Integrating score-based diffusion models with machine learning-enhanced localization for advanced data assimilation in geological carbon storage - Gabriel Serrão Seabra; Nikolaj T. Mücke; Vinicius Luiz Santos Silva; Alexandre A. Emerick; Denis Voskov; Femke C. Vossepoel (Computational Geosciences, 2026)
Accurate characterization of subsurface heterogeneity is important for the safe and effective implementation of geological carbon storage (GCS) projects. This paper explores how machine learning methods can enhance data assimilation for GCS with a framework that integrates score-based diffusion models with machine learning-enhanced localization in channelized reservoirs during CO $$_2$$ 2 injection. We employ a machine learning-enhanced localization framework that uses large ensembles ( $$N_s = 5000$$ N s = 5000 ) with permeabilities generated by the diffusion model and states computed by simple ML algorithms to improve covariance estimation for the Ensemble Smoother with Multiple Data Assimilation (ESMDA). We apply ML algorithms to a prior ensemble of channelized permeability fields, generated with the geostatistical model FLUVSIM. Our approach is applied on a CO $$_2$$ 2 injection scenario simulated using the Delft Advanced Research Terra Simulator (DARTS). Our ML-based localization maintains significantly more ensemble variance than when localization is not applied, while achieving comparable data-matching quality. This framework has practical implications for GCS projects, helping improve the reliability of uncertainty quantification for risk assessment.

Using a machine learning proxy for localization in ensemble data assimilation - Johann M. Lacerda; Alexandre A. Emerick; Adolfo P. Pires (Computational Geosciences, 2021)

Recent developments combining ensemble smoother and deep generative networks for facies history matching - Smith W. A. Canchumuni; Jose D. B. Castro; Júlia Potratz; Alexandre A. Emerick; Marco Aurélio C. Pacheco (Computational Geosciences, 2021)

Data-space inversion with ensemble smoother - Mateus M. Lima; Alexandre A. Emerick; Carlos E. P. Ortiz (Computational Geosciences, 2020)

Deterministic ensemble smoother with multiple data assimilation as an alternative for history-matching seismic data - Alexandre A. Emerick (Computational Geosciences, 2018)

Investigation on Principal Component Analysis Parameterizations for History Matching Channelized Facies Models with Ensemble-Based Data Assimilation - Alexandre A. Emerick (Mathematical Geosciences, 2017)

Investigation of the sampling performance of ensemble-based methods with a simple reservoir model - Alexandre A. Emerick; Albert C. Reynolds (Computational Geosciences, 2013)

History matching time-lapse seismic data using the ensemble Kalman filter with multiple data assimilations - Alexandre A. Emerick; Albert C. Reynolds (Computational Geosciences, 2012)