A Multiresolution Approach to Reservoir Parameter Estimation Using Wavelet Analysis (2000)

A Multiresolution Approach to Reservoir Parameter Estimation Using Wavelet Analysis Pengbo Lu; Pengbo Lu Stanford University Search for other works by this author on: This Site Google Scholar Roland N. Horne Roland N. Horne Stanford University Search for other works by this author on: This Site Google Scholar Paper presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, October 2000. Paper Number: SPE-62985-MS https://doi.org/10.2118/62985-MS Published: October 01 2000 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Lu, Pengbo, and Roland N. Horne. "A Multiresolution Approach to Reservoir Parameter Estimation Using Wavelet Analysis." Paper presented at the SPE Annual Technical Conference and Exhibition, Dallas, Texas, October 2000. doi: https://doi.org/10.2118/62985-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Annual Technical Conference and Exhibition Search Advanced Search 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.IntroductionThe most commonly encountered and probably the most challenging task in reservoir engineering is to describe the reservoir accurately and efficiently. An accurate description of a reservoir is crucial to the management of production and the efficiency of oil recovery. Reservoir characterization has been a major research subject in reservoir engineering since the 1960s1–4. The goal is to estimate the spatial distribution of the reservoir properties, e.g., permeability and porosity, by integration of all kinds of available information. The information is divided into two categories: prior information and dynamic information. The prior information includes all the phenomenological information and static data (core, logging, seismic and geologic information, etc.). The dynamic information includes production data, pressure transient data, tracer testing data, long-term pressure history, etc. Recently, companies have begun performing 4-D seismic surveys, providing another type of dynamic data5,6,7. Keywords: artificial intelligence, parameter estimation, pressure transient analysis, reservoir parameter estimation, information, estimation, wavelet, coefficient, reservoir characterization, pressure transient testing Subjects: Reservoir Characterization, Formation Evaluation & Management, Pressure transient analysis This content is only available via PDF. 2000. Society of Petroleum Engineers You can access this article if you purchase or spend a download.