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Volume 41 Issue 5
Oct.  2023
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SUN Chao, YUE JianHua, CAI LaiXing, PAN DongMing. Investigation on Elastic Parameters of Glutenite by Multi-Frequency-Band Measurements: A case study for rocks from the steep slope zone in Dongying Sag[J]. Acta Sedimentologica Sinica, 2023, 41(5): 1543-1558. doi: 10.14027/j.issn.1000-0550.2023.049
Citation: SUN Chao, YUE JianHua, CAI LaiXing, PAN DongMing. Investigation on Elastic Parameters of Glutenite by Multi-Frequency-Band Measurements: A case study for rocks from the steep slope zone in Dongying Sag[J]. Acta Sedimentologica Sinica, 2023, 41(5): 1543-1558. doi: 10.14027/j.issn.1000-0550.2023.049

Investigation on Elastic Parameters of Glutenite by Multi-Frequency-Band Measurements: A case study for rocks from the steep slope zone in Dongying Sag

doi: 10.14027/j.issn.1000-0550.2023.049
Funds:

National Natural Science Foundation of China 42104111

National Natural Science Foundation of China 41906188

State Key Laboratory of Oil and Gas Resources and Exploration Open Project, No. PRP/open 2207 PRP/open 2207

Xuzhou Science and Technology Bureau Young Talents Project KC22018

  • Received Date: 2023-04-05
  • Accepted Date: 2023-06-30
  • Rev Recd Date: 2023-06-05
  • Available Online: 2023-06-30
  • Publish Date: 2023-10-10
  • Glutenites, which significantly contribute to gravity flow sedimentation studies, have become prime targets for oil, gas, and groundwater exploration, owing to their advantageous reservoir conditions. However, their complex depositional attributes, heterogeneous composition, and intricate pore structures pose challenges, affecting the elastic response of seismic waves. This complexity can compromise the reliability of fluid inversion methodologies and the outcomes of fluid identification processes. Consequently, a comprehensive understanding of the interrelationship between reservoir lithology, its physical properties, involved fluids, and seismic attributes is vital. Such understanding, attainable through petrophysical experiments, is critical for accurately deciphering the petrophysical response characteristics of glutenites. We undertook multi-frequency-band measurements (employing seismic frequencies at 1⁃100 Hz and ultrasonic frequency at 1 MHz) on the elastic parameters of the glutenite from the Dongying Sag in the Bohai Bay Basin. Initially, we selected a series of samples from each grain level for porosity and permeability tests, followed by classification and analysis of the rock types and physical properties. We then measured the P- and S-wave ultrasonic velocities in samples saturated with varying fluids, assessing the effects of fluid, lithology, and pressure on velocity. This led to the determination of the relationship between pressure and velocity by evaluating different water-saturated samples, followed by the calculation of indicative factors to identify rock types and fluid sensitivity parameters. Furthermore, we measured the Young’s modulus and Poisson’s ratio to calculate the P- and S-wave velocities of the seismic band. Lastly, we computed the crack density and soft porosity using ultrasonic velocity and compared the velocity dispersion with the seismic frequency band measurements. Our results suggest that: (1) sensitive parameters ρ, K, λ, λρ, and λϕ help distinguish glutenite from shale and conglomerate, but not sandstone. For fluids, gas-water can be separated through υ, ρVP/VS, ρυ, and λυ, whereas oil and water cannot; (2) at in-situ reservoir pressure, glutenite demonstrates larger P-wave velocity dispersion, crack density, and corresponding closure pressure compared to those of sandstone, conglomerate, and mudstone; (3) the dispersion can be explained by the "squirt flow" mechanism, aligning with a crack aspect ratio distribution that mirrors the actual scenario. The velocity dispersion range of oil-saturated samples is found at lower frequencies, with a larger dispersion span, thus positioning seismic band dispersion range as a sensitive parameter for differentiating between oil and water, overcoming the limitations of traditional sensitive parameters. This study offers insights into lithology and fluid-sensitive parameters via ultrasonic experiments and explores the pronounced velocity dispersion characteristics of glutenite under in-situ reservoir conditions through seismic frequency band experimental results, thereby identifying the key dispersion physical mechanism in glutenite. We assert that the fractured glutenite possesses excellent reservoir properties, exhibiting velocity dispersion characteristics markedly different from marl with fluid sensitivity. This prompts the consideration of frequency effects in fluid identification for traditional inversion methods. Our work systematically encapsulates the petrophysical response characteristics of glutenite bodies, establishing a theoretical groundwork for studying seismic wave response and identifying fluids in glutenite reservoirs. This could enhance reservoir prediction accuracy and effectively guide the geophysical exploration of glutenite reservoirs.
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  • Received:  2023-04-05
  • Revised:  2023-06-05
  • Accepted:  2023-06-30
  • Published:  2023-10-10

