi formation in the northern Guizhou area

    由于只考虑宏孔的高压压汞分形情况,根据分形理论所得的五峰组—龙马溪组页岩储层孔隙表现出单一的整体分形特征,拟合曲线与数据点几乎在同一直线上,未出现N2分形维数多重分形的现象,整体分布在2.0904~2.3736(表4),表明页岩储层宏孔孔隙结构差异不大。不同岩相之间分形维数DHg存在一定差异,硅质页岩宏孔分形维数最大高于黏土质页岩与混合质页岩,而不同TOC含量页岩储层宏孔的孔隙结构复杂程度及表面粗糙程度基本相同,表明TOC含量对宏孔孔隙的发育形态,分布特征及非均质性的影响较小。

    表 4.  黔北地区五峰组—龙马溪组不同岩相页岩高压压汞分形维数
    Table 4.  High pressure mercury injection fractal dimensions of different lithofacies shales from the Wufeng Formation−Longmaxi Formation in the northern Guizhou area
    岩相孔隙度
    /%
    渗透率
    /mD
    孔体积
    /(cm3/g)
    比表面积
    /(m2/g)
    平均孔径
    /nm
    最大进汞饱和度/%拟合系数
    /R2
    分形维数
    /DHg
    USS−171.62562.19750.00652.7359.5116.000.99422.2198
    RSS−112.17800.15820.00873.13311.1015.950.90062.2978
    RMS−11.64592.12540.00631.77314.1114.550.91962.1404
    MSS−11.08141.22440.00411.11214.5914.220.97532.1230
    MMS−132.71136.05400.01072.96414.4515.590.96562.0904
    LSS−151.70960.55400.00640.76833.2713.990.98112.3736
    LMS−13.170215.42310.01181.51331.1213.820.99662.1320
    LCS−253.08752.80050.01154.24910.7813.810.97862.1037
     | Show Table
    DownLoad: CSV

    高压压汞分形维数DHg与页岩储层孔隙度和渗透率之间存在一定的负相关性,相关性较差(图9a、d)。N2吸附中表征较大孔隙非均质性的分形维数D2与页岩物性之间呈负相关,与孔隙度的相关性更好;而表征较小孔隙的分形维数D1与岩石物性二者之间也存在一定的负相关性(图10a、b)。表明岩石的孔隙度和渗透率的增大和提高可以减小储层多孔结构的复杂程度,与较大孔隙之间的相关性较好,说明储层的物性主要是对页岩孔隙结构复杂程度影响较大,对其表面分形影响较小;其次,较大孔隙的增加可以减弱储层的非均质性,大孔隙的均质性更好,复杂程度更低,连通性更好,对应的物性更好。

    图 9.  高压压汞分形维数DHg与物性、孔隙体积及比表面积相关性分析
    Figure 9.  Correlation relationship between high pressure mercury injection fractal dimension DHg and physical properties, pore volume and pore specific surface area
    图 10.  N2分形维数D1D2与物性、孔隙体积及比表面积相关性分析
    Figure 10.  Correlation relationship between N2 fractal dimension D1, D2 and porosity, permeability, pore volume and pore specific surface area

    高压压汞分形维数DHg与孔体积及表面积参数之间无明显相关性;而N2吸附分形维数D1D2均表现出与岩石孔隙体积与孔隙比表面积参数之间的正相关性,整体上与孔体积之间的相关性较差,而与孔隙比表面积之间的相关性好(图9c、d,图10c、d);其中,N2吸附分形维数D1D2与页岩的孔隙比表面积之间的相关系数可达0.738、0.7158。说明页岩储层中发育的主要孔径范围内的孔隙体积的增加可在一定范围内提高页岩孔隙结构的非均质性,但孔隙比表面积的增加对分形维数起主导影响作用;而高压压汞表征宏孔的分形维数DHg与孔隙比表面积则无相关性,证明五峰组—龙马溪组孔隙结构的表面非均质性主要受微孔和中孔的比表面积变化影响较大,而宏孔的比表面积增加对其非均质性影响较小。因为页岩的宏孔孔隙表面更光滑,形状更均一(图4c、f)。由此认为N2吸附所得分形维数D1D2可以准确地反映岩石孔隙表面的非均质性强弱,页岩分形维数越大,则可为页岩气提供更多的吸附空间。

