Ewha Womans University · 工学
Professor Baehyun Min's research lab specializes in advanced reservoir engineering and computational methods for enhanced oil recovery (EOR) and geological carbon storage. The lab focuses on developing innovative simulation techniques, machine learning integration, and data assimilation strategies to improve reservoir characterization, history matching, and uncertainty quantification in complex, channelized reservoirs. Key research directions include CO2-based EOR processes such as CWAG and WAG, coupled with carbon capture and storage (CCS), as well as the application of artificial intelligence and sparse representation methods to enhance geological plausibility and predictive accuracy.
Figures are computed from collected data and may differ slightly.
This study investigates the potential of a novel CO2-carbonated water-alternating-gas (CWAG) injection method for enhanced oil recovery (EOR) and geological carbon storage. The Weyburn fluid data acquired from Canada are used in a compositional reservoir simulation of a CO2-CWAG case study with seven cycles in order to analyze the effects of carbonated water (CW) upon the oil recovery and CO2 storage capacity of a multi-phase CO2/brine/oil system. The study includes an assessment of the CO2 plum
Abstract This article presents an efficient approach to determine the optimal drilling location for maximizing the cumulative production without the need for a reservoir simulation, of which scheme is based on artificial neural network incorporating the productivity potential. A reservoir simulator can provide an accurate result, but is sometimes inefficient due to the enormous computing requirements. The typical artificial neural network scheme used in multiwell placement shows lower predictabi
This study couples an iterative sparse coding in a transformed space with an ensemble smoother with multiple data assimilation (ES-MDA) for providing a set of geologically plausible models that preserve the non-Gaussian distribution of lithofacies in a channelized reservoir. Discrete cosine transform (DCT) of sand-shale facies is followed by the repetition of K-singular value decomposition (K-SVD) in order to construct sparse geologic dictionaries that archive geologic features of the channelize
For an ensemble-based history matching of a channelized reservoir, loss of geological plausibility is challenging because of pixel-based manipulation of channel shape and connectivity despite sufficient conditioning to dynamic observations. Regarding the loss as artificial noise, this study designs a serial denoising autoencoder (SDAE) composed of two neural network filters, utilizes this machine learning algorithm for relieving noise effects in the process of ensemble smoother with multiple dat
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