复杂孔隙储层三维岩石物理模版

复杂孔隙储层往往同时发育孔缝洞等多种孔隙类型,这种孔隙结构的复杂性使得岩石的速度与孔隙度之间的相关性很差.经典的二维岩石物理模版只研究弹性参数与孔隙度和饱和度之间的定量关系,而不考虑孔隙结构的影响,用这样的模版来预测复杂孔隙储层的物性参数时带来很大偏差.本文首先证明多重孔隙岩石的干骨架弹性参数可以用一个等效孔隙纵横比的单重孔隙岩石物理模型来模拟;进而基于等效介质岩石物理理论和Gassmann方程,建立一个全新的三维岩石物理模版,用它来建立复杂孔隙岩石的弹性性质与孔隙扁度及孔隙度和饱和度之间的定量关系;在此基础上,预测复杂储层的孔隙扁度、孔隙度以及孔隙中所包含的流体饱和度.实际测井和地震反演数据试验表明,三维岩石物理模版可有效提高复杂孔隙储层参数的预测精度.     

Rock physics modeling of heterogeneous carbonatereservoirs： porosity estimation and hydrocarbon detection

In heterogeneous natural gas reservoirs, gas is generally present as small patch-like pockets embedded in the water-saturated host matrix. This type of heterogeneity, alsocalled ＂patchy saturation＂, causes significant seismic velocity dispersion and attenuation. Toestablish the relation between seismic response and type of fluids, we designed a rock physicsmodel for carbonates. First, we performed CT scanning and analysis of the fluid distributionin the partially saturated rocks. Then, we predicted the quantitative relation between the waveresponse at different frequency ranges and the basic lithological properties and pore fluids.A rock physics template was constructed based on thin section analysis of pore structuresand seismic inversion. This approach was applied to the limestone gas reservoirs of the rightbank block of the Amu Darya River. Based on poststack wave impedance and prestack elasticparameter inversions, the seismic data were used to estimate rock porosity and gas saturation.The model results were in ~ood a~reement with the production regime of the wells.     

致密砂岩气藏裂隙-孔隙型弹性岩石物理模板研究:以川西坳陷A区为例

相对于常规砂岩,致密砂岩在岩石物理性质、力学性质等方面具有明显差异,并呈现出很强的非均质性.岩石物理模型能将储层参数与地震特性信息联系起来,因此可以作为致密砂岩储层参数与地震特性信息转换的桥梁.常规的岩石物理模型通常只考虑单一因素(例如非均匀性,单一孔隙,单一尺度等),建立的岩石物理模板并不适用于致密砂岩.本文针对高饱和气、微裂隙发育、非均质性强的致密砂岩储层,利用Voigt-Reuss-Hill模型计算混合矿物的弹性模量,采用微分等效介质(DEM)模型描述含裂隙、孔隙岩石的骨架弹性模量,基于Biot-Rayleigh波动方程构建了岩石物理弹性模板,给出了致密砂岩储层弹性参数与物性的关系.基于测井数据和实验数据对岩石物理弹性模板进行校正,并将校正后的岩石物理弹性模板结合叠前地震资料应用于川西地区储层孔隙度与裂隙含量预测.结果显示,反演裂隙含量、孔隙度与储层试气报告、测井孔隙度基本吻合,表明该模板能够较合理地应用于致密砂岩储层孔隙度及裂隙含量解释中.     

基于数字岩心的碳酸盐岩孔隙结构对弹性性质的影响研究(下篇):储层孔隙结构因子表征与反演

碳酸盐岩复杂的孔隙结构如何影响其弹性性质一直是地球物理研究的难点问题,在此基础上如何半定量甚至是定量地对碳酸盐岩储层预测,特别是如何有效地获取孔隙结构参数相关的地震属性体一直是油气工业界追求的目标,本研究从数字岩心角度入手,联合测井以及地震数据尝试探究这一问题的解决方案.首先针对代表不同孔隙结构类型的有限数目的碳酸盐岩样品获得其对应的高精度数字岩心数据体,为了获得更加可靠的具有地球物理含义的弹性性质随孔隙度变化的统计规律,我们通过子网格的技术,在有限数目的碳酸盐岩数字岩心数据体上获得了大量的数字岩心子网格样本,对于每个子网格样本可以分别获得其对应的数字岩心图像孔隙度、表征孔隙软硬程度的孔隙结构参数(γ)、以及基于有限元法模拟的弹性性质,由此基于数字岩心的研究思路,我们最终获得了基于孔隙结构因子表征与分类下的弹性性质与孔隙度的定量化解释量版.与此同时,在地震尺度上通过叠前地震资料获取的纵横波及密度属性体后,基于如上获得的定量化解释量版,我们最终获得了针对碳酸盐岩储层的新的属性体——孔隙结构参数(γ)属性体,这使得在地震尺度上预测碳酸盐岩储层的孔隙结构类型成为可能,也使利用地震数据在孔隙结构参数表征与分类下的碳酸盐岩储层反演精度的提高成为可能.     

