Ana González-Nicolás

Imaging and quantification of spreading and trapping of carbon dioxide in saline aquifers using meter‐scale laboratory experiments

The role of capillary forces during buoyant migration of CO2 is critical toward plume immobilization within the postinjection phase of a geological carbon sequestration operation. However, the inherent heterogeneity of the subsurface makes it very challenging to evaluate the effects of capillary forces on the storage capacity of these formations and to assess in situ plume evolution. To overcome the lack of accurate and continuous observations at the field scale and to mimic vertical migration and entrapment of realistic CO2 plumes in the presence of a background hydraulic gradient, we conducted two unique long-term experiments in a 2.44 m × 0.5 m tank. X-ray attenuation allowed measuring the evolution of a CO2-surrogate fluid saturation, thus providing direct insight into capillarity-dominated and buoyancy-dominated flow processes occurring under successive drainage and imbibition conditions. The comparison of saturation distributions between two experimental campaigns suggests that layered-type heterogeneity plays an important role on nonwetting phase (NWP) migration and trapping, because it leads to (i) longer displacement times (3.6 months versus 24 days) to reach stable trapping conditions, (ii) limited vertical migration of the plume (with center of mass at 39% versus 55% of aquifer thickness), and (iii) immobilization of a larger fraction of injected NWP mass (67.2% versus 51.5% of injected volume) as compared to the homogenous scenario. While these observations confirm once more the role of geological heterogeneity in controlling buoyant flows in the subsurface, they also highlight the importance of characterizing it at scales that are below seismic resolution (1–10 m).

Stochastic injection-strategy optimization for the preliminary assessment of candidate geological storage sites

