English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Because the geological conditions of the reservoir are complicated and involve many factors, the inversion of reservoir parameters is realized by using numerical simulation technology and history matching method. At present, Ensemble Kalman Filter method is widely used in history matching. But in the fact, the Ensemble Kalman Filter has problem such as inaccurate gradient calculation and pseudo correlation. In this paper, the Ensemble Kalman Filter based on shrinkage covariance matrix estimation is used to construct the localization matrix. By gradually matching production performance, the gradient of data assimilation method is corrected, the pseudo correlation is weakened, the reservoir model is updated, and the optimal estimate is obtained. By an example, we compare the Ensemble Kalman Filter and Ensemble Kalman Filter based on shrinkage covariance matrix estimation. The results show that Ensemble Kalman Filter based on shrinkage covariance matrix estimation is superior to Ensemble Kalman Filter in the accuracy of model production dynamic matching.

Reservoir automatic history matching method using ensemble Kalman filter based on shrinkage covariance matrix estimation