岩石物理模版在储层定量解释中的应用

岩石物理模板采用测井解释的各类岩性矿物骨架点值,选取适合该地区的岩石物理模型,模拟在不同储层组合、不同孔隙及不同饱和度情况下,储层岩石物理参数变化引起的储层测井参数及地球物理响应特征的变化,定量地建立起储层参数同地球物理弹性参数间的解释关系模版.本文根据新场JS42气藏储层参数分析结果,尝试性地将岩石物理解释模板应用于储层定量解释中,对储层高产气区、含水区域进行定量解释,并预测了该气藏的气水界面,该预测结果与实钻井测试情况吻合,证实了该方法的科学性.     

Multiscale rock-physics templates for gas detection in carbonate reservoirs

The heterogeneous distribution of fluids in patchy-saturated rocks generates significant velocity dispersion and attenuation of seismic waves. The mesoscopic Biot–Rayleigh theory is used to investigate the relations between wave responses and reservoir fluids. Multiscale theoretical modeling of rock physics is performed for gas/water saturated carbonate reservoirs. Comparisons with laboratory measurements, log and seismic data validate the rock physics template. Using post-stack and pre-stack seismic inversion, direct estimates of rock porosity and gas saturation of reservoirs are obtained, which are in good agreement with oil production tests of the wells.     

Rock skeleton models and seismic porosity inversion

By substituting rock skeleton modulus expressions into Gassmann approximate fluid equation, we obtain a seismic porosity inversion equation. However, conventional rock skeleton models and their expressions are quite different from each other, resuling in different seismic porosity inversion equations, potentially leading to difficulties in correctly applying them and evaluating their results. In response to this, a uniform relation with two adjusting parameters suitable for all rock skeleton models is established from an analysis and comparison of various conventional rock skeleton models and their expressions including the Eshelby-Walsh, Pride, Geertsma, Nur, Keys-Xu, and Krief models. By giving the two adjusting parameters specific values, different rock skeleton models with specific physical characteristics can be generated. This allows us to select the most appropriate rock skeleton model based on geological and geophysical conditions, and to develop more wise seismic porosity inversion. As an example of using this method for hydrocarbon prediction and fluid identification, we apply this improved porosity inversion, associated with rock physical data and well log data, to the ZJ basin. Research shows that the existence of an abundant hydrocarbon reservoir is dependent on a moderate porosity range, which means we can use the results of seismic porosity inversion to identify oil reservoirs and dry or water-saturated reservoirs. The seismic inversion results are closely correspond to well log porosity curves in the ZJ area, indicating that the uniform relations and inversion methods proposed in this paper are reliable and effective.     

地震尺度下碳酸盐岩储层的岩石物理建模方法(英文)

碳酸盐岩油藏的强非均质性以及孔隙结构的复杂性,使得作为连接油藏参数与地震参数重要桥梁的岩石物理模型,以及作为油藏预测和定量表征最有效工具的流体替换成为岩石物理建模的难点与重点。在碳酸盐岩储层复杂孔隙结构与地震尺度下碳酸盐岩储层非均质性分析基础上,研究采用岩石网格化方法,将地震尺度下非均质碳酸盐岩储层岩石划分为具有独立岩石参数的均质岩石子体,根据岩石孔隙成因与结构特征采用不同岩石物理模型分步计算岩石子块干岩石弹性模量,并根据不同孔隙连通性进行流体替换,计算饱和不同流体岩石弹性模量。基于计算的岩石子块弹性模量,采用Hashin-Shtrikman-Walpole弹性边界计算理论方法实现地震尺度下碳酸盐岩储层弹性参数计算。通过对含有不同类型孔隙组合碳酸盐岩储层模型的弹性模量进行计算与分析,明确不同孔隙对岩石弹性参数的影响特征,模拟分析结果与实际资料认识一致。     

