Highly repeatable time-lapse seismic with distributed compressive sensing — Mitigating effects of calibration errors

Abstract

Recently, we demonstrated that combining joint recovery with low-cost nonreplicated randomized sampling tailored to time-lapse seismic can give us access to high-fidelity, highly repeatable, dense prestack vintages, and high-grade time lapse. To arrive at this result, we assumed well-calibrated surveys — i.e., we presumed accurate postplot source/receiver positions. Unfortunately, in practice, seismic surveys are prone to calibration errors, which are unknown deviations between actual and postplot acquisition geometry. By means of synthetic experiments, we analyze the possible impact of these errors on vintages and on time-lapse data obtained with our joint-recovery model from compressively sampled surveys. Supported by these experiments, we demonstrate that highly repeatable time-lapse vintages are attainable despite the presence of unknown calibration errors in the positions of the shots. We assess the repeatability quantitatively for two scenarios by studying the impact of calibration errors on conventional dense but irregularly sampled surveys and on low-cost compressed surveys. To separate time-lapse effects from calibration issues, we consider the idealized case in which the subsurface remains unchanged and the practical situation in which time-lapse changes are restricted to a subset of the data. In both cases, the quality of the recovered vintages and time lapse decreases gracefully for low-cost compressed surveys with increasing calibration errors. Conversely, the quality of vintages from expensive densely periodically sampled surveys decreases more rapidly as unknown and difficult-to-control calibration errors increase.