Investigation of the sampling performance of ensemble-based methods with a simple reservoir model

• Published in: Computational geosciences Vol. 17; no. 2; pp. 325 - 350
• Main Authors: Emerick, Alexandre A., Reynolds, Albert C.
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.04.2013; Springer Nature B.V
• Subjects: Algorithms; Computational efficiency; Computer simulation; Crude oil; Earth and Environmental Science; Earth Sciences; Geotechnical Engineering & Applied Earth Sciences; Hydrogeology; Kalman filters; Markov chains; Mathematical Modeling and Industrial Mathematics; Monte Carlo methods; Monte Carlo simulation; Nonlinearity; Original Paper; Petroleum; Reservoirs; Sampling; Soil Science & Conservation; Uncertainty; Uncertainty quantification; Ensemble smoother; Randomized maximum likelihood; History matching; Ensemble Kalman filter; Markov chain Monte Carlo

The application of the ensemble Kalman filter (EnKF) for history matching petroleum reservoir models has been the subject of intense investigation during the past 10 years. Unfortunately, EnKF often fails to provide reasonable data matches for highly nonlinear problems. This fact motivated the devel...