基于统计岩石物理模型的各向异性页岩储层参数反演

提出了各向异性页岩储层统计岩石物理反演方法.通过统计岩石物理模型建立储层物性参数与弹性参数的定量关系,使用测井数据及井中岩石物理反演结果作为先验信息,将地震阻抗数据定量解释为储层物性参数、各向异性参数的空间分布.反演过程在贝叶斯框架下求得储层参数的后验概率密度函数,并从中得到参数的最优估计值及其不确定性的定量描述.在此过程中综合考虑了岩石物理模型对复杂地下介质的描述偏差和地震数据中噪声对反演不确定性的影响.在求取最大后验概率过程中使用模拟退火优化粒子群算法以提高收敛速度和计算准确性.将统计岩石物理技术应用于龙马溪组页岩气储层,得到储层泥质含量、压实指数、孔隙度、裂缝密度等物性,以及各向异性参数的空间分布及相应的不确定性估计,为页岩气储层的定量描述提供依据.     

非均质天然气藏的岩石物理模型及含气饱和度反演

非均质气藏中,天然气一般呈"斑块状"分布于含水岩石内部,这种非均匀分布特征会导致地震波显著的频散与衰减现象.为发展适用于碳酸盐岩储层中流体检测的岩石物理模型,本文基于Biot-Rayleigh波动方程,实现了对非饱和岩石的多尺度理论建模,预测了不同尺度下波响应与岩性、流体间的定量联系.将此项建模技术应用于阿姆河右岸的灰岩气藏,给出了多尺度的岩石物理学图板.通过与实验数据、测井精细解释结果及地震数据的对比分析,本文论证了图板的正确性与可适用性.结合叠后波阻抗反演与叠前弹性参数反演,基于地震资料进行了储层含气饱和度与孔隙度的反演,反演结果与各井实际的产气情况吻合.     

基于叠前反演的流体敏感属性实验研究及应用

提取叠前地震振幅信息的叠前反演技术已成为储层预测的重要手段,其能获得各种岩石弹性参数,丰富储层预测方法.因目标储层的差异性,优选并建立有利的流体敏感参数对储层流体检测尤为重要.本文基于岩石物理实验, 测量并分析了岩石弹性参数随流体饱和度的变化特征, 进一步根据岩石物理理论建立组合流体敏感参数, 达到对油气检测的最佳敏感效果.定义了流体敏感量,定量分析岩石弹性参数的流体敏感性.最后本文在X区块进行了叠前地震反演的应用, 结果表明通过岩石物理实验分析并建立获得的流体敏感参数能明显的提高储层的识别能力.     

基于流体替换技术的地震AVO属性气藏识别(英文)

传统上,油藏地球物理工程师是基于测井数据进行流体替换,计算油藏饱和不同流体时的弹性参数,并通过地震正演模拟分析油藏饱和不同流体时的地震响应,从而进行油气藏识别研究。该研究方案为油藏研究提供了重要的弹性参数和地震响应信息,但这些信息仅限于井眼位置。对于实际油藏条件,地下储层参数都是随位置变化而变化的,如孔隙度、泥质含量和油藏厚度等,因此基于传统流体替换方案得到的流体变化地震响应信息对于油气藏识别具有很大的局限性。研究通过设定联系油藏弹性参数与孔隙度、矿物组分等参数的岩石物理模型,并基于三层地质模型,进行地震正演模拟与AVO属性计算。得到油藏孔隙度、泥质含量和储层厚度变化时地震AVO属性,并建立了饱和水储层和含气储层对应AVO属性(包括梯度与截距)之间的定量关系。建立的AVO属性之间的线性关系可以实现基于地震AVO属性直接进行流体替换。最后,应用建立的流体替换前后AVO属性之间线性方程,对模拟地震数据直接进行流体替换,并通过流体替换前后AVO属性交汇图分析实现了气藏识别。     

Integration of rock physics and seismic inversion for rock typing and flow unit analysis: A case study

Rock typing and flow unit detection are more challenging in clastic reservoirs with a uniform pore system. An integrated workflow based on well logs, inverted seismic data and rock physics models is proposed and developed to address such challenges. The proposed workflow supplies a plausible reservoir model for further investigation and adds extra information. Then, this workflow has been implemented in order to define different rock types and flow units in an oilfield in the Persian Gulf, where some of these difficulties have been observed. Here, rock physics models have the leading role in our proposed workflow by providing a diagnostic framework in which we successfully differentiate three rock types with variant characteristics on the given wells. Furthermore, permeability and porosity are calculated using the available rock physics models to define several flow units. Then, we extend our investigation to the entire reservoir by means of simultaneous inversion and rock physics models. The outcomes of the study suggest that in sediments with homogeneous pore size distribution, other reservoir properties such as shale content and cementation (which have distinct effects on the elastic domain) can be used to identify rock types and flow units. These reservoir properties have more physical insights for modelling purposes and can be distinguished on seismic cube using proper rock physics models. The results illustrate that the studied reservoir mainly consists of rock type B, which is unconsolidated sands and has the characteristics of a reservoir for subsequent fluid flow unit analysis. In this regard, rock type B has been divided into six fluid units in which the first detected flow unit is considered as the cleanest unit and has the highest reservoir process speed about 4800 to 5000 mD. Here, reservoir quality decreases from flow unit 1 to flow unit 6.     

