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多属性融合技术在苏14井区的应用 总被引：2，自引：1，他引：1
In this study area the geological conditions are complicated and the effective sandstone is very heterogeneous. The sandstones
are thin and lateral and vertical variations are large. We introduce multi-attribute fusion technology based on pre-stack
seismic data, pre-stack P- and S-wave inversion results, and post-stack attributes. This method not only can keep the fluid
information contained in pre-stack seismic data but also make use of the high SNR characteristics of post-stack data. First,
we use a one-step recursive method to get the optimal attribute combination from a number of attributes. Second, we use a
probabilistic neural network method to train the nonlinear relationship between log curves and seismic attributes and then
use the trained samples to find the natural gamma ray distribution in the Su-14 well block and improve the resolution of seismic
data. Finally, we predict the effective reservoir distribution in the Su-14 well block. 相似文献
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The main problems in seismic attribute technology are the redundancy of data and the uncertainty of attributes, and these problems become much more serious in multi-wave seismic exploration. Data redundancy will increase the burden on interpreters, occupy large computer memory, take much more computing time, conceal the effective information, and especially cause the "curse of dimension". Uncertainty of attributes will reduce the accuracy of rebuilding the relationship between attributes and geological significance. In order to solve these problems, we study methods of principal component analysis (PCA), independent component analysis (ICA) for attribute optimization and support vector machine (SVM) for reservoir prediction. We propose a flow chart of multi-wave seismic attribute process and further apply it to multi-wave seismic reservoir prediction. The processing results of real seismic data demonstrate that reservoir prediction based on combination of PP- and PS-wave attributes, compared with that based on traditional PP-wave attributes, can improve the prediction accuracy. 相似文献
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基于频率域峰值属性的河道砂体定量预测及应用(英文) 总被引：1，自引：1，他引：0
河道砂体是陆相含油气盆地最重要的储集类型之一,其边界识别和厚度定量预测是储层预测的热点难题。本文在总结现有方法技术的基础上,提出一种利用频率域峰值属性进行河道砂体边界识别和厚度定量预测的新方法。对典型河道薄砂体地震反射进行了正演模拟,构造了一种新的地震属性——峰值频率-振幅比,研究表明:峰值频率属性对地层厚度变化敏感,振幅属性对地层岩性变化敏感,两者比值突出河道砂体的边界,同时,借助峰值频率与薄层厚度间存在的定量关系进行薄砂体厚度计算。实际数据应用表明,地震峰值频率属性可以较好的刻画河道的平面展布特征;峰值频率-振幅比属性可以提高对河道砂体边界的识别能力;利用频率域地震属性进行砂体边界识别及厚度定量预测是可行的。 相似文献
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东营凹陷沙三、沙四沉积时期，发育了大量不同时期的砂砾岩体，它们是非常规油气勘探中重要的储层类型。由于砂砾岩体具有纵向厚度变化大、横向展布不均匀、岩相变化快等特点，在地震属性分析与厚度预测时，用单一属性对储层厚度描述具有很大的不确定性。为此，提取了多种地震属性，采用主成分分析法（PCA）进行优化、去除冗余信息。考虑到随机森林（RF）具有预测精度高、对异常值容忍性强、训练速度快且不易过拟合等特点，引入该方法对砂砾岩储层厚度进行预测。针对属性自相似问题，PCA采用了两种方法:①直接对全部属性做降维处理，提取主成分进行预测（PCA-RF1）；②先对相似属性做降维处理，再组合其他属性进行预测（PCA-RF2）。原始RF、PCA-RF1、PCA-RF2方法还与人工神经网络方法（ANN）进行了效果对比，结果表明，基于相似属性降维处理的PCA-RF2方法，具有最佳应用效果。 相似文献
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因为地震数据的三维空间分布优势,地震属性已经被广泛应用于含油气性预测、储层厚度预测、孔隙度预测等。但也存在地震属性之间信息冗余、属性与储层物性参数关系模糊的问题。针对这两个问题,将模糊粗糙理论和机器学习引入到储层参数预测中来。通过模糊粗糙集理论对地震属性进行约简,去除冗余信息,得到最优化的地震属性组合;将约简后的属性作为机器学习的输入,实现从地震属性到储层物性参数的非线性映射。该方法既保留了地震属性中有效信息,又避免了因输入变量过多而导致的网络模型训练困难。实际数据应用表明,属性约简的机器学习预测结果分辨率更高,并与数据吻合更好。 相似文献
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基于流体替换技术的地震AVO属性气藏识别(英文) 总被引：2，自引：1，他引：1
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。 相似文献
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在地震解释与储层预测中,地震属性扮演着重要角色.在砂砾岩储层研究过程中,常规属性效果并不理想,无法满足实际应用的需要.而过零点个数,虽是一种不被人经常使用的地震属性,但我们发现在砂砾岩体油藏预测中应用效果良好.为此,研究过零点个数地震属性的定义与计算方法,讨论子波主频、砂岩厚度、泥岩夹层和地层韵律性等与它的关系,通过模型测试与实际资料应用对其做较全面的诠释.研究表明:(1)对于单一界面地震记录,过零点个数不随子波主频和波长的变化而变化;(2)砂岩厚度与过零点个数呈正相关,厚砂岩的过零点个数要比薄砂岩大;(3)砂体间存在的泥岩夹层,厚度变化对过零点个数影响较小,但它会影响波组的形态;(4)不规则沉积与均匀沉积相比,过零点个数属性值要大,因此,此属性比较适合多期次发育的砂砾岩体或其他薄互层结构的储层预测.总的来说,过零点个数地震属性能够有效地识别砂砾岩体发育区,可为此类储层预测提供了一种新的优势属性. 相似文献
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储层物性参数作为描述储层特性、储层建模和流体模式的重要指标,其准确估算可以为储层预测提供有力参考依据,但传统储层物性参数反演方法无法兼顾反演精度及空间连续性。针对上述问题,本文引入地震属性作为深度学习算法输入,针对地震属性之间存在的信息冗余特征,利用随机森林-递归消除法对地震属性进行约简预处理,最终建立一种基于地震属性约简的储层物性参数预测方法。实际数据测试结果表明,地震属性约简的深度学习储层物性参数预测结果具有良好的精度及横向分辨率,证实本文方法的有效性。 相似文献
