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首页> 外文会议>SPE Asia Pacific improved oil recovery conference (APIORC 01) >Reservoir Characterization - Integrated Interpretation of Borehole Data and Surface Seismic Data
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Reservoir Characterization - Integrated Interpretation of Borehole Data and Surface Seismic Data

机译：储层表征-井眼数据和地表地震数据的综合解释

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Precise reservoir characterization is of primary importance tornreservoir evaluation and optimization. Effective integration ofrnborehole and surface seismic data is the key to achieve thisrnobjective. However, such an integration is well known to be arncomplex task due to various difficulties, e.g. differentrnresolutions between well data and seismic.rnIn this paper, we show an integrated workflow in whichrnborehole data and surface seismic data are integrated forrnreservoir characterization. The workflow consists of threernsteps as follow:rn1. Formation evaluation. Accurate petrophysical evaluationrnof well logs with other borehole data is carried out tornbuild a petrophysical model that describes formationrncomposition, lithology and fluid type.rn2. Fluid substitution and AVO modeling. This establishesrnthe link between the seismic responses and petrophysicalrnparameters. Borehole seismic data provide the calibrationrnfor the sonic data, and also the input for the anisotropicrnand inelastic effects. Fluid substitution investigates therneffects of pore fluids on the propagation speeds of waves.rnAVO modeling generates offset dependent seismicrnresponses for the models of interest with knownrnpetrophysical parameters.rn3. Seismic classification. Knowledge extrapolation isrnperformed to populate the seismic volumes or sectionsrnwith the calibrated AVO response obtained at the wellrnlocation. A geostatistical and neural network analysisrnestablishes the relationship between the seismic attributesrnand formation properties (lithofacies, porosity,rnpermeability, pore fluids, etc) at the well location. Thernquantified knowledge between formation parameters andrnseismic response at the well location is exported to thernrest of the seismic data (2D sections and/or 3D seismicrnvolumes).rnThis workflow will reveal if there is enough acousticrndifference between different lithofacies for them to berndistinguished from seismic data. When there is sufficientrnacoustic difference between different lithofacies, therndistribution and variation of these lithofacies can be trackedrnusing the seismic data.rnIt can provide direct hydrocarbon indicators for thernoptimization of well locations. When the acoustic impedancerndifference between different fluids in the formation is largernenough to be captured by AVO analysis, direct hydrocarbonrndetection is possible and the well locations can be optimized.rnThis integrated approach adds tremendous value to thernexisting data and shows what is needed for future datarnacquisition.
机译：精确的储层表征对于储层评估和优化至关重要。有效整合井眼和地表地震数据是实现这一目标的关键。然而，由于各种困难，例如，由于这种困难，众所周知这种集成是复杂的任务。在本文中，我们展示了一个集成的工作流程，其中井眼数据和地表地震数据被集成用于储层表征。工作流程包括以下三个步骤：1。编队评估。进行了准确的岩石物理评价以及其他井眼数据，以建立描述岩石的组成，岩性和流体类型的岩石物理模型。流体替代和AVO建模。这在地震响应和岩石物理参数之间建立了联系。钻孔地震数据为声波数据提供了校准，也为各向异性和非弹性效应提供了输入。流体替代研究了孔隙流体对波传播速度的影响。rnAVO建模为具有已知岩石物理参数的感兴趣模型生成偏移相关的地震响应。地震分类。进行知识外推，以利用在测井处获得的校准后的AVO响应来填充地震体或地震剖面。地统计学和神经网络分析建立了井位地震属性与地层性质（岩相，孔隙度，渗透率，孔隙流体等）之间的关系。井眼位置的地层参数和地震响应之间的量化知识将导出到地震数据的其余部分（2D剖面和/或3D地震体积）。该工作流程将揭示不同岩相之间是否存在足够的声波差异，以使其与地震数据区分开。当不同岩相之间的声波差异足够大时，可以利用地震数据追踪这些岩相的分布和变化。它可以为油气井位置的优化提供直接的碳氢化合物指示剂。当地层中不同流体之间的声阻抗差足够大以至于可以通过AVO分析捕获时，就可以直接进行烃探测并优化井位。这种综合方法为现有数据增加了巨大价值，并显示了未来数据采集所需要的。

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