Integrating basin modeling with seismic technology and rock physics

We explore the link between basin modelling and seismic inversion by applying different rock physics models. This study uses the E‐Dragon II data in the Gulf of Mexico. To investigate the impact of different rock physics models on the link between basin modelling and seismic inversion, we first model relationships between seismic velocities and both (1) porosity and (2) effective stress for well‐log data using published rock physics models. Then, we build 1D basin models to predict seismic velocities derived from basin modelling with different rock physics models, in a comparison with average sonic velocities measured in the wells. Finally, we examine how basin modelling outputs can be used to aid seismic inversion by providing constraints for the background low‐frequency model. For this, we run different scenarios of inverting near angle partial stack seismic data into elastic impedances to test the impact of the background model on the quality of the inversion results. The results of the study suggest that the link between basin modelling and seismic technology is a two‐way interaction in terms of potential applications, and the key to refine it is establishing a rock physics models that properly describes changes in seismic signatures reflecting changes in rock properties.     

基于数字岩心的碳酸盐岩孔隙结构对弹性性质的影响研究(上篇):图像处理与弹性模拟

碳酸盐岩复杂的孔隙结构如何影响其弹性性质一直是地球物理研究的难点问题,在此基础上如何半定量甚至是定量地对碳酸盐岩储层预测,特别是如何有效地获取孔隙结构参数相关的地震属性体一直是油气工业界追求的目标.本研究从数字岩心角度入手,联合测井以及地震数据尝试探究这一问题的解决方案,包括如下几个方面:(1)代表性碳酸盐岩储层样品获取;(2)CT扫描数字岩心数据体获取;(3)数字岩心数据的图像处理;(4)数字岩心数据的静态弹性模拟;(5)数字岩心子数据体的孔隙结构因子提取;(6)孔隙结构因子表征与分类下的弹性性质与孔隙度的定量化量版建立;(7)数字岩心-井-地震联合的孔隙度属性提取;(8)孔隙结构因子的地震属性体获取.本研究分为两篇系列文章上篇与下篇,上篇主要阐述如上提出的(1)—(4)方面,重点在于针对碳酸盐岩二值化图像处理的流程建立与验证,以及数字岩心静态弹性模拟的理论方面,这两方面是基于数字岩心获得精确的碳酸盐岩弹性性质模拟结果的关键所在;下篇主要阐述利用数字岩心数据获得孔隙结构因子的思路、理论与流程,以及为碳酸盐岩储层预测为目标而获得孔隙结构因子的地震属性体的实际应用方面.由于两篇文章共享数字岩心数据,同时所涉及的研究思路与流程形成一个有机整体,因此写成两篇系列文章而非两篇独立文章.本文为两篇系列文章的第一篇:上篇.     

基于计算岩石物理方法的页岩储层弹性参数提取

发展了应用数值计算方法获取页岩储层的速度、各向异性参数的计算岩石物理系列方法.该系列方法包括了大尺度精细地质模型数值建模、计算网格尺度的地球物理建模和地震波数值模拟提取岩石物理弹性参数.本文方法利用储层的统计数据而不是具体岩心的测量数据,可获得储层岩石物理弹性参数的变化规律.相比于基于岩心测试的岩石物理方法,本文方法可精细考虑实际储层的非均匀特征,可得到岩心测试难以求取的与尺寸效应高度相关的弹性参数,也避免了求取弹性参数变化规律时获取不同地质特征岩心的困难.本文发展了计算岩石物理方法,为计算岩石物理面临的大尺度地质建模和计算能力限制问题提供了有效的解决方案.文中以胜利罗家的页岩储层为例,求得了储层TOC含量从3%到21%变化情况下储层的P波、S波速度以及各向异性参数变化规律.     

