CN105308479B - 通过与偏移距相关的弹性fwi的多参数反演 - Google Patents
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Abstract
一种用于使用弹性反演的多参数反演方法。该方法将数据分解为偏移距/角度组并且以顺序次序对它们执行反演。该方法可以显著加快迭代反演处理的收敛,并且因此当用于全波场反演(FWI)时是最有利的。本发明方法利用发射能量与反射角之间的关系或者相等地弹性FWI中的偏移距相关性。该发明使用小角度(近偏移距)反射的振幅很大程度上仅由声阻抗(1)确定而与大部分Vp/Vs无关的认识。大角度(中和远偏移距)反射受到Ip、Vp/Vs(2)以及诸如密度(3)和各向异性的其它地球参数的影响。因此,本发明方法在执行多参数FWI中将数据分解为角度或偏移距组,以减小在反演中所确定的不同模型参数之间的串扰。
Description
相关申请的交叉引用
本申请要求2013年5月24日提交的题为“Multi-Parameter Inversion throughOffset Dependent Elastic FWI”的美国临时专利申请61/827,474的权益,通过引用将其全部内容并入本文。
技术领域
本发明一般涉及地球物理勘探领域,包括油气勘探领域,并且更特别地,涉及地震数据处理。具体地,本发明是用于地震数据的弹性全波场反演(“FWI”)以获取多个物理参数的地下模型的方法。
背景技术
地球物理数据处理中的反演处理通常(并且在本文件的情况中同样)指的是将地震反射数据转换为地下地球模型形式的储层的定量岩石性质描述。如果假定该模型是各向同性的,则这种模型需要三种参数:密度(ρ)、纵波速度(Vp)和横波速度(Vs)来对其进行描述。在包括各向异性和衰减的更一般的地下模型中需要附加参数。在地震分辨率反演中使用许多技术,诸如叠后或叠前AVO反演和全波场反演(FWI)。
众所周知,垂直入射角处的PP反射(纵波下/纵波上)很大程度上由声阻抗Ip=ρVp确定。为了从地震数据估算Ip,在FWI中只考虑纵波传播通常是足够的,以节省处理时间。为此,波传播建模只依赖于ρ和Vp。然而,单独的Ip不一定是储层岩石和类型的良好指标。众所周知,从诸如Vp/Vs的弹性参数中可以更好地恢复流体类型。因此,针对声波和弹性参数两者的多参数反演在储层表征中已经成为理想的,或许几乎是必须的。
通过弹性FWI的多参数反演在描绘储层特征中具有独特作用,因为它是基于弹性波传播的精确建模的。出于两个主要原因,弹性FWI是非常昂贵的处理。第一,由于用于剪切波传播的计算机模拟所需的更密集的计算网格,有限差分模拟比声波(只有纵波)假设下变得昂贵得多。第二,多参数反演比声波FWI需要更多的迭代,以获得收敛并且减小不同参数之间的串扰。在储层表征中,描述岩石性质最重要的参数是声阻抗Ip和速度比Vp/Vs。因此,需要可以在最小数目的迭代(优选约10)中可靠地反演Ip和Vp/Vs的FWI方法,以使其在诸如储层表征和速度模型建立的商业应用中实用。
存在广泛的各种方法来从地震数据估算岩石性质。由Hampson等(2005)提议的程序提出了叠前AVO反演中的典型工作流程。在他们的工作流程中,Ip、Is和密度是基于角道集中的AVO和Aki-Richards方程(Aki和Richards,2002)同时估算的。他们的方法是基于反射率的线性近似而不是模拟弹性波和匹配波形的迭代处理。因此,由于线性近似,在叠前反演中计算成本要便宜的多。相反,尽管弹性FWI是昂贵得多的处理,但是其具有生成优越结果的潜力。
发明内容
