Quantifying and predicting naturally fractured reservoir behavior with continuous fracture models
Quantifying and predicting naturally fractured reservoir behavior with continuous fracture models
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DOI：
10.1306/07130909016
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发表时间：
2009-11
期刊：
影响因子：
3.5
通讯作者：
C. Jenkins;A. Ouenes;A. Zellou;Jeff Wingard
中科院分区：
文献类型：
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作者：
C. Jenkins;A. Ouenes;A. Zellou;Jeff Wingard
本文介绍了连续裂缝建模 (CFM) 中使用的工作流程及其在多个项目中的成功应用。我们的 CFM 工作流程包括四个基本步骤：（1）解释关键地震层位并生成叠前和叠后地震属性； (2) 使用这些属性以及测井和岩心数据来建立岩性、孔隙度、含水饱和度等地震约束的地质细胞模型； (3) 将导出的地质细胞模型与叠前和叠后地震属性以及附加地质力学模型相结合，导出高分辨率三维（3-D）裂缝模型； (4) 通过测试动态储层模拟器中的 3-D 裂缝模型与油井动态的匹配能力来验证它们。我们的 CFM 工作流程使用神经网络方法来集成所有可用的静态和动态数据。这使得模型能够更好地识别裂缝区域并量化其对油井和储层流动行为的影响。该技术已成功应用于众多砂岩和碳酸盐岩储层，以了解储层行为并确定在何处钻探额外的井。三个现场案例研究用于说明 CFM 方法的功能。
This article describes the workflow used in continuous fracture modeling (CFM) and its successful application to several projects. Our CFM workflow consists of four basic steps: (1) interpreting key seismic horizons and generating prestack and poststack seismic attributes; (2) using these attributes along with log and core data to build seismically constrained geocellular models of lithology, porosity, water saturation, etc.; (3) combining the derived geocellular models with prestack and poststack seismic attributes and additional geomechanical models to derive high-resolution three-dimensional (3-D) fracture models; and (4) validating the 3-D fracture models in a dynamic reservoir simulator by testing their ability to match well performance. Our CFM workflow uses a neural network approach to integrate all of the available static and dynamic data. This results in a model that is better able to identify fractured areas and quantify their impact on well and reservoir flow behavior. This technique has been successfully applied in numerous sandstone and carbonate reservoirs to both understand reservoir behavior and determine where to drill additional wells. Three field case studies are used to illustrate the capabilities of the CFM approach.