油藏描述中的地震相分析新技术.ppt

油藏描述中的地震相分析新技术,油藏描述中的地震相分析新技术,绪论地震相分析技术在油藏描述中的作用波形分类地震相分析技术的特点和应用实例-以Stratimagic软件为例地震相分析技术存在的问题和发展趋势,绪论,地震相是个“古老”，宽泛的名词，概念。60年代中期，有人开始使用，70年代初随着地震地层学的兴起，被广泛使用。地震相的定义，多种多样不统一，本人愿意定义为：地震信号特征的一种表征形式，并且这种表征形式所表征的信号特征可以在横向或纵向上划分成单元或分类。地震信号外形，内部结构，振幅，相位，频率，速度等均可以作为地震相。现在一般将振幅，相位等表征地震动力学特征的信号称为属性，而把反射外形等静力学特征的信号定义为地震相的较多。地震相可应用于地震地层学，岩性地震学以及油藏描述中的储层预测等。本次讲座主要集中在地震相的概念，以及工业界已应用的地震相分析方法的原理，实例的介绍上。,油藏描述中的地震相分析新技术,绪论地震相分析技术在油藏描述中的作用波形分类地震相分析技术的特点和应用实例-以Stratimagic软件为例地震相分析技术存在的问题和发展趋势,地震相分析技术在油藏描述中的作用,油藏描述的任务：-储层/油藏分布预测-储层/油藏物性的确定解决油藏描述的地震方法：-属性/地震相分析方法-井约束AI/EI反演方法推荐的工作流程,构造解释,沿层选定目的段,层段内地震相分析,靶区的井约束反演,地震相和反演结果的综合解释,定性到定量的地震相分析,What is Results from the Seismic Facies Analysis Technology?,Comparison with Traditional Methods,Map of Amplitude Standard Deviation(the best Amplitude Map),Seismic Facies superimposed on the Amplitude map,EACH real trace is assigned a color according to whichmodel trace it is most closely correlated.,The Seismic Facies Map,地震方法用于油藏描述的现状,Attributies Analysis/地震相 地震属性分析方法所提取属性种类不断增加（20，50种，更多？）用户选择属性缺少合适的方法对多种属性解释地质意义不明确。Well Calibration and Inversion 地震的井标定和反演外推估算地震信号的横向变化通常是困难的需要先验的初始模型花费和计算吞吐量仍是系统化工业化应用的障碍先验约束往往出现误差,地震相分析技术在油藏描述中的作用,快速进行地震信号特征的分类，研究地震信号的变化规律从地震信号某种/多种特征的变化规律中确定反映地质体沉积，物性等变化的规律，从而直接进行沉积相研究，储层预测和物性预测等。地震相分析可以快速的为井约束AI/EI反演等确定靶区，指导反演结果的解释。新的地震相分析方法可以进一步确定地震微相，地震相的定量化等，从而进行油藏的精细描述。,Well 1,Well 2,Example-Channel Definition(1),Conventional Instantaneous/Average Amplitude Maps,Example-Channel Definition(2),Seismic Facies Map of isolated channel region,using a Neural Network derived classification,Model-12 classes,B,A,Example-Channel Definition(3),Detail of Central ChannelDifferences in production from wells A and B are explainedWell B-central(clean)channelWell A-point bar,地震相分析技术在油藏描述中的作用,快速进行地震信号特征的分类，研究地震信号的变化规律从地震信号某种/多种特征的变化规律中确定反映地质体沉积，物性等变化的规律，从而直接进行沉积相研究，储层预测和物性预测等。地震相分析可以快速的为井约束AI/EI反演等确定靶区，指导反演结果的解释。新的地震相分析方法可以进一步确定地震微相，地震相的定量化等，从而进行油藏的精细描述。,油藏描述中的地震相分析新技术,绪论地震相分析技术在油藏描述中的作用波形分类地震相分析技术的特点和应用实例-以Stratimagic软件为例地震相分析技术存在的问题和发展趋势,波形分类地震相分析技术的特点和实例,Attributies Analysis/地震相 地震属性分析方法所提取属性种类不断增加（20，50种，更多？）用户选择属性缺少合适的方法对多种属性解释地质意义不明确。Well Calibration and Inversion 地震的井标定和反演外推估算地震信号的横向变化通常是困难的需要先验的初始模型花费和计算吞吐量仍是系统化工业化应用的障碍先验约束往往出现误差,Stratimagic-地震地层解释/地震相分析软件,专门用于解释岩性，地层，油藏，地质相对比的新的地震解释技术源于ELF公司获得专利的波形分类技术，由CGG-FLAGSHIP开发为软件产品。2002年Paradigm购并Flagship后，进一步与其它的地震相分析技术结合，如Seisfacies,NexModel,VoxelGeo等，使其更加完整，功能强大。,Stratimagic:a unique solution Stratimagic:独特的解决方案波形分类地震相分析,A process:characterization based on trace shape 一种处理：基于道形状的特征描述Trace shape classification represents the true heterogenity of the seismic signal 道形状分类代表了地震信号的真实的横向异常A technology:self-organizing neural networks 一项技术：自组织的神经网络An industrial shape-recognition process,robust and unaffected by noise or spurious events一个工业化的形状识别处理，它稳定，不受噪音和假同相轴的影响A method:many years of operational success applied.一种方法：成功地应多年to exploration,appraisal and reservoir studiesin clastics and carbonatesfor oil and gas,onshore or offshoreon 5 continents,from sea-bottom to 20.000 ft.可用于勘探评价和油藏研究，碎屑岩和碳酸岩，油或气，陆上和海上。,The Basic Assumption is Changes in any of the physical parameters of the subsurface are always reflected in a change in shape of the seismic trace.For example change in porosity will result in a differently shaped trace.