Technical Content

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 ...

Carbonate velocity model building is challenging due to the complex geometry and sharp velocity contrasts associated with carbonates. Full-waveform inversion (FWI), together with long offsets, wide azimuth and good low frequency data, is known to be a powerful tool to address these challenges. Unfortunately ...

There have been many advances in natural language processing in recent years but most of the work have been focused on texts from a general domain or medicine and so datasets in the geology domain are sadly lacking. We demonstrate how existing taxonomy and ...

Seismic images obtained through conventional migration methods have limitations as amplitude distortions and migration artifacts. To mitigate these limitations, we can generate FWI Images by computing the reflector-normal derivatives of the high-frequency FWI velocities. Given the resolution, accuracy, and geological consistency of the velocities ...

Compared with towed-streamer acquisitions, ocean bottom nodes (OBN) generally provide fuller-azimuth illumination of the subsurface, longer offsets, higher signal-to-noise ratio (S/N), and improved low frequencies. These advantages provide the necessary ingredients for two key elements of seismic exploration and monitoring: (i) full-waveform inversion (FWI) ...

Seismic time-lapse (4D) surveys have been widely used to quantitatively monitor the geophysical property changes within hydrocarbon reservoirs due to production effects. Full-waveform inversion (FWI), which has become one of the most reliable tools for velocity model building (VMB), is a natural technology choice ...

After many years of processing seismic streamer data acquired over the Shenzi field in deep-water Gulf of Mexico (GOM) with the best available technology, the workflows have remained highly interpretive, far from robust, often time-consuming, and ultimately proved inadequate for resolving the complexity of ...