Investigation on Elastic Parameters of Glutenite by Multi-Frequency-Band Measurements: A case study for rocks from the steep slope zone in Dongying Sag

doi: 10.14027/j.issn.1000-0550.2023.049
Funds:

National Natural Science Foundation of China 42104111

National Natural Science Foundation of China 41906188

State Key Laboratory of Oil and Gas Resources and Exploration Open Project, No. PRP/open 2207 PRP/open 2207

Xuzhou Science and Technology Bureau Young Talents Project KC22018

Abstract: Glutenites, which significantly contribute to gravity flow sedimentation studies, have become prime targets for oil, gas, and groundwater exploration, owing to their advantageous reservoir conditions. However, their complex depositional attributes, heterogeneous composition, and intricate pore structures pose challenges, affecting the elastic response of seismic waves. This complexity can compromise the reliability of fluid inversion methodologies and the outcomes of fluid identification processes. Consequently, a comprehensive understanding of the interrelationship between reservoir lithology, its physical properties, involved fluids, and seismic attributes is vital. Such understanding, attainable through petrophysical experiments, is critical for accurately deciphering the petrophysical response characteristics of glutenites. We undertook multi-frequency-band measurements (employing seismic frequencies at 1⁃100 Hz and ultrasonic frequency at 1 MHz) on the elastic parameters of the glutenite from the Dongying Sag in the Bohai Bay Basin. Initially, we selected a series of samples from each grain level for porosity and permeability tests, followed by classification and analysis of the rock types and physical properties. We then measured the P- and S-wave ultrasonic velocities in samples saturated with varying fluids, assessing the effects of fluid, lithology, and pressure on velocity. This led to the determination of the relationship between pressure and velocity by evaluating different water-saturated samples, followed by the calculation of indicative factors to identify rock types and fluid sensitivity parameters. Furthermore, we measured the Young’s modulus and Poisson’s ratio to calculate the P- and S-wave velocities of the seismic band. Lastly, we computed the crack density and soft porosity using ultrasonic velocity and compared the velocity dispersion with the seismic frequency band measurements. Our results suggest that: (1) sensitive parameters ρ, K, λ, λρ, and λϕ help distinguish glutenite from shale and conglomerate, but not sandstone. For fluids, gas-water can be separated through υ, ρVP/VS, ρυ, and λυ, whereas oil and water cannot; (2) at in-situ reservoir pressure, glutenite demonstrates larger P-wave velocity dispersion, crack density, and corresponding closure pressure compared to those of sandstone, conglomerate, and mudstone; (3) the dispersion can be explained by the "squirt flow" mechanism, aligning with a crack aspect ratio distribution that mirrors the actual scenario. The velocity dispersion range of oil-saturated samples is found at lower frequencies, with a larger dispersion span, thus positioning seismic band dispersion range as a sensitive parameter for differentiating between oil and water, overcoming the limitations of traditional sensitive parameters. This study offers insights into lithology and fluid-sensitive parameters via ultrasonic experiments and explores the pronounced velocity dispersion characteristics of glutenite under in-situ reservoir conditions through seismic frequency band experimental results, thereby identifying the key dispersion physical mechanism in glutenite. We assert that the fractured glutenite possesses excellent reservoir properties, exhibiting velocity dispersion characteristics markedly different from marl with fluid sensitivity. This prompts the consideration of frequency effects in fluid identification for traditional inversion methods. Our work systematically encapsulates the petrophysical response characteristics of glutenite bodies, establishing a theoretical groundwork for studying seismic wave response and identifying fluids in glutenite reservoirs. This could enhance reservoir prediction accuracy and effectively guide the geophysical exploration of glutenite reservoirs.

