Quantitative assessment of monitoring strategies for conformance verification of CO2 storage projects

Barros, Eduardo; Leeuwenburgh, O.; Szklarz, S.P.

doi:10.1016/j.ijggc.2021.103403

Cited by 8 publications

(3 citation statements)

References 54 publications

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“…Monitoring the CO2 injection gives operators assurance that the project is in conformance, reduces uncertainties existing at the outset of the operation, and addresses societal concerns (Dean & Tucker, 2017;Barros et al, 2021). Monitoring plans need to balance cost-efficiency and value of information (Bourne et al, 2014).…”

Section: Site Development and Engineeringmentioning

confidence: 99%

Subsurface carbon dioxide and hydrogen storage for a sustainable energy future

Krevor

Coninck

Gasda

et al. 2023

Nat Rev Earth Environ

174

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Limiting climate change to less than 1.5 o C would require vast quantities of CO2 storage in subsurface geological formations. Global injection rates projected by integrated assessment models synthesised in IPCC reports are on the order of ten gigatonnes per year by 2050. Industrial experience with megatonne per year storage projects allows us to evaluate the feasibility and potential limitations of a transition to the gigatonne scale. The successes with CO2 have also led to interest in new energy technologies using subsurface fluids, including hydrogen storage underground. We review the role of subsurface CO2 and H2 storage in a sustainable energy transition. We have found that current deployment demonstrates the viability of CO2 storage in a variety of geological, social, economic, and technological contexts, and is making contributions to climate change mitigation today commensurate with the impact of solar photovoltaics in the USA market. The implications of this are that CO2 storage is well positioned to play an important role in the energy transition, and H2 storage may benefit from this experience. However, these are not certain outcomes, with many hurdles -the development of multi-site regional scale storage, viable business models for accelerated deployment, demonstrating environmental sustainability and achieving societal acceptability -yet to be addressed.

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Section: Site Development and Engineeringmentioning

confidence: 99%

Subsurface carbon dioxide and hydrogen storage for a sustainable energy future

Krevor

Coninck

Gasda

et al. 2023

Nat Rev Earth Environ

174

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“…We refer to information reported in the literature (Zeng et al, 2013;Diao et al, 2017;Fan et al, 2014;Wang et al, 2014;Jin et al, 2017;Wang et al, 2018) and consider broad ranges of variability for the values of each uncertain model parameter 𝜃 𝑖 (see Table 2). Selection of the support ranges of each 𝜃 𝑖 values is set to represent broad ranges of variability based on typical measurement uncertainties (see, e.g., Sifuentes et al, 2009;Li and Liu, 2016;Ajayi et al, 2019;Barros et al, 2021). We set the support of 𝜃 1 = 𝐻 and 𝜃 2 = 𝐴 considering a ± 50 % and ± 20% range of variability around the corresponding mean values reported in the literature.…”

Section: Uncertainty Assessmentmentioning

confidence: 99%

Assessment and uncertainty quantification of onshore geological CO2 storage capacity in China

Ranaee

Khattar

Inzoli

et al. 2022

International Journal of Greenhouse Gas Control

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“…Carbon Capture and Storage (CCS) is a technology potentially reduce to reduce carbon emissions by up to 85% by 2050 [1]. The way the CCS system works is that when it is finished being injected, the CO2 in the permeable storage reservoir will be controlled by several conditions (fluid pressure, temperature, composition, and stress field) as well as rock properties (porosity, permeability, density) [2]. Above the permeable layer there is a seal (caprock) so that CO2 cannot move directly upwards.…”

Section: Introductionmentioning

confidence: 99%

The Implementation of a Deep Neural Network (DNN) Approach in a Case Study Predicting the Distribution of Carbon Dioxide (CO₂) Gas Saturation

Tsaniyah,

Komara,

Utama

2024

IOP Conf. Ser.: Earth Environ. Sci.

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Predicting the distribution of CO2 gas saturation is one example of how multiphase flow might be evaluated in Carbon Capture and Storage (CCS). The TOUGH2 simulator is one of the numerical simulations commonly used for multiphase flow simulation. Ordinary numerical simulations have several issues, including high grid spatial resolution and high processing costs. One of the most effective deep learning approaches to predicting the distribution of CO2 gas saturation is the deep neural network (DNN). A deep neural network is a network with three interconnected layers, there are input, hidden, and output layers. DNN learns about the previously constructed architecture from the input data. DNN requires a large quantity of data as input. Thus, in this study, we use 700 data points for each of the train_a and train_b variables. The distribution of CO2 gas saturation will be predicted automatically by the trained DNN model. This technique can handle complex data patterns, such as gas saturation in multiphase flow problems. The reconstruction loss findings show that the loss value decreases as the number of epochs increases. Furthermore, we used 3 and 4 epochs to determine the difference in results between the two. As a result, the model with 4 epochs and 10−3 regularization weight obtained the lowest error value of 0.4305. In summary, this model is capable of predicting CO2 gas saturation distribution, but more research is needed to produce more optimal results. This research hopes to help monitor multiphase flow in CCS systems in the future by forecasting the distribution of CO2 gas saturation.

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Quantitative assessment of monitoring strategies for conformance verification of CO2 storage projects

Cited by 8 publications

References 54 publications

Subsurface carbon dioxide and hydrogen storage for a sustainable energy future

Subsurface carbon dioxide and hydrogen storage for a sustainable energy future

Assessment and uncertainty quantification of onshore geological CO2 storage capacity in China

The Implementation of a Deep Neural Network (DNN) Approach in a Case Study Predicting the Distribution of Carbon Dioxide (CO₂) Gas Saturation

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Quantitative assessment of monitoring strategies for conformance verification of CO2 storage projects

Cited by 8 publications

References 54 publications

Subsurface carbon dioxide and hydrogen storage for a sustainable energy future

Subsurface carbon dioxide and hydrogen storage for a sustainable energy future

Assessment and uncertainty quantification of onshore geological CO2 storage capacity in China

The Implementation of a Deep Neural Network (DNN) Approach in a Case Study Predicting the Distribution of Carbon Dioxide (CO2) Gas Saturation

Contact Info

Product

Resources

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The Implementation of a Deep Neural Network (DNN) Approach in a Case Study Predicting the Distribution of Carbon Dioxide (CO₂) Gas Saturation