The development of time-lag FWI (TLFWI) in recent years has enabled the use of the full wavefield (primary reflection, multiple, ghost, and diving waves) in inversion. With this advance it is now possible to include ever more detail in the velocity model, ultimately reaching ...
Technical Content
Pushing seismic resolution to the limit with FWI Imaging
Pushing seismic resolution to the limit with FWI Imaging
Although the resolution of a seismic image is ultimately bound by the spatial and temporal sampling of the acquired seismic data, the seismic images obtained through conventional imaging methods normally fall far short of this limit. Conventional seismic imaging methods take a piecemeal approach ...
CGG’s Dr. Ellie MacInnes looks at how geoscience can help deliver economical, efficient geothermal energy in line with global sustainability targets.
Ps Imaging on the Edvard Grieg Field: Application of Ps Reflection Fwi and Fwi Imaging
Ps Imaging on the Edvard Grieg Field: Application of Ps Reflection Fwi and Fwi Imaging
Multi-component data recording from ocean-bottom seismic (OBS) surveys captures both PP and PS (converted wave) events. Processing such data can produce superior images compared to those obtained from conventional streamer acquisitions. In addition, PP and PS images can provide valuable insights into reservoir properties ...
The Value of Dual-Azimuth Acquisition: Imaging, Inversion and Development over the Dugong Area
The Value of Dual-Azimuth Acquisition: Imaging, Inversion and Development over the Dugong Area
The Dugong area in the Norwegian North Sea was surveyed by North-South (N–S) orientated, variable depth streamer data, and recently, East-West (E–W) orientated triple source multi-sensor data. By reprocessing the original N-S data in combination with the E–W, we found that a combined dual-azimuth ...
Towards Using Neural Networks to Complement Conventional Seismic Processing Algorithms
Towards Using Neural Networks to Complement Conventional Seismic Processing Algorithms
Convolutional-based neural network (CNN-based) architectures have shown promise in performing denoising tasks. However, it can be demonstrated that their predictions are of limited use for some tasks because they produce signal leakage. For these tasks, a possible improvement is to incorporate CNN-based architectures as ...
Unlocking Value from Unstructured Documents Using Machine Learning: a Geochemistry Case Study, Us Gulf of Mexico
Unlocking Value from Unstructured Documents Using Machine Learning: a Geochemistry Case Study, Us Gulf of Mexico
Over two million files, containing geochemical information, have been collected from tens of thousands of wells drilled during decades of exploration in the Gulf of Mexico (GOM) and are available to geoscientists in the public domain. While these files represent a vast knowledgebase covering ...
Depth Imaging in North Kuwait: Challenges and Solutions
Depth Imaging in North Kuwait: Challenges and Solutions
We present the main results of a tailored velocity model building workflow on a recent broadband survey from North Kuwait. Depth imaging in Kuwait presents several challenges, including the need to capture the strong velocity variations of a complex near surface that generates long ...
Elastic Land Full-Waveform Inversion in the Middle East: Method and Applications
Elastic Land Full-Waveform Inversion in the Middle East: Method and Applications
Applications of full-waveform inversion (FWI) to land data have proven much more challenging than to marine data. The difficulties are linked to a lower signal-to-noise ratio but also to a greater influ-ence of elastic wave phenomena in these data sets, especially those characterized by ...