SUN Chao, YUE JianHua, CAI LaiXing, PAN DongMing. Investigation on Elastic Parameters of Glutenite by Multi-Frequency-Band Measurements: A case study for rocks from the steep slope zone in Dongying Sag[J]. Acta Sedimentologica Sinica, 2023, 41(5): 1543-1558. doi: 10.14027/j.issn.1000-0550.2023.049
Citation: SUN Chao, YUE JianHua, CAI LaiXing, PAN DongMing. Investigation on Elastic Parameters of Glutenite by Multi-Frequency-Band Measurements: A case study for rocks from the steep slope zone in Dongying Sag[J]. Acta Sedimentologica Sinica, 2023, 41(5): 1543-1558. doi: 10.14027/j.issn.1000-0550.2023.049
  • 砂砾岩体,是指主要由粒径大于2 mm的砾石和砂岩一并构成的粗粒碎屑沉积体,其内部砾岩、砂岩和泥质杂基含量不一[12],常见冲积扇、近岸水下扇、扇三角洲、辫状河三角洲、深水浊积扇、滑塌浊积扇等不同成因的扇体在断陷湖盆陡坡带形成快速堆积、有序发育的扇体群[34]。作为重力流沉积的主要研究对象,砂砾岩体受到全球沉积学界的重点关注[57],同时也在油气勘探和水文地质勘探领域占据重要地位[1,810],尤其是准噶尔盆地玛湖凹陷特大型砾岩油藏和渤海湾盆地渤中19-6大型整装凝析气田的连续发现,使得砂砾岩体迅速成为我国油气资源规模增储的接替阵地[1,1114]。由于砂砾岩体多靠近生烃中心,常与深湖相烃源岩直接接触或被其包裹,因而具有优越的成藏条件[1,4,8]。然而,在构造活动强烈、古地形起伏多变、物源近、坡度陡的沉积背景下,砂砾岩体往往表现出沉积动力复杂、扇体迁移叠置、岩性岩相多样、内部非均质性强等突出特征,导致砂体成层性差、储层相变快且预测风险较高[1,1518]。另外,埋藏后的差异性流体―岩石作用过程进一步加剧了储层孔喉结构和油水关系的复杂性[13,1920]

    岩石作为不同沉积、成岩过程的产物,其物理性质必然携带不同地质过程的地震属性信息[13,21]。显然,借助岩石物理实验建立储层岩性、物性、流体与地震属性之间的关系,能够有效约束地震反演,进而提高储层预测精度[22]。在众多属性中,由于地震波频散特征与流体流动密切相关而显得极为重要。当地震波经过岩石储层时,诱发流体流动,导致能量损耗,从而产生地震波频散现象,称为波致流[2326]。Gregory[27]对孔隙度为4%~41%的岩石样品进行了超声纵波速度测量,发现在低孔隙度岩石中,流体饱和度对纵波速度的影响要比在高孔隙度岩石中更加显著。Domenico[28]测量了盐水饱和度对疏松含气砂岩纵波速度的影响,发现在低盐水饱和度时,速度测量值与理论值基本一致,而在高盐水饱和度时,速度测量值与理论结果存在差异,并认为这种差异与波致流现象相关。Toksöz et al.[29]在实验室中使用超声波法测量了盐水饱和的砂岩和灰岩,发现存在纵波衰减,且随着有效压力增加,纵波衰减显著减小。King et al.[30]使用超声透射法测量了砂岩储层样品的纵、横波速度,对于盐水饱和样品,研究发现实验测量结果与Gassmann[31]预测结果不相符,孔隙尺度上的流体流动即喷射流理论可为其测量结果提供合理的解释。Lebedev et al.[32]使用超声波研究了饱和度对砂岩的影响,在低饱和度时的测量结果接近Gassmann-Wood(GW)边界,而在高饱和度时,测量结果接近Gassmann-Hill(GH)边界,在中等饱和度时位于GW和GH边界之间,同样认为这种现象由波致流现象引起。

    实验研究表明,频散和衰减普遍存在,但测量频段主要集中在超声频段,难以确定地震频段相应的频散和衰减机制。为实施地震频段测量,Spencer[33]首先利用应力—应变法在4~400 Hz测量了水饱和砂岩的衰减,并使用应力松弛理论解释实验数据。Batzle et al.[34]在地震频段测量了砂岩速度频散和衰减,并定义“流体流动性”控制频散和衰减特征。Mikhaltsevitch et al.[35]对完全水饱和状态下的低渗透砂岩开展地震频段衰减测量,但未解释测量结果。Subramaniyan et al.[36]测量了枫丹白露砂岩的频散和衰减,其实验结果表明喷射流存在。Pimienta et al.[37]在对饱和枫丹白露砂岩的宽频带(0.005~100 Hz,1 MHz)测量中观察到两个衰减峰值,并将其解释为与喷射流和排水/不排水过渡的组合。未晛等[38]利用应力—应变低频岩石物理测量装置研究了孔隙流体对不同渗透率岩石地震波速度的影响,揭示在低饱和度下,致密砂岩在地震和超声频段下没有明显的频散,但在高饱和度下纵波速度的频散变得明显。另外,针对砂岩储层[39]和灰岩储层[40],笔者曾分别测量了二者在地震频段与超声频段的频散和衰减特征并分析了其机理,认为流体在不同尺度的流动是引发地震波频散和衰减的本质原因,为使用地震波速度频散和衰减属性预测流体性质提供了可能。

