Domenico Baù

Importance of poroelastic coupling in dynamically active aquifers of the Po river basin, Italy.

Uncoupling between the flow field and the stress field in pumped aquifers is the basis of the classical groundwater hydrology. Recently, some authors have disputed the assumption of uncoupling with regard to both fluid dynamics and porous medium deformation. The issue is very important as it could undermine the traditional approach to simulate subsurface flow, analyze pumping tests, and predict land subsidence caused by fluid withdrawal. The present paper addresses the problem of coupling versus uncoupling in the Po river plain, a normally consolidated and normally pressurized basin which has experienced in the last 50 years a pronounced pore pressure drawdown because of water and gas removal and where a large hydromechanical database is available from the ground surface down to 4000 m depth. A numerical study is performed which shows that the matrix which relates flow to stress is very similar to the capacity matrix of the uncoupled flow equation. A comparison of results obtained with the finite element integration of the coupled and uncoupled models indicates that pore pressure is rather insensitive to coupling anywhere within the pumped formation while in the adjacent aquitard-aquifer units, coupling induces a slight overpressure which quickly dissipates in time with a small initial influence on medium deformation, and specifically on land subsidence. As a major consequence the uncoupled solutions to the fluid dynamic and the structural problems appear to be fully warranted on any timescale of practical interest in a typical normally consolidated and pressurized basin.

Optimal design of pump-and-treat systems under uncertain hydraulic conductivity and plume distribution.

In this work, we present a stochastic optimal control framework for assisting the management of the cleanup by pump-and-treat of polluted shallow aquifers. In the problem being investigated, hydraulic conductivity distribution and dissolved contaminant plume location are considered as the uncertain variables. The framework considers the subdivision of the cleanup horizon in a number of stress periods over which the pumping policy implemented until that stage is dynamically adjusted based upon new information that has become available in the previous stages. In particular, by following a geostatistical approach, we study the idea of monitoring the cumulative contaminant mass extracted from the installed recovery wells, and using these measurements to generate conditional realizations of the hydraulic conductivity field. These realizations are thus used to obtain a more accurate evaluation of the initial plume distribution, and modify accordingly the design of the pump-and-treat system for the remainder of the remedial process. The study indicates that measurements of contaminant mass extracted from pumping wells retain valuable information about the plume location and the spatial heterogeneity characterizing the hydraulic conductivity field. However, such an information may prove quite soft, particularly in the instances where recovery wells are installed in regions where contaminant concentration is low or zero. On the other hand, integrated solute mass measurements may effectively allow for reducing parameter uncertainty and identifying the plume distribution if more recovery wells are available, in particular in the early stages of the cleanup process.

Residual land subsidence near abandoned gas fields raises concern over northern Adriatic coastland

Many gas fields have been detected in the Northern Adriatic basin in the last 50 years. Gas production began in the early 1950s, and today some of the reservoirs are depleted. Other fields are currently under production or awaiting development in the near future (Figure 1a). One major environmental consequence of withdrawing gas from the ground is land subsidence. This can be a matter of great concern if the field is located below or close to low-lying and densely urbanized coastal areas. A ground elevation loss of only a few centimeters in these areas can enhance the ingress of sea water inland and expose the shore to flooding during high tides and severe storm events (Figure 2a).

Planning of Groundwater Supply Systems Subject to Uncertainty Using Stochastic Flow Reduced Models and Multi-Objective Evolutionary Optimization

The typical modeling approach to groundwater management relies on the combination of optimization algorithms and subsurface simulation models. In the case of groundwater supply systems, the management problem may be structured into an optimization problem to identify the pumping scheme that minimizes the total cost of the system while complying with a series of technical, economical, and hydrological constraints. Since lack of data on the subsurface system most often reflects upon the development of groundwater flow models that are inherently uncertain, the solution to the groundwater management problem should explicitly consider the tradeoff between cost optimality and the risk of not meeting the management constraints. This work addresses parameter uncertainty following a stochastic simulation (or Monte Carlo) approach, in which a sufficiently large ensemble of parameter scenarios is used to determine representative values selected from the statistical distribution of the management objectives, that is, minimizing cost while minimizing risk. In particular, the cost of the system is estimated as the expected value of the cost distribution sampled through stochastic simulation, while the risk of not meeting the management constraints is quantified as the expected value of the intensity of constraint violation. The solution to the multi-objective optimization problem is addressed by combining a multi-objective evolutionary algorithm with a stochastic model simulating groundwater flow in confined aquifers. Evolutionary algorithms are particularly appropriate in optimization problems characterized by non-linear and discontinuous objective functions and constraints, although they are also computationally demanding and require intensive analyses to tune input parameters that guarantee optimality to the solutions. In order to drastically reduce the otherwise overwhelming computational cost, a novel stochastic flow reduced model is thus developed, which practically allows for averting the direct inclusion of the full simulation model in the optimization loop. The computational efficiency of the proposed framework is such that it can be applied to problems characterized by large numbers of decision variables.

