METHOD FOR UNCERTAINTY QUANTIFICATION USING DEEP LEARNING

摘要

The present invention relates to a method for evaluating uncertainty of a reservoir by using deep learning. According to the present invention, the method comprises a step of preparing static data; a step of generating a plurality of reservoir models by using the static data; a learning step of learning the reservoir models with an auto-encoder; an encoding step of extracting feature vectors of the reservoir models with the learned auto-encoder; a step of evaluating similarity (distance) of the reservoir models in accordance with the extracted feature vectors; a grouping step of grouping similar models by a clustering scheme based on the similarity; a representative model selecting step; a first simulation step of performing a reservoir simulation with respect to the representative models; a determining step of determining whether dynamic data, observed from the reservoir, exists; a step of evaluating uncertainty when observed dynamic data do not exist as a determination result in the determining step; a step of selecting an optimal representative model and a final model and evaluating uncertainty when observed data exists as the determination result in the determining step; and an inversion operating step of performing an inversion operation algorithm. The method of the present invention has an effect of evaluating, with reliability, the degree of uncertainty of a reservoir.