    上述研究获得了大量的频散、衰减数据和重要的理论认识,有效地指导了砂岩和灰岩储层的地球物理勘探工作,但目前缺少对断陷湖盆砂砾岩体岩石物理响应特征的系统总结,其复杂的沉积特征、孔隙结构和非均质性对地震波弹性响应的影响在根本上限制着流体反演方法的可靠性和流体识别结果。因此,本文针对东营凹陷陡坡带典型砂砾岩样品开展跨频段(地震频段和超声频段)弹性参数测量与分析,通过实验结果探究敏感弹性参数,总结频散规律及机制,为砂砾岩储层的岩性和烃类检测提供岩石物理依据。

  • 本文测量装置分为地震频带测量单元和超声频带测量单元。地震频带测量单元的有效测量频率范围为1~100 Hz,设备配置与Borgomano et al.[41]研发的设备基本一致。图1显示了该装置的设计图,包括A:轴压加载,用以加载轴向压力;B:压电陶瓷震源,用以激励震源产生正弦信号;C:胡克腔,用以加载围压,可加载到60 MPa;D:加温套,用来加载温度,可达150 ℃;E:底座,用于调整高度;F:应变片,用以测量岩石样品和铝制样品的轴向和径向应变;G:超声探头,用以测量岩石的超声波速度;H:参考铝样;I:加压流体泵;J:惠斯通电桥,用以识别应变片的微弱信号;K:气瓶。

    Figure 1.  Multi⁃frequency band apparatus diagram

    地震频带测量单元的工作原理为应力—应变法。在测量过程中,测试样品和铝制参考样品被黏合在一起并沿其轴线垂直对齐,以使应力能够在横向和轴向方向均匀分布。当在测试样品上施加特定频率的正弦应力时,应变片可以检测轴向和径向应变,据此计算岩石模量和泊松比:

    E=Eal×εal/εax (1)
    v=εra/εax (2)

    式中:Eal 是参考铝样的杨氏模量,值为72 GPa。εal 为参考铝样的轴向应变,εax 为测试样品轴向应变,εra 为参考铝样的径向应变。基于各向同性假设,可以利用杨氏模量,泊松比和密度计算岩石样品的纵、横波速度,具体参考岩石物理手册第二章相关内容[42]

    超声频带测量单元的工作原理为行波法。P波和S波探头镶嵌在参考铝样内部(图1,H),上部探头激发频率为1 MHz的信号,下部探头接收该信号,假设样品长度为L,对零时间为t0(声波经过参考铝样的时间),时间为tr,则纵、横波速度为:

    VP,S=Ltr-t0 (3)
  • 济阳坳陷东营凹陷北部陡坡带因坡度陡、物源近、构造活动强烈,在沙河街组三段、四段时期发育冲积扇、扇三角洲、近岸水下扇、浊积扇等多种类型的砂砾岩沉积体[2,8,15]。本次实验共钻取沙三段40块岩心样品,颗粒粒度涵盖细砾岩、砂质砾岩、砾质砂岩、细砂岩等各粒级代表性样品。孔隙度的测试结果(图2)显示,大部分样品孔隙度集中在0~14%,少量样品孔隙度大于20%,其中不同岩性孔隙度分布范围重叠;渗透率测试结果(图3)显示,大部分样品的渗透率集中在10-5~10-3 μm2,少量样品渗透率高于10-2 μm2,不同岩性渗透率分布范围重叠交叉且渗透率和孔隙度呈弱正相关关系。整体来看,以扇中分支水道含砾砂岩、细砂岩孔渗条件最优,其次为扇根主水道砾岩,泥岩物性最差。

    Figure 2.  Porosity of 40 samples in the Third member of the Shahejie Formation (Es3)

    Figure 3.  Permeability of 40 samples in in the Third member of the Shahejie Formation (Es3)

