Generating Seismic Horizon Using Multiple Seismic Attributes

Today’s 3-D seismic surveys usually contain hundreds of inline and crossline vertical seismic slices. Seismic interpreters usually need to spend weeks or even months for manually picking horizons on vertical seismic slices. Researchers have developed algorithms to accelerate horizon picking and most algorithms employ the seismic reflector’s dip as the input. However, the computed seismic reflector’s dip is usually inaccurate near and across the discontinuities in the seismic images. Note that the time samples which belong to the same horizon should have approximate similar seismic instantaneous phase values. We propose to automatically track the seismic horizon simultaneously considering the seismic reflector’s dip and instantaneous phase attributes. Our algorithm aims to achieve three objectives: 1) minimizing the difference between the dip computed using tracked horizon and seismic dip attribute, 2) minimizing the difference among instantaneous phase value of the time samples on the tracked horizon, and 3) the tracked horizon exactly passes through user-defined control points (seeds). A constrained conjugate least-square algorithm is employed to solve our optimization problem. The applications show that the tracked horizon which only uses dip attribute would “jump” from one seismic event to another seismic event near the unconformity zone. However, the horizon tracked using the proposed method strictly follows the same seismic event over the whole seismic survey.

[1]  Janis Keuper,et al.  Extracting horizon surfaces from 3D seismic data using deep learning , 2020 .

[2]  Hao Wu,et al.  Semiautomated seismic horizon interpretation using the encoder-decoder convolutional neural network , 2019, GEOPHYSICS.

[3]  Bo Zhang,et al.  Accurate seismic dip and azimuth estimation using semblance dip guided structure tensor analysis , 2019, GEOPHYSICS.

[4]  Sanyi Yuan,et al.  Geosteering Phase Attributes: A New Detector for the Discontinuities of Seismic Images , 2019, IEEE Geoscience and Remote Sensing Letters.

[5]  Bo Zhang,et al.  Automatic horizon picking using multiple seismic attributes , 2018, SEG Technical Program Expanded Abstracts 2018.

[6]  Xinming Wu,et al.  Directional structure tensors in estimating seismic structural and stratigraphic orientations , 2017 .

[7]  D. Hale,et al.  Horizon volumes with interpreted constraints , 2014 .

[8]  A. Barnes A tutorial on complex seismic trace analysis , 2007 .

[9]  J. Claerbout,et al.  Flattening without picking , 2006 .

[10]  Franz Schreier,et al.  Iteratively regularized Gauss–Newton method for bound-constraint problems in atmospheric remote sensing , 2003 .

[11]  H. Zeng,et al.  Stratal slicing, Part I: Realistic 3-D seismic model , 1998 .

[12]  Xiaokai Wang,et al.  Robust Seismic Volumetric Dip Estimation Combining Structure Tensor and Multiwindow Technology , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Trygve Randen,et al.  Automated Geometry Extraction From 3D Seismic Data , 2003 .