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随着油气田开发程度越来越高,勘探难度越来越大,如东部的老油田已经进入开发的后期,如何识别薄层砂体是非常重要的工作之一,解决这些难题这势必需要更先进的技术.地震属性能够很好的反映砂体横向展布特征,但是单一属性无法定量预测砂体厚度,而多属性之间又存在多解性,因此有必要提炼地震属性之间的共同点,将地震属性进行信息融合,形成新的融合属性.针对这一问题,本文提出首先利用高频谐波提高地震数据的分辨率,在此基础上着重研究基于概率核的地震属性融合方法,融合了几种常见的地震属性,并结合波阻抗反演方法,预测了N873区块沙三6-3小层砂体厚度.结果显示该方法能够很好的反映砂体横向展布特征,避免了地震属性多解性问题,为提高砂体预测的精度,提供了新的思路和方法. 相似文献
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Mehdi Rezvandehy H. Aghababaei S.H. Tabatabaee Raissi 《Journal of Applied Geophysics》2011,73(3):187-195
Three dimensional seismic operation of Gorgan Plain was studied around a well, which is situated in North of Iran following the hitting of a thin overpressure gas layer (thickness of 9.6 m), with the purpose of the accurate modeling of geological structures and determining the approximate gas storages. The geological structures of the reservoir were modeled using the seismic attributes (coherence, instantaneous amplitude and spectral decomposition (FFT)). The obtained results clearly demonstrated the shape and volume of the existing structural traps in the studied area. In order to estimate the thickness of gas layer in the 3D seismic volume and determining the gas storage, the thickness changes based on the seismic amplitudes were used because its thickness was less than the critical resolution thickness for this layer. However, due to its low thickness, the lack of indicator peak in seismic sections and strong faults of area, it was difficult to pursue this layer in the seismic volume and map its exact amplitude. Considering this issue, a new method with integrating of seismic attributes was recommended. First, the instantaneous amplitude attribute of the thin reservoir layer reflector in computed synthetic seismogram were fabricated and then the frequency regarding the highest amount (dominant frequency) was chosen by Fourier Transform. Finally, spectral decomposition (FFT) with the resulting frequency was gained over the cross-section of the layer's instantaneous amplitude attribute in the 3D seismic volume choosing a proper time window. In such a situation, an increase of its thickness was seen as its amplitude increase and the minimum gas storage of this reservoir was calculated using the area of the restricted part of high thickness (over 9.6 m). 相似文献
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Prediction of effective porosity from seismic attributes using locally linear model tree algorithm 
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下载免费PDF全文 Ahmad Ashtari Talkhestani 《Geophysical Prospecting》2015,63(3):680-693
In this study, a locally linear model tree algorithm was used to optimize a neuro‐fuzzy model for prediction of effective porosity from seismic attributes in one of Iranian oil fields located southwest of Iran. Valid identification of effective porosity distribution in fractured carbonate reservoirs is extremely essential for reservoir characterization. These high‐accuracy predictions facilitate efficient exploration and management of oil and gas resources. The multi‐attribute stepwise linear regression method was used to select five out of 26 seismic attributes one by one. These attributes introduced into the neuro‐fuzzy model to predict effective porosity. The neuro‐fuzzy model with seven locally linear models resulted in the lowest validation error. Moreover, a blind test was carried out at the location of two wells that were used neither in training nor validation. The results obtained from the validation and blind test of the model confirmed the ability of the proposed algorithm in predicting the effective porosity. In the end, the performance of this neuro‐fuzzy model was compared with two regular neural networks of a multi‐layer perceptron and a radial basis function, and the results show that a locally linear neuro‐fuzzy model trained by a locally linear model tree algorithm resulted in more accurate porosity prediction than standard neural networks, particularly in the case where irregularities increase in the data set. The production data have been also used to verify the reliability of the porosity model. The porosity sections through the two wells demonstrate that the porosity model conforms to the production rate of wells. Comparison of the locally linear neuro‐fuzzy model performance on different wells indicates