Reservoir characterization and paleo-stratigraphic imaging over Okari Field, Niger Delta, using neural networks
Reservoir characterization and paleo-stratigraphic imaging over Okari Field, Niger Delta, using neural networks
复制标题
使用神经网络对尼日尔三角洲 Okari 油田进行储层表征和古地层成像
DOI：
10.1190/1.3599150
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发表时间：
2011
期刊：
影响因子：
3.3
通讯作者：
M. Olorunniwo
中科院分区：
文献类型：
--
作者：
Muslim B. Aminu;M. Olorunniwo
尼日尔三角洲的储层几何形状和内部结构在短距离内会发生变化，岩性和孔隙度在横向和纵向上会发生快速变化。了解这些变化对于为该盆地的前景设计最佳开发战略至关重要。为了全面了解尼日尔三角洲Okari油田储集相和内部结构的变化，开展了这项研究。传统的地震解释、属性分析和随后的钻井已经在滚动背斜中找到了一堆储层。为了揭示油田的古地层学并充分利用测井特性填充油田，我们使用多层前馈神经网络（MLFN）从地震和测井数据集预测页岩体积和孔隙度。早些时候，已经进行了岩石物理分析，以了解该领域的岩石流体相关联，并协助进一步定量解释和校准神经网络预测.
Reservoir geometry and internal architecture in the Niger Delta can vary over short distances with rapid lateral and vertical changes in lithology and porosity. Understanding such variations is critical to designing an optimum development strategy for prospects in this basin. It was in order to fully understand the variations in reservoir facies and internal architecture over Okari oil field in the Niger Delta that this study was undertaken. Conventional seismic interpretation, attribute analyses, and subsequent drilling had located a stack of reservoirs in a rollover anticline. To unravel the paleo-stratigraphy of the field and fully populate the field with log properties, we used a multilayered feed-forward neural network (MLFN) to predict shale volume and porosity from seismic and well-log data sets. Earlier, rock physics analyses had been undertaken to understand litho-fluid facies associations in the field and assist in further quantitative interpretation and calibration of neural-network predictions...