本发明是用于使用弹性FWI的多参数反演的可靠且有效率的计算机实现方法。该方法将数据分解为偏移距或角度组,并且以顺序次序对它们执行弹性FWI。与没有本发明的改进所执行的弹性FWI相比,该方法可以显著加快收敛,在一些示例中大约为10倍。本发明方法利用反射能量与反射角之间的关系或相等地弹性FWI中的偏移距相关性(offsetdependence)。根据Aki和Richards(1980)的经典AVO理论,众所周知小角度(近偏移距)反射的振幅很大程度上仅由声阻抗确定而与大部分Vp/Vs无关。大角度(中和远偏移距)反射受到Ip、Vp/Vs以及诸如密度和各向异性的其它地球参数的影响。因此,本发明方法在执行多参数FWI中将数据分解为角度/偏移距组,以减小不同模型参数之间(即反演未知参数之间)的串扰。为了本公开内容,包括随附的权利要求,应当理解将数据分解为角度组相当于将数据分解为偏移距组,并且应当理解一个术语包括另一个。
在一个实施例中,本发明是用于地震数据的反演以推断包括纵波速度、横波速度和密度的地下物理性质参数的计算机实现的方法,所述方法包括:从地震数据中只提取PP模式,以及将PP模式数据顺序地反演为两个或更多个不同的偏移距范围,每个偏移距范围反演确定至少一个物理性质参数,其中在第二以及随后的反演中,在先前反演中所确定的参数保持固定。
在另一实施例中,本发明是用于地震数据的反演以推断至少纵波速度、横波速度和密度的方法,所述方法包括:(a)从地震数据中只获取PP模式,以及将地震数据划分为近偏移距范围、中偏移距范围和远偏移距范围,其中范围可以或可以不重叠;(b)使用利用声波反演算法编程的计算机反演针对纵波声阻抗Ip的近偏移距范围;(c)在Ip固定在(b)中它的值的状态下,使用弹性反演算法反演针对横波声阻抗Is或针对纵波速度Vp除以横波速度Vs的中偏移距范围;(d)在Ip固定在(b)中它的值以及Vp/Vs固定在从(c)中的Is值所确定的值的状态下,使用弹性反演算法反演针对密度的远偏移距范围;以及(e)使用声阻抗的定义和如在(d)中所确定的密度从Ip和Is计算Vp和Vs。
在典型的情况中,近偏移距范围可以小于500m,其中远偏移距范围大于2km,并且中偏移距范围在近偏移距范围和远偏移距范围之间。
附图说明
通过参考下面的详细描述和附图,本发明的优点被更好地理解,其中:
图1是示出本发明的地震处理方法的一个实施例中的基本步骤的流程图;
图2示出了用来生成合成道集的真实Vp、Vs和密度剖面,以及一个炮点道集;
图3示出了与真实Ip和合成数据相比,使用近偏移距数据和数据误差的Ip的反演;
图4示出了不知道Vp/Vs,只有Ip是不能够解释中偏移距数据的;
图5示出了利用图2中所固定的Ip的Vp/Vs反演解释直到中偏移距的地震数据;以及
图6示出了利用图2和图4中所固定的Ip和Vp/Vs,来自远偏移距数据的密度的反演结果。
许多附图是彩色原件,由于专利法关于颜色使用的限制其被转换为灰度标。
本发明将结合示例实施例被描述。然而,就下面的详细描述是针对本发明的特定实施例或特定用途来说,其意在只是示例性的,并且将不被解释为限制本发明的范围。相反,其意在涵盖可以包括在如随附的权利要求所限定的本发明的范围内的所有替换物、修改和等价物。
具体实施方式
在由(缩写为“SSB”)Sears、Singh和Barton(2008)所提出的弹性FWI方法中,提议三阶段工作流程来从纵波和横波地震数据估算Vp和Vs:第一阶段,使用垂直入射和广角纵波数据针对短和中尺度Vp的反演;第二阶段,使用广角纵波数据针对中Vs的反演;和第三阶段,使用纵横波数据针对短尺度Vs的反演。短尺度和中尺度是SSB论文中所使用的术语。一般而言,短尺度指可以从地震数据中的高频反射能量直接推断的空间尺度,并且大尺度指反射频率低于典型的地震源(例如,4-6Hz海上采集)的空间尺度。因此,大尺度是典型地从偏移速度分析推断的。大尺度和短尺度之间的间隔通常被称为中尺度。
