Reservoir automatic history matching: Methods, challenges, and future directions

Reservoir history matching refers to the process of continuously adjusting the parameters of the reservoir model, so that its dynamic response will match the historical observation data, which is a prerequisite for making forecasts based on the reservoir model. With the development of optimization theory and machine learning algorithms, automatic history matching has made numerous breakthroughs for practical applications. In this perspective, the existing automatic history matching methods are summarized and divided into model-driven and surrogate-driven history matching methods according to whether the reservoir simulator needs to be run during the automatic history matching process. Then, the basic principles of these methods and their limitations in practical applications are outlined. Finally, the future trends of reservoir automatic history matching are discussed.

Automated summary

Reservoir history matching refers to continuously adjusting the parameters of the reservoir model to match the historical observation data, which is essential for making forecasts based on the reservoir model. Automatic history matching has made significant progress for practical applications due to advances in optimization theory and machine learning algorithms. This article summarizes the existing automatic history matching methods, categorizing them as model-driven or surrogate-driven based on whether the reservoir simulator needs to be run. The basic principles and limitations of these methods in practical applications are outlined, followed by a discussion on the future trends of reservoir automatic history matching.