Technical Content

Sub-basalt seismic imaging is very challenging due to large impedance contrasts at sediment-basalt interfaces. The impedance of basalt usually gives a strong reflection coefficient at the top of basalt, and thus generates strong multiples. Offshore western India, this issue is compounded by short-period seabed ...

The attenuation of surface related multiples is typically one of the most challenging steps in the processing of shallow water marine projects. Least-squares wave-equation deconvolution imaging is a powerful tool to address this challenge, but images derived from a deep target level may produce ...

The Taranaki Basin is one of New Zealand’s largest basins. Initially forming as a Cretaceous rift basin, it has over 400 exploration and production wells. Early basin history is characterized by extensional fault blocks, and as basin evolution continued, thrusting and inversion associated with ...

The desire for ultra-high density (UHD) seismic surveys is now becoming more achievable for future exploration and field development with the increasing availability of versatile nodal land systems. Acquisition geometry design using a higher density of sources as well as receivers considerably reduces the ...

A high-resolution seismic image is of great importance to exploration and production in many ways, such as bypassing drilling hazards and identifying compartmentalized reservoirs. To achieve seismic resolution as high as possible, the conventional seismic imaging process takes more of a linear approach to ...

4D time-lapse seismic imaging is typically performed using the same velocity model to migrate the baseline and monitor data. However, in complex cases where one producing reservoir sits above another, significant changes in the properties of the overlying reservoir can result in a 4D ...

Convolutional based deep neural networks can be used in addition to existing workflows, to improve turnaround or as a ‘guide’ for further processing. Whilst a lot of effort has been made to try to improve the DNN architecture for processing tasks or to understand ...

Learning how to best mimic seismic processing algorithms or workflows with deep learning (DL) has become a very active field of research. However, seismic processing own particularities may necessitate adaptations of current DL methods. In this paper, we explain and illustrate how the different ...

Depth imaging of converted-wave (P-to-S) ocean-bottom seismic (OBS) data requires a depth model for both the P- and S-wave velocities. Building the S-wave velocity model is very challenging: conventional techniques include PP and PS image registration, or joint PP and PS tomography. These approaches ...