虽然SSB方法可能初看来类似于本文所公开的3步骤发明方法,但存在区分它们的重要特征。首先,SSB方法贯穿3个阶段使用不同的波模式。本发明方法贯穿3个步骤使用相同的波模式(PP波),但不同的反射角/偏移距。众所周知,PP波数据代表典型的地震勘测中的大部分记录能量,并且因此代表海上拖缆采集中的大部分值。其次,SSB方法在第1阶段不分离垂直入射和广角纵波,并且同时使用它们。本发明方法在步骤1只使用小角度反射数据,其是加快收敛的关键步骤。
一个综合示例被用来展示该方法在恢复Ip和Vp/Vs方面是非常可靠且有效的。获得Ip和Vp/Vs所需的迭代总数约为10。在该综合示例中,在步骤3(见图1流程图)中恢复密度信息可以要求附加的10-15次迭代。对现场数据的测试示出在约10次迭代内同样可以获取Ip和Vp/Vs的精确且可靠的估算。然而,在现场数据情况中,密度反演的可靠性强烈受制于速度模型,包括各向异性和在远偏移距处的数据质量,的精确度。
该综合示例遵循在图1的流程图中所说明的本发明方法的实施例。合成(计算机模拟)数据被在该测试示例中使用以展示本发明。数据集由基于图2中示出的层状(一维)地球模型的各向同性弹性有限差分模拟生成,其中Vp、Vs和密度相对地下深度绘制。速度和密度的单位是m/s和kg/m3。合成“测量”数据的共炮点道集也在图2中的8处示出。以秒为单位的时间被绘制在纵轴上,并且以米为单位的偏移距被绘制在横轴上。地球模型的最大深度是2.3km,并且可用的最大偏移距是5km。由于专利法关于颜色使用的限制,所描绘的炮点道集8是彩色数据显示的灰度标转换,其中颜色用来代表地震振幅的量级。图3-图6中示出的模拟与测量数据以及误差的对比同样如此。
步骤1:从近偏移距数据反演Ip。首先,使用近偏移距PP数据(偏移距小于500m)来执行声波FWI以获取Ip的估算,其在图3中绘制。如上所解释的,在小反射角处(相等地,该示例中的小偏移距)的PP波数据由声阻抗Ip确定。弹性参数对小角度PP反射数据影响极微。执行声波FWI需要初始Vp和密度模型。初始Vp模型可以从传统偏移速度分析获取,并且针对该综合测试,使用图2中的平滑版本的“真实”Vp剖面(用来正演模拟合成数据)。初始密度模型可以从密度和Vp之间的经验关系获取。为简单起见,使用恒定密度(1000kg/m3)模型开始。根据数学定义
Ip=ρVp,(1)
很显然,用Ip除以密度ρ后,使用已知密度ρ反演的Ip可以直接反演成Vp。Ip和Vp的迭代5次的结果在图3中以时间域和深度域两者示出,其中黑线是反演模型并且轻阴影线是合成模型。在该情况中,反演的未知参数是Ip。根据方程(1)通过用反演的Ip除以ρ可以接着获取Vp的估算。在图3中,为了比较,反演的模型与真实的合成模型重叠。所有的反演在深度域(米)中执行;结果在11和12处示出。为了在某一频率范围中的对比,反演结果通过使用图2中的光滑版本的真实Vp按照深时转换而被转换为时间(秒)。在应用带通滤波器之后,时间域(9和10)中的对比被限制在5-40Hz内。从9和11,可以看出反演的Ip非常好地匹配合成模型。因为Vp是根据方程(1)基于假设恒定的ρ从反演的Ip中获取的,所以不期望获取的Vp和真实的Vp之间的良好匹配(还没有执行ρ的更新估算)。因此,初始密度模型(恒定的)与合成密度模型(图2中的7)非常不同,并且由于方程(1)该差异被反映在Vp中。这在10中通过在大约1.75s处的时间域中的不匹配和在大约1800m处的深度域(12)中的类似的不匹配被特别地指示。在9和11中可以看出,在那个特定的时间和深度处针对Ip的不匹配要小的多。
