共查询到19条相似文献，搜索用时 203 毫秒
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通过叠前反演获得的单参数或组合参数都有一定的流体识别能力,但如何将多种流体识别因子有效融合是目前进行流体识别的一个难题.利用人工参与进行流体性质的综合解释是目前流体识别因子融合的主要途径,但这种方法人为干扰较大,不确定性强.鉴于此,本文提出了一种基于近似支持向量机的流体识别方法.该方法首先以实际工区测井资料为依据,优选出对工区内储层所含流体特征敏感的流体识别因子作为输入参数,然后通过近似支持向量机进行流体性质的判别,实例证明该方法的识别结果客观准确,是一种可靠的流体识别方法. 相似文献
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碳酸盐岩储层的AVO响应要比碎屑岩储层响应微弱,因此,弹性阻抗（EI）即使在确定了最佳入射角的情况下,要判断碳酸盐岩储层中的流体性质,依然十分困难.本文提出了一种多角度扩展弹性阻抗（MEEI）反演进行碳酸盐岩储层流体识别方法.利用已有钻井的测井数据,通过计算目的层段的MEEI,建立相应的含流体模式,进而建立了碳酸盐岩储层流体检测中的有效气水判据.通过不同入射角下EEI随入射角变化的不同趋势,可以有效识别储层中含气或含水情况,判别储层好坏.本文方法的有效性在四川盆地某地区实际资料应用中取得了良好的流体检测效果. 相似文献
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砂岩储层孔隙中的流体识别一直是石油勘探开发过程中重要的环节,传统方法主要依赖于测井数据,但是在测井数据缺失的条件下较难得到准确的流体识别结果.本文提出一种只依靠地震数据的砂岩中流体识别的新方法,并选择地球物理方法可测或可求的地球物理参数σ、ρλ和ρμ作为流体识别因子,然后进行模型实验.首先,设置典型流体状态,用Gassmann方程进行流体替换,将得到的流体识别因子作为支持向量机的训练集数据,并定义支持向量机的分类标签;之后,设置随机流体状态,利用Gassmann方程计算流体因子,将得到的结果作为支持向量机的测试集数据.将训练集、测试集数据集输入支持向量机,进行分类,得出测试集数据的分类结果.模型实验分类结果表明,支持向量机法可以判别砂岩孔隙中流体的主要属性. 相似文献
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致密砂岩流体识别难度大,智能算法能够较好地建立其流体识别模型.相较于单一智能算法,分类委员会机器通过联合多个专家(智能算法)有助于提升智能模型整体性能.而针对分类委员会机器中单个专家性能难以提升的问题,添加门网络构建动态分类委员会机器是一种更有效的模块化学习方式.本研究首先采用门网络将输入数据划分为多个子数据集,然后联合决策树、概率神经网络、贝叶斯分类、BP神经网络、最近邻算法分别训练子数据集得到多个子模型,最后利用组合器最优化子模型组合得到最佳的流体识别模型.针对塔里木盆地库车坳陷大北、克深、博孜地区致密砂岩地层测井数据和测试数据,采用平均影响值法优选敏感测井系列作为输入,构建了动态的测井流体识别模型,其训练、验证准确率分别为96.29%和91.39%.利用此模型以BZ9井为例进行流体类型判别,预测结果与测试结果一致.该方法将无监督与有监督学习相结合,引入门网络提高了数据集利用效率,避免了数据集分布不均衡对模型构建的影响;采用投票机制集成多种专家,建立了子模型与专家的适应关系,流体识别模型预测精度和泛化能力大大提高. 相似文献
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《地球物理学进展》2015,(6)
储层含气时,岩石的弹性力学参数会发生改变,利用岩石的弹性性质可以对流体性质进行识别.引入流体敏感度评价参数,优选出反映储层含气性敏度高的5个弹性参数,并将这5个弹性参数作为流体识别算法的输入参数.分别利用贝叶斯算法、K近邻算法、广义神经网络算法、主成分分析算法、支持向量机算法对不同流体性质进行识别,并将识别结果采用投票从众的原则,构建多算法协同分类方法(BKGPS).采用BKGPS算法对苏里格气田西部盒8、山1致密砂泥岩储层6口井的75个样本进行判别,样本的识别符合率达到93%,BKGPS识别结果与5种单算法相比,能够提高流体识别的准确性与稳定性.对研究区致密砂岩储层实际资料处理,BKGPS算法处理结果与试气结论一致,验证了该方法的可靠性. 相似文献
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雷电物理学的发展和雷电防护新理论与新技术的研究需要对雷云荷电结构进行深入探索.利用地面电场观测数据对雷云荷电模型进行地球物理学反演是一个可行的研究途径.实际雷云荷电结构复杂多变,反演目标函数高度非线性,传统的反演方法往往显得无能为力,利用量子反演方法可尝试解决此问题.在总结分析近年发展比较成熟的量子遗传算法(QGA)、量子退火算法(QA)和量子粒子群算法(QPSO)的基础上,针对Amoruso和Lattarulo提出的带电圆盘雷云荷电模型建立反演模型,分别用三种改进的量子反演算法对理论模型计算结果进行了反演实验,发现QA对此模型的反演准确度最高,而QGA的全局收敛速度最快.通过用QGA对一组实际观测数据分别进行的三层、四层、五层带电圆盘模型的反演,对比分析了不同模型结构对实际反演结果的影响. 相似文献
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杜向东 《CT理论与应用研究》2013,(3):409-420
以地震资料为主体的储层流体识别技术对现阶段油气勘探具有重要意义。本文利用岩石模量参数与品质因子作为储层流体指示因子,从提高流体指示因子估算可靠性的角度出发,分别研究了基于弹性阻抗反演的弹性模量参数直接估算方法与基于小波域QVO的品质因子估算方法。岩石模量参数直接估算方法在充分利用弹性阻抗反演高抗噪性与实用性的同时,有效地避免了间接累计误差的影响。品质因子的估算方法基于零相位子波假设,通过对小波域叠前品质因子的归零处理消除叠加平均效应。通过模型试算验证两种方法的流体判识可行性;并且结合实际资料进一步表明岩石模量与品质因子信息的综合应用可以提高储层流体的检测质量。 相似文献
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碳酸盐岩裂缝性储层测井识别及评价技术综述与展望 总被引：5，自引：0，他引：5
