Heterogeneity-assisted carbon dioxide storage in marine sediments

Dai, Zhenxue; Zhang, Ye; Bielicki, Jeffrey M.; Amooie, Mohammad Amin; Zhang, Mingkan; Yang, Changbing; Zou, Youqin; Ampomah, William; Xiao, Ting; Jia, Wei; Zhang, Wen; Sun, Youhong; Moortgat, Joachim; Soltanian, Mohamad Reza; Stauffer, Philip H.

doi:10.1016/j.apenergy.2018.05.038

Cited by 99 publications

(31 citation statements)

References 49 publications

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“…Geological carbon storage (GCS) is considered a feasible option to reverse the trend of rising carbon dioxide (CO 2 ) concentrations in atmosphere (Flett et al, 2007;Oldenburg and Doughty, 2011;Li et al, 2016;Abdi-Khanghah et al, 2018;Dai et al, 2018;Amooie et al, 2019;Soltanian et al, 2019;Bemani et al, 2020a;Erfani et al, 2020;Sadatshojaie and Rahimpour, 2020;Sturmer et al, 2020). Depleted oil and gas reservoirs, un-mineable coal seams, and deep saline aquifers are among the primary candidate formations for GCS.…”

Section: Introductionmentioning

confidence: 99%

CO2 geological sequestration in heterogeneous binary media: Effects of geological and operational conditions

Ershadnia

Wallace

Soltanian

2020

Adv. Geo-Energy Res.

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Realistic representation of subsurface heterogeneity is essential to better understand and effectively predict the migration and trapping patterns of carbon dioxide (CO 2) during geological carbon sequestration (GCS). Many candidate aquifers for GCS have sedimentary architectures which reflect fluvial deposition, where coarser-grained facies with higher-permeability (e.g., sandstone) are juxtaposed within finer-grained facies with lower-permeability (e.g., shale). Because the subsurface is difficult to access and sample, geostatistical methods are often used to model the spatial distribution of geological facies across different scales. We use a transition-probability based approach to simulate heterogeneous systems with binary facies distributions and the resulting petrophysical properties at the field scale. The approach produces heterogeneity fields which honor observable and physical facies attributes (e.g., volumetric proportions, mean lengths, and juxtapositional tendencies). Further, we use the associated facies-dependent properties for both relative permeability and capillary pressure relations and their hysteretic behavior. Heterogeneous facies models are used to investigate the sensitivity of different trapping mechanisms (i.e., dissolution, residual trapping) as well as CO 2 plume dynamics to variability in (1) the spatial organization and connectivity of sedimentary facies types; (2) aquifer temperature; (3) CO 2 injection period; (4) perforation length; and (5) the level of impurity, represented here as methane (CH 4) present in injected CO 2 streams. Model results show that the magnitudes of residual and solubility trapping are reduced by increasing the percentage and degree of connectivity of high-permeability facies. An increase in aquifer temperature leads to a decrease in residual trapping and an increase in solubility trapping. Results also reveal that for a given volume of injected CO 2 , shorter injection times yield higher total amounts of trapped CO 2. Similarly, wells perforated over a shorter thickness of the aquifer contribute to an increase in both residual and solubility trapping. We also find that increased CH 4 concentrations in the injected CO 2 streams decrease residual trapping while increasing solubility trapping. This effect is more pronounced at shallower depths, where the pressure and temperature of the aquifer are lower.

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Section: Introductionmentioning

confidence: 99%

CO2 geological sequestration in heterogeneous binary media: Effects of geological and operational conditions

Ershadnia

Wallace

Soltanian

2020

Adv. Geo-Energy Res.

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“…The computational costs associated with simulating these aspects of CCS can be prohibitive, necessitating the use of surrogate models. Although a large number of surrogate modeling studies have been conducted for CCS in the context of risk assessment, sensitivity analysis, UQ, and monitoring network design (Dai et al, ; Jeong & Srinivasan, , ; Keating et al, ; Oladyshkin et al, ; Pawar et al, ; Sun et al, , ), development of high‐fidelity surrogate models remains a challenging subject in the high‐dimensional decision space.…”

Section: Introductionmentioning

confidence: 99%

Predicting CO₂ Plume Migration in Heterogeneous Formations Using Conditional Deep Convolutional Generative Adversarial Network

