Time-Lapse One-Step Least-Squares Migration

Time-lapse seismic imaging is an essential and effective tool in characterizing reservoir changes due to oil and gas production or CO2 injection. We herein focus on imaging with the time-lapse difference between observed datasets using reverse-time migration (RTM). RTM has no dip limitation but remains an adjoint imaging operator with Hessian effects, such as amplitude imbalance and blurring effects. Its corresponding inverse operator, least-squares RTM (LSRTM), promises much higher imaging quality but at an expensive cost. To balance computational overhead and imaging quality, we take the one-step LSRTM, which relieves the Hessian effects through a data-domain adaptive deconvolution. We verify the proposed approach on a synthetic dataset from a modified Marmousi model containing three fluid- or gas-related anomalies. The results show that our approach can detect and describe the detailed time-lapse reservoir changes in high resolution.