ASSISTED HISTORY MATCHING FOR PETROLEUM RESERVOIRS IN THE SOCIAL COMPUTING ERA | Science Publications

American Journal of Applied Sciences

Dario Viberti¹  Michel Cancelliere¹  Francesca Verga¹ 

关键词: Reservoir Simulation;    History Matching;    Multi-objective Optimization;    Evolutionary Strategies;    Collective Computing;   

DOI  :  10.3844/ajassp.2013.901.916

学科分类：自然科学（综合）

来源: Science Publications

【 摘 要 】

The exploitation strategy of hydrocarbon reservoirs can be technically and economically optimized only if a reliable numerical model of the reservoir under investigation is available to predict the system response for different production scenarios. A numerical model can be reasonably trustworthy after calibration only, which means the model has at least proved its ability to reproduce the historical behavior of the reservoir it represents. The calibration procedure, also known as history matching, is the most time consuming phase in a reservoir study workflow. Over the last decades several methods, classified as Assisted History Matching (AHM), have been proposed for a partial automation of the model calibration procedure. Meta-heuristic methods have been used to iteratively reduce the misfit between simulated and historical data. However, the main limit for the application of these algorithms is the amount of computational time necessary for the evaluation of the objective function, thus for the simulation runs. On the other hand, the new trend on collective computing offers a solution to CPU intensive tasks by distributing the work among several computers located in different places but globally connected through the World Wide Web. In this study a novel workflow for assisted history matching is proposed. The results proved that this workflow provides better and more representative solutions in a fraction of the time needed by traditional approaches.

【 授权许可】

Unknown