Facies-constrained FWI: Toward application to reservoir characterization

Nishant Kamath, Ilya Tsvankin, Ehsan Zabihi Naeini

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


The most common approach to obtaining reservoir properties from seismic data exploits the amplitude variation with offset response of reflected waves. However, structural complexity and errors in the velocity model can severely reduce the quality of the inverted results. Full-waveform inversion (FWI) has shown a lot of promise in obtaining high-resolution velocity models for depth imaging. We propose supplementing FWI with rock-physics constraints obtained from borehole data to invert for reservoir properties. The constraints are imposed by adding appropriately weighted regularization terms to the objective function. The advantages of this technique over conventional FWI algorithms are shown by conducting synthetic tests for both isotropic and VTI (transversely isotropic with a vertical symmetry axis) models. The medium parameterization for FWI is selected using radiation (scattering) patterns of perturbations in the model parameters.
Original languageEnglish (US)
Pages (from-to)924-930
Number of pages7
JournalThe Leading Edge
Issue number11
StatePublished - Nov 1 2017
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported by the Consortium Project on Seismic Inverse Methods for Complex Structures at the Center for Wave Phenomena (CWP) and competitive research funding from the King Abdullah University of Science and Technology (KAUST). We are grateful to Tariq Alkhalifah (KAUST) and Antoine Guitton (CWP) for fruitful discussions. The reproducible numerical examples are generated with the Madagascar open-source software package freely available from www.ahay.org.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


Dive into the research topics of 'Facies-constrained FWI: Toward application to reservoir characterization'. Together they form a unique fingerprint.

Cite this