    在此基础上,选取21块代表性样品开展超声测量,依据岩性分组:砾岩6块,砂岩8块,含砾砂岩5块,泥质砂岩1块,灰岩1块。为揭示砂砾岩体的频散特征,优选出4块典型岩石样品进行地震频带测量,样品信息为:16号砂质砾岩(图4a)、8号细砾岩(图4b)、10号细砂岩(图4c)和13号砾质砂岩(图4d)。样品所在深度、物性参数、矿物组成和颗粒体积模量等信息如表1~3所示,颗粒体积模量K0 通过Voigt-Reuss-Hill平均法[42]计算得出。因4块样品的矿物组分类似(表3),对应体积模量差异不大。从渗透率的角度分析,细砂岩和砾质砂岩的渗透率较低,而砂质砾岩和细砾岩的渗透率较高;从孔隙度的角度分析,砾质砂岩和砂质砾岩的孔隙度较高,而细砂岩和细砾岩的孔隙度较低。

    Figure 4.  Representative core samples for low⁃frequency measurement

    Figure 5.  Evolution of P⁃and S⁃wave velocity versus increasing pressure for 21 samples of saturated gas, water, and oil

    Figure 6.  P⁃and S⁃wave velocity versus frequency under different effective pressures for glutenite saturated with water

    Figure 7.  Lithology indicator factors

    Figure 8.  Fluid indicator factors

    Figure 9.  Crack density versus pressure

    Figure 10.  Soft⁃porosity distribution versus aspect ratio

    岩心序号岩性深度/m
    16砂质砾岩2 991.5
    8细砾岩2 713.8
    10细砂岩2 822.8
    13砾质砂岩2 744.7
    岩心序号密度/(g/cm3孔隙度/%渗透率/×10-3 μm2
    162.3313.8822.23
    82.408.6217.70
    102.429.910.15
    132.3612.843.52
    岩心序号黏土总量/%石膏/%石英/%钾长石/%斜长石/%方解石/%铁白云石/%白云石/%菱铁矿/%K0/GPa
    166.33.327.4034.215.43.79.7062.2
    85.03.826.6029.423.83.87.7063.2
    104.7025.84.416.733.34.77.92.566.2
    135.03.826.6029.423.83.87.7063.2
  • 对21块样品分别饱和气、水和油(表4)后进行超声测量,获得纵横波速度(图5)。

    流体密度/(g/cm3黏度/cP体积模量/GPa
    空气10-310-510-3
    112.2
    0.89681.8

    对于干燥样品,纵、横波速度特征如下:灰岩具有最高的纵波速度,超过了6 560.0 m/s,而泥岩的纵波速度次之,介于5 500.0~5 700.0 m/s;砾岩和砂岩的纵波速度差异较大,分布范围分别为3 900.0~6 400.0 m/s和2 170.0~6 600.0 m/s。横波速度特征类似,灰岩同样具有最高的横波速度,超过4 050.0 m/s,而泥岩的横波速度最低,基本介于3 260.0~3 350.0 m/s;砾岩和砂岩的横波速度也存在较大的差异,分别为1 990.0~4 000.0 m/s和1 200.0~4 060.0 m/s。

    对于饱和水的样品,其纵、横波速度具有以下特点:灰岩的纵波速度最高,超过6 660.0 m/s,泥岩次之,速度介于5 800.0~5 890.0 m/s。砾岩、含砾砂岩和砂岩的纵波速度跨度较大,速度介于3 270.0~6 600.0 m/s。对比干燥样品,饱和水纵波速度整体提升。另一方面,灰岩的横波速度最高,超过4 000.0 m/s,而砾岩的横波速度介于2 000.0~4 000.0 m/s,砂岩的横波速度介于1 200.0~3 700.0 m/s。对比干燥样品可知,流体对横波存在一定影响,但比对纵波的影响幅度小。

    对于饱和油的样品,其纵、横波速度具有以下特点:灰岩纵波速度最高,大于6 640.0 m/s;泥岩次之,纵波速度在5 800.0 m/s以上;砾岩的纵波速度介于4 820.0~6 640.0 m/s;砂岩的纵波速度介于3 200.0~6 500.0 m/s。从横波速度看,灰岩横波速度同样最高,超过4 000.0 m/s;泥岩横波速度为3 300.0 m/s左右;砾岩横波速度介于2 000.0~4 000.0 m/s;砂岩横波速度介于1 200.0~4 000.0 m/s。对比饱和水样品,饱和油与饱和水的纵波速度基本相似。

