共查询到16条相似文献，搜索用时 125 毫秒
1.
地震岩相识别能够提供具有不同储层特征的岩相分布信息,对岩相识别的不确定性开展定量评价分析可降低后期油藏建模与储层评价的风险.考虑了地震岩相识别中测井岩相定义、岩石物理建模、井震尺度匹配及地震反演等环节的不确定性对岩相识别的影响,基于概率统计方法,引入熵函数实现了地震岩相识别不确定性定量评价,并结合岩相概率、重建率等多角度综合定量分析不确定性的构成及传递特征,系统地实现了地震岩相识别不确定性评价流程的整体连通.提出了结合属性交绘特征约束反演参数空间,提高地震岩相识别运算效率.模拟数据分析表明利用熵函数可精确实现岩相识别不确定性地定量表征,利用属性交绘特征约束参数空间既大幅度减少运算量,也可降低地震岩相识别的不确定性. 相似文献
2.
岩相和储层物性参数是油藏表征的重要参数，地震反演是储层表征和油气藏勘探开发的重要手段.随机地震反演通常基于地质统计学理论，能够对不同类型的信息源进行综合，建立具有较高分辨率的储层模型，因而得到广泛关注.其中，概率扰动方法是一种高效的迭代随机反演策略，它能综合考虑多种约束信息，且只需要较少的迭代次数即可获得反演结果.在概率扰动的优化反演策略中，本文有效的联合多点地质统计学与序贯高斯模拟，并结合统计岩石物理理论实现随机反演.首先，通过多点地质统计学随机模拟，获得一系列等可能的岩相模型，扰动更新初始岩相模型后利用相控序贯高斯模拟建立多个储层物性参数模型；然后通过统计岩石物理理论，计算相应的弹性参数；最后，正演得到合成地震记录并与实际地震数据对比，通过概率扰动方法进行迭代，直到获得满足给定误差要求的反演结果.利用多点地质统计学，能够更好地表征储层空间特征.相控序贯高斯模拟的应用，能够有效反映不同岩相中储层物性参数的分布.提出的方法可在较少的迭代次数内同时获得具有较高分辨率的岩相和物性参数反演结果，模型测试和实际数据应用验证了方法的可行性和有效性. 相似文献
3.
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. 相似文献
4.
《中国科学:地球科学》2020,(6)
地震岩石物理是连接岩石弹性参数与储层物性参数的桥梁,叠前地震反演是实现地下岩石弹性、物性、岩性及含流体性质定量表征的重要方法.文章构建了碎屑岩地震岩石物理高阶近似模型,推导了利用岩石模量高阶近似(Jacobian、Hessian矩阵)表征的叠前地震AVO反射特征方程,并分析了岩石孔隙度、泥质含量及流体饱和度对AVO反射率的贡献度,探讨了此方程在岩石物性参数直接预测方面的可行性.以此为基础,在待反演模型参数服从混合概率先验模型的前提下,文章提出了基于差分进化-马尔可夫链蒙特卡罗随机模型的相约束叠前地震概率化反演方法,兼具差分进化算法的全局寻优特性和马尔可夫链蒙特卡罗模型的不确定性分析能力;通过多条马尔可夫链的交叉并行,可以同步获得待反演模型参数的多个随机解,进而模拟待反演模型的后验概率密度分布,后验均值作为待反演模型的最优解,方差与置信区间用来评价反演结果的不确定性,实现储层弹性、物性、离散岩相及干岩石骨架等参数的同步预测.通过理论试验和实际资料处理验证了该理论方法的有效性. 相似文献
5.
