Enabling site-specific well leakage risk estimation during geologic carbon sequestration using a modular deep-learning-based wellbore leakage model

Baek, Seunghwan; Bacon, Diana H.; Huerta, Nicolas J.

doi:10.1016/j.ijggc.2023.103903

Title: Enabling site-specific well leakage risk estimation during geologic carbon sequestration using a modular deep-learning-based wellbore leakage model

Journal Article · 05 May 2023 · International Journal of Greenhouse Gas Control

DOI: https://doi.org/10.1016/j.ijggc.2023.103903 · OSTI ID:1988162

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Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Geologic carbon sequestration (GCS) is a promising technology for mitigating net carbon emissions and growing climate concern by storing CO₂ in reservoirs. Oil and gas brownfields are an attractive option for CO₂ storage, but these sites have many historical wellbores from petroleum production and can be a potential leakage pathway for CO₂ or formation brine. Therefore, risk management of GCS operations requires an assessment of potential well leakage. Due to the high uncertainty of the system, stochastic approaches are ideal for quantifying the range of risk behaviors, but they must be computationally efficient in the face of complex physics. Here, we develop a new physics-centric deep learning wellbore model to predict the leakage of CO₂ and brine through leaky wellbores. Multi-physics numerical simulations were used to generate data sets, and physics-informed features were introduced. Neural networks were optimized with an automated searching algorithm. Feature analysis quantifies the impact of each feature on model prediction and confirms the role of physics-inspired parameters. The model shows high predictive performance across a wide range of geologic and injection conditions and well attributes. In conclusion, a case study illustrates how the model is applied to assess well leakage in GCS operations.

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Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

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

Grant/Contract Number:: AC05-76RL01830; AC0576RL01830

OSTI ID:: 1988162

Alternate ID(s):: OSTI ID: 2000640

Report Number(s):: PNNL-SA-173631

Journal Information:: International Journal of Greenhouse Gas Control, Vol. 126; ISSN 1750-5836

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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58 GEOSCIENCES
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Title: Enabling site-specific well leakage risk estimation during geologic carbon sequestration using a modular deep-learning-based wellbore leakage model

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