An Ensemble 4D Seismic History Matching Framework with Sparse Representation Based on Wavelet Multiresolution Analysis

TL;DR

This paper introduces a novel ensemble 4D seismic history matching framework that utilizes wavelet multiresolution analysis and sparse representation to improve reservoir characterization without intermediate inversion processes.

Contribution

It combines wavelet-based sparse data representation with iterative ensemble algorithms, avoiding inversion uncertainties and reducing data size for more effective history matching.

Findings

Effective noise estimation in wavelet coefficients

Reduced data size through sparse wavelet representation

Improved reservoir characterization accuracy

Abstract

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)