Nonlinear simultaneous inversion of pore structure and physical parameters based on elastic impedance

Carbonate reservoirs have complex pore structures, which not only significantly affect the elastic properties and seismic responses of the reservoirs but also affect the accuracy of the prediction of the physical parameters. The existing rockphysics inversion methods are mainly designed for clastic rocks, and the inversion objects are generally porosity and water saturation. The data used are primarily based on the elastic parameters, and the inversion methods are mainly linear approximations. To date, there has been a lack of a simultaneous pore structure and physical parameter inversion method for carbonate reservoirs. To solve these problems, a new Bayesian nonlinear simultaneous inversion method based on elastic impedance is proposed. This method integrates the differential effective medium model of multiple-porosity rocks, Gassmann equation,Amplitude Versus Offset(AVO) theory, Bayesian theory, and a nonlinear inversion algorithm to achieve the simultaneous quantitative prediction of the pore structure and physical parameters of complex porous reservoirs. The forward modeling indicates that the contribution of the pore structure, i.e., the pore aspect ratio, to the AVO response and elastic impedance is second only to that of porosity and is far greater than that of water saturation. The application to real data shows that the new inversion method for determining the pore structure and physical parameters directly from pre-stack data can accurately predict a reservoir's porosity and water saturation and can evaluate the pore structure of the effective reservoir.     

岩石孔隙结构的地震信号特征

岩石孔隙结构是控制砂岩和碳酸盐岩的地震波速度和渗透率的重要参数之一。如果两种类似的岩石其孔隙度一定,而渗透率不同,那么它们的声波速度相差2km/s,而渗透率两者则可能相差近6个数量级,即从0.01mD到20mO。在本文中我们总结了由一个广义孔隙弹性理论简化的一个双参数弹性速度模型，以描述孔隙结构对弹性波的影响。由于矿物和储层的流体是给定的,我们用孔隙度和骨架的柔性(挠性)因子来确定速度模型,这个模型可以用于地震反演和储层表征,已改善孔隙度和储量的计算骨架柔性因子可用于岩石结构(PST)类型的定量分类,并可以利用叠前、叠后的两种地震资料将其与孔隙的连通性和渗透率联系起来。本项研究同时也有助于说明为什么振幅与偏移距(AVO)分析用于流体检测在某些情况下失败。这是由于孔隙结构对地震波的影响能够掩盖所有流体效应，特别是在碳酸岩中。     

Seismic driven probabilistic classification of reservoir facies for static reservoir modelling: a case history in the Barents Sea

In this paper we present a case history of seismic reservoir characterization where we estimate the probability of facies from seismic data and simulate a set of reservoir models honouring seismically‐derived probabilistic information. In appraisal and development phases, seismic data have a key role in reservoir characterization and static reservoir modelling, as in most of the cases seismic data are the only information available far away from the wells. However seismic data do not provide any direct measurements of reservoir properties, which have then to be estimated as a solution of a joint inverse problem. For this reason, we show the application of a complete workflow for static reservoir modelling where seismic data are integrated to derive probability volumes of facies and reservoir properties to condition reservoir geostatistical simulations. The studied case is a clastic reservoir in the Barents Sea, where a complete data set of well logs from five wells and a set of partial‐stacked seismic data are available. The multi‐property workflow is based on seismic inversion, petrophysics and rock physics modelling. In particular, log‐facies are defined on the basis of sedimentological information, petrophysical properties and also their elastic response. The link between petrophysical and elastic attributes is preserved by introducing a rock‐physics model in the inversion methodology. Finally, the uncertainty in the reservoir model is represented by multiple geostatistical realizations. The main result of this workflow is a set of facies realizations and associated rock properties that honour, within a fixed tolerance, seismic and well log data and assess the uncertainty associated with reservoir modelling.     

Research on seismic fluid identification driven by rock physics

Seismic fluid identification works as an effective approach to characterize the fluid feature and distribution of the reservoir underground with seismic data. Rock physics which builds bridge between the elastic parameters and reservoir parameters sets the foundation of seismic fluid identification, which is also a hot topic on the study of quantitative characterization of oil/gas reservoirs. Study on seismic fluid identification driven by rock physics has proved to be rewarding in recognizing the fluid feature and distributed regularity of the oil/gas reservoirs. This paper summarizes the key scientific problems immersed in seismic fluid identification, and emphatically reviews the main progress of seismic fluid identification driven by rock physics domestic and overseas, as well as discusses the opportunities, challenges and future research direction related to seismic fluid identification. Theoretical study and practical application indicate that we should incorporate rock physics, numerical simulation, seismic data processing and seismic inversion together to enhance the precision of seismic fluid identification.