数据误差15,即测量数据13(来自合成模型)和模拟数据14(根据(1)来自反演的Ip、恒定的密度和获取的Vp)之间的差异在图3中被示出。该差异实际是可以忽略的。在现场(实际)数据的反演期间,数据误差对于收敛检查是非常重要的标准,因为在现场数据应用中,“真实模型”是很少已知的。一般而言,当其它条件是类似的,更好的数据误差通常但不总是指示反演产品中的更高置信度。可忽略的误差量指示近偏移距数据可以仅通过Ip很好地解释。
步骤2:利用从先前步骤所固定的Ip从中偏移距(小于2km)数据反演Is或Vp/Vs。下面是已知的,简单的关系:
Is=ρVs,(2)
其中,方程(3)从方程(1)和(2)直接得出。在该步骤2中,反演需要是弹性的,并且反演未知参数是Vp/Vs。因为Ip是从先前步骤所固定的,所以根据(3)在该步骤中针对Vp/Vs的反演相当于针对Is的反演。选择性地,该反演未知参数可以是Is。图4在18中示出了初始Vs模型(黑线,恒定的)和合成模型(轻阴影线)之间的差异,并且比Vp/Vs在19中示出。利用该初始Vs模型和Vp(在17中示出)以及来自步骤1的密度(恒定的),当将偏移距延伸到2km时可以在嵌板22中观察到大的数据误差,如在图4中指示的。这是因为单独的Ip是不足以解释中反射角(偏移距)数据的。需要针对第二个参数(Vp/Vs)的良好估算来解释中偏移距数据。然而,在近偏移距处的数据误差仍然同图3(15)中一样小,因为根据步骤1,Ip是固定的(16,9)。
跟随如图3在显示步骤1反演结果中使用的相同的布局,图5示出了5次迭代后反演的Vp/Vs(黑线,26),其与合成模型(轻阴影线,26)重叠。该反演模型良好地匹配合成模型。如在嵌板29中指示的,在中偏移距范围(500m至2km,比例尺未在附图中示出)处的数据误差被具有反演的Vp/Vs模型的益处大大减小了。在步骤2反演中,根据步骤1,Ip(23)和Vp(24)是固定的。从方程(3)中,精确的Is可以从Ip和Vp/Vs的精确反演结果中获取。但是,如果密度信息是缺少或不精确的,来自方程(2)的Vs将不会一样精确。这在图5中的嵌板25中时间约为1.75s处被指示,其中可以看出从Vp/Vs获取的Vs没有匹配合成模型到与Vp/Vs相同的程度。
步骤3:利用从前述两个步骤所固定的Ip和Vp/Vs从远偏移距(直到5km)数据反演密度。数学关系(1)至(3)指示利用被固定的Ip和Vp/Vs的任何密度更新导致更新Vp和Vs。因此利用固定的Ip和Vp/Vs的密度的反演等同于Vp的反演。在步骤3,利用从步骤1和2所固定的Ip和Vp/Vs,所有可用的直到5km的偏移距(在该示例中)被用来执行密度的弹性反演。图6示出了10次迭代后的反演密度(黑线,33),其与合成模型(轻阴影线,33)重叠,其中合成模型是图2中的7被转换为时间域。同时,由于更新的密度剖面33,步骤3导致与图3的Vp相比改进的Vp预测(31,黑线)。如在图3中36处示出的,数据误差主要在远偏移距(2km至5km)处。
前述描述针对本发明的特定实施例,以为了说明发明。然而,对本领域的技术人员将明显的是对本文所描述的实施例的许多修改和变化是可能的。所有这些修改和变化意在如由随附的权利要求所限定的本发明的范围内。
参考文献
Aki和Richards,Quantitative Seismology,Theory and Methods,chapter5.20,W.H.Freeman & Co.(1980).
Lazaratos,S.,Chikichev,I.和Wang,K.,2011,Improving convergence rate ofFull Wavefield Inversion(FWI)using spectral shaping,PCT专利申请公开WO2012/134621.
