Downhole pressure and chemical monitoring for CO2 and brine leak detection in aquifers above a CO2 storage reservoir

Buscheck, Thomas A.; Mansoor, Kayyum; Wainwright, Haruko M.; Carroll, Susan A.

doi:10.1016/j.ijggc.2019.102812

Cited by 17 publications

(8 citation statements)

References 43 publications

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“…Moreover, the presence of a less conductive unit magnifies pressure buildups (Figures 7 and 8) as well as the alterations to the aquifer flow field (Figure 9). Therefore, aquifer pressure monitoring at different depths and locations within the aquifer could be indeed useful to detect and characterize a CO 2 leakage since its early stages (Strandli and Benson 2013; Cameron et al 2016; Buscheck et al 2019), especially if combined with other downhole monitoring and surface‐based geophysical techniques (Yang et al 2019).…”

Section: Resultsmentioning

confidence: 99%

“…Several scientific contributions have addressed this issue so far. However, the majority of these, whether numerical (Lindeberg 1997; Ide et al 2007; Zhou et al 2008; Birkholzer et al 2009; Kopp et al 2010; Strandli and Benson 2013; Zheng et al 2013; Bachu 2015, and references therein; Bandilla et al 2015, and references therein; Gershenzon et al 2015, 2017; Buscheck et al 2019), analytical (Nordbotten et al 2005a; Nordbotten et al 2005b; Nordbotten and Celia 2006; Cihan et al 2012; Szulczewski et al 2014), or experimental (Pini et al 2012; Trevisan et al 2014; Krevor et al 2015, and references therein; Trevisan et al 2017), mostly focused on carbon dioxide behavior in the deep subsurface under conditions resembling its liquid or supercritical state. Existing field scale experiments of gaseous CO 2 releases in the subsurface and associated numerical efforts (Lewicki et al 2007; Cahill and Jakobsen 2013, 2015; Cahill et al 2014; Basirat et al 2016; Pezard et al 2016) put much emphasis on the impacts on shallow groundwater quality and on the response of near‐surface leakage detection methodologies rather than on carbon dioxide flow behavior.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Modeling GaseousCO₂Flow Behavior in Layered Basalts: Dimensional Analysis and Aquifer Response

et al. 2021

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Particular attention is paid to the risk of carbon dioxide (CO2) leakage in geologic carbon sequestration (GCS) operations, as it might lead to the failure of sequestration efforts and to the contamination of underground sources of drinking water. As carbon dioxide would eventually reach shallower formations under its gaseous state, understanding its multiphase flow behavior is essential. To this aim, a hypothetical gaseous leak of carbon dioxide resulting from a well integrity failure of the GCS system in operation at Hellisheiði (CarbFix2) is here modeled. Simulations show that migration of gaseous carbon dioxide is largely affected by formation stratigraphy, intrinsic permeability, and retention properties, whereas the initial groundwater hydraulic gradient (0.0284) has practically no effect. In two different scenarios, about 18.3 and 30.6% of the CO2 that would have been injected by the GCS system for 3 days could be potentially released again into the atmosphere due to a sustained leakage of the same duration. As the gaseous leak occurs, the aquifer experiences high pressure buildups, and the presence of a less conductive layer further magnifies these. Strikingly, the dimensional analysis showed that buoyant and viscous forces can be comparable over time within the predicted gaseous plumes, even far from the leakage source. Local pressure gradients, buoyant, viscous, and capillary forces all play an important role during leakage. Therefore, neglecting one or more of these contributions might lead to a partial prediction of gaseous CO2 flow behavior in the subsurface, giving space to incorrect interpretations and wrong operational choices.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Modeling GaseousCO₂Flow Behavior in Layered Basalts: Dimensional Analysis and Aquifer Response

et al. 2021

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“…The Kimberlina dataset is generated based on a hypothetical numerical model built on the geologic structure of a commercial-scale geologic carbon sequestration (GCS) reservoir at the Kimberlina site in the southern San Joaquin Basin, 30 km northwest of Bakersfield, CA, USA. The P-wave and S-wave velocity maps used in this work belong to the geophysical model, which is created based on the realistic geologic-layer properties from the GCS site [3].…”

Section: B Data Descriptionmentioning

confidence: 99%

“…This augmentation employs the forward model to produce new training data that are more representative of the solution we seek. To validate its performance, we applied our inversion method to detect carbon sequestration leakage using synthetic seismic data sets generated using a subsurface model for a potential CO 2 storage site at Kimberlina, California [3].…”

Section: Introductionmentioning

confidence: 99%

Physics-Consistent Data-Driven Waveform Inversion With Adaptive Data Augmentation

Rojas-Gomez

Yang

Lin

et al. 2022

IEEE Geosci. Remote Sensing Lett.

