  In this work we propose an ensemble 4D seismic history matching framework for
reservoir characterization. Compared to similar existing frameworks in
reservoir engineering community, the proposed one consists of some relatively
new ingredients, in terms of the type of seismic data in choice, wavelet
multiresolution analysis for the chosen seismic data and related data noise
estimation, and the use of recently developed iterative ensemble history
matching algorithms.
  Typical seismic data used for history matching, such as acoustic impedance,
are inverted quantities, whereas extra uncertainties may arise during the
inversion processes. In the proposed framework we avoid such intermediate
inversion processes. In addition, we also adopt wavelet-based sparse
representation to reduce data size. Concretely, we use intercept and gradient
attributes derived from amplitude versus angle (AVA) data, apply multilevel
discrete wavelet transforms (DWT) to attribute data, and estimate noise level
of resulting wavelet coefficients. We then select the wavelet coefficients
above a certain threshold value, and history-match these leading wavelet
coefficients using an iterative ensemble smoother.
  (The rest of the abstract is omitted for exceeding the limit of length)