    页岩中有机质、石英与黏土矿物等矿物组分对页岩孔隙的发育具有较强的影响作用,为明确黔北地区五峰组—龙马溪组页岩分形维数与其总有机碳含量及矿物组分之间的关系,对各因素与分形维数之间进行相关性分析,发现不同孔径孔隙分形维数与TOC含量及矿物组分之间存在不同的相关性特征(图11图12)。N2吸附分形维数D1D2与TOC含量之间呈良好的正相关性(图12a),页岩的分形维数随着总有机碳含量的增加明显增大,可能是有机质的富集导致储层中有机质孔的数量增多,而有机质孔隙以不规则状为主,且孔隙表面粗糙,增强了其非均质性;同时宏孔分形维数DHg与TOC之间无明显相关性的结果表明微孔和中孔的孔隙结构非均质性受TOC影响较大(图11a)。与上文中孔隙结构定量表征结果相一致,有机质含量的增多可为页岩气储层提供更多的微孔和中孔级别的有机质孔储集空间。页岩中有机质含量越高,页岩的多孔结构越复杂,分布越不均一。因此,当TOC含量越高,岩石中微孔占比越大,孔隙结构越复杂,表现出分形维数D1D2越大。

    图 11.  高压压汞分形维数DHg与TOC、矿物组分相关性分析
    Figure 11.  Correlation relationship between high−pressure mercury intrusion fractal dimension DHg and TOC, mineral composition
    图 12.  N2分形维数D1D2与TOC、矿物组分相关性分析
    Figure 12.  Correlation relationship between N2 fractal dimension D1, D2 and TOC, mineral composition

    宏孔分形维数DHg与岩石中脆性矿物含量(石英+长石+黄铁矿)之间呈线性正相关,相关系数为0.6306(图11b);与黏土矿物之间呈线性负相关,相关系数为0.4915(图11b、c);而分形维数D1与脆性矿物含量及黏土矿物含量之间无明显相关性,分形维数D2与这两种矿物含量之间的相关性较弱(图12b、c);表明脆性矿物因其自身具有更好的刚性,硬度更大,在地层沉积形成与机械压实的成岩演化过程中能够有效保护有机质孔等微孔和中孔孔隙结构的完整性;而宏孔的结构可能发生变形,导致其孔隙表面形状不规则,增强了宏孔的非均质性,表现出分形维数DHg增大。黏土矿物由于具有更好的延展性,质地更软,导致其在成岩过程中不易形成较大孔隙或者原本存在的大孔遭受压实作用被挤压消失,故表现出宏孔分形维数DHg随黏土矿物含量的增加而减小的现象。同时,黏土矿物的增多会导致页岩中黏土矿物层间孔的增多,而因其孔隙宽度多大于100 nm(图4c),可能导致黏土矿物表面存在更多的束缚水,在其表面形成水膜,从而使其孔隙表面变光滑,分形维数DHg变小。

    研究表明(Ji et al.,2016),页岩N2吸附分形维数D1D2与甲烷吸附最大吸附量及Langmuir压力之间均存在一定的相关性。分形维数D1与Langmuir压力呈负相关,而分形维数D2与Langmuir压力呈正相关。相同条件下,分形维数越大,页岩对甲烷的吸附能力越强,甲烷吸附量越大。分形维数越高表明其样品多孔结构表面越不规则,孔隙形态的非均质性越强;同时由上述分形维数与页岩孔隙结构参数的相关性分析可知,更大的分形维数会引起孔隙比表面积增加,从而为甲烷吸附提供更多的可吸附空间。通过对比不同岩相高压压汞分形维数DHg及N2吸附分形维数D1D2大小发现(表2表3),极富有机质页岩分形维数整体上最大,其次为富有机质页岩、中等有机质页岩,贫有机质页岩各类分形维数均较小,而矿物组分对页岩分形维数的影响主要体现在对宏孔孔隙分形的控制上,对微孔和中孔的影响较小。因此,黔北地区五峰组—龙马溪组海相页岩的孔隙非均质性主要受TOC的控制,总有机碳的含量增加,在为页岩储层提供微孔和中孔更多孔隙空间的同时,也使孔隙的比表面积增大,导致分形维数变大,对页岩气具有更强的吸附能力。

