Engagement 4 survey – Acquisition started May 2023
3,537 km2 of ultralong-offset, enhanced template-matching full-waveform inversion (FWI) OBN data in the US Gulf of Mexico
Illuminate the subsurface in deepwater US Gulf of Mexico
High-resolution velocity models for a range of E&P scenarios and geological settings
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.
Complying with the half-wavelength criteria is a fundamental challenge for FWI. When the criteria is violated, cycle skipping issues between the predicted and acquired data can cause the inversion to converge at a local minimum, resulting in an inaccurate model. Conventional least-squares FWI (LsFWI) mitigates this risk by using a highly accurate initial model, restricting its scope of use to basins with mature velocity models.
In locations where an accurate initial model is not available, our adjustive FWI (AdFWI) algorithm is designed to build the relationship between traveltime shift and model error to correct the erroneous background model while also mitigating cycle-skipping issues.
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.
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.
Our multiparameter FWI algorithm simultaneously solves for both velocity and anisotropy in a single inversion. As the velocity field nears convergence, a joint inversion of the velocity and anisotropy fields is applied to the data. This inversion algorithm incorporates both diving and reflection energy to mitigate the nonuniqueness of the solutions caused by the coupling between the vertical velocity and anisotropy field—providing you with a high-resolution velocity model.
In areas where highly absorptive geologic features such as gas pockets dominate the subsurface, our viscoacoustic FWI (QFWI) can simultaneously estimate both a high-resolution velocity field and Q-model. It explicitly separates Q-induced dispersion from amplitude attenuation and compensates for phase dispersion. Q propagation in FWI correctly accounts for phase modulation induced in the propagation of refracted energy and the diving wave zone, providing you with improved geologic detail in the near surface.
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.
WesternGeco 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.
Our elastic and 4D FWI algorithms help you make better drilling and production decisions. Elastic FWI (EFWI) is applied to ocean bottom seismic (OBS) data. It simultaneously solves for both Vp and Vs, and the velocity fields are validated through elastic RTM (ERTM). When elastic properties are provided, EFWI can be used to detect gas, sand, and shale zones, helping you select optimal drilling locations.
During the production stage, 4D FWI is used to monitor reservoir fluid migration. Typical 4D FWI workflows use identical processing flows to identify image differences between baseline and monitor datasets. Our approach instead creates a baseline model using traditional FWI workflows that is then used as a constraint for the monitor model update. This ensures slight differences at the reservoir are captured in the right place and shape. Changes in the reservoir are detected through model parameter changes rather than simple data differences, mitigating the risk of misclassifying differences caused by survey geometry to fluid migration.
Well-constrained FWI uses geological information beyond what is available from seismic data to achieve model updates. Our approach includes building an a priori model from well information to derive a model misfit term. The misfit term is incorporated into the objective function and used to constrain the model update and produce a velocity field consistent with the recorded well data.