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首页> 外文期刊>Journal of geophysics and engineering >Improved modeling of channel prediction based on gray relational analysis and a support vector machine: a case study on the X pilot area in the Daqing oilfield in China
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Improved modeling of channel prediction based on gray relational analysis and a support vector machine: a case study on the X pilot area in the Daqing oilfield in China

机译：基于灰色关系分析和支持向量机的信道预测建模 - 以中国大庆油田X试验区为例

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摘要

Considering the complex reservoir conditions and rapid changes in lithological facies, it is difficult to predict the channel distributions in the Heidimiao oil layer in the X pilot area of the Daqing oilfield. To address this problem, a model for fluvial reservoir prediction under complex geological conditions is established by combining gray relational analysis (GRA) and a support vector machine (SVM). Attribute selection is firstly processed based on 2D forward modeling. A predictive model of the main channel combining GRA and SVM methods is then built using the selected attributes as inputs. The predictive pay thickness is our proposed model is well validated with the realistic pay thickness data interpreted from 18 wells, and all the relative errors are within 10%. Channel predictions from our proposed models also confirmed the accuracy based on historical oil production.
机译：考虑到复杂的储层条件和岩性相的快速变化，难以预测大庆油田X试验区的海德米亚油层中的通道分布。 为了解决这个问题，通过组合灰色关系分析（GRA）和支持向量机（SVM）来建立复杂地质条件下的河流储层预测模型。 首先基于2D转发建模处理属性选择。 然后使用所选属性作为输入，构建组合GRA和SVM方法的主通道的预测模型。 预测薪酬厚度是我们提出的模型，良好地验证了从18个井中解释的现实付费厚度数据，所有相对误差都在10％以内。 我们拟议模型的渠道预测还基于历史石油生产确认了准确性。

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