| 基于地震属性约简的深度学习储层物性参数预测:以莺歌海盆地乐东区为例 |
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| 引用本文： | 刘仕友,曲福良,周凡,邓利峰.基于地震属性约简的深度学习储层物性参数预测:以莺歌海盆地乐东区为例[J].CT理论与应用研究,2022,31(5):577-586. |
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| 作者姓名： | 刘仕友 曲福良 周凡 邓利峰 |
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| 作者单位： | 1.中海石油（中国）有限公司海南分公司, 海口570000 |
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| 基金项目： | 中海油有限公司重大科技项目（南海西部油田上产2000万方关键技术研究（CNOOC-KJ 135 ZDXM38 ZJ02ZJ））。 |
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| 摘 要： | 储层物性参数作为描述储层特性、储层建模和流体模式的重要指标，其准确估算可以为储层预测提供有力参考依据，但传统储层物性参数反演方法无法兼顾反演精度及空间连续性。针对上述问题，本文引入地震属性作为深度学习算法输入，针对地震属性之间存在的信息冗余特征，利用随机森林-递归消除法对地震属性进行约简预处理，最终建立一种基于地震属性约简的储层物性参数预测方法。实际数据测试结果表明，地震属性约简的深度学习储层物性参数预测结果具有良好的精度及横向分辨率，证实本文方法的有效性。
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| 关 键 词： | 深度学习 地震属性约简 随机森林算法 储层物性参数 |
| 收稿时间： | 2021-11-23 |
Deep Learning Reservoir Parameter Prediction Based on Seismic Attribute Reduction: Take Ledong Area of Yinggehai Basin as an Example |
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| Institution: | 1.Haikou Branch, CNOOC China Limited, Haikou 570000, China2.School of Geosciences, China University of Petroleum (East China), Qingdao 266580, China |
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| Abstract: | As an important indicator to describe reservoir characteristics, reservoir modeling and fluid model, the accurate estimation of reservoir physical parameters can provide a powerful reference for reservoir prediction, but the traditional inversion method of reservoir physical parameters can not give consideration to inversion accuracy and spatial continuity. To solve the above problems, this paper introduced seismic attributes as input of deep learning algorithm. Aiming at the information redundancy among seismic attributes, random forest-recursive elimination method was used to reduce the seismic attributes, thus a prediction method of reservoir physical property parameters based on seismic attribute reduction was finally established. The actual data test results showed that the prediction results of reservoir physical parameters by deep learning based on seismic attribute reduction presented good accuracy and lateral resolution, which confirmed the effectiveness of the proposed method. |
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