Latent diffusion model parameterization allows MCMC and SMC to outperform latent-space ESMDA in data mismatch and uncertainty reduction for 3D subsurface DA, while model-space ESMDA produces unrealistic posteriors.
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2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Develops a posterior-informed two-stage stochastic multi-objective optimization framework for exploration well portfolio selection under uncertainty, solved via sample average approximation and NSGA-II.
citing papers explorer
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A two-stage stochastic programming framework for oil and gas exploration well portfolio optimization under geological and economic uncertainty
Develops a posterior-informed two-stage stochastic multi-objective optimization framework for exploration well portfolio selection under uncertainty, solved via sample average approximation and NSGA-II.