    综上,灰岩的速度高于砾岩、泥质砂岩和含砾砂岩,表明当砂岩中存在泥质或砾石时,其非均质性更强,速度差异也更大。

    除了流体和岩性影响速度外,压力同样影响速度。由测量结果可知,无论是干燥还是饱和状态,五种岩石的纵、横波速度随着压力的增大而增大,总体呈现非线性增长,其变化率在较高压力处小,在较低压力处大。引起这种变化规律的原因是岩石中存在裂缝等纵横比较小的软(易压缩)孔隙,其随有效压力增加逐渐闭合,导致岩石骨架刚度变大,进而引发相应的纵、横波速度以及弹性模量的增大。由于砂岩和砂砾岩具有较高的孔隙度,需要40~50 MPa的压力才足以使得岩石中软孔隙完全闭合,即速度压力曲线呈现轻微的变缓的趋势。相对而言,泥岩和灰岩孔隙度较低,随着压力的变化规律并不敏感。将岩石完全饱和水或油后,砂岩的纵波速度压力敏感性降低,其原因是可压缩性更小的水替代了孔隙中原有的空气,降低了岩石的整体可压缩性(即岩石刚度提升)。

  • 通过杨氏模量和泊松比计算得出16号样品在水饱和状态下的纵波速度和横波速度(图6a,b)。设备所测杨氏模量E的不确定度约为5%,泊松比的不确定度约为4%。基于这些不确定度推导出的纵波速度不确定度约为6%[40]。在1 MPa压力下时,随着频率从1 Hz增加到1 MHz,16号样品纵波速度从2 913.6 m/s增加到3 430.9 m/s,频散幅度约为17%。当压力增加到5 MPa时,随着频率从1 Hz增加到1 MHz,纵波速度从3 066.7 m/s增加到3 546.8 m/s,频散幅度约为15.7%。当压力增加到20 MPa时,纵波速度从3 644.8 m/s增加到3 968.8 m/s,频散幅度约为9%。因此可以明确,随着压力增加,频散幅度减小。通过表1可知,16号样品所在深度为2 991.0 m,根据Avseth et al.[43]中提到的关于有效压力与深度间的换算关系,当孔隙压力为静水压力时,30 MPa的有效压力能够近似岩心所处地层的压力条件。在30 MPa压力下,随着频率从1 Hz到1 MHz,地震波速度从3 860.0 m/s增加到4 059.0 m/s,频散幅度降低到6%,表明在地下原位储层中,16号样品的频散变低。

    在水饱和状态下,8号样品的纵波速度和横波速度(图6c,d)的不确定度约为6%[40]。在1 MPa压力下,随着频率从1 Hz增加到1 MHz,纵波速度从4 292.2 m/s增加到4 849.1 m/s,频散幅度约为12%。当压力增加到5 MPa时,随着频率从1 Hz增加到1 MHz,纵波速度从4 514.0 m/s增加到4 985.0 m/s,频散幅度约为10.4%。当压力增加到20 MPa时,纵波速度从4 928.0 m/s增加到5 125.0 m/s,频散幅度约为4%。与16号样品所得规律一致,随着压力增加,频散幅度减小。通过表1可知,8号样品所在深度为2 713.0 m,根据Avseth et al.[43]中提到的关于有效压力与深度间的换算关系,当孔隙压力为静水压力时,30 MPa的有效压力能够近似岩心所处深度地层的压力条件。在30 MPa压力下,随着频率从1 Hz增加到1 MHz,纵波速度从5 103.0 m/s增加到5 202.0 m/s,频散幅度降低到2%,这表明在地下原位储层压力下,8号样品的频散变得很低,基本可以忽略不计。

    在水饱和状态下,10号样品的纵波速度和横波速度(图6e,f)的不确定度约为6%[40]。当压力为1 MPa时,随着频率从1 Hz增加到1 MHz,纵波速度从3 960.0 m/s增加到4 369.0 m/s,频散幅度约为10%。当压力增加到5 MPa时,随着频率从1 Hz增加到1 MHz,纵波速度从4 017.0 m/s增加到4 395.0 m/s,频散幅度约为9%。当压力增加到20 MPa时,纵波速度从4 499.0 m/s增加到4 646.0 m/s,频散幅度约为3%。随着压力增加,频散幅度减小。通过表1可知,10号样品所在深度为2 822.8 m,根据Avseth et al.[43]中提到的关于有效压力与深度间的换算关系,当孔隙压力为静水压力时,30 MPa的有效压力能够近似岩心所处2 800.0 m深度地层的压力条件。在30 MPa压力下,随着频率从1 Hz增加到1 MHz,地震波速度从4 645.0 m/s增加到4 723.3 m/s,频散幅度降低到2%,这表明在地下原位储层中,10号样品的频散相对较低,可以忽略不计。

