Using nonseismic data to aid velocity model building and complex imaging: A case study from western China

Abstract

A workflow for inverting high-velocity conglomerates by simultaneous joint inversion (SJI) of seismic and nonseismic data was developed during a depth-migration project using land 3D data sets from western China. Large localized velocity variations caused by conglomerates severely limited the effectiveness of ray-based seismic tomography in generating an accurate velocity field. Single-domain inversion indicated different responses of particular lithologies for various nonseismic methods, and well-log crossplots enabled estimation of the relationships between models generated from different geophysical measurements. Using the high-resistivity property of the conglomerates, SJI of seismic first-break and magnetotelluric (MT) data was conducted to estimate spatial thickness and velocity. Combining the relatively low-resolution information derived from the MT data with the higher-resolution seismic image, the distribution of conglomerates could be interpreted with the help of well markers and well-logging horizons. The velocity model then was adjusted iteratively. The model that was updated by using the SJI workflow showed better consistency with borehole data compared with the initial tomographic model. The depth-migrated image created by using the updated model showed more geologically plausible structures both at target level and in the overburden.

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“…Furthermore, Panzner et al (2016) use a sequential analysis of the different data sets, where inversions are performed individually, but information is exchanged between the separate inversions. These kinds of integrated workflows are often used in hydrocarbon exploration instead of full joint inversions, either because a full joint inversion is considered intractable (Um et al 2014), or because the main goal of the inversion is to generate improved models for migrating seismic reflection data (Colombo and Stefano 2007;Colombo et al 2008;De Stefano et al 2011;Colombo et al 2014;Cui et al 2015;Takam Takougang et al 2015) or full waveform inversion (Zerilli et al 2016). Heincke et al (2017), in contrast, perform a full joint inversion of all three data sets.…”