“shape”is quantified in the change of sample value from sample to sample.,What is the Seismic Facies Classification Technology Mentioned Here?,What do you see?,Your brain is a neural network-SHAPE is used to decidehow many different types of vegetable are here.NOT color(how many peppers?)or size(how many tomatoes?).,Are these the same shape?,Now What Do You See?,What is the Seismic Facies Classification Technology Mentioned Here?,How to understand the meaning of seismic data through Facies Identification and Classification using Trace shape?,-XX%amplitude,+/-2ms,Sampling to nearest 4ms sample generates+/-2ms unbiased noise on timeup to 25%biased noise on amplitude,FIXED VERTICAL SAMPLING,Reduces sampling noise Takes full advantage of propagation beyond seismic sample,TRACE RECONSTRUCTION,Trace Reconstruction:a critical step.,WHAT ARE BASIC NEURAL NETWORKS?,Signal Flow:Input Output,Synapse,INPUT SEISMICINTERVAL,OUTPUT TRACES,Dendrites,Cell Body,Synapses,Axon,Looking for seismic shape changes,Neural Network,Clustering analysis,A Neural Network looks for a suite of traces that describe the progressive changes in the seismic shape.,Looking for seismic shape changes,Neural Networkordered color changes,Clustering analysisabrupt color changes,What Do We Classify?,Whole cube?Significantly exceeds actual volume of interest(reservoir),good for early exploratory work onlyAttribute maps?Demands prior knowledge,can be used to refine insight,but not to define itProblem:Which maps to use as input?Problem:Some information could be bypassedTrace shape in interval?Focused on geological volume of interestSeismic signal shape includes all attributes,Comparison of Benefits and Drawbacks,What is the Seismic Facies Classification Technology Mentioned Here?,How to understand the meaning of seismic data through Facies Identification and Classification using Trace shape?,工作流程(work flow)I.Learning from the data,and only the data从地震数据中学习，且仅仅从地震数据,The model traces 模型道These synthetic traces are constructed by the neural network process,using a learning set extracted from the seismic interval.No well data is used at this stage.The user has no influence on the selection of data,and there are no weighting criteria.The result is 100%repeatable.这些合成道是用从地震层段中提取出来的由神经网络处理建造的，这一阶段不需要井数据。用户在数据选择方面没有影响，没有加权标准，结果.100%可重复。,INPUT SEISMICINTERVAL,OUTPUT TRACES,Synapses,Dendrites,Cell Body,Axon,What are Basic Neural Networks?,Signal Flow:Input Output Synapse,The Process,The Neural Network trains itself on the actual trace shapes within a 3D seismic interval,and constructs synthetic seismic traces that represent the signal diversity over the entire defined volume,Traces are refined by an iterative processuntil the best correlation to the real datais obtained,The Seismic Facies Map,EACH real trace is assigned a color according to whichmodel trace it most closely correlates to,NEURAL NETWORK PARAMETERS,Number of model traces(number of colours in the output facies map),Number of iterationsRate of learning(epsilon),Continuity(sigma),Reference surfaces,interval thickness,sub-sampling parameter,OUTPUT,INPUT,PROCESSING,Classification Maps:Class Range 2 to 100,3 Classes,7 Classes,15 Classes,Increasing the number of classes results in greater detail,Small number of classes identifies first order trace variability,Unlike clustering,Neural Networks do not require preconceived ideas about the number of classes,Number of Iterations:Range 1 to 100,1 Iteration,20 Iterations,50 Iterations,100 Iterations,CLASSIFICATION MAPS,The seismic facies map 地震相图,The map 地震相图Each trace has been assigned the number(and color)of the model trace to which it has the best correlation.每一道赋给它与模型道最相关的号码和颜色。By observing the distribution of color on this map,we can assess the distribution of seismic shapes throughout the interpreted area.通过观察图上颜色的分布，我们可以评定解释区域的地震形状的分布。反映了岩性，地层，地质相的变化。,Projecting facies information on seismic将相的信息投影到地震剖面上,The classification result can be projected directly above the interval on which the process was applied,allowing a one-to-one visualization of the actual data traces and their corresponding assignement to one of the classes.分类结果可以直接投影到处理过的层段的上，允许一对一的实际数据道及其中一个相应的赋值分类的可视化，为地震相的变化确定其具体反射特征。