储层弹性与物性参数可直接应用于储层岩性预测和流体识别,是储层综合评价和油气藏精细描述的基本要素之一.现有的储层弹性与物性参数地震同步反演方法大都基于Gassmann方程,使用地震叠前数据,通过随机优化方法反演储层弹性与物性参数;或基于Wyllie方程,使用地震叠后数据,通过确定性优化方法反演储层弹性与物性参数.本文提出一种基于Gassmann方程、通过确定性优化方法开展储层弹性和物性参数地震叠前反演的方法,该方法利用Gassmann方程建立储层物性参数与叠前地震观测数据之间的联系,在贝叶斯反演框架下以储层弹性与物性参数的联合后验概率为目标函数,通过将目标函数的梯度用泰勒公式展开得到储层弹性与物性参数联合的方程组,其中储层弹性参数对物性参数的梯度用差分形式表示,最后通过共轭梯度算法迭代求解得到储层弹性与物性参数的最优解.理论试算与实际资料反演结果证明了方法的可行性. 相似文献
6.
《中国科学:地球科学》2015,(1)
地震流体识别指利用地震资料对储层含流体特征进行识别与描述.含流体储层地震岩石物理是地震流体识别的基础,是搭建储层弹性参数与物性参数的桥梁,是实现含油气储层流体定量表征的重要发展方向.岩石物理驱动下地震流体识别研究有助于认识地下油气储层含流体特征及分布规律.文章概述地震流体识别及相关基础研究中的关键科学问题,着重评述国内外岩石物理驱动下地震流体识别研究的主要进展,探究地震流体识别研究面临的机遇,挑战及未来的研究方向.理论研究和实际应用表明,地震流体识别要以岩石物理及数值模拟为理论基础,发展有效的流体敏感参数构建及评价方法;以地震资料为数据支撑,形成有效的地震资料品质评价方法;以地震反演为技术保障,发展可靠的地震反演策略. 相似文献
7.
岩相信息能够反映储层岩性及流体特征,在地震储层预测中具有重要作用.常规方法主要利用与岩相信息关系密切的弹性参数定性或定量地转化为岩相信息.在实际应用中,弹性参数的获取主要基于叠前地震反演技术.而不同弹性参数的叠前地震反演精度间存在着差异,势必影响岩相的整体预测精度.本文提出对弹性参数进行加权统计来预测岩相.首先,基于贝叶斯理论,引入权重系数来调节弹性参数信息的采用量,构建出最终的目标反演函数;其次,考虑到勘探初期缺少明确的测井岩相信息,提出利用高斯混合分布函数来自动估算岩相先验概率;最后,根据输入弹性参数的取值,计算每类岩相对应的后验概率密度,将目标反演函数取最大后验概率密度时对应的岩相类别作为最终预测的岩相.新方法旨在减少弹性参数精度间的精度差异对岩相预测结果的影响,以期提高地震岩相的预测精度.模型与实际资料测试均表明该方法可行、有效且预测精度较高. 相似文献
8.
肯吉亚克油田石炭系油藏属持低孔渗、异常高压碳酸盐岩油藏，它除了具有埋深大，非均质性强，油气成藏控制因素复杂等特点外，其上还覆盖巨厚盐丘，造成盐下地震反射时间和振幅畸变严重，地震成像差、信噪比低和分辨率低，给储层预测工作带来极大困难。如何正确预测油藏高产带分布规律是高效开发这类油藏的关键，本文研究从分析形成碳酸盐岩油藏高产带的主控因素入手，通过井震标定，优选反映碳酸盐岩岩相、岩溶、物性和裂缝的地震属性，结合地震、地质、测井、油藏工程和钻井资料，把盐下特低孔渗碳酸盐岩油藏高产带预测问题分解成构造解释、岩相预测、岩溶预测、物性预测、裂缝预测和综合评价等六个环节。宏观上，通过建立断裂、岩相、岩溶模式，定性预测储层分布有利区带；微观上，通过多参数储层特征反演和多属性综合分析，定量、半定量预测有利储层分布，有效解决盐下碳酸盐岩油藏高产带预测难题，基本搞清本区碳酸盐岩油藏高产带分布规律，为优选有利勘探和开发目标提供依据。文中提出的方法和技术对解决国内外碳酸盐岩油藏高产带预测和其他复杂储层预测问题有借鉴作用。 相似文献
9.
