3D GSMP General Surface Multiple Prediction | Schlumberger

3D GSMP

General surface multiple prediction

Superior imaging regardless of sparse, missing, or irregular field seismic data

3D GSMP general surface multiple prediction algorithm is a full-3D true-azimuth implementation of the surface-related multiple elimination (SRME) technique. It is used for accurately predicting complex multiples, including diffracted and scattered multiple energy.

Solution for complex imaging challenges

3D GSMP algorithm preserves double bounces and other complex primary events for removal with complementary techniques, such as conventional or shifted-apex Radon demultiple. This approach enables correctly migrating these events by using imaging algorithms, including reverse time migration (RTM), for the best-possible reservoir image.

Gulf of Mexico wide-azimuth data example.
Images performed by 3D GSMP algorithm over a common receiver gather. Highlighted areas show complex and diffracted surface multiples attenuated by our algorithm.

Suitable for all survey types: conventional, wide-azimuth, towed-streamer, ocean bottom seismic (OBS), single- and multivessel full azimuth acquisition, and land surveys.

 High-amplitude surface multiple (left) is completely removed after 3D GSMP general surface multiple prediction is used in Coil Shootin acquisition.
Shot gather from a survey acquired with Coil Shooting single-vessel full-azimuth acquisition. The high-amplitude surface multiple (left) is removed after using 3D GSMP algorithm (right). Using this technique, the underlying primary data is preserved—eliminating the need for further multiple attenuation processing.

High-quality multiple modeling

Minimal preprocessing is required because interpolation, regularization, and extrapolation are conducted with the 3D GSMP prediction algorithm. To produce a high-quality multiple model, 3D GSMP algorithm realizes the multiples at true azimuth to ensure an accurate match with the multiples in the input data.

Optimal input data selection

In areas where there are multiple overlapping vintages of data or large infill volumes, datasets can differ in their signal-to-noise ratio, offset distribution, and other characteristics. The 3D GSMP algorithm uses the highest-grade input data to refine modeling of multiples for multisurvey and 4D or time-lapse seismic projects.

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