赵宇璇, 黎小伟, 袁春艳, 范久霄. 多属性优选的神经网络技术在鄂尔多斯盆地L区块煤层储层预测中的应用[J]. 海洋地质前沿, 2019, 35(2): 65-72. DOI: 10.16028/j.1009-2722.2019.02009
    引用本文: 赵宇璇, 黎小伟, 袁春艳, 范久霄. 多属性优选的神经网络技术在鄂尔多斯盆地L区块煤层储层预测中的应用[J]. 海洋地质前沿, 2019, 35(2): 65-72. DOI: 10.16028/j.1009-2722.2019.02009
    ZHAO Yuxuan, LI Xiaowei, YUAN Chunyan, FAN Jiuxiao. APPLICATION OF NEURAL NETWORK TECHNOLOGY OPTIMIZED BY MULTIPLE SEISMIC ATTRIBUTES TO PREDICTION OF C81 SANDSTONE RESERVOIRS IN COAL SEAM AREA IN L BLOCK OF ORDOS BASIN[J]. Marine Geology Frontiers, 2019, 35(2): 65-72. DOI: 10.16028/j.1009-2722.2019.02009
    Citation: ZHAO Yuxuan, LI Xiaowei, YUAN Chunyan, FAN Jiuxiao. APPLICATION OF NEURAL NETWORK TECHNOLOGY OPTIMIZED BY MULTIPLE SEISMIC ATTRIBUTES TO PREDICTION OF C81 SANDSTONE RESERVOIRS IN COAL SEAM AREA IN L BLOCK OF ORDOS BASIN[J]. Marine Geology Frontiers, 2019, 35(2): 65-72. DOI: 10.16028/j.1009-2722.2019.02009

    多属性优选的神经网络技术在鄂尔多斯盆地L区块煤层储层预测中的应用

    APPLICATION OF NEURAL NETWORK TECHNOLOGY OPTIMIZED BY MULTIPLE SEISMIC ATTRIBUTES TO PREDICTION OF C81 SANDSTONE RESERVOIRS IN COAL SEAM AREA IN L BLOCK OF ORDOS BASIN

    • 摘要: 研究区煤层较为发育,且煤层厚度差异大,导致目的层属性值存在区域性差异,采用同一种参数提取属性不能有效表征该区地质体特征;同时由于地震属性值是地质体特征(沉积特征,岩性特征、孔隙结构等)的综合反映,单属性参数具有多解性。在地质体分区的基础上,利用多地震属性优化下的神经网络技术对研究区进行分区砂岩储层预测。实践表明, 该技术有效避免了单属性多解性和多属性综合判别精度低的缺点, 提高了利用地震属性预测砂岩储层的精度。

       

      Abstract: Seismic attribute values are usually dependant on some geological parameters, such as sedimentary characteristics, lithology and pore structures. However, as the seismic reflection is a kind of integrated reflection of a geological body, a single attribute parameter may have multiple solutions. On the basis of individual geological body, the Neural network technique optimized with multi-seismic attributes is used by the authors to predict the reservoir in JHER well area. Our results show that this technique may avoid the disadvantages of multiple solution for single attribute, enhance the resolution of multi-attribute discrimination, and thus improve the accuracy and efficiency of seismic reservoir prediction.

       

    /

    返回文章
    返回