布谷鸟马尔科夫链蒙特卡洛混合高斯地质统计学随机反演 总被引：2，自引：0，他引：2
地质统计学随机反演可以获得比常规反演更高分辨率的结果,目前已成为储层高分辨率预测的主流方法.地下不同岩相储层参数存在明显差异,本文在地质统计学反演框架下构建了岩相和储层参数同步反演目标函数,实现不同岩相条件下储层参数分布精细描述.在求解该高维数据多参数同步反演问题时,本文将可以动态调节搜索步长的布谷鸟算法与马尔科夫链蒙特卡洛方法融合,采用多条马尔科夫链进行Levy飞行产生新解的策略扩大解的空间范围,通过适应度最佳选择输出最优解实现全局优化迭代,有效提升了反演方法的稳定性和全局最优性,避免了传统马尔科夫链蒙特卡洛方法因抽样随机性而陷入局部最优的问题.通过含噪声模型和实际数据分析验证了本文方法的有效性. 相似文献
10.
基于频变AVO技术对多尺度裂缝内流体属性反演与识别（英文） 总被引：5，自引：3，他引：2
通过地震数据获取裂缝储藏中流体的性质并对流体类型进行识别,是地震勘探岩性反演的重要问题之一。由于地震波的速度、储层的密度等弹性参数对某些流体不具有很强的敏感性,使只依赖振幅信息进行流体识别的传统AVO方法面临困境。作为传统叠前振幅反演的一个拓展,频变AVO（FDAVO）技术进一步考虑了振幅对频率的依赖关系,将这种依赖关系与地下裂缝结构、流体填充对应起来,能带来更丰富的流体信息。利用该技术,本文提出了一种基于地震数据参数化Chapman模型的贝叶斯反演新方法（BIDCMP）,它包含两步算法,即,FDAVO反演储层的非弹性属性和贝叶斯框架下的流体识别。首先,通过匹配观测数据和模型数据,构造差函数反演裂缝储层非弹性参数。随后,在贝叶斯框架下,使用马尔科夫随机场（MRF）作为先验模型,联合多参数场识别流体。本方法在计算过程中,除综合考虑了弹性参数场、测井资料等常规信息外,还特别地加人了第一步中反演得的非弹性参数的约束,从而充分利用了流体粘性差异,最后在最大后验概率（MAP）准则下输出最佳岩性一流体识别结果。分别对合成地震记录和模拟岩性—流体剖面验证本文方法的有效性,结果证明本文方法获得的流体识别结果准确可信。 相似文献
11.
Seismic driven probabilistic classification of reservoir facies for static reservoir modelling: a case history in the Barents Sea 总被引：2，自引：0，他引：2
Dario Grana Enrico Paparozzi Silvia Mancini Cristiano Tarchiani 《Geophysical Prospecting》2013,61(3):613-629
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. 相似文献
12.
利用能够整合测井信息与井间地震信息的地质统计学随机模拟方法,结合传统的地质统计学反演思路,得到了一种能够同时整合测井、井间地震与地面地震三种先验信息的地质统计学反演与储层建模方法.由于井间射线信息、测井信息与地面地震数据在随机反演与建模过程当中都得到了尊重,因此与传统地质统计学反演仅利用了测井与地面地震数据相比,本文的地质统计学反演与建模方法更充分地利用了先验信息,有效提高了反演的精度,降低了随机建模中的多解性.基于理论数据的测试证实了上述观点. 相似文献
13.
