Full-Waveform Inversion | Schlumberger

Full Waveform Inversion

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

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.

 

FWI uses a two-way wave equation to invert for high-resolution velocity and anisotropy models derived from seismic data.

Mitigate cycle skipping and improve model accuracy

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.

The slider shows a seismic inline and depth slice of a smoothed PSTM velocity model (left) and the updated model after AdFWI (right). The white dashed line on the inline section indicates the location of the corresponding depth slice.

Seismic inline and depth slice of a smoothed PSTM velocity model updated Seismic inline and depth slice of a smoothed PSTM velocity model after Adjustive FWI

Accurately position subsalt reservoirs

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.

Legacy model Legacy model with FWI

The FWI-derived velocity model can be used as an input to calculate pore pressure and acoustic impedance.

Simultaneously solve for anisotropy and velocity

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.

Resolve overburden geological features to improve image clarity

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.
FWI before FWI after

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. Our 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.

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.

When used with advanced imaging algorithms such as reverse time migration (RTM), the FWI-derived velocity model gives you the ability to perform highly detailed stratigraphic interpretation and geohazard analysis.

Optimize drilling decisions

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.

Monitor reservoir fluid migration

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.

Achieve high-resolution earth models with well-constrained FWI

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.

 

 

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