针对塔里木盆地库车坳陷克深地区地震成像存在的静校正处理难度大、原始资料品质低、速度建模及叠前深度偏移难度大3大难题,研究盐下高陡构造成像技术。基于误差反向传播神经网络和最小平方QR分解双尺度层析反演方法获取复杂近地表高精度速度模型,解决盐下高陡构造地震成像静校正问题;在应用高精度静校正和均方根速度的基础上,采用十字排列锥体滤波和球面扩散补偿技术提高地震资料信噪比、恢复深层有效反射信号,解决盐下高陡构造原始地震数据品质低问题;在地质、测井、钻井等多信息的约束下,基于实体模型速度更新和网格层析速度反演的双尺度速度建模技术获取复杂地下构造的高精度速度模型,并应用真地表叠前深度偏移技术提高剧烈起伏地表条件下的地震成像效果,解决盐下高陡构造速度建模及叠前深度偏移问题。通过上述技术获得盐下高陡构造高质量地震成像成果,地震成像预测结果与实钻井吻合好,并成功部署3口超深井。图15表1参16
The seismic imaging has three difficulties in the Keshen area of the Kuqa depression in Tarim Basin: difficult static correction, poor original data, difficult velocity modeling and pre-stack depth migration. A dual-scale tomography inversion approach based on BP neural network and LSQR was developed to obtain the accurate near-surface velocity of the complex near-surface structure, to address the static correction of subsalt high steep structure imaging. On the basis of applying high-precision static correction and root-mean-square (RMS) velocity to the seismic data, three dimensional cone filtering and spherical spreading amplitude compensation were used to enhance the signal to noise ratio and restore the deep effective signals to cope with the poor quality of original seismic data. Under the constraints of geologic, well logging and drilling data, the dual-scale velocity modeling technology based on model-based velocity updating and grid-based tomography was adopted to obtain the precise velocity model of the complex substructure, and then the pre-stack depth migration was taken to improve the imaging effect of structure with complex surface conditions, to solve the problem of subsalt high steep structure velocity modeling and pre-stack depth migration. By applying these three techniques, the high-quality imaging achievements of subsalt high steep structure were obtained. The results of seismic imaging prediction are in good agreement with drilling results and three ultra-deep wells have been drilled successfully.
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