A Multiresolution Approach to Reservoir Parameter Estimation Using Wavelet Analysis

Abstract

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractIn this paper, a new multiresolution approach is proposed for reservoir parameter estimation, i.e., to estimate the spatial distribution of the reservoir properties by proper integration of all types of data available (either static or dynamic). We considered the integration of production history data, seismic data and well test data in this work.One of the key issues in parameter estimation is to develop an efficient and reliable nonlinear regression procedure. We adapted wavelet analysis to describe the distribution of sensitivity coefficients, since wavelet analysis is a powerful tool in multiresolution analysis and is widely used in signal and image processing. Wavelet analysis can compress the parameter space, stabilize the algorithm and avoid local minima. This new approach also improves the computational efficiency significantly by varying the resolution of the estimation at different regression stages.Wavelet analysis also has the capability to integrate different types of data efficiently, using different levels of wavelets to incorporate different data types. We can account explicitly for the resolving power of different data and estimate reservoir properties with nonuniform resolution.We have applied the procedure to multiphase reservoir examples that demonstrate the reliability and flexibility of the approach. We also gained very good convergence and excellent computational efficiency compared to conventional methods. The most important conclusion of this paper is that wavelet analysis is a very useful tool for reservoir parameter estimation and can speed up the computation and improve the performance. The nonlinear regression procedure integrating wavelet analysis has substantial advantages over the conventional algorithms.