Analytical Model for Leakage Detection in CO<sub>2</sub> Sequestration in Deep Saline Aquifers: Application to ex Situ and in Situ CO<sub>2</sub> Sequestration Processes

Ahmadi, Mohammad Ali; Chen, Zhangxin

doi:10.1021/acsomega.9b02668

Cited by 11 publications

(5 citation statements)

References 54 publications

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“…Analytical solutions have been presented in the literature for studying the CO 2 leakage and plume migration along the leakage paths. Among these, the analytical models reported by Norbotten et al, 33 Ebigbo et al, 34 and Ahmadi and Chen 35 were developed using assumptions such as constant fluid properties, no gravity effect, and uniform temperature and pressure. Albeit the simplicity of the analytical models, the assumed parameters play a significant role in the migration and storage of CO 2 in geological formations.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Leaky Deep Saline Aquifer for Storing sc-CO₂

Pavan,

Devarapu,

Govindarajan

2024

Energy Fuels

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The increase in the emissions of greenhouse gases is driving the rapid climate change in the earth's environment. Carbon dioxide is one of the most abundant greenhouse gases emitted into the atmosphere. Capturing CO 2 gas is essential to limit gas emissions released into the surrounding environment. CO 2 injection into deep geological formations is a popular method of storing CO 2 . Among these formations, deep saline aquifers are known to possess higher compatibility for the better storage of CO 2 . However, the leakage pathways formed due to poorly sealed abandonment wells, faults, and fracture networks in the caprock reduce the CO 2 storage efficiency. Determining the leakage rates is essential for understanding the storage efficiency of the aquifer. Therefore, a numerical model is developed to evaluate the leakage rates economically. The present study considers a domain consisting of two interconnected aquifers with a leakage in the caprock to understand the influence of various characteristics of the leakage path and aquifer on the CO 2 leakage rates. The normalized leakage rate is also evaluated with time during CO 2 gas injection. The results from the developed model depicted the variations in leakage path distance, thickness of the leakage path, permeability, and depth of the aquifer to affect the CO 2 migration in the bottom aquifer and storage efficiency. The evaluated normalized leakage rates varied from 0.2 to 0.175 with a change in leakage distance, and these rates significantly increased from 0.04 to 0.38 with an increase in leakage thickness. In addition, the normalized leakage rate is changed from 0.22 to 0.0004 with a decrease in permeability in orders from 10 −13 to 10 −15 m 2 and becomes uniform at 0.2 with injection time for depth variation. In addition, the porosity of the leakage path has a negligible impact on the leakage rates and gas migration into the upper aquifer. Overall, the study ascertained the influence of the leakage path on the efficacy of the aquifer for storing CO 2 .

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Section: Introductionmentioning

confidence: 99%

Characterization of Leaky Deep Saline Aquifer for Storing sc-CO₂

Pavan,

Devarapu,

Govindarajan

2024

Energy Fuels

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“…Different methods have been developed to model the storage capacity, including analytical or semi-analytical studies and numerical approaches. For instance, the analytical study of quantifying leakage rates and storage used analytical models in the Laplace domain for CO 2 sequestration methods [29]. In addition, a semi-analytical solution was developed to provide a simple and practical approach to evaluating the potential of CO 2 leakage through abandoned wells in the subsurface storage site [30].…”

Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis of CO2 Storage in Deep Saline Aquifers Using Machine Learning and Bayesian Optimization

