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From obc seismic to porosity volume: A pre-stack analysis of a turbidite reservoir, deepwater Campos Basin, Brazil.

机译:从地震到孔隙体积:巴西坎普斯盆地深水浊积岩储层的叠前分析。

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摘要

The Campos Basin is the best known and most productive of the Brazilian coastal basins. Turbidites are, by far, the main oil-bearing reservoirs. Using a four component (4-C) ocean-bottom-cable (OBC) seismic survey I set out to improve the reservoir characterization in a deep-water turbidite field in the Campos Basin. In order to achieve my goal, pre-stack angle gathers were derived and PP and PS inversion were performed. The inversion was used as an input to predict the petrophysical properties of the reservoir. Converting seismic reflection amplitudes into impedance profiles not only maximizes vertical resolution but also minimizes tuning effects.;Mapping the porosity is extremely important in the development of a hydrocarbon reservoirs. Combining seismic attributes derived from the P-P data and porosity logs I use linear multi-regression and neural network geostatistical tools to predict porosity between the seismic attributes and porosity logs at the well locations. After predicting porosity in well locations, those relationships were applied to the seismic attributes to generate a 3-D porosity volume. The predicted porosity volume highlighted the best reservoir facies in the reservoir. The integration of elastic impedance, shear impedance and porosity improved the reservoir characterization.
机译:坎波斯盆地是巴西沿海盆地中最著名,生产力最高的盆地。到目前为止,浊石是主要的含油储层。我使用四分量(4-C)海底电缆(OBC)地震勘测方法着手改善Campos盆地深水浊积场中的储层特征。为了实现我的目标,推导了叠前角度集,并进行了PP和PS反演。反演被用作预测储层岩石物理特性的输入。将地震反射振幅转换为阻抗曲线,不仅可以最大程度地提高垂直分辨率,而且可以最大程度地减小调谐效果。孔隙度的映射在油气藏开发中极为重要。结合从P-P数据和孔隙度测井得到的地震属性,我使用线性多元回归和神经网络地统计工具来预测井位置的地震属性和孔隙度测井之间的孔隙度。在预测完井位置的孔隙度后,将这些关系应用于地震属性以生成3-D孔隙度体积。预测的孔隙度突出了储层中最好的储层相。弹性阻抗,剪切阻抗和孔隙度的综合改善了储层特征。

著录项

  • 作者

    Martins, Luiz M. R.;

  • 作者单位

    Colorado School of Mines.;

  • 授予单位 Colorado School of Mines.;
  • 学科 Geophysics.
  • 学位 M.S.
  • 年度 2013
  • 页码 108 p.
  • 总页数 108
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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