    在水饱和状态下,13号样品的纵波速度和横波速度(图6g,h)的不确定度约为6%[40]。在1 MPa压力下,随着频率从1 Hz增加到1 MHz,纵波速度从3 104.1 m/s增加到3 677.7 m/s,频散幅度约为18%。当压力增加到5 MPa时,随着频率从1 Hz增加到1 MHz,纵波速度从3 227.4 m/s增加到3 744.9 m/s,频散幅度约为16%。当压力增加到20 MPa时,纵波速度从3 752.1 m/s增加到4 009.3 m/s,频散幅度约为7%。随着压力增加,频散幅度减小。通过表1可知,13号样品所在深度为2 744.7 m,根据Avseth et al.[43]中提到的关于有效压力与深度间的换算关系,当孔隙压力为静水压力时,30 MPa的有效压力能够近似岩心所处2 744.7 m 深度地层的压力条件。在30 MPa压力下,随着频率从1 Hz增加到1 MHz,地震波速度从3 959.0 m/s增加到4 081.2 m/s,频散幅度降低到3%,这表明在地下原位储层中,13号样品的频散相对较低。

  • 在常规岩石物理分析中,寻找敏感参数区分岩性及流体是常用的分析手段。本文采用Dillon et al.[44]提出的统计学方法寻找敏感参数,即构建指示因子区分两组数据,从而获得敏感属性参数:

    I=X-X'σX' (4)

    式中:I为指示因子,X为特定物相的特定属性的平均值,X′为参考物相的特定属性的平均值。σX′ 为参考物相的特定属性的标准差。当I大于2时,表明两组物相数据的中心点距离大于参考相数据的范围,因此两组物相数据可以区分。在区分砂岩和砾岩时,X为砾岩特定属性平均值,X′为砂岩特定属性平均值,σX′ 为砂岩特定属性标准差。在区分流体时,例如水和气,则X为样品饱和水属性平均值,X′为样品饱和气属性平均值,σX′ 为样品饱和气属性标准差。以泥岩为参考相,获得饱和气、水和油时的砾岩、砂砾岩及砂岩的指示因子(图7a~c)。在三种饱和状态下,主要的敏感弹性参数为ρ,K,λ,λρ,λΦ。对比砂砾岩、砾岩及砂岩的指示因子可知,砂砾岩与泥岩的区分度最大。图7d为油饱和样品在选取λΦK作为敏感参数时的交会图,可见砂砾岩确实较砂岩和砾岩与泥岩区分度更好。此外,在敏感参数选取λΦK时,砂砾岩的指示因子大约是砾岩指示因子的两倍,因此,砂砾岩和砾岩可区分(图7d黄色和青色数据点)。

    以水为参考相,可计算得到砂岩、砾岩及砂砾岩的流体指示因子(图8a~c)。综合三种岩性,气—水差异明显,主要的敏感弹性参数为v,ρVp/Vs,ρv,λv。然而,油—水差异很小,没有敏感弹性参数可以区分。选取vρVp/Vs作为敏感参数时的交会图(图8d)分析砂砾岩储层,可见气—水可明显区分,但油—水无法区分。

    综合上述敏感因子分析可知:从岩性上看,砂砾岩与其他岩性较容易区分;从流体上看,砂岩、砾岩及砂砾岩中饱和流体为水—气和油—气容易区分,但油—水之间不易区分。

  • 参照邓继新等[45]的研究认识,利用超声频带纵、横波速度计算表1中各样品的裂隙密度:

    Kd=Kh(1+16[1-(vh)2]ε9(1-2vh))-1 (5)
    Gd=Gh(1+321-vh[5-vh]ε45(2-vh))-1 (6)

    式中:Kh,vhGh 是岩石骨架无微裂隙体积模量,泊松比和剪切模量,可通过纵、横波速度高压极限测量结果获得,ε是裂隙密度。KdGd 是由纵、横波计算所得干燥样品体积模量和剪切模量。图9是裂隙密度随压力的演化结果:在1 MPa压力下,砂质砾岩(图9a)和砾质砂岩(图9d)裂隙密度约为0.9,而砂岩(图9c)和砾岩(图9b)的裂隙密度约为0.2;随着压力增加,所有样品的裂隙密度均降低。上述四块样品所在的深度为2 700.0~2 900.0 m,根据Avseth et al.[43]中提到的关于有效压力与深度间的换算关系,当孔隙压力为静水压力时,20~30 MPa的有效压力能够近似岩心所处深度地层的压力条件。因此,对于砂砾岩(图9a,d)而言,原位压力裂隙密度约0.2,而砾岩(图9b)和砂岩(图9c)的原位压力裂隙密度约0.04,是砂砾岩裂隙密度的1/5。

    参照Duan et al.[46]和Sun et al.[47]的研究,基于裂隙密度,可计算岩石中未闭合软孔隙的最小初始纵横比:

