Minimum acceptance criteria for subsurface scenario-based uncertainty models from single image generative adversarial networks (SinGAN)
Evaluating and checking subsurface models is essential before their use to support optimum
subsurface development decision making. Conventional geostatistical modeling workflows …
subsurface development decision making. Conventional geostatistical modeling workflows …
Fast and Reliable History Matching of Channel Reservoirs Using Initial Models Selected by Streamline and Deep Learning
Ensemble-based methods involve using multiple models for model calibration correct initial
models based on observed data. The assimilated ensemble models allow probabilistic …
models based on observed data. The assimilated ensemble models allow probabilistic …
Deep learning for spatial nonstationarity: evaluation, mitigation, and generation
L Liu - 2024 - repositories.lib.utexas.edu
Spatial nonstationarity, the location variance of features' statistical distributions, is ubiquitous
in many natural settings. While the advent of deep learning technologies has enabled new …
in many natural settings. While the advent of deep learning technologies has enabled new …
이산화탄소지중저장을위한기계학습기반4-D 탄성파자료통합및배사구조채널대수층특성화
김현민， 김남화， 신현돈， 조홍근 - 한국자원공학회지, 2024 - dbpia.co.kr
본 연구에서는 채널대수층의 이산화탄소 지중저장에서 4-D 탄성파자료를 통합해 불확실성을
정량화하고 신뢰도를 향상하기 위해 기계학습의 하나인 Pix2Pix 기반의 4-D 탄성파자료 …
정량화하고 신뢰도를 향상하기 위해 기계학습의 하나인 Pix2Pix 기반의 4-D 탄성파자료 …