Temporal Integration of Seismic Traveltime Tomography

Abstract

Time-lapse geophysical measurements and seismic imaging methods in particular are powerful techniques for monitoring changes in reservoir properties. Traditional time-lapse processing methods treat each dataset as an independent unit and estimate changes in reservoir state through differencing these separate inversions. We present a general least-squares approach to jointly inverting time-varying property models through use of spatio-temporal coupling operators. Originally developed within the medical imaging community, this extension of traditional Tikhonov regularization allows us to constrain the way in which models vary in time, thereby reducing artifacts observed in traditional time-lapse imaging formulations. The same methodology can also accommodate changes in experiment geometry as a function of time thus allowing inversion of incremental or incomplete surveys. In this case, temporal resolution is traded for improved spatial coverage at individual timesteps. We use seismic traveltime tomography as a model problem although almost any geophysical inversion task can be posed within this formalism. We apply the developed time-lapse inversion algorithm to a synthetic crosswell dataset designed to replicate a CO2 sequestration monitoring experiment.