齐颖, 范久霄, 黎小伟, 杨靖. 振幅属性在M气田盒1段储层预测中的应用[J]. 海洋地质前沿, 2017, 33(6): 65-70. DOI: 10.16028/j.1009-2722.2017.06010
    引用本文: 齐颖, 范久霄, 黎小伟, 杨靖. 振幅属性在M气田盒1段储层预测中的应用[J]. 海洋地质前沿, 2017, 33(6): 65-70. DOI: 10.16028/j.1009-2722.2017.06010
    QI Ying, FAN Jiuxiao, LI Xiaowei, YANG Jing. APPLICATION OF AMPLITUDE ATTRIBUTES IN RESERVOIR PREDICTION FOR THE P1X1 SECTION OF M GAS FIELD[J]. Marine Geology Frontiers, 2017, 33(6): 65-70. DOI: 10.16028/j.1009-2722.2017.06010
    Citation: QI Ying, FAN Jiuxiao, LI Xiaowei, YANG Jing. APPLICATION OF AMPLITUDE ATTRIBUTES IN RESERVOIR PREDICTION FOR THE P1X1 SECTION OF M GAS FIELD[J]. Marine Geology Frontiers, 2017, 33(6): 65-70. DOI: 10.16028/j.1009-2722.2017.06010

    振幅属性在M气田盒1段储层预测中的应用

    APPLICATION OF AMPLITUDE ATTRIBUTES IN RESERVOIR PREDICTION FOR THE P1X1 SECTION OF M GAS FIELD

    • 摘要: M气田的主力储层为二叠统下石盒子组盒1段河道砂体储层,属于典型的低孔、低渗岩性油气藏,地震预测难度大。针对这一难题,从储层和围岩的地球物理特征出发,运用地震原理方法,经过正演模型验证、对比,总结出本区盒1段地震反射特征和提取叠后地震数据的振幅属性,建立储层预测与识别的依据。经过统计本区40口实际钻井资料,振幅属性与实际钻井的符合率达到75%以上,表明该方法对本区盒1段储层含气性有很好的预测效果,是该层段储层预测的有效的实用方法之一。

       

      Abstract: The P1x1 (the He-1 Section of the Xiashihezi Formation of the Permian) channel sand body, the main reservoir of the M gas field, is the most important exploration target in this region. It is a typical lithologic reservoir with low porosity and permeability and thus hard to make seismic prediction. In this paper, we presented a practical way for reservoir prediction. Upon the principle of seismics and using of method of statistics and data analysis, actual drilling data, in addition to the geophysical characteristics of actual logging data are collected and processed by the seismic forward modeling. As the results, the seismic section features of P1x1 are summarized, and then the amplitude attributes of the post-stack seismic data, which can be used as a criterion for distribution prediction, are extracted from these features. According to the actual drilling data of 40 wells in this area, the coincidence rate of amplitude attributes and actual drilling data is as high as 75%, which shows a good effect of prediction. The process to draw amplitude attributes from seismic post-stack data is rather simple. It requires relatively small amount of data and the workload is not heavy. It is easy to carry out and only requires limited hardware support.

       

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