이화여자대학교 · 공학
Baehyun Min 교수의 연구실은 오일리커버리(Enhanced Oil Recovery, EOR)와 지구권 탄소 저장(Geological Carbon Storage)을 융합한 혁신적 기술 개발에 주력하고 있습니다. 특히 CO2-탄산화수 주입, 다주기 WAG(수압교환가스) 프로세스, 그리고 인공지능 기반 최적화 기법을 활용한 고도화된 reservoir 모델링과 역사 매칭 기법을 개발하고 있습니다. 연구는 실용적이고 지속 가능한 자원 개발을 목표로 하며, 정밀한 시뮬레이션과 머신러닝 기반 예측 모델을 통해 지질학적 타당성과 동적 성능을 동시에 확보합니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
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
