On CO2 Behavior in the Subsurface, Following Leakage from aGeologic Storage Reservoir

Pruess, Karsten

doi:10.2172/920245

Cited by 7 publications

(3 citation statements)

References 43 publications

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“…The injection of supercritical CO 2 into the storage complex perturbs the physical, chemical and thermal environment of the reservoir [Newell and Ilgen, 2019]. Because CO 2 injection increases the pressure, this process may trigger CO 2 leakage across the seal when the pressure increase induces opening of pre-existing faults or fractures zones [Pruess, 2006, Ringrose, 2020. To ensure safe operations of CO 2 storage, we develop a quantitative leakage detection tool based on a deep neural classifier.…”

Section: Deep Neural Network Classifier For Co 2 Leakage Detectionmentioning

confidence: 99%

De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection

Yin¹,

Erdinc²,

Gahlot³

et al. 2022

Preprint

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Geological carbon storage represents one of the few truly scalable technologies capable of reducing the CO2 concentration in the atmosphere. While this technology has the potential to scale, its success hinges on our ability to mitigate its risks. An important aspect of risk mitigation concerns assurances that the injected CO2 remains within the storage complex. Amongst the different monitoring modalities, seismic imaging stands out with its ability to attain high resolution and high fidelity images. However, these superior features come, unfortunately, at prohibitive costs and time-intensive efforts potentially rendering extensive seismic monitoring undesirable. To overcome this shortcoming, we present a methodology where time-lapse images are created by inverting non-replicated time-lapse monitoring data jointly. By no longer insisting on replication of the surveys to obtain high fidelity time-lapse images and differences, extreme costs and time-consuming labor are averted. To demonstrate our approach, hundreds of noisy time-lapse seismic datasets are simulated that contain imprints of regular CO2 plumes and irregular plumes that leak. These time-lapse datasets are subsequently inverted to produce time-lapse difference images used to train a deep neural classifier. The testing results show that the classifier is capable of detecting CO2 leakage automatically on unseen data and with a reasonable accuracy.

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Section: Deep Neural Network Classifier For Co 2 Leakage Detectionmentioning

confidence: 99%

De-risking geological carbon storage from high resolution time-lapse seismic to explainable leakage detection

Yin¹,

Erdinc²,

Gahlot³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…As the bubbles rise and expand, they will cool from the Joule-Thomson effect (Pruess, 2006;Oldenburg, 2006). According to Pruess (2006) Joule-Thomson cooling could be appreciable if heat exchange with surroundings is ignored.…”

Section: Joule-thomson Coolingmentioning

confidence: 99%

“…According to Pruess (2006) Joule-Thomson cooling could be appreciable if heat exchange with surroundings is ignored. Oldenburg (2006) in a simulation of the injection of CO 2 into a reservoir determined the cooling effect would be small when heat exchange is taken into account.…”

Section: Joule-thomson Coolingmentioning

confidence: 99%