An Ensemble 4D Seismic History Matching Framework with Wavelet Multiresolution Analysis - A 3D Benchmark Case Study

In a previous work (Luo et al., 2016), we proposed an ensemble 4D seismic history matching framework, which has some relatively new ingredients, in terms of the type of seismic data in choice, the way to handle big seismic data and related data noise estimation, and the use of a recently developed iterative ensemble history matching algorithm. In seismic history matching, it is customary to use inverted seismic parameters as the observations. In doing so, extra uncertainties may arise during the inversion processes. We avoid such intermediate inversion processes by adopting amplitude versus angle (AVA) data. To handle the big-data problem in seismic history matching, we adopt a wavelet-base sparse representation procedure. Concretely, we apply a discrete wavelet transform to seismic data, and estimate noise in resulting wavelet coefficients. We then use an iterative ensemble smoother to history-match leading wavelet coefficients above a certain threshold value. In the previous work (Luo et al., 2016), we applied the proposed framework to a 2D synthetic case. In the current study, we extend our investigation to the 3D Brugge benchmark case. Numerical results indicate that, the proposed framework is very efficient in handling big seismic data, while achieving reasonably good history matching performance.