Hampson,Russell和Bankhead,“Simultaneous inversion of pre-stackseismic data,”75th Annual International Meeting,SEG,Expanded Abstract,1633-1637(2005)。
Sears,Singh和Barton,“Elastic full waveform inversion of multi-component OBC seismic data,”Geophysical Prospecting 56,843-862(2008)。
Claims (20)
1.一种用于地震数据的全波场反演以推断包括纵波速度、横波速度和密度的地下物理性质参数的计算机实现的方法,所述方法包括:从地震数据中只提取PP模式,以及使用全波场反演算法将PP模式数据顺序地反演为两个或更多个不同的偏移距范围,每个偏移距范围全波场反演确定至少一个物理性质参数,其中在第二以及随后的全波场反演中,在先前反演中所确定的参数保持固定。
2.根据权利要求1所述的方法,其中近偏移距范围是按顺序第一被反演的,并且第一全波场反演使用利用声波全波场反演算法编程的计算机推断纵波声阻抗Ip。
3.根据权利要求2所述的方法,其中中偏移距范围是按顺序第二被反演的,并且第二全波场反演在Ip固定在第一全波场反演中它的值的状态下推断横波声阻抗Is或者纵波速度Vp除以横波速度Vs,所述第二全波场反演使用弹性全波场反演算法。
4.根据权利要求3所述的方法,其中远偏移距范围是按顺序第三被反演的,并且第三全波场反演在Ip固定在第一全波场反演中它的值以及Vp/Vs固定在从第二全波场反演中所确定的值的状态下,使用弹性全波场反演算法来推断密度或Vp。
5.根据权利要求4所述的方法,其中使用声阻抗的定义并且使用如在第三全波场反演中所推断的密度从Ip和Is中计算Vp和Vs。
6.根据权利要求4所述的方法,其中在第三全波场反演中推断Vp,从关系Ip=ρVp中计算密度,并且如在第一全波场反演中确定Ip。
7.根据权利要求4所述的方法,其中在执行所述方法中使用关系Ip=ρVp和Is=ρVs中的一者或两者。
8.根据权利要求4所述的方法,所述方法还包括至少重复一次顺序全波场反演,以更新所推断的物理性质参数。
9.根据权利要求1所述的方法,其中所述两个或更多个不同的偏移距范围中的至少一些重叠。
10.根据权利要求1所述的方法,其中所述两个或更多个不同的偏移距范围不重叠。
11.一种用于地震数据的全波场反演以推断包括纵波速度、横波速度和密度的地下物理性质参数的计算机实现的设备,所述设备包括:用于从地震数据中只提取PP模式的装置,以及使用全波场反演算法将PP模式数据顺序地反演为两个或更多个不同的偏移距范围的装置,每个偏移距范围全波场反演确定至少一个物理性质参数,其中在第二以及随后的全波场反演中,在先前反演中所确定的参数保持固定。
12.根据权利要求11所述的设备,其中近偏移距范围是按顺序第一被反演的,并且第一全波场反演使用利用声波全波场反演算法编程的计算机推断纵波声阻抗Ip。
13.根据权利要求12所述的设备,其中中偏移距范围是按顺序第二被反演的,并且第二全波场反演在Ip固定在第一全波场反演中它的值的状态下来推断横波声阻抗Is或者纵波速度Vp除以横波速度Vs,所述第二全波场反演使用弹性全波场反演算法。
14.根据权利要求13所述的设备,其中远偏移距范围是按顺序第三被反演的,并且第三全波场反演在Ip固定在第一全波场反演中它的值以及Vp/Vs固定在从第二全波场反演中所确定的值的状态下,使用弹性全波场反演算法来推断密度或Vp。
15.根据权利要求14所述的设备,其中使用声阻抗的定义并且使用如在第三全波场反演中所推断的密度从Ip和Is中计算Vp和Vs。
16.根据权利要求14所述的设备,其中在第三全波场反演中推断Vp,从关系Ip=ρVp中计算密度,并且如在第一全波场反演中确定Ip。
17.根据权利要求14所述的设备,其中在所述设备中使用关系Ip=ρVp和Is=ρVs中的一者或两者。
18.根据权利要求14所述的设备,所述设备还包括用于至少重复一次顺序全波场反演,以更新所推断的物理性质参数的装置。
19.根据权利要求11所述的设备,其中所述两个或更多个不同的偏移距范围中的至少一些重叠。
20.根据权利要求11所述的设备,其中所述两个或更多个不同的偏移距范围不重叠。
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KR101861060B1 (ko) | 2018-05-28 |
CA2909105C (en) | 2018-08-28 |
MX346526B (es) | 2017-03-23 |
US20140350861A1 (en) | 2014-11-27 |
CA2909105A1 (en) | 2014-11-27 |
RU2615591C1 (ru) | 2017-04-05 |
KR20160013970A (ko) | 2016-02-05 |
SG11201508195PA (en) | 2015-12-30 |
AU2014268976A1 (en) | 2015-12-10 |
MY169125A (en) | 2019-02-18 |
AU2014268976B2 (en) | 2016-12-22 |
BR112015025516A2 (pt) | 2017-07-18 |
CN105308479A (zh) | 2016-02-03 |
MX2015014703A (es) | 2016-03-07 |
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