由于碳酸盐岩裂缝发育机制的复杂性,开展碳酸盐岩裂缝性储层的识别与评价一直是测井分析的热点和难点.本文基于大量文献调研,梳理了碳酸盐岩裂缝的定义、成因和分类,系统归纳了碳酸盐岩裂缝性储层的测井响应特征及评价方法,并进行了实例分析.研究表明,常规测井方法对碳酸盐岩裂缝均有不同程度的揭示,基于不同原理的常规测井方法对碳酸岩盐裂缝的响应程度差异显著,利用常规测井方法对碳酸岩盐储层裂缝识别应遵循综合识别原则;碳酸盐岩裂缝成像测井识别方法中,电阻率成像测井、偶极声波测井和核磁共振测井反映裂缝较为直观,但成本高;基于常规测井资料的灰色关联识别方法、神经网络识别方法、小波多尺度识别方法等非线性数学方法,弥补了常规测井和成像测井识别碳酸盐岩裂缝的不足,并且取得较好的应用效果. 相似文献
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J. Hou 《Geophysical Prospecting》2002,50(5):453-462
The evaluation of coalbed methane reservoirs using log data is an important approach in the exploration and development of coalbed methane reservoirs. Most commonly, regression techniques, fuzzy recognition and neural networks have been used to evaluate coalbed methane reservoirs. It is known that a coalbed is an unusual reservoir. There are many difficulties in using regression methods and empirical qualitative recognition to evaluate a coalbed, but fuzzy recognition, such as the fuzzy comprehensive decision method, and neural networks, such as the back-propagation (BP) network, are widely used. However, there are no effective methods for computing weights for the fuzzy comprehensive decision method, and the BP algorithm is a local optimization algorithm, easily trapped in local minima, which significantly affect the results. In this paper, the recognition method for coal formations is developed; the improved fuzzy comprehensive decision method, which uses an optimization approach for computing weighted coefficients, is developed for the qualitative recognition of coalbed methane reservoirs. The homologous neural network, using a homologous learning algorithm, which is a global search optimization, is presented for the quantitative analysis of parameters for coalbed methane reservoirs. The applied procedures for these methods and some problems related to their application are also discussed. Verification of the above methods is made using log data from the coalbed methane testing area in North China. The effectiveness of the methods is demonstrated by the analysis of results for real log data. 相似文献
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常规测井资料识别裂缝性储层流体类型方法研究 总被引：8，自引：2，他引：6
裂缝性储层流体类型识别一直是测井界亟待解决的难题．一方面裂缝性储层具有岩性复杂、储集类型多样、物性变化大和非均质性强等特点,因而其测井解释和流体类型识别要复杂和困难得多．另一方面,复杂岩性油气藏越来越得到人们的重视．本次研究中,通过对流体类型影响因素的分析,认识到不同流体类型与电阻率比值的高低、粘土含量的多少以及孔隙度的大小密切相关．提出了综合考虑上述三项因素的流体类型识别参数FTI．该方法应用于辽河盆地大民屯凹陷变质岩和碳酸盐岩潜山地层流体类型识别,效果良好． 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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地震尺度下碳酸盐岩储层的岩石物理建模方法(英文) 总被引：3，自引：3，他引：0
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。 相似文献
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Gustavo Côrte Jesper Dramsch Hamed Amini Colin MacBeth 《Geophysical Prospecting》2020,68(7):2164-2185