that there is a distinct discrepancy in the performance of this model compared with the other techniques. This discrepancy in the performance is a function of the correlation between the model inputs and output. In the case where the strength of the relationship between seismic attributes and effective porosity decreases, the neuro‐fuzzy model results in more accurate prediction than regular neural networks, whereas the neuro‐fuzzy model has a close performance to neural networks if there is a strong relationship between seismic attributes and effective porosity. The effective porosity map, presented as the output of the method, shows a high‐porosity area in the centre of zone 2 of the Ilam reservoir. Furthermore, there is an extensive high‐porosity area in zone 4 of Sarvak that extends from the centre to the east of the reservoir. 相似文献
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基于AVO梯度、走时及速度的纵波方位角裂缝检测已有较多研究和应用.由于裂缝及所含流体的综合作用,碳酸岩裂缝性储层的地震响应具有高频衰减、频带低移及方位各向异性的特点,因此应用衰减属性进行裂缝预测具有重要价值.基于小波变换计算的瞬时频域振幅谱及以此为基础提取的衰减属性,由于在时域和频域都有较好的分辨率能力,能够准确地刻画出碳酸岩裂缝性储层的非均质性.结合三维地震纵波数据的分方位角处理和分析技术,可以由方位衰减属性估算裂缝相对密度.对Z区下古生界潜山碳酸岩储层进行的裂缝预测表明,由衰减梯度和85%能量比频率两种属性导出的裂缝发育密度具有很好的相关性,且与实际情况符合. 相似文献
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Hydrocarbon prediction from seismic amplitude and amplitude‐versus‐offset is a daunting task. Amplitude interpretation is ambiguous due to the effects of lithology and pore fluid. In this paper, we propose a new attribute “J” based on a Gassmann–Biot fluid substitution to reduce ambiguity. Constrained by seismic and rock physics, the J attribute has good ability to detect hydrocarbons from seismic data. There are currently many attributes for hydrocarbon prediction. Among the existing attributes, far‐minus‐near times far and fluid factor are commonly used. In this paper, the effectiveness of these two existing attributes was compared with the new attribute. Numerical modelling was used to test the new attribute “J” and to compare “J” with the two existing attributes. The results showed that the J attribute can predict the existence of hydrocarbon in different porosity scenarios with less ambiguity than the other two attributes. Tests conducted with real seismic data demonstrated the effectiveness of the J attribute. The J attribute has performed well in scenarios in which the other two attributes gave inaccurate predictions. The proposed attribute “J” is fast and simple, and it could be used as a first step in hydrocarbon analysis for exploration. 相似文献
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D-S证据理论为融合不确定信息提供了一条很好的思路。本文提出将D-S证据理论用于地震多属性融合的方法,首先在钻孔实测煤层气含量值的指导下优选对煤层气含量值变化敏感的地震属性,然后基于D-S证据理论对优选的地震属性进行融合处理,并将融合结果用于煤层气富集区的预测。实际应用效果表明:预测结果与钻孔实测煤层气含量值基本吻合,本文提出的基于D-S证据理论的地震多属性融合方法用于预测煤层气富集区是可行的。 相似文献
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Formation’s properties can be estimated indirectly using joint analysis of compressional and shear wave velocities. Shear wave data is not usually acquired during well logging, which is most likely for cost saving purposes. Even if shear data is available, the logging programs provide only sparsely sampled one-dimensional measurements: this information is inadequate to estimate reservoir rock properties. Thus, if the shear wave data can be obtained using seismic methods, the results can be used across the field to estimate reservoir properties. The aim of this paper is to use seismic attributes for prediction of shear wave velocity in a field located in southern part of Iran. Independent component analysis (ICA) was used to select the most relevant attributes to shear velocity data. Considering the nonlinear relationship between seismic attributes and shear wave velocity, multi-layer feed forward neural network was used for prediction of shear wave velocity and promising results were presented. 相似文献
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地震属性技术在岩性和构造解释等方面得到了越来越广泛的应用，特别是在煤、油气资源勘探中具有重要的作用.基于淮南矿区谢桥1区13 1煤层地震勘探资料，提取了28种地震属性数据；通过地震属性的分析，优选出平均峰值振幅、振幅的峰态、最大绝对振幅、瞬时频率斜率等4种地震属性作为煤层厚度预测模型基本参数，结合已知钻孔资料，利用多元多项式回归以及BP人工神经网络方法，求出了各属性与煤厚之间的多元多项式回归模型及人工神经网络模型，并对模型进行了误差分析和应用结果对比分析，反映出人工神经网络模型在煤厚预测中具有好的应用效果. 相似文献
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介绍了地震属性分类及地质含义,并以彩16井区为例优选出对储层含油气性敏感的地震属性参数，建立它们与含油气性的关系，利用单属性及多属性聚类分析评价了目的层的储层质量，并进行了有利储层预测，并指出下一步的有利勘探目标. 相似文献