Multicomponent Seismic Monitoring

Landrø, Martin

doi:10.1017/9781316480724.006

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Note that a rock physics model used for converting the Kimberlina model into the P‐wave velocity model is based on the average of the upper and lower Hashin–Shtrikman bounds (Mavko et al., 2020; Yang et al., 2019), Thus, the seismic training data are sensitive to a broader range of CO 2 saturation even at high CO 2 saturation levels. However, in practice, if a rock's physical relationship between P‐wave velocity and CO 2 saturation is close to the lower Hashin–Shtrikman bound, the sensitivity of the seismic imaging to the high concentration of CO 2 saturation would diminish (Davis et al., 2019; Gasperikova & Li, 2021; Kim et al., 2010). The non‐seismic methods such as EM and gravity methods can help to fill the gap.…”

Section: Deep Learning Multiphysics Imaging Experimentsmentioning

confidence: 99%

“…Geological carbon storage (GCS) is a viable option for reducing CO 2 emissions into the atmosphere (Benson & Cole, 2008; Davis et al., 2019; Metz et al., 2005; Ringrose, 2020). A large amount of CO 2 is captured from fossil‐fuel power stations and other major industrial CO 2 sources and is injected into depleted reservoirs or saline aquifers.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning multiphysics network for imaging CO₂ saturation and estimating uncertainty in geological carbon storage

Alumbaugh

Commer

et al. 2022

Geophysical Prospecting

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Multiphysics inversion exploits different types of geophysical data that often complement each other and aims to improve overall imaging resolution and reduce uncertainties in geophysical interpretation.Despite the advantages, traditional multiphysics inversion is challenging because it requires a large amount of computational time and intensive human interactions for preprocessing data and finding tradeoff parameters. These issues make it nearly impossible for traditional multiphysics inversion to be applied as a real-time monitoring tool for geological carbon storage. In this paper, we present a deep-learning (DL) multiphysics network for imaging CO 2 saturation in real time. The multiphysics network consists of three encoders for analyzing seismic, electromagnetic, and gravity data, and shares one decoder for combining imaging capabilities of the different geophysical data for better predicting CO 2 saturation. The network is trained on pairs of CO 2 label models and multiphysics data so that it can directly image CO 2 saturation. We use the bootstrap aggregating method to enhance the imaging accuracy and estimate uncertainties associated with CO 2 saturation images. Using realistic CO 2 label models and multiphysics data derived from the Kimberlina CO 2 storage model, we evaluate the performance of the DL multiphysics network and compare their imaging results to those from the DL single-physics networks.Our modeling experiments show that the DL multiphysics network for seismic, electromagnetic, and gravity data not only improves the imaging accuracy but also reduces uncertainties associated with CO 2 saturation images. Our results also suggest that the DL multiphysics network for the non-seismic data (i.e., electromagnetic and gravity) can be used as an effective low-cost monitoring tool in between regular seismic monitoring.

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Section: Deep Learning Multiphysics Imaging Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep learning multiphysics network for imaging CO₂ saturation and estimating uncertainty in geological carbon storage

Alumbaugh

Commer

et al. 2022

Geophysical Prospecting

View full text Add to dashboard Cite

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2021

Seismic Reservoir Modeling

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Joint Inversion of Geophysical Data for Geologic Carbon Sequestration Monitoring: A Differentiable Physics‐Informed Neural Network Model

et al. 2023

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Geologic CO 2 sequestration (GCS) is one of the main mitigation strategies to prevent carbon dioxide (CO 2 ) from entering the atmosphere by capturing and injecting it into subsurface geological formations, such as depleted oil reservoirs and deep saline aquifers (Aminu et al., 2017;Metz et al., 2005). To ensure safe and long-term CO 2 storage, geophysical monitoring surveys are periodically acquired to surveil the behavior of the injected CO 2 and make informed decisions for storage management (Davis et al., 2019). Time-lapse (4-D) seismic acquisition is one of the monitoring techniques widely deployed in GCS projects, where a series of seismic surveys are sequentially acquired to quantify changes in reservoir properties (e.g., elastic properties, fluid pressure and saturation) during and after CO 2 injection (Caspari et al.

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Multicomponent Seismic Monitoring

Cited by 5 publications

References 13 publications

Deep learning multiphysics network for imaging CO₂ saturation and estimating uncertainty in geological carbon storage

Deep learning multiphysics network for imaging CO₂ saturation and estimating uncertainty in geological carbon storage

References

Joint Inversion of Geophysical Data for Geologic Carbon Sequestration Monitoring: A Differentiable Physics‐Informed Neural Network Model

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Multicomponent Seismic Monitoring

Cited by 5 publications

References 13 publications

Deep learning multiphysics network for imaging CO2 saturation and estimating uncertainty in geological carbon storage

Deep learning multiphysics network for imaging CO2 saturation and estimating uncertainty in geological carbon storage

References

Joint Inversion of Geophysical Data for Geologic Carbon Sequestration Monitoring: A Differentiable Physics‐Informed Neural Network Model

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Deep learning multiphysics network for imaging CO₂ saturation and estimating uncertainty in geological carbon storage

Deep learning multiphysics network for imaging CO₂ saturation and estimating uncertainty in geological carbon storage