利用专门的解释工具（Reflector Termination&Envelops)等，逐线解释出地震相变化的位置和形状，如上超，下超，不整合等。,利用Termination和Envelope解释,利用Termination 和Envelope 进行解释,Where do we go from here?,Fitting the facies map to well informationThe relevance of the facies map(s)relative to a geological setting can be assessed by fitting in well information.Interval scopeThe process is obviously sensitive to data that is included(or excluded)from the volume of interest.While intervals should be larger than the strict time-thickness of interest(to catch e.g.tuning effects),it is interesting to try different thicknesses.Area of interestOnce a general-purpose map has indicated some major features,the process can be focused on the zones of interest,to obtain a sharper,more detailed picture.,II.将地震相结果与井信息匹配进一步细分地震相 Fitting well information:与井信息匹配,Seismic signal at well position:井位置的地震信号For each well,select either a synthetic seismogram from a list,or the actual seismic trace at the penetration of the interval.Correlation to all models is computed,and a color is assigned according to best correlation.对于每一口井，选择或者合成记录，或者层段位置的地震道，与所有的模型道计算相关，按照最好的相关赋给颜色。,Fitting Well Information:Comparing Seismic Response,Real trace at well location is compared with the models,Which is the best model?Where else can we seethis model type?,Substituting traces in the model table:替换模型道,Stratimagic allows the user to substitute one of the model traces with the trace currently in the selector window,be it a seismic trace from the dataset,or a synthetic seismogram computed from the well data.Stratimagic 允许用户用现有选择窗口的道替换一个模型道，可以是来自数据体的地震道，或者是由井数据计算的合成记录。,Recalculating on an area of interest 重新计算关心的区域,To reveal more detail in the channel,we could increase the number of classes.However,it is more efficient to process a new interval restricted to the prospective area.为了揭示河道内更细的细节，可以增加分类的数目，而更有效的方法是在限定的区域内处理新的层段。,III.Quantifing Seismic Facies with Petrophysical parameters地震相的定量化-岩石物性参数模拟,NexModel,Piloted seismic facies analysis using NexModelTM and StratimagicTM,NexModelTM Basic Workflow,Load log data,Create layered impedance modelfor well,Load seismic at well location,Create wellsynthetic,Tie well syntheticto seismic&optimise,Basic/Advancedmodelling,Calibrate/quantifyseismic facies,Export newfacies groups,SUPERVISEDCLASSIFICATION,UNSUPERVISEDCLASSIFICATION,reducing risk&adding value to subsurfaceevaluation,A powerful starting point,The NexModel synthetic is correlated to.The nearby seismic tracesThe Stratimagic NNT trace models,DT,RHOB,GR,Synthetic Seismogram,Seismic tracesat borehole,with horizons,Synthetic trace,Stratimagicbest fitmodel Trace,Acoustic impedencecolumn,Nexmodel Nexmodel&Stratimagic,Modeledtrace A,Modeledtrace B,Guided classification in Stratimagic,through model traces substitution with Nexmodel synthetic traces.Insertion of:the well A synthetic trace the pseudo-well synthetic traces,Nexmodel Nexmodel&Stratimagic,Nexmodel&Stratimagic,Well A,Facies A,Well B,Modeledtrace A,Modeledtrace B,Other main functions,Conventional Attributes analysis常规的属性分析工具3D propagation3D 自动追踪 VoxelGeo3D可视化StratiQC成图 Conventional Interpretation tools常规的解释工具,Add-on Value and Project database integration 高附加值与其它数据库的高度集成,Preserving your investment保护你的投资Your current OpenWorks or GeoFrame project databases are accessed你现有的数据库是OpenWorks还是GeoFrame?Stratimagic 都可以访问,包括地震数据体，层位和井数据。Your users continue to use their familiar structural interpretation tools你的用户继续使用他们熟悉的构造解释工具Put to work immediately立即投入工作Without duplication,access large-volume seismic datasets over the network.不需要备份，可以通过网络访问大的地震数据体。,沿层或层间提取多种属性，作为岩性，地层，油藏解释的辅助手段,沿层或层间提取14大类30多种属性，得到属性平面图，作为描述岩性变化等辅助特性。利用叠合图（Mix Map)手段，将所有属性结果，地震相图等任意叠合在一起，利用方便的色彩管理工具，突出它们的共同特征，定性的综合对比，为解释岩性，地层，油藏等提供更多的依据。