1,720,969 research outputs found
Characterization of flow through random media via Karhunen–Loève expansion: an information theory perspective
Full text linkWe leverage on information theory to assess the fidelity of approximated numerical stochastic groundwater flow simulations. We consider flow in saturated heterogeneous porous media, where the Karhunen–Loève (KL) expansion is used to express the hydraulic conductivity as a spatially correlated random field. We quantify the impact of the KL expansion truncation on the uncertainty associated with punctual values of hydraulic conductivity and flow velocity. In particular, we compare the statistical dependence between variables by considering (a) linear correlation metrics (Pearson coefficient of correlation) and (b) metrics capable of accounting for nonlinear dependence (coefficient of uncertainty based on mutual information). We test the selected metrics by analyzing the relationship between hydraulic conductivity fields generated via Monte Carlo sampling with different levels of truncation of the KL expansion and the corresponding fluid velocity fields, obtained through the numerical solution of Darcy’s flow. Our analysis shows that employing linear correlation metrics leads to a general overestimation of the correlation level and information theory based indicators are valuable tools to assess the impact of the KL truncation on the output velocity values. We then analyze the impact of the number of retained modes on the spatial organization of the velocity field. Results indicates that (i) as the number of modes decrease the spatial correlations of the velocity field increases; (ii) linear indicators of spatial correlation are again larger than their nonlinear counterparts
Impact of reservoir geochemistry on low salinity waterflooding: Global sensitivity analysis
No full textLow salinity waterflooding (LSW) has been receiving an increasing attention in the industry as it has several advantages over alternative strategies to enhanced oil recovery (EOR), i.e. i) outperforms conventional EOR methods in terms of additional oil recovery, ii) involves lower chemical costs, iii) it is environmentally friendly, and iv) requires a relatively simple field process implementation. We quantify low salinity effect (LSE) on oil recovery upon employing a mechanistic geochemical model under parametric uncertainty. Our ultimate goal is to characterize the impact of the reservoir rock and fluid properties on the performance of LSW. To this end, we propose a geochemical model of LSW built within a commercial compositional reservoir simulator. The model is designed to account for geochemical processes recently identified through experimental and pore-scale analyses, i.e. double layer expansion, multiple ion exchange and mineral dissolution/precipitation. The geochemical model outputs were first compared with two low salinity coreflood experiments reported in the literature and then applied to predict oil recovery under parametric uncertainty. Our study encompasses the effect of formation water ionic concentrations, rock mineralogy and reservoir temperature on LSW performance. Global sensitivity indices were calculated considering reasonable intervals of variability of the studied parameters. Our analysis focuses on the total oil recovery at three different time scales: 6 months (before water breakthrough), 2 years (after water breakthrough), and 10 years (the end of the injection). The results are discussed in the frame of quantitative screening criteria development, which is necessary to assess whether a given reservoir may be a promising candidate for LSW. Our results indicate that reservoir temperature is the parameter which demonstrated the most significant impact on the LSW performance. Ion concentration also displays a significant influence on oil recovery, and we identify different chemical species in carbonate and sandstone environments. On the contrary, mineral composition shows a limited influence on oil recovery. In particular, our results show that clay presence might be not essential for LSW to be effective in sandstone reservoirs. We discuss the impact of our results in the context of experimental design aimed at an improved constraining of LSW parameterization. Our study suggests that reservoir fluid composition (e.g., concentration of Ca2+, SO42− and Na+), in addition to reservoir temperature, should be prioritized in future experimental campaigns to better understand its influence on LSW under different reservoir and operation conditions
Formulation and probabilistic assessment of reversible biodegradation pathway of Diclofenac in groundwater
Full text linkWe present a conceptual and mathematical framework leading to the development of a biodegradation model capable to interpret the observed reversibility of the Pharmaceutical Sodium Diclofenac along its biological degradation pathway in groundwater. Diclofenac occurrence in water bodies poses major concerns due to its persistent (and bioactive) nature and its detection in surface waters and aquifer systems. Despite some evidences of its biodegradability at given reducing conditions, Diclofenac attenuation is often interpreted with models which are too streamlined, thus potentially hampering appropriate quantification of its fate. In this context, we propose a modeling framework based on the conceptualization of the molecular mechanisms of Diclofenac biodegradation which we then embed in a stochastic context, thus enabling one to quantify predictive uncertainty. We consider reference environmental conditions (biotic and denitrifying) associated with a set of batch experiments that evidence the occurrence of a reversible biotransformation pathway, a feature that is fully captured by our model. The latter is then calibrated in the context of a Bayesian modeling framework through an Acceptance-Rejection Sampling approach. By doing so, we quantify the uncertainty associated with model parameters and predicted Diclofenac concentrations. We discuss the probabilistic nature of uncertain model parameters and the challenges posed by their calibration with the available data. Our results are consistent with the recalcitrant behavior exhibited by Diclofenac in groundwater and documented through experimental data and support the observation that unbiased estimates of the hazard posed by Diclofenac to water resources should be assessed through a modeling strategy which fully embeds uncertainty quantification
Modeling solute transport and mixing in heterogeneous porous media under turbulent flow conditions
Full text linkWe develop and test a modeling approach to quantify turbulence-driven solute transport and mixing