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首页> 外文学位 >Three-dimensional seismic interpretation and synthetic modeling of the Atoka and Morrow formations, in the Buffalo Valley Field (Delaware Basin, New Mexico, Chaves County) for reservoir characterization using neural networks.
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Three-dimensional seismic interpretation and synthetic modeling of the Atoka and Morrow formations, in the Buffalo Valley Field (Delaware Basin, New Mexico, Chaves County) for reservoir characterization using neural networks.

机译：布法罗河谷油田（特拉华州盆地，新墨西哥州，查韦斯县）中Atoka和Morrow地层的三维地震解释和综合建模，利用神经网络进行储层表征。

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摘要

While the advantages of Artificial Neural Networks (ANN) for reservoir characterization are widely known, this project expands such benefits by providing a method by which an ANN can be trained prior to its application to real data. First, a geological and 3D seismic interpretation of the lower Pennsylvanian Atoka-Morrow sequence in the Buffalo Valley Field, New Mexico was executed. Then, to design the ANN and to test its predictions, I generated well-logs and synthetic seismic models for seismic attributes extraction. In order to bridge the vertical resolution gap between well-logs (high resolution) and seismic data (low resolution), VSP data was added as an intermediate step to train the ANN. This results in more effective predictions of density and sonic velocity values of the interval of interest.; A synthetic model based on well-log correlation, seismic interpretation, and regional information provided the data set for the ANN training. The synthetic modeling also guided the 3D seismic interpretation of the Atoka and Morrow Formations. (Abstract shortened by UMI.)
机译：虽然人工神经网络（ANN）用于储层表征的优势广为人知，但该项目通过提供一种在将ANN应用于实际数据之前可以对其进行训练的方法，扩大了此类益处。首先，对新墨西哥州布法罗山谷地区的宾夕法尼亚州下阿托卡-莫罗序列进行了地质和3D地震解释。然后，为了设计ANN并测试其预测，我生成了测井曲线和用于地震属性提取的综合地震模型。为了弥合测井曲线（高分辨率）和地震数据（低分辨率）之间的垂直分辨率差距，添加了VSP数据作为训练ANN的中间步骤。这样可以更有效地预测感兴趣区间的密度和声速值。基于测井相关性，地震解释和区域信息的综合模型为ANN训练提供了数据集。综合建模还指导了Atoka和Morrow地层的3D地震解释。 （摘要由UMI缩短。）

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