Interferometric Imaging and its Application to 4D Imaging

Abstract

This thesis describes new interferometric imaging methods for migration and waveform inversion. The key idea is to use reflection events from a known reference reflector to ”naturally redatum” the receivers and sources to the reference reflector. Here, ”natural redatuming” is a data-driven process where the redatuming Green’s functions are obtained from the data. Interferometric imaging eliminates the statics associated with the noisy overburden above the reference reflector. To mitigate the defocussing caused by overburden errors I first propose the use of interferometric least-squares migration (ILSM) to estimate the migration image. Here, a known reflector is used as the reference interface for ILSM, and the data are naturally redatumed to this reference interface before imaging. Numerical results on synthetic and field data show that ILSM can significantly reduce the defocussing artifacts in the migration image.

Next, I develop a waveform tomography approach for inverting the velocity model by mitigating the velocity errors in the overburden. Unresolved velocity errors in the overburden velocity model can cause conventional full-waveform inversion to get stuck in a local minimum. To resolve this problem, I present interferometric full-waveform inversion (IFWI), where conventional waveform tomography is reformulated so a velocity model is found that minimizes the objective function with an interferometric crosscorrelogram misfit. Numerical examples show that IFWI, compared to FWI, computes a significantly more accurate velocity model in the presence of a nearsurface with unknown velocity anomalies.

I use IFWI and ILSM for 4D imaging where seismic data are recorded at different times over the same reservoir. To eliminate the time-varying effects of the near surface both data sets are virtually redatumed to a common reference interface before migration. This largely eliminates the overburden-induced statics errors in both data sets. Results with synthetic and field data show that ILSM and IFWI can suppress the artifacts caused by non-repeatability in time-lapse surveys. This can lead to a much more accurate characterization of the movement of fluids over time. In turn, this information can be used to optimize the extraction of resources in enhanced oil recovery (EOR) operations.