,Dip map,final channel base,Zoomed area,Channel thickness attributes,This attribute highlights the features of interest that have been seen on other maps.,Average amplitude of peakevents in interval,Facies map(channel area alone),Running the facies classification process on the restricted area of the channels yields a more representative characterization,Seismic facies map 12 classes,MixMap-Facies map-Avg.Ampl.Peaks,Geological model,prospectivity,Prospective features were mapped as envelopes(3D delineation of volume)Channel leveePaleo-channelPoint bar,Enlargement on attribute map of average amplitudeof interval peaks,Random profile,Time Slice 1660 ms,Stratimagic 2.0New Features,Multi-Attribute Seismic Facies Classification using NNT,Multi-volume trace shape classification on constant and non-constant intervalsMulti-attribute map classification,Integration with VoxelGeo,Viewing and manipulation of Stratimagic data in VoxelGeo with no workflow disruptionsSeismic volumesSeismic facies and attribute mapsWells(boreholes,tops,logs),SeisFaciesMulti-attribute seismic analysis,Introducing a new software solution jointly developed by ENI Agip Division and Flagship Geo,多属性全数据体分类,SeisFacies Benefits,Facies distribution(and its associated geological meaning)can be applied for:Preliminary screening of seismic data in exploration activityRanking of prospectsConditioning of geostatistical reservoir modellingReservoir characterizationGeohazard risk assessment,CLASSIFIED SEISMIC TRACES OR SAMPLES CAN BE DIRECTLY CALIBRATED FOR QUANTITATIVE DEFINITION OF RESERVOIR PROPERTIES,SeisFacies,SeisFacies incorporates technologies and methods developed by ENI AGIP DIVISION,Classification,Calibration,Fusion,3D SEISMIC TRACE CLASSIFICATION MULTIATTRIBUTE MAPS CLASSIFICATION MULTIATTRIBUTE BLOCK CLASSIFICATION,SeisFacies Classification Processes,ClassificationNNT&Hierarchical,BLOCKS,MAPS,TRACES,Input Data:Multiple 3D seismic volumesVariable or constant time interval,Input Data:Interval attribute mapsHorizon mapsClassification maps,Input Data:Multiple 3D seismic volumesVariable or constant time interval,PCA(Recommended),Output Data:3D Seismic Facies Volume,ClassificationHierarchical,Output Data:Attribute Facies Map,ClassificationHierarchical,PCA(Optional),Output Data:Seismic Facies Map,Zonation(Optional),PCA(Optional),Amplitude,PCA 1,PCA 2,Coherency,Impedance,PCA 2,PCA 1,PCA 2,PCA Components,Amplitude,Coherency,Impedance,Input Seismic Volumes,PCA 1,SeisFacies PCA,APPLICATION OF REGRESSION FUNCTION(S),CONTROL VARIABLE,DEFINITION OFREGIONS,OUTPUT:CALIBRATED VOLUME,SeisFacies Multi-Attribute Calibration,Classification,SEMBLANCEVOLUME,SEMBLANCE MAP,IMPEDANCE MAP,IMPEDANCE VOLUME,MIIXATTR RESULTS“MIXED VOLUME”,“MIXED MAP“,RESERVOIRCHARACTERIZATION,The SeisFacies“Fusion”Approach,Turbidite System,Base of Turbidite,Horizon Slice:Facies Block,Horizon Slice:Fusion Semblance/Impedance,Semblance,Impedance,SeisFacies FusionExample,SeisFacies Conclusions,Integrated component and extension to Stratimagic;shares Stratimagics user interface and infrastructureRobust solution for multi-attribute classification and calibration of seismic data,incorporating technologies and methods developed by ENI AGIPEnables effortless work on multiple versions of a seismic survey,or a set of attributes computed over timeEnables a detailed description of the reservoir,resulting in better informed business decisions based on more accurate prediction of reservesImproves reservoir characterization within field development projects,A New Methodology Based on Seismic Facies Analysis and Litho-Seismic ModelingThe Elkhorn Slough Field Pilot Project Solano County California,By Manuel Poupon(Flagship Geosciences,today Paradigm)and Kostia Azbel(CGG-Geoscience)Offshore,March 1999,The Elkhorn Slough Field Pilot Project Solano County