Geological carbon sequestration (GCS) has been identified as having the potential to reduce increasing atmospheric concentrations of carbon dioxide (CO2). However, a global impact will only be achieved if GCS is cost-effectively and safely implemented on a massive scale. This work presents a computationally efficient methodology for identifying optimal injection strategies at candidate GCS sites having uncertainty associated with caprock permeability, effective compressibility, and aquifer permeability. A multi-objective evolutionary optimization algorithm is used to heuristically determine non-dominated solutions between the following two competing objectives: (1) maximize mass of CO2 sequestered and (2) minimize project cost. A semi-analytical algorithm is used to estimate CO2 leakage mass rather than a numerical model, enabling the study of GCS sites having vastly different domain characteristics. The stochastic optimization framework presented herein is applied to a feasibility study of GCS in a brine aquifer in the Michigan Basin (MB), USA. Eight optimization test cases are performed to investigate the impact of decision-maker (DM) preferences on Pareto-optimal objective-function values and carbon-injection strategies. This analysis shows that the feasibility of GCS at the MB test site is highly dependent upon the DM’s risk-adversity preference and degree of uncertainty associated with caprock integrity. Finally, large gains in computational efficiency achieved using parallel processing and archiving are discussed.RésuméLa séquestration géologique du carbone (SGC) a été identifiée comme offrant des potentialités de limitation de l’accroissement de la concentration du dioxyde de carbone (CO2) dans l’atmosphère. Cependant, un effet global ne sera obtenu que si la SGS est réalisée de manière rentable et sécuritaire à très grande échelle. Le présent travail expose une méthode analytiquement efficace pour identifier les stratégies d’injection optimales dans les sites candidats à la SGS présentant une incertitude associée à la perméabilité de la roche couverture, la compressibilité effective et la conductivité hydraulique de l’aquifère. Un algorithme d’optimisation évolutive multi-objectifs est utilisé pour déterminer de manière heuristique les solutions non dominées entre les deux objectifs compétitifs suivants : (1) maximiser la masse de CO2 séquestrée et (2) minimiser le coût de projet. Un algorithme semi-analytique est utilisé, de préférence à un modèle numérique, pour estimer la fuite massique de CO2, ce qui permet d’étudier des sites SGC ayant des caractéristiques physiques extrêmement différentes. La structure d’optimisation stochastique présentée ici est appliquée à l’étude de faisabilité de la séquestration géologique du carbone (SGC) dans un aquifère sursalé du Bassin du Michigan (BM), Etats Unis. Huit cas-tests d’optimisation sont réalisés pour reconnaître l’impact des choix du décideur sur les valeurs de la fonction de Pareto - objectif optimal - et les stratégies d’injection du carbone. Cette analyse montre que la faisabilité de la SGC dans le site test du Bassin du Michigan est fortement dépendante du choix du décideur en matière de tolérance au risque et du degré d’incertitude relative à l’intégrité de la roche de couverture. Pour finir sont discutés les gains importants d’efficacité analytique obtenus en utilisant en parallèle des traitements et l’archivage.ResumenEl secuestro geológico de carbono (GCS) se ha identificado por tener potencial de reducir el incremento de las concentraciones atmosféricas de dióxido de carbono (CO2). Sin embargo, un impacto global se logrará si el GCS es económico, eficaz y seguro de ser implementado en una escala masiva. Este trabajo presenta una metodología eficiente computacionalmente para identificar estrategias óptimas de inyección en los sitios pretendidos teniendo una incertidumbre asociada a la permeabilidad de la roca sello, a la compresibilidad efectiva y a la permeabilidad del acuífero. Un algoritmo de optimización evolutiva multiobjetivo se utiliza para determinar heurísticamente soluciones no dominadas entre los siguientes dos objetivos contrapuestos: (1) maximizar la masa de CO2 secuestrado y (2) minimizar el costo del proyecto. Se usó un algoritmo semianalítico para estimar las fugas en masa de CO2 en lugar de un modelo numérico, lo que permite el estudio de sitios GCS que tienen muy diferentes características de dominio. El marco de optimización estocástica presentado en este documento se aplica a un estudio de viabilidad de GCS en un acuífero de salmuera en la Cuenca de Michigan (MB), EE.UU. Ocho casos de pruebas de optimización se llevan a cabo para investigar el impacto de la toma de decisiones (DM), las preferencias en los valores de la función objetivo y las estrategias de inyección de carbono óptima en el sentido de Pareto. Este análisis muestra que la viabilidad del GCS en el sitio de la prueba en MB es altamente dependiente de la preferencia al riesgo de la adversidad de la DM y su grado de incertidumbre asociado a la integridad de la roca sello. Finalmente, se discuten las grandes ganancias en eficiencia computacional logradas utilizando el procesamiento y archivo en paralelo.摘要地质碳封存被认定具有降低日益增加的二氧化碳大气含量的潜力。然而,如果地质碳封存能够大规模低成本和安全地实施,才能取得全球性的效果。本研究介绍了确定那些盖层渗透性、有效压缩性和含水层渗透性具有不确定性的候选地质碳封存场地最优注入战略的一种计算上高效率的方法。利用多目标进化优化算法确定了下面两个相互矛盾的目标之间的非主导解决方案:(1)使封存的二氧化碳量最大;(2)使项目成本最小。利用半解析算法而不是数值模型估算了二氧化碳的泄漏量,使地质碳封存场地的研究具有更大不同的域特征。在此展示的随机最优化框架应用到了美国密西根流域一个卤水含水层地质碳封存的可行性研究中。进行了八次最优化实验以调查决策者参数选择对帕累托-最优目标函数值和碳注入战略的影响。这项分析显示,密西根实验场地地质碳封存的可行性高度依赖于决策者的风险-灾难参数选择和与盖层完整性相关的不确定性程度。最后,论述了采用平行处理和归档获取的计算效率方面的成果。ResumoO sequestro geológico de carbono (SGC) tem sido identificado como tendo o potencial de reduzir as concentrações de dióxido de carbono (CO2) na atmosfera. Entretanto, o impacto global somente será alcançado se o SGC for implementado de forma rentável e segura em larga escala. Este trabalho apresenta uma metodologia computacional eficiente para identificar as melhores estratégias de injeção em locais candidatos à SGC com incertezas associadas à permeabilidade da rocha de cobertura, compressão efetiva e permeabilidade do aquífero. Um algoritmo de otimização evolutiva multi-objetivo é usado para determinar heuristicamente soluções não-dominadas entre os dois seguintes objetivos conflitantes: (1) maximizar a massa de CO2 sequestrada e (2) minimizar o custo do projeto. Um algoritmo semi-analítico é utilizado para estimar a massa de CO2 liberada ao invés de um modelo numérico, permitindo o estudo de locais de SGC com uma vasta diferença de características dominantes. O arcabouço estocástico de otimização aqui apresentado é aplicado a um estudo de viabilidade de SGC em um aquífero salino na Bacia de Michigan (BM), EUA. Foram realizados oito testes de otimização para investigar o impacto das preferencias do tomador de decisão (TD) nos valores da função objetivo da eficiência Pareto e nas estratégias de injeção de carbono. Esta análise mostra que a viabilidade do SGC na BM é altamente dependente das preferências risco-adversidade do TD e do grau de incerteza associado à integridade da rocha de cobertura. Por fim, são discutidos grandes ganhos de eficiência computacional obtidos utilizando processamento paralelo e arquivamento.

