Normal or abnormal? Machine learning for the leakage detection in carbon sequestration projects using pressure field data

S Sinha, RP de Lima, Y Lin, AY Sun, N Symons… - International journal of …, 2020 - Elsevier
The international commitments for atmospheric carbon reduction will require a rapid
increase in carbon capture and storage (CCS) projects. The key to any successful CCS …

Deep Learning Model for CO2 Leakage Detection Using Pressure Measurements

Z Zhang, X He, M AlSinan, Y Li, H Kwak… - SPE Annual Technical …, 2022 - onepetro.org
Geologic CO2 sequestration (GCS) has been considered a viable engineering measure to
decrease global CO2 emissions. The real-time monitoring to detect possible CO2 leakage is …

Leak detection in carbon sequestration projects using machine learning methods: Cranfield site, Mississippi, USA

S Sinha, R Pires De Lima, Y Lin, A Y. Sun… - SPE Annual Technical …, 2020 - onepetro.org
Due to international commitments on carbon capture and storage (CCS), an increase in
CCS projects is expected in the near future. Saline aquifers and depleted hydrocarbon …

Characterization of seismic-scale petrofacies variability in the Arbuckle Group using supervised machine learning: Wellington Field, Kansas

AB Caf, D Lubo-Robles, KJ Marfurt, H Bedle… - Interpretation, 2024 - library.seg.org
Abstract The Arbuckle Group in southern Kansas has been investigated for carbon
geosequestration-related studies. In this study, we evaluated the seismic-scale …

Quantifying the effects of pressure management for the Williston basin Brine Extraction and Storage Test (BEST) site using machine learning

X Yu, T Jiang, CB Williamson, RJ Klapperich… - International Journal of …, 2024 - Elsevier
Active reservoir management (ARM) through brine extraction can reduce pressure buildup
during large-scale implementation of carbon capture and storage (CCS) projects. This study …

[BOOK][B] Reinforcement learning for well location optimization

K Dawar - 2021 - search.proquest.com
The strategic placement of exploratory wells during the process of hydrocarbon production is
critical for both the determination of the reservoir properties and the eventual profitability of …

[PDF][PDF] Petrographic analysis with deep convolutional neural networks

R Pires de Lima - 2019 - core.ac.uk
Machine learning (ML) techniques have been successfully applied, with considerable
success, in the geosciences for almost two decades. Applications of ML by the geoscientific …

[CITATION][C] SPE-209959-MS

Z Zhang, X He, S Marwah AlSinan - 2022