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Seismic full-waveform inversion (FWI) is a nonlinear computational imaging technique that can provide detailed estimates of subsurface geophysical properties. Solving the FWI problem can be challenging due to its ill-posedness and high computational cost. In this work, we develop a new hybrid computational approach to solve FWI that combines physicsbased models with data-driven methodologies. In particular, we develop a data augmentation strategy that can not only improve the representativity of the training set, but also incorporate important governing physics into the training process and therefore improve the inversion accuracy. To validate the performance, we apply our method to synthetic elastic seismic waveform data generated from a subsurface geologic model built on a carbon sequestration site at Kimberlina, California. We compare our physics-consistent data-driven inversion method to both purely physics-based and purely data-driven approaches and observe that our method yields higher accuracy and greater generalization ability.

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“…The simulation procedure consists of four modules: a CO 2 storage reservoir model (Fig. 1(a)), a wellbore [27], [28].…”

Section: B Small Co 2 Leak Detection and Kimberlina Datasetmentioning

confidence: 99%

Making Invisible Visible: Data-Driven Seismic Inversion with Spatio-temporally Constrained Data Augmentation

Yang,

Zhang,

Guan

et al. 2021

Preprint

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Deep learning and data-driven approaches have shown great potential in scientific domains. The promise of datadriven techniques relies on the availability of a large volume of high-quality training datasets. Due to the high cost of obtaining data through expensive physical experiments, instruments, and simulations, data augmentation techniques for scientific applications have emerged as a new direction for obtaining scientific data recently. However, existing data augmentation techniques originating from computer vision, yield physically unacceptable data samples that are not helpful for the domain problems that we are interested in. In this paper, we develop new physicsinformed data augmentation techniques based on convolutional neural networks. Specifically, our generative models leverage different physics knowledge (such as governing equations, observable perception, and physics phenomena) to improve the quality of the synthetic data. To validate the effectiveness of our data augmentation techniques, we apply them to solve a subsurface seismic full-waveform inversion using simulated CO2 leakage data. Our interest is to invert for subsurface velocity models associated with very small CO2 leakage. We validate the performance of our methods using comprehensive numerical tests. Via comparison and analysis, we show that data-driven seismic imaging can be significantly enhanced by using our physics-informed data augmentation techniques. Particularly, the imaging quality has been improved by 15% in test scenarios of general-sized leakage and 17% in small-sized leakage when using an augmented training set obtained with our techniques.

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Downhole pressure and chemical monitoring for CO2 and brine leak detection in aquifers above a CO2 storage reservoir

Cited by 17 publications

References 43 publications

Modeling GaseousCO₂Flow Behavior in Layered Basalts: Dimensional Analysis and Aquifer Response

Modeling GaseousCO₂Flow Behavior in Layered Basalts: Dimensional Analysis and Aquifer Response

Physics-Consistent Data-Driven Waveform Inversion With Adaptive Data Augmentation

Making Invisible Visible: Data-Driven Seismic Inversion with Spatio-temporally Constrained Data Augmentation

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Downhole pressure and chemical monitoring for CO2 and brine leak detection in aquifers above a CO2 storage reservoir

Cited by 17 publications

References 43 publications

Modeling GaseousCO2Flow Behavior in Layered Basalts: Dimensional Analysis and Aquifer Response

Modeling GaseousCO2Flow Behavior in Layered Basalts: Dimensional Analysis and Aquifer Response

Physics-Consistent Data-Driven Waveform Inversion With Adaptive Data Augmentation

Making Invisible Visible: Data-Driven Seismic Inversion with Spatio-temporally Constrained Data Augmentation

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Product

Resources

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Modeling GaseousCO₂Flow Behavior in Layered Basalts: Dimensional Analysis and Aquifer Response

Modeling GaseousCO₂Flow Behavior in Layered Basalts: Dimensional Analysis and Aquifer Response