    (1)黔北地区五峰—龙马溪组主要发育9种岩相类型,其中硅质页岩岩相是研究区的主要岩相类型。黔北地区五峰组—龙马溪组页岩储层孔隙孔径以<100 nm为主,微孔和中孔最为发育,宏孔发育较少,有机质孔是孔体积与比表面积的主要贡献者。

    (2)五峰组—龙马溪组页岩具有多重分形特征,通过N2吸附FHH模型、高压压汞分形理论计算获得页岩分形维数。N2吸附低压分形维数D1为2.5351~2.6722,高压分形维数D2为2.8311~2.9113,二者存在明显差异,D2表现出更强的非均质性;高压压汞宏孔分形维数DHg为2.0904~2.3736,整体分形维数小于N2吸附分形维数。不同岩相之间分形维数差异较大,极富有机质页岩各类分形维数整体较大,页岩孔隙发育程度复杂,非均质性强。

    (3)页岩分形维数DHg与脆性矿物含量呈正相关,与黏土矿物含量呈负相关,与页岩孔隙度、渗透率呈弱负相关,与孔隙体积、比表面积呈弱正相关,与TOC含量之间无明显相关性。表明不同影响因素对DHg产生的影响不同,而主要影响因素为矿物组分,脆性矿物含量越多,黏土矿物含量越少,则页岩的宏孔孔隙结构越复杂。

    (4)分形维数D1D2与TOC含量、孔隙比表面积之间呈较好的线性正相关,相关系数均在0.7左右;分形维数D1与孔隙体积呈弱正相关性,与储层物性呈弱负相关性,与矿物组分之间无明显相关性。分形维数D1代表页岩中较小孔的非均质性特征,页岩TOC含量越高,孔隙比表面积越大,孔隙结构越复杂。分形维数D2与储层物性呈现较好的负相关性,与孔体积、脆性矿物含量呈弱正相关性,与黏土矿物含量呈弱负相关性。TOC含量越高,孔隙比表面积越大,页岩分形维数D2越大,非均质性越强。

    (5)页岩孔隙结构分形维数越大,其多孔结构发育越复杂、孔隙表面越粗糙,越有利于页岩气的富集。极富有机质页岩分形维数数值最大且脆性矿物(石英+长石+黄铁矿)含量最高,而硅质页分形维数及脆性矿物含量均大于混合质页岩与黏土质页岩。因此,黔北地区五峰组—龙马溪组筛选页岩气开发有利层段时,首先考虑TOC含量的影响,其次考虑矿物组分的影响,综合认为极富有机质硅质页岩、富有机质页岩、富有机质硅质页岩为优质岩相,其次为中等有机质硅质页岩。

  • 本文采用多种方法对黔北地区海相页岩储层宏观与微观孔隙非均质性特征进行了研究,利用N2吸附及高压压汞实验数据计算了分形维数参数,分别讨论了不同类型孔隙与分形维数的相关性,并结合岩相分析,优选出硅质页岩岩相是黔北地区五峰组—龙马溪组页岩储层最有利的岩相,为黔北地区进一步的勘探开发提供理论指导。

  • Through multiple methods, this paper investigated the macroscopic and microscopic heterogeneity characteristics of marine shale reservoirs from the Wufeng−Longmaxi Formation in the northern Guizhou area. The fractal dimensions were calculated based on N2 adsorption and high−pressure mercury injection experimental data. to the relationship between different types of fractal dimensions and different types of pores has been discussed. In combination with lithofacies analyses, we consider that the siliceous shale lithofacies are the most favorable lithofacies of shale reservoirs in the Wufeng−Longmaxi Formation shales in the northern Guizhou area. Our results provide important guidance for exploration and extraction of shale gas in the northern Guizhou area.