Zhi

Sun

Jeong

2019

Water Resources Research

149

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Numerical simulation of flow and transport in heterogeneous formations has long been studied, especially for uncertainty quantification and risk assessment. The high computational cost associated with running large‐scale numerical simulations in a Monte Carlo sense has motivated the development of surrogate models, which aim to capture the important input‐output relations of physics‐based models but require only a fraction of the cost of full model runs. In this work, we formulate a conditional deep convolutional generative adversarial network (cDC‐GAN) surrogate model to learn the dynamic functional mappings in multiphase models. The cDC‐GAN belongs to a class of semisupervised learning methods that can be used to learn the data generation processes. Like the original GAN, a main strength of the cDC‐GAN is that it includes a self‐training scheme for improving the quality of generative modeling in a game theoretic framework, without requiring extensive statistical knowledge and assumptions on input data distributions. In particular, our cDC‐GAN model is designed to learn cross‐domain mappings between high‐dimensional input (e.g., permeability) and output (e.g., phase saturations) pairs, with the ability to incorporate conditioning information (e.g., prediction time). As a use case, we demonstrate the performance of cDC‐GAN for predicting the migration of carbon dioxide (CO2) plume in heterogeneous carbon storage reservoirs, which has both numerical and practical significance because of the safe storage requirements now mandated in many countries. Results show that cDC‐GAN achieves high accuracy in predicting the spatial and temporal evolution patterns of the injected CO2 plume, as compared to the original results obtained using a compositional reservoir simulator. The performance of cDC‐GAN models, trained using the same number of training samples, stays relatively robust when the level of spatial heterogeneity is increased. Our cDC‐GAN is pattern based and is not limited by the underlying physics. Thus, it provides a general framework for developing surrogate models, and for conducting uncertainty analyses for a wide range of physics‐based models used in both groundwater and subsurface energy exploration applications.

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“…Enhanced oil recovery techniques involve reservoir injection of gases, such as carbon dioxide and light hydrocarbons, among gas injection type medium, carbon dioxide is preferred over hydrocarbon gases because of its high flooding utility, relative lower cost and the potential for accompanying environmental profits through its disposal in the porous media (Gale, 2004;Haszeldine, 2009;Gershenzon, 2015;Dai, 2018). However, carbon dioxide displacement system brings about several changes of multiphase and multicomponent behavior of reservoir, and often causes organic solid phase deposition that blocks porous media throats and thus decreases reservoir permeability (Shelton & Yarborough, 1977;Stalkup, 1983;Tuttle, 1983;Monger, 1985;Mazzocchi et al, 1998).…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Law of Organic Solid Phase Precipitation and Deposition during Carbon Dioxide Displacement Process

Yu¹

2019

GEP

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Carbon Capture, Utilization and Storage (CCUS) has been regarded as an indispensable, strategic and pressing technology to reduce anthropogenic carbon dioxide emissions, and mitigate the severe consequences of climate change. Its utilization and storage play important roles in this system and they can be applied for oceanic and underground geological sequestration especially for the oil gas reservoir that needs to improve recovery. For the carbon dioxide flooding process, the crude oil displacement generally shows a better performance with the increase of the pressure. However, carbon disposal is always complex. It could encounter organic solid phase precipitation and deposition in near miscibility environment. The law of multiphase and multicomponent diversification in the whole processes is still poorly understood. We thus used the method of slim tube to get dynamic data during the process. Indeed, the interval of near minimum miscibility pressure was determined. Analysis results of injectivity index and productivity index show that the reservoir blockage primarily appears as the displacement pressure is higher than the near minimum miscibility lower limit pressure and plays an important role in the production capacity. Extortionate or low pressure is not conducive to carbon dioxide displacement.

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Heterogeneity-assisted carbon dioxide storage in marine sediments

Cited by 99 publications

References 49 publications

CO2 geological sequestration in heterogeneous binary media: Effects of geological and operational conditions

CO2 geological sequestration in heterogeneous binary media: Effects of geological and operational conditions

Predicting CO₂ Plume Migration in Heterogeneous Formations Using Conditional Deep Convolutional Generative Adversarial Network

Analysis of the Law of Organic Solid Phase Precipitation and Deposition during Carbon Dioxide Displacement Process

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Heterogeneity-assisted carbon dioxide storage in marine sediments

Cited by 99 publications

References 49 publications

CO2 geological sequestration in heterogeneous binary media: Effects of geological and operational conditions

CO2 geological sequestration in heterogeneous binary media: Effects of geological and operational conditions

Predicting CO2 Plume Migration in Heterogeneous Formations Using Conditional Deep Convolutional Generative Adversarial Network

Analysis of the Law of Organic Solid Phase Precipitation and Deposition during Carbon Dioxide Displacement Process

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Predicting CO₂ Plume Migration in Heterogeneous Formations Using Conditional Deep Convolutional Generative Adversarial Network