    αi=34πε0εp1Kd(εp)-1Khεpdpdεpdεp (7)

    式中:εp 为特定压力的裂隙密度。Kd (εp )为特定压力情况含气体积模量,可通过特定压力的纵、横波速度(图6)计算。ε0εp 分别为压力为0和p时的裂隙密度。则样品中软孔隙的纵横比值随压力的变化关系为:

    αp=αi-0p4(1-v2)3Kπ(1-2v)dp (8)

    通过裂隙密度和裂隙纵横比计算软孔隙度Φc 为:

    ϕc=4π3αε (9)

    软孔隙随纵横比分布的演化关系(图10)表明:在1 MPa压力下,砂砾岩(图10a,d)样品中裂隙纵横比分布的最大值为2.2×10-3,而砂岩(图10c)和砾岩(图10d)的裂隙纵横比分布的最大值为1.2×10-3。随着压力增加,裂隙纵横比分布范围逐渐减小,这是因为压力增加使小纵横比的裂隙闭合,同时使大纵横比的微裂隙纵横比变小。当压力达到储层原位压力时(20~30 MPa),砂砾岩的纵横比分布的最大值为1.2×10-3,砂岩和砾岩的纵横比分布的最大值为0.6×10-3,与此同时,软孔隙度变少,但砂砾岩的软孔隙度约为细砾岩和细砂岩软孔隙度的10倍。

    裂隙密度及纵横比与岩石的频散特征密切相关[4849],参照邓继新等[45]计算速度的方法,可计算表1中样品饱和水时纵、横波速度的频散特征(图6,实线)。该预测结果与实验结果完美匹配,表明砂砾岩体频散特征满足“喷射流”机制。基于喷射流理论,相对于水,油的高黏度使流体交换需要更多的时间来达到平衡,特征频率向低频移动,降低频散出现频率范围,从而使地震频带的频散范围增大;反之,可以利用频散范围区分油和水,从而解决传统敏感参数无法区分油水的问题。图6中的加号为20 MPa下饱和油样品的速度,可以看到,与上文分析一致,频散发生的频率范围更低,频散幅度更大。

    压力增加将导致裂隙密度(图9)减小,进而改变速度频散特征。因此,探究储层压力下岩石样品的裂隙密度及其对应的频散特征十分必要。不同岩性下速度频散范围(图11a,b)、裂隙密度(图11c)及裂隙闭合压力(图11d)之间的关系显示:砂砾岩纵波速度频散最大,最大值为16%,砂岩次之,最高值为12%,砾岩更小,最大值为10%,泥灰岩最小,最大值为4%;各岩性横波速度频散范围均小于5%,其中砂砾岩横波频散范围偏大,泥灰岩频散最小,几乎可以忽略;砂砾岩体裂隙密度最大,砂岩次之,砾岩和泥灰岩裂隙密度较小;砂砾岩微裂隙闭合压力介于40~45 MPa,砂岩的闭合压力介于20~40 MPa,泥灰岩需要的压力最小,介于6~10 MPa。

    Figure 11.  Comprehensive analysis of sandstone, conglomerate, glutenite, and mudstone⁃limestone

    总体而言,砂砾岩裂隙发育,其速度频散特征与泥灰岩存在明显差异且具有流体敏感性。因此,传统流体反演方法,如常用的Gassmann流体项反演方法[49]等,均需要考虑频率的影响才可对砂砾岩储层内部流体进行有效识别和区分。研究中尚未考虑砂砾岩储层的强非均质性,其导致的强各向异性对于地震波弹性响应的影响同样值得深入探究。此外,由于喷射流机制对应的特征频率出现在较高频段,而在103~106 Hz之间缺少相应的实验数据与预测结果进行比对,后续研究工作还需在高频段为其寻找更加有力的证据。

  • (1) 超声频带测量结果表明,区分砂砾岩与泥灰岩的主要敏感弹性参数(优选属性)为ρ,K,λ,λρ,λΦ,区分气—水的主要敏感弹性参数为v,ρVp/Vs,ρv,λv,无敏感参数可区分油—水。

    (2) 地震频带测量结果表明,储层压力下砂砾岩纵、横波速度频散较泥、灰岩更强,反演所得裂隙密度更大。

    (3) 联合超声和地震频带纵、横波速度测量结果,确定了砂砾岩中的重要频散物理机制为喷射流机制,预测其频散特征的裂隙纵横比为一组分布。基于喷射流理论,饱和油和水的岩石样品的频散所在的频带不同,因此可作为区分油—水的敏感参数。

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