An iterative global pressure solution for the semi-analytical simulation of geological carbon sequestration

Successful large-scale implementation of geological CO2 sequestration (GCS) will require the preliminary assessment of multiple potential injection sites. Risk assessment and optimization tools used in this effort typically require large numbers of simulations. This makes it important to choose the appropriate level of complexity when selecting the type of simulation model. A promising multi-phase semi-analytical method proposed by Nordbotten et al. (Environ. Sci. Technol. 43, 743–749 2009) to estimate key system attributes (i.e., pressure distribution, CO2 plume extent, and fluid migration) has been found to reduce computational run times by three orders of magnitude when compared to other standard numerical techniques. The premise of the work presented herein is that the existing semi-analytical leakage algorithm proposed by Nordbotten et al. (Environ. Sci. Technol. 43, 743–749 2009) may be further improved in computational efficiency by applying a fixed-point-type iterative global pressure solution to eliminate the need to solve large sets of linear equations at each time step. Results show that significant gains in computational efficiency are obtained with this new methodology. In addition, this modification provides the same enhancement to similar semi-analytical algorithms that simulate single-phase injection into multi-layer domains.

Investigation of representing hysteresis in macroscopic models of two‐phase flow in porous media using intermediate scale experimental data

Author(s): Cihan, A; Birkholzer, J; Trevisan, L; Gonzalez-Nicolas, A; Illangasekare, T | Abstract:

Metamodeling-based approach for risk assessment and cost estimation: Application to geological carbon sequestration planning

Abstract Carbon capture and storage (CCS) is being evaluated globally as a geoengineering measure for significantly reducing greenhouse emission. However, long-term liability associated with potential leakage from these geologic repositories is perceived as a main barrier of entry to site operators. Risk quantification and impact assessment help CCS operators to screen candidate sites for suitability of CO 2 storage. Leakage risks are highly site dependent, and a quantitative understanding and categorization of these risks can only be made possible through broad participation and deliberation of stakeholders, with the use of site-specific, process-based models as the decision basis. Online decision making, however, requires that scenarios be run in real time. In this work, a Python based, Leakage Assessment and Cost Estimation (PyLACE) web application was developed for quantifying financial risks associated with potential leakage from geologic carbon sequestration sites. PyLACE aims to assist a collaborative, analytic-deliberative decision making processes by automating metamodel creation, knowledge sharing, and online collaboration. In PyLACE, metamodeling, which is a process of developing faster-to-run surrogates of process-level models, is enabled using a special stochastic response surface method and the Gaussian process regression. Both methods allow consideration of model parameter uncertainties and the use of that information to generate confidence intervals on model outputs. Training of the metamodels is delegated to a high performance computing cluster and is orchestrated by a set of asynchronous job scheduling tools for job submission and result retrieval. As a case study, workflow and main features of PyLACE are demonstrated using a multilayer, carbon storage model.