Joint AVO inversion, wavelet estimation and noise-level estimation using a spatially coupled hierarchical Bayesian model 总被引：1，自引：0，他引：1
The main objective of the AVO inversion is to obtain posterior distributions for P-wave velocity, S-wave velocity and density from specified prior distributions, seismic data and well-log data. The inversion problem also involves estimation of a seismic wavelet and the seismic-noise level. The noise model is represented by a zero mean Gaussian distribution specified by a covariance matrix. A method for joint AVO inversion, wavelet estimation and estimation of the noise level is developed in a Bayesian framework. The stochastic model includes uncertainty of both the elastic parameters, the wavelet, and the seismic and well-log data. The posterior distribution is explored by Markov-chain Monte-Carlo simulation using the Gibbs' sampler algorithm. The inversion algorithm has been tested on a seismic line from the Heidrun Field with two wells located on the line. The use of a coloured seismic-noise model resulted in about 10% lower uncertainties for the P-wave velocity, S-wave velocity and density compared with a white-noise model. The uncertainty of the estimated wavelet is low. In the Heidrun example, the effect of including uncertainty of the wavelet and the noise level was marginal with respect to the AVO inversion results. 相似文献
14.
15.
Seismic Rock physics plays a bridge role between the rock moduli and physical properties of the hydrocarbon reservoirs. Prestack seismic inversion is an important method for the quantitative characterization of elasticity, physical properties, lithology and fluid properties of subsurface reservoirs. In this paper, a high order approximation of rock physics model for clastic rocks is established and one seismic AVO reflection equation characterized by the high order approximation(Jacobian and Hessian matrix) of rock moduli is derived. Besides, the contribution of porosity, shale content and fluid saturation to AVO reflectivity is analyzed. The feasibility of the proposed AVO equation is discussed in the direct estimation of rock physical properties. On the basis of this, one probabilistic AVO inversion based on differential evolution-Markov chain Monte Carlo stochastic model is proposed on the premise that the model parameters obey Gaussian mixture probability prior model. The stochastic model has both the global optimization characteristics of the differential evolution algorithm and the uncertainty analysis ability of Markov chain Monte Carlo model. Through the cross parallel of multiple Markov chains, multiple stochastic solutions of the model parameters can be obtained simultaneously, and the posterior probability density distribution of the model parameters can be simulated effectively. The posterior mean is treated as the optimal solution of the model to be inverted.Besides, the variance and confidence interval are utilized to evaluate the uncertainties of the estimated results, so as to realize the simultaneous estimation of reservoir elasticity, physical properties, discrete lithofacies and dry rock skeleton. The validity of the proposed approach is verified by theoretical tests and one real application case in eastern China. 相似文献
16.
Honoré Yenwongfai Nazmul Haque Mondol Isabelle Lecomte Jan Inge Faleide Johan Leutscher 《Geophysical Prospecting》2019,67(4):1020-1039
Seismic petro-facies characterization in low net-to-gross reservoirs with poor reservoir properties such as the Snadd Formation in the Goliat field requires a multidisciplinary approach. This is especially important when the elastic properties of the desired petro-facies significantly overlap. Pore fluid corrected endmember sand and shale depth trends have been used to generate stochastic forward models for different lithology and fluid combinations in order to assess the degree of separation of different petro-facies. Subsequently, a spectral decomposition and blending of selected frequency volumes reveal some seismic fluvial geomorphological features. We then jointly inverted for impedance and facies within a Bayesian framework using facies-dependent rock physics depth trends as input. The results from the inversion are then integrated into a supervised machine learning neural network for effective porosity discrimination. Probability density functions derived from stochastic forward modelling of endmember depth trends show a decreasing seismic fluid discrimination with depth. Spectral decomposition and blending of selected frequencies reveal a dominant NNE trend compared to the regional SE–NW pro-gradational trend, and a local E–W trend potentially related to fault activity at branches of the Troms-Finnmark Fault Complex. The facies-based inversion captures the main reservoir facies within the limits of the seismic bandwidth. Meanwhile the effective porosity predictions from the multilayer feed forward neural network are consistent with the inverted facies model, and can be used to qualitatively highlight the cleanest regions within the inverted facies model. A combination of facies-based inversion and neural network improves the seismic reservoir delineation of the Snadd Formation in the Goliat Field. 相似文献