Abdulwahab

Yan

et al. 2023

Energies

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Geological CO2 sequestration (GCS) has been proposed as an effective approach to mitigate carbon emissions in the atmosphere. Uncertainty and sensitivity analysis of the fate of CO2 dynamics and storage are essential aspects of large-scale reservoir simulations. This work presents a rigorous machine learning-assisted (ML) workflow for the uncertainty and global sensitivity analysis of CO2 storage prediction in deep saline aquifers. The proposed workflow comprises three main steps: The first step concerns dataset generation, in which we identify the uncertainty parameters impacting CO2 flow and transport and then determine their corresponding ranges and distributions. The training data samples are generated by combining the Latin Hypercube Sampling (LHS) technique with high-resolution simulations. The second step involves ML model development based on a data-driven ML model, which is generated to map the nonlinear relationship between the input parameters and corresponding output interests from the previous step. We show that using Bayesian optimization significantly accelerates the tuning process of hyper-parameters, which is vastly superior to a traditional trial–error analysis. In the third step, uncertainty and global sensitivity analysis are performed using Monte Carlo simulations applied to the optimized surrogate. This step is performed to explore the time-dependent uncertainty propagation of model outputs. The key uncertainty parameters are then identified by calculating the Sobol indices based on the global sensitivity analysis. The proposed workflow is accurate and efficient and could be readily implemented in field-scale CO2 sequestration in deep saline aquifers.

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“…Hayek et al (2009) investigated CO2 stratification in the deep layered reservoir with consideration of the gravity effects. Ahmadi and Chen (2019) studied the CO2 storage capacity by predicting the pressure response in the sequestration process. Qiao et al (2021) introduced a semi-analytical solution to the pressure distribution and the leakage rate using the pressuretransient analysis, in which fluid compressibility is considered.…”

Section: Introductionmentioning

confidence: 99%

Locating CO2 Leakage in Subsurface Traps Using Bayesian Inversion and Deep Learning

Zhang

et al. 2023

Day 1 Sun, February 19, 2023

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Geologic CO2 sequestration (GCS) is a promising engineering measure to reduce global greenhouse emissions. However, accurate detection of CO2 leakage locations from underground traps remains a challenging problem. This study proposes a workflow that combines Bayesian inversion and deep learning algorithms to detect the sites of CO2 leakage. There are four main steps in the workflow. Step 1: we identify the key uncertainty parameters. Here we mean the CO2 leakage location. Then we get the training set using Latin Hypercube Sampling (LHS) method and perform the high-fidelity simulation using CMG. Step 2: we train the surrogate model using the data set collected from the last step, in which the Bayesian optimization is used to tune the hyperparameters automatically. Step 3: we perform the Bayesian inversion to invert the CO2 leakage location, in which the surrogate serves as the forward model to reduce the computational expense. Step 4: we feed the inverted CO2 leakage location into the high-fidelity model to produce the pressure response. If the error between the pressure response between the surrogate and the high-fidelity model is small enough, the solution is accepted. Otherwise, the accuracy of the surrogate model and the convergence of the Bayesian inversion process are revisited. We validate this method using a synthetic model of CO2 injection. Results show that the proposed Bayesian inversion assisted by the deep learning algorithm can accurately detect the CO2 leakage location with narrow uncertainties. This approach provides an accurate and efficient way to detect CO2 leakage locations in real-time applications.

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Analytical Model for Leakage Detection in CO₂ Sequestration in Deep Saline Aquifers: Application to ex Situ and in Situ CO₂ Sequestration Processes

Cited by 11 publications

References 54 publications

Characterization of Leaky Deep Saline Aquifer for Storing sc-CO₂

Characterization of Leaky Deep Saline Aquifer for Storing sc-CO₂

Uncertainty Analysis of CO2 Storage in Deep Saline Aquifers Using Machine Learning and Bayesian Optimization

Locating CO2 Leakage in Subsurface Traps Using Bayesian Inversion and Deep Learning

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Analytical Model for Leakage Detection in CO2 Sequestration in Deep Saline Aquifers: Application to ex Situ and in Situ CO2 Sequestration Processes

Cited by 11 publications

References 54 publications

Characterization of Leaky Deep Saline Aquifer for Storing sc-CO2

Characterization of Leaky Deep Saline Aquifer for Storing sc-CO2

Uncertainty Analysis of CO2 Storage in Deep Saline Aquifers Using Machine Learning and Bayesian Optimization

Locating CO2 Leakage in Subsurface Traps Using Bayesian Inversion and Deep Learning

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Analytical Model for Leakage Detection in CO₂ Sequestration in Deep Saline Aquifers: Application to ex Situ and in Situ CO₂ Sequestration Processes

Characterization of Leaky Deep Saline Aquifer for Storing sc-CO₂

Characterization of Leaky Deep Saline Aquifer for Storing sc-CO₂