In this work, we tackle the challenge of quantitative estimation of reservoir dynamic property variations during a period of production, directly from four-dimensional seismic data in the amplitude domain. We employ a deep neural network to invert four-dimensional seismic amplitude maps to the simultaneous changes in pressure, water and gas saturations. The method is applied to a real field data case, where, as is common in such applications, the data measured at the wells are insufficient for properly training deep neural networks, thus, the network is trained on synthetic data. Training on synthetic data offers much freedom in designing a training dataset, therefore, it is important to understand the impact of the data distribution on the inversion results. To define the best way to construct a synthetic training dataset, we perform a study on four different approaches to populating the training set making remarks on data sizes, network generality and the impact of physics-based constraints. Using the results of a reservoir simulation model to populate our training datasets, we demonstrate the benefits of restricting training samples to fluid flow consistent combinations in the dynamic reservoir property domain. With this the network learns the physical correlations present in the training set, incorporating this information into the inference process, which allows it to make inferences on properties to which the seismic data are most uncertain. Additionally, we demonstrate the importance of applying regularization techniques such as adding noise to the synthetic data for training and show a possibility of estimating uncertainties in the inversion results by training multiple networks. 相似文献
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致密碳酸盐岩在成岩和后成岩过程中形成了复杂的孔隙结构特征,其速度等地震弹性参数不仅与孔隙度有关,而且还与孔隙结构特征密切相关.为了进一步研究致密碳酸盐岩内部流体相关的速度频散特征,针对致密碳酸盐岩进行实验室的频散测量与频散理论分析尤为重要.本研究选用了一块典型的致密型碳酸盐岩样品,在对样品进行了精细的包括CT扫描与镜下薄片的孔隙结构描述基础上,进行了实验室跨频段(从地震频段-超声频段)的频散测量与频散响应分析.比较实验室跨频段岩石物理频散测量可以获得如下认识:1)较之于典型的"喷射流"机制,改进的"喷射流"模型可以半定量地解释频散测量的结果,这大大提高了对致密碳酸盐岩频散响应的理解与认识;2)改进的"喷射流"模型还不能完全精确地匹配实验室频散测量结果,这说明除了微观尺度下的"喷射流"机制,还存在着其他控制频散与衰减的机制;3)本项工作对研究致密碳酸盐岩储层中不同频段地震波响应以及对储层预测与流体识别提供了理论依据. 相似文献
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低渗、特低渗储层是国内外油田地质研究的重点,而其流体识别问题更是一个焦点问题.本文根据以往的研究成果,再认识了低渗、特低渗储层流体识别的难点,进而梳理和归纳了流体识别的常规技术和新技术.研究表明,利用单一的测井解释方法容易引起多解性,技术风险高;基于地区地质、岩心、录井等资料,结合多种测井曲线,开展流体的综合识别评价是一个重要的技术思路;将现代地质数学方法和信号分析技术引入流体识别中将会产生积极地意义,本文对此进行了展望. 相似文献
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裂缝型储层的描述包括预测裂缝分布特征和识别裂隙充填物.依据等效介质理论计算的纵波速度随裂缝密度的增大而减小.正演地震记录显示,裂缝介质含气时反射振幅最大,且变化程度比含油或含水时大.叠前方位AVO反演所得的各向异性梯度Bani与裂缝密度成正比,可用于描述有效裂缝发育强度.对于不同的裂缝密度,各向异性梯度Bani与各向同性梯度Biso的比值I(1)fluid近似为常数,且对流体敏感.经裂缝纵横比和背景介质拉梅常数修正后,流体因子Ifluid既不随纵横比变化,又不受背景介质的影响,是裂缝型储层敏感的流体识别因子.在塔里木盆地塔北哈拉哈塘地区热瓦普区块碳酸盐岩储层裂缝发育区域,运用该参数在井点处的流体识别效果与钻井结果一致. 相似文献