Assessing the value of permeability data in a carbon capture and storage project

Puerta-Ortega, Carlos; Bickel, J. Eric; Hovorka, Susan

doi:10.1016/j.ijggc.2013.06.003

Cited by 3 publications

(1 citation statement)

References 36 publications

(25 reference statements)

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“…CO 2 is to be injected in a block delimited by faults and covering an area of 4.64 km 2 (equivalent reservoir radius is $1200 m). On the basis of the hydrocarbon accumulation and field history, the limiting faults may be sealing and the reservoir is considered closed [40,41], restricting the amount of CO 2 injection because of geomechanical constraints [42]. A high overpressure may jeopardize mechanical stability of the caprock (e.g.…”

Section: Example 3: Co 2 Storage Site Casementioning

confidence: 99%

Analytical model of well leakage pressure perturbations in a closed aquifer system

Zeidouni

2014

Advances in Water Resources

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Section: Example 3: Co 2 Storage Site Casementioning

confidence: 99%

Analytical model of well leakage pressure perturbations in a closed aquifer system

Zeidouni

2014

Advances in Water Resources

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A sequential decision and data analytics framework for maximizing value and reliability of CO2 storage monitoring

Tadjer

Hong

Bratvold

2021

Journal of Natural Gas Science and Engineering

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Absolute permeability estimation from microtomography rock images through deep learning super-resolution and adversarial fine tuning

de Castro Vargas Fernandes,

Duarte Vidal,

Carvalho Medeiros

et al. 2024

Sci Rep

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The carbon capture and storage (CCS) process has become one of the main technologies used for mitigating greenhouse gas emissions. The success of CCS projects relies on accurate subsurface reservoir petrophysical characterization, enabling efficient storage and captured $$\textrm{CO}_2$$ CO 2 containment. In digital rock physics, X-ray microtomography ($$\upmu $$ μ -CT) is applied to characterize reservoir rocks, allowing a more assertive analysis of physical properties such as porosity and permeability, enabling better simulations of porous media flow. Estimating petrophysical properties through numeric simulations usually requires high-resolution images, which are expensive and time-inefficient to obtain with $$\upmu $$ μ -CT. To address this, we propose using two deep learning models: a super-resolution model to enhance the quality of low-resolution images and a surrogate model that acts as a substitute for numerical simulations to estimate the petrophysical property of interest. A correction process inspired by generative adversarial network (GAN) adversarial training is applied. In this approach, the super-resolution model acts as a generator, creating high-resolution images, and the surrogate network acts as a discriminator. By adjusting the generator, images that correct the errors in the surrogate’s estimations are produced. The proposed method was applied to the DeePore dataset. The results shows the proposed approach improved permeability estimation overall.

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Assessing the value of permeability data in a carbon capture and storage project

Cited by 3 publications

References 36 publications

Analytical model of well leakage pressure perturbations in a closed aquifer system

Analytical model of well leakage pressure perturbations in a closed aquifer system

A sequential decision and data analytics framework for maximizing value and reliability of CO2 storage monitoring

Absolute permeability estimation from microtomography rock images through deep learning super-resolution and adversarial fine tuning

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