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

Yu, Xue; Jiang, Tao; Williamson, Cody B.; Klapperich, Ryan J.; Hamling, John A.; Azzolina, Nicholas A.; Pekot, Lawrence J.

doi:10.1016/j.ijggc.2023.104041

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

Journal Article · Wed Dec 20 23:00:00 EST 2023 · International Journal of Greenhouse Gas Control

DOI:https://doi.org/10.1016/j.ijggc.2023.104041· OSTI ID:2564392

^[1]; Jiang, Tao ^[2]; Williamson, Cody B. ^[2]; Klapperich, Ryan J. ^[2]; ^[2]; Azzolina, Nicholas A. ^[2]; Pekot, Lawrence J. ^[2]

University of North Dakota, Grand Forks, ND (United States); Univ. of North Dakota, Grand Forks, ND (United States). Energy and Environmental Research Center
University of North Dakota, Grand Forks, ND (United States)

Active reservoir management (ARM) through brine extraction can reduce pressure buildup during large-scale implementation of carbon capture and storage (CCS) projects. This study used machine learning (ML)-assisted approaches to analyze bottomhole pressure (BHP) responses to various brine injection and extraction scenarios. Field monitoring data were collected over a 2-year operation period at two injection wells and one extraction well (about 400 meters away) as part of a Brine Extraction and Storage Test (BEST) in the North Dakota portion of the Williston Basin. Injection activities increased the BHPs at the injection wells by around 0.70 MPa (~100 psi) during the operation period. Extraction activities demonstrated the capability to decrease the BHPs at the injection wells by approximately 0.21–0.34 MPa (30–50 psi) depending on the ratio of the extraction and injection well flow rates (the “extraction ratio” – a normalization procedure used in the analysis). The pressure reduction provided by the extraction well equated to 30%–50% of the pressure buildup at the injection well. Furthermore, this work shows how ML analytics can play a key role in estimating reservoir pressure responses for complex injection and extraction activities during pressure management practices of CCS projects.

View Accepted Manuscript (DOE)

Research Organization:: University of North Dakota, Grand Forks, ND (United States). Energy and Environmental Research Center

Sponsoring Organization:: USDOE Office of Fossil Energy and Carbon Management (FECM)

Grant/Contract Number:: FE0026160

OSTI ID:: 2564392

Journal Information:: International Journal of Greenhouse Gas Control, Journal Name: International Journal of Greenhouse Gas Control Vol. 131; ISSN 1750-5836

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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