Full-Waveform Inversion

High-resolution velocity models for a range of E&P scenarios and geological settings

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Overcome your imaging challenges

With full-waveform inversion (FWI) solutions for every exploration, appraisal, or production environment, we can create highly detailed velocity models that honor the geologic structures in your reservoir. Not only do our FWI algorithms work with all acquisition geometries, but they also complement the low frequencies inherent to broadband seismic data. The result is a high-fidelity image that enables you to achieve a wide range of subsurface objectives across the E&P life cycle.

ETM-FWI, unlock a step-change in subsalt imaging

Enhanced template-matching FWI (ETM-FWI) is an 全国快3信誉最好的老平台 innovation that delivers exceptional improvement in earth model building and imaging. ETM-FWI leverages the combined power of both kinematic and dynamic components of the seismic wavefield derived from an entire shot record. This approach effectively overcomes the limitations associated with traditional travel time-based FWI methodologies. Furthermore, ETM-FWI ensures the robust handling of the misfit between real data and simulated wavefield across multiple dimensions through a multi-dimensional template matching approach. This becomes particularly crucial in complex geology settings

ETM-FWI has been applied successfully with all difference complex geological settings across the world. Combined with newly acquired sparse ocean bottom node (OBN) acquisition data, it delivers a giant leap in subsalt imaging in Gulf of Mexico.

FWI model with RTM overlay

ETM-FWI and sparse OBN data on a regional scale

Step change in legacy model built with decades of streamer data, revealed now on regional scale with ETM-FWI and sparse OBN technology.

EFWI: The Next Wave in Seismic Processing

To simulate how the seismic wave propagates through the whole earth, you need an elastic simulator.

Achieve greater accuracy and detail with Elastic FWI

Elastic full-waveform inversion (EFWI) takes into account both the compressional (P-wave) and shear (S-wave) components of the seismic wavefield during seismic wave propagation simulation, which leads to more accurate and detailed estimation of the subsurface velocity models.

Elastic full-waveform inversion (EFWI) is particularly useful for velocity model building in complex geological settings where there are large velocity contrasts, such as around salt or subsalt formations. In these environments, traditional inversion techniques that rely solely on the acoustic wave equation can struggle to accurately image the subsurface, as they cannot account for the shear (S-wave) component of the seismic wavefield.

Reveal steeply dipping structures with FWI Derived Reflectivity (FDR)

FWI is a robust algorithm that can produce a high-resolution velocity model update when using the higher bandwidths in the input data. A pseudo-reflectivity volume (FDR/FWI imaging) could be directly derived from the FWI velocity field by computing the impedance contrast normal to a structure interface, which can be accurately obtained by considering the structural tensor computed from the FWI update velocity model. Compared with a conventional migrated image, FWI-derived reflectivity can improve the imaging of steeply dipping reflectors.

Bridging gaps in existing acquisition limitations with RFWI

Deepwater subsalt prospects are difficult to image because of the complexities of the overburden and the limited penetration depth of the refraction energy needed for conventional FWI.

Our reflection-based FWI (RFWI) algorithm overcomes these obstacles by producing reliable, low-wavenumber model updates.

Initially, a Born modeling–based gradient kernel is implemented to directly compute the reflection-based low-wavenumber components of a conventional FWI gradient.
Then, a robust kinematics-oriented objective function ensures that the low-wavenumber components update the model in the correct directions.

Through their combined use, RFWI can derive more accurate velocity models at depths where traditional FWI is limited, even when starting with a smooth initial model.

RFWI improves the velocity model of the section deep below the salt. Move the slider to see the improved geologic detail between the legacy model and the model updated with RFWI.

FWI applied to data acquired on land captures subsurface geologic detail that cannot be obtained through traditional tomography. The legacy tomography model (left) has been updated using FWI (right), providing significant uplift.

Address near-surface challenges with FWI for land data

Low-frequency transmitted seismic energy is crucial for the success of FWI to overcome sensitivity to starting velocity fields. Unfortunately, the low-frequency portion of data acquired on land has a low signal-to-noise ratio (S/N), which can negatively affect the quality of your model.

全国快3信誉最好的老平台 acoustic FWI application for land data uses a semblance-based high-resolution Radon (HR-Radon) inversion approach to enhance the S/N of the low-frequency part of the input data and improve the convergence of the land FWI workflow. To mitigate the impact of elastic effects, we include only the diving and postcritical early arrivals in the waveform inversion. With the aid of HR-Radon preconditioning and a carefully designed workflow, acoustic FWI can derive a reliable high-resolution near-surface model that could not be otherwise recovered through traditional tomographic methods.