Land surface uplift above compacting overconsolidated reservoirs

An investigation on land surface deformation resulting from the compaction of producing overconsolidated gas/oil reservoirs is presented. If the depleted formation is significantly less compressible than the surrounding medium the vertical reservoir shrinkage is relatively smaller than the horizontal one, thus generating a possible swelling of the overburden and a consequent pronounced decrease of land subsidence or even a surface uplift. Such an effect may also cause an additional land settlement during the post-productive recovery phase over fields which exhibit a stiffer behavior in expansion. In the present paper the surface displacements caused by the depletion of an overconsolidated disk-shaped reservoir are analyzed by a finite element model taking into account the influence of the disk burial depth and areal extent, the Poisson ratio of the medium, and the underburden stiffness. The results obtained with a more realistic three-dimensional setting are also discussed to address the possible effects of an irregular reservoir geometry.

Estimating spatially-variable first-order rate constants in groundwater reactive transport systems

Numerical reactive transport models are often used as tools to assess aquifers contaminated with reactive groundwater solutes as well as investigating mitigation scenarios. The ability to accurately simulate the fate and transport of solutes, however, is often impeded by a lack of information regarding the parameters that define chemical reactions. In this study, we employ a steady-state Ensemble Kalman Filter (EnKF), a data assimilation algorithm, to provide improved estimates of a spatially-variable first-order rate constant λ through assimilation of solute concentration measurement data into reactive transport simulation results. The methodology is applied in a steady-state, synthetic aquifer system in which a contaminant is leached to the saturated zone and undergoes first-order decay. Multiple sources of uncertainty are investigated, including hydraulic conductivity of the aquifer and the statistical parameters that define the spatial structure of the parameter field. For the latter scenario, an iterative method is employed to identify the statistical mean of λ of the reference system. Results from all simulations show that the filter scheme is successful in conditioning the λ ensemble to the reference λ field. Sensitivity analyses demonstrate that the estimation of the λ values is dependent on the number of concentration measurements assimilated, the locations from which the measurement data are collected, the error assigned to the measurement values, and the correlation length of the λ fields.

Waterdrive dynamics and enhanced land subsidence over productive gas fields: Application to Dosso degli Angeli reservoir, Ravenna, Italy

An active waterdrive (or confining aquifer) generally plays a favorable role in sustaining the pore-pressure in productive gas reservoirs. However, it may contribute significantly to spread the pressure decline and enlarge the land settlement bowl around the field. Moreover, it may continue to compact, and hence produce additional subsidence, for a long time after the field abandonment. The dynamics of waterdrive depletion, and related compaction, are primarily controlled by the aquifer extent and its rock hydromechanical properties for any given field pumping program. After production has been completed water progressively floods the reservoir with a moving water-gas interface until a stable equilibrium is ultimately achieved. The process of residual waterdrive depressurization and concurrent reservoir repressurization is a nonlinear one, where nonrecoverable sediment compaction exerts an important influence on the overall occurrence. A nonlinear finite element model to predict land subsidence over a depleted gas field and the associated waterdrive is developed and applied to the gas reservoir of Dosso degli Angeli, Ravenna, Italy. Numerical results show that the waterdrive compaction accounts for two-thirds of the maximum land subsidence of more than 30 cm recorded in 1992 at the well shutdown and suggest that a residual land settlement of almost 10 cm is to be expected by the year 2042 over the two areas of Porto Garibaldi and Casal Borsetti, located a few kilometers south and north of the field, respectively.

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.

Geostatistical solution to the inverse problem using surrogate functions for remediation of shallow aquifers

Pump-and-treat (PAT) techniques are often applied to the remediation of dissolved chemicals from shallow aquifers. A related management problem typically consists of the selection of the pumping strategy and the most appropriate treatment method, in order to minimize the total cleanup cost while meeting a set of technical, economic and social constraints. However, due to scarcity of information about the hydrogeological system, stochastic modeling approaches seem more appropriate. Of primary concern is the inherent spatial variability of hydraulic conductivity. In general, the implementation of a remediation strategy assessed based on uncertain hydrogeological parameters leads to a decision involving the risk of constraint violations. The decision-making process may then be formulated as a multiobjective optimization framework where the optimality of a pumping pattern is traded off against its reliability. Operations may be structured into a stochastic optimal control problem, in which the remediation strategy is sequentially updated based upon new measurements collected during the actual cleanup process. The procedure requires the implementation of an inverse simulation model to estimate the stochastic hydrogeological parameters based on a set of potential measurements. In this work, we follow a geostatistical conceptual model where the spatial distribution of hydraulic conductivity is represented as a realization of a log- normally distributed stationary process, characterized by an exponential covariance function. Using the maximum likelihood method, the parameter estimation problem is solved by determining the set of geostatistical parameters -- average, variance, and correlation scales. Available data may include direct measurements of hydraulic conductivity, water table elevation at a number of monitoring wells, and contaminant mass extracted from active remediation wells. A rigorous solution to this optimization problem would require a stochastic flow and transport model to be included in the optimization loop to calculate the expected values and the covariance matrix of the available measurements as functions of the decision variables. Because of the overwhelming computational effort involved, a surrogate model or response surface is introduced to approximate the objective function. The surrogate model is estimated using a multidimensional kriging interpolation over a set of data points or measurements obtained from a series of stochastic flow and transport simulations for pre-established combinations of the decision variables. Since the goodness of the solution is ultimately determined by the error of estimation of the objective function, which in turn depends on the number and the location of measurements, an optimal search procedure is used in order to optimize the pattern of data points. The method turns out to be computationally efficient and produces results that well approximate the actual geostatistical distribution of hydraulic conductivity.