首页> 外文期刊>Journal of Applied Geophysics >Rock formation characterization for carbon dioxide geosequestration: 3D seismic amplitude and coherency anomalies, and seismic petrophysical facies classification,Wellington and Anson-Bates Fields, Kansas, USA
【24h】

Rock formation characterization for carbon dioxide geosequestration: 3D seismic amplitude and coherency anomalies, and seismic petrophysical facies classification,Wellington and Anson-Bates Fields, Kansas, USA

机译:二氧化碳地质封存的岩层表征:3D地震振幅和相干异常以及地震岩石物相分类,美国堪萨斯州惠灵顿和安森-贝茨油田

获取原文
获取原文并翻译 | 示例
           

摘要

Higher resolution rock formation characterization is of paramount priority, amid growing interest in injecting carbon dioxide, CO_2, into subsurface rock formations of depeleting/depleted hydrocarbon reservoirs or saline aquifers in order to reduce emissions of greenhouse gases. In this paper, we present a case study for a Mississippian carbonate characterization integrating post-stack seismic attributes, well log porosities, and seismic petrophysical facies classification. We evaluated changes in petrophysical lithofacies and reveal structural facies-controls in the study area. Three cross-plot clusters in a plot of well log porosity and acoustic impedance corroborated a Neural Network petrophysical facies classification, which was based on training and validation utilizing three petrophysically-different wells and three volume seismic attributes, extracted from a time window including the wavelet of the reservoir-top reflection. Reworked lithofacies along small-throw faults has been revealed based on comparing coherency and seismic petrophysical facies. The main objective of this study is to put an emphasis on reservoir characterization that is both optimized for and subsequently benefiting from pilot tertiary CO_2 carbon geosequestration in a depleting reservoir and also in the deeper saline aquifer of the Arbuckle Group, south central Kansas. The 3D seismic coherency attribute,we calculated fromawindow embracing the Mississippian top reflection event, indicated anomalous features that can be interpreted as a change in lithofacies or faulting effect. An Artificial Neural Network (ANN) lithofacies modeling has been used to better understand these subtle features, and also provide petrophysical classes, which will benefit flow-simulation modeling and/or time-lapse seismic monitoring feasibility analysis. This paper emphasizes the need of paying greater attention to small-scale features when embarking upon characterization of a reservoir or saline-aquifer for CO_2 based carbon geosequestration.
机译:随着人们越来越关注将二氧化碳CO_2注入到去油气/贫化碳氢化合物储层或盐水层的地下岩层中,以减少温室气体的排放,对高分辨率岩层的表征成为重中之重。在本文中,我们提出了一个密西西比碳酸盐岩特征的案例研究,该特征综合了叠后的地震属性,测井孔隙度和地震岩石物理相分类。我们评估了岩石物理岩相的变化,并揭示了研究区的构造相控制。测井孔隙度和声阻抗图上的三个交叉图簇证实了神经网络岩石物理相分类,该分类基于训练和验证,利用三个岩石物理不同的井和三个体积地震属性,并从包括小波的时间窗口中提取水库顶反射。通过比较相干性和地震岩石物理相,揭示了沿小倾角断层改造的岩相。这项研究的主要目的是强调油藏特征,该特征既可针对枯竭的油藏以及堪萨斯州中南部阿巴克勒集团的深层含水层中的三次三次CO_2碳固碳试验进行优化,并从中受益。我们从包含密西西比顶部反射事件的窗口中计算出的3D地震相干属性表示异常特征,可以将其解释为岩相变化或断层作用。人工神经网络(ANN)岩相建模已被用来更好地理解这些细微特征,并且还提供了岩石物理分类,这将有利于流量模拟建模和/或延时地震监测可行性分析。本文强调在着手进行基于CO_2的碳地质封存的储层或含盐水层的表征时,需要更加关注小尺度特征。

著录项

相似文献

  • 外文文献
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号