Transforming 2D Seismic into Pseudo3D

We have developed a methodology known as Pseudo3D. This advanced technique is uses Machine Learning to enhance the value of 2D seismic data by transforming it into a more informative 3D format. Pseudo3D leverages input data from all available 2D migrated stacks and from various 3D vintages of seismic surveys, if available.

2D seismic data remains crucial for companies assessing new permits or exploring new basins, providing foundational insights that guide their decision-making processes. This type of data is particularly valuable in the initial stages of exploration, where comprehensive and detailed information about subsurface structures is essential.

While 2D seismic data is invaluable during the exploration phase, the lack of lateral continuity may make these datasets less suitable for appraisal and development stages. Additionally, the complexities associated with multi-vintage datasets necessitate careful and detailed analysis to ensure accurate and reliable results.

Our Pseudo3D is primarily a post-stack data-driven process, thus improving efficiency by not requiring complex and resource intensive methods that involve pre-stack processing such as de-migration and migration. We leverage machine learning models to approximate results of pre-stack quality while only using post-stack data.