Simultaneous joint migration inversion as a high‐resolution time‐lapse imaging method: Feasibility and robustness study

Abstract

The conventional time‐lapse processing workflow is usually sensitive to the non‐repeatable uncertainties between different vintages caused by noise, acquisition designs and independent processing. Therefore, in order to reduce these non‐repeatable uncertainties, all the datasets are usually acquired from well‐sampled and well‐repeated acquisition surveys, and the independent processing is always carefully tailored to maximally reduce the non‐repeatable uncertainties during processing. Moreover, the conventional time‐lapse analysis method, based on a time‐shift map, is not always a good indicator of the actual velocity differences due to its local one‐dimensional subsurface assumption. In order to relax these rigid requirements and have a better velocity change indicator, a robust high‐resolution simultaneous joint migration inversion was proposed as an effective time‐lapse tool for reservoir monitoring. The method combines a simultaneous data‐processing strategy with the joint migration inversion method. Joint migration inversion is a full wavefield inversion method with a parameterization in terms of reflectivity and propagation velocity. The simultaneous strategy allows the baseline and monitor parameters to communicate and compensate with each other dynamically during inversion, thus, suppressing the non‐repeatable uncertainties during the time‐lapse processing. To investigate the feasibility of using high‐resolution simultaneous joint migration inversion in practice, some numerical experiments are conducted to test the dependence of high‐resolution simultaneous joint migration inversion on the quality of the time‐lapse datasets including the following aspects: random noise; sparse surveys; ocean bottom node versus streamer (different types of monitoring surveys); non‐repeated sources, including source positioning errors and non‐repeated source wavelets; spatial weighting operators in the L2‐norm penalty terms; and sensitivity to weak time‐lapse effects. All the experiments are carried on the basis of a realistic synthetic time‐lapse model based on the Grane field offshore Norway. These experiments show that high‐resolution simultaneous joint migration inversion is robust to random noise, survey sparsity, survey non‐repeatability, source positioning errors and source wavelet discrepancies. Furthermore, high‐resolution simultaneous joint migration inversion remains effective when the spatial weighting operators in the L2‐norm penalty terms are largely relaxed, and high‐resolution simultaneous joint migration inversion is capable of detecting weak time‐lapse changes (e.g. velocity changes down to ±35 m/s).