Multiparameter Elastic Full Waveform Inversion with Facies-based Constraints
KAUST DepartmentPhysical Sciences and Engineering (PSE) Division
Earth Science and Engineering Program
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AbstractFull waveform inversion (FWI) incorporates all the data characteristics to estimate the parameters described by the assumed physics of the subsurface. However, current efforts to utilize full waveform inversion beyond improved acoustic imaging, like in reservoir delineation, faces inherent challenges related to the limited resolution and the potential trade-off between the elastic model parameters. Some anisotropic parameters are insufficiently updated because of their minor contributions to the surface collected data. Adding rock physics constraints to the inversion helps mitigate such limited sensitivity, but current approaches to add such constraints are based on including them as a priori knowledge mostly valid around the well or as a global constraint for the whole area. Since similar rock formations inside the Earth admit consistent elastic properties and relative values of elasticity and anisotropy parameters (this enables us to define them as a seismic facies), utilizing such localized facies information in FWI can improve the resolution of inverted parameters. We propose a novel approach to use facies-based constraints in both isotropic and anisotropic elastic FWI. We invert for such facies using Bayesian theory and update them at each iteration of the inversion using both the inverted models and a prior information. We take the uncertainties of the estimated parameters (approximated by radiation patterns) into consideration and improve the quality of estimated facies maps. Four numerical examples corresponding to different acquisition, physical assumptions and model circumstances are used to verify the effectiveness of the proposed method.
CitationZhang Z, Alkhalifah T, Naeini EZ, Sun B (2018) Multiparameter Elastic Full Waveform Inversion with Facies-based Constraints. Geophysical Journal International. Available: http://dx.doi.org/10.1093/gji/ggy113.
SponsorsWe thank Juwon Oh, Vladimir Kazei and Yike Liu (IGG, CAS) for their helpful discussions. For computer time, this research used the resources of the Supercomputing Laboratory at King Abdullah University of Science & Technology (KAUST) in Thuwal, Saudi Arabia. The research was partly funded by the National Natural Science Foundation of China (Grant Nos. 41730425).
PublisherOxford University Press (OUP)