Assessing pore network heterogeneity across multiple scales to inform CO2 injection models

Butler, Shane K.; Barajas-Olalde, César; Yu, Xue; Burton-Kelly, Matthew E.; Mibeck, Blaise A.F.; Kong, Lingyun; Kurz, Bethany A.; Crandall, Dustin

doi:10.1016/j.ijggc.2023.104017

Cited by 2 publications

(5 citation statements)

References 44 publications

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“…First, the geological evaluation grade of the research area is determined. The evaluation of the potential and suitability for CO 2 geological storage in China is classified from low to high accuracy into national grade, basin grade, target area grade, site grade, and injection grade [37]. Guo et al [38] classified CO 2 storage sites into regional grade, basin grade, target area/target area grade, and site grade by adjusting the in scale and evaluation methods.…”

Section: Methods Of Ahp and Wim Modelsmentioning

confidence: 99%

“…Guo et al [38] classified CO 2 storage sites into regional grade, basin grade, target area/target area grade, and site grade by adjusting the in scale and evaluation methods. Previous classification standards [37,38] divide the CO 2 storage sites in coal goaf into regional grade, mine field grade, mine grade, and perfusion grade based on the distribution characteristics and discontinuity of the goaf in coal. The object, or the scale size of data collection, and field investigation of CO 2 storage sites are determined according to the degree of work in the research area (Table 1).…”

Section: Methods Of Ahp and Wim Modelsmentioning

confidence: 99%

“…The raw data are first standardized because of the significant dimensional differences between different indicators. The nondimensional data all between 0 and 1, compared with the standard, can be combined with the geological conditions of CO2 storage facilities at home and abroad [37][38][39] Third, the evaluation units are divided and refined using a GIS grid. The evaluation units for CO 2 storage are divided based on structures (faults, folds) and tunnels, etc., as boundaries.…”

Section: Methods Of Ahp and Wim Modelsmentioning

confidence: 99%

“…The raw data are first standardized because of the significant dimensional differences between different indicators. The nondimensional data all between 0 and 1, compared with the standard, can be combined with the geological conditions of CO 2 storage facilities at home and abroad [37][38][39]. The main influencing factors for the storage conditions of oil, natural gas, and coalbed methane are the geological safety factors, storage potential factors, socio-economic factors, and environmental protection factors.…”

Section: Methods Of Ahp and Wim Modelsmentioning

confidence: 99%

“…The main influencing factors were geological safety, storage potential, socio-economic, and environmental protection. These were based on the storage conditions of oil, natural gas, and coalbed methane combined with the geological conditions of CO 2 storage facilities at home and abroad [37][38][39].…”

Section: Classification Influencing Factorsmentioning

confidence: 99%

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Combined Analytic Hierarchy Process and Weighted Interval Method Models for the Geological Evaluation of CO2 Storage in Coal Goaf

Hou,

Xiao,

Liu

et al. 2024

Energies

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The increasing concentration of CO2 in the atmosphere is a major factor contributing to climate change. CO2 storage in coal goaf is a convenient, effective, and economical solution. Methods to quickly and effectively evaluate geological conditions are urgently required. The main influencing factors are geological safety, storage potential, economics, and environmental protection; these include 4 aspects, 38 indexes, and 4 index levels that can be quantified using classification levels. We established a geological evaluation model, using analytic hierarchy process (AHP) and weighted interval methods. AHP was used to determine its elements, indicators, and inter-layer relationships, as well as to clarify its structural relationships. The weight interval method is used to evaluate unstable elements, reducing their difficulty, and constant values are used to assign weights of stable elements to increase accuracy. This model was applied to assess the suitability of the goaf in Yaojie mine for geological CO2 storage. The results revealed that this goaf is an above average CO2 storage space, which was consistent with previous research. This geological CO2 storage evaluation model may also be used to assess the CO2 storage suitability of other coal goafs.

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Section: Methods Of Ahp and Wim Modelsmentioning

confidence: 99%

Section: Methods Of Ahp and Wim Modelsmentioning

confidence: 99%

Section: Methods Of Ahp and Wim Modelsmentioning

confidence: 99%

“…The raw data are first standardized because of the significant dimensional differences between different indicators. The nondimensional data all between 0 and 1, compared with the standard, can be combined with the geological conditions of CO 2 storage facilities at home and abroad [37][38][39]. The main influencing factors for the storage conditions of oil, natural gas, and coalbed methane are the geological safety factors, storage potential factors, socio-economic factors, and environmental protection factors.…”

Section: Methods Of Ahp and Wim Modelsmentioning

confidence: 99%

Section: Classification Influencing Factorsmentioning

confidence: 99%

See 3 more Smart Citations

Combined Analytic Hierarchy Process and Weighted Interval Method Models for the Geological Evaluation of CO2 Storage in Coal Goaf

Hou,

Xiao,

Liu

et al. 2024

Energies

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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 pore network heterogeneity across multiple scales to inform CO2 injection models

Cited by 2 publications

References 44 publications

Combined Analytic Hierarchy Process and Weighted Interval Method Models for the Geological Evaluation of CO2 Storage in Coal Goaf

Combined Analytic Hierarchy Process and Weighted Interval Method Models for the Geological Evaluation of CO2 Storage in Coal Goaf

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

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