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    • 表 1.  黔北五峰组—龙马溪组页岩有机地化与物性参数
      Table 1.  The organic geochemistry and physical property parameters of the Wufeng−Longmaxi Formation shales in the northern Guizhou area

      井号
      有机地化参数 物性参数
      TOC/% Ro 有机质类型 Φ/% K/mD
      AY−1 2.925.974.67 2.002.072.02 I 0.131.460.66 0.00040.30340.0350
      SD−1 0.336.142.33 2.102.432.32 I 0.292.001.05 0.00080.06970.0204
      BZ−1 0.294.951.85 2.512.752.65 I/II1 0.153.231.29 0.00061.69000.2400
        注:
       | Show Table
      DownLoad: CSV
    • 表 2.  页岩实验样品基础信息
      Table 2.  Information of analyzed shale samples
      编号井号深度/m层位岩相
      USS−17AY−12323.80龙马溪组极富有机质硅质页岩
      RSS−11BZ−11113.00龙马溪组富有机质硅质页岩
      RMS−1AY−12331.20五峰组富有机质混合质页岩
      MSS−1BZ−11118.25五峰组中等有机质硅质页岩
      MMS−13BZ−11112.80龙马溪组中等有机质混合质页岩
      LSS−15BZ−11112.00龙马溪组贫有机质硅质页岩
      LMS−1SD−11140.80五峰组贫有机质混合质页岩
      LCS−25BZ−11089.75龙马溪组贫有机质黏土质页岩
       | Show Table
      DownLoad: CSV
    • 表 3.  黔北地区五峰组—龙马溪组不同岩相页岩N2吸附分形维数
      Table 3.  N2 adsorption fractal dimension of different lithofacies shales from the Wufeng−Longmaxi Formation in the northern Guizhou area


      岩相
      不同孔径范围累积
      孔体积/(cm3/g)
      不同孔径范围累积
      比表面积/(m2/g)
      P/P0<0.5 P/P0>0.5
      0~2 nm 2~50 nm >50 nm 0~2 nm 2~50 nm >50 nm 拟合系数R2 分形维数D1 拟合系数R2 分形维数D2
      USS−17 0.0052 0.0170 0.0184 16.134 25.261 25.323 0.9931 2.6722 0.8987 2.9113
      RSS−11 0.0050 0.1070 0.0191 15.888 24.910 24.995 0.9944 2.6488 0.9243 2.8884
      RMS−1 0.0046 0.0146 0.0167 14.285 21.834 21.920 0.9936 2.6478 0.9482 2.8892
      MSS−1 0.0031 0.0115 0.0133 9.4016 16.064 16.136 0.9981 2.6101 0.9523 2.8864
      MMS−13 0.0016 0.0078 0.0095 4.7206 9.2929 9.3620 0.9988 2.6104 0.9640 2.8622
      LSS−15 0.0012 0.0063 0.0078 3.3643 7.0746 7.1357 0.9992 2.5351 0.9705 2.8422
      LMS−1 0.0015 0.0083 0.0106 4.2739 8.9615 9.0539 0.9995 2.5922 0.9761 2.8311
      LCS−25 0.0034 0.0151 0.0181 10.196 18.794 18.920 0.9991 2.5968 0.9717 2.8539
       | Show Table
      DownLoad: CSV
    • 表 4.  黔北地区五峰组—龙马溪组不同岩相页岩高压压汞分形维数
      Table 4.  High pressure mercury injection fractal dimensions of different lithofacies shales from the Wufeng Formation−Longmaxi Formation in the northern Guizhou area
      岩相孔隙度
      /%
      渗透率
      /mD
      孔体积
      /(cm3/g)
      比表面积
      /(m2/g)
      平均孔径
      /nm
      最大进汞饱和度/%拟合系数
      /R2
      分形维数
      /DHg
      USS−171.62562.19750.00652.7359.5116.000.99422.2198
      RSS−112.17800.15820.00873.13311.1015.950.90062.2978
      RMS−11.64592.12540.00631.77314.1114.550.91962.1404
      MSS−11.08141.22440.00411.11214.5914.220.97532.1230
      MMS−132.71136.05400.01072.96414.4515.590.96562.0904
      LSS−151.70960.55400.00640.76833.2713.990.98112.3736
      LMS−13.170215.42310.01181.51331.1213.820.99662.1320
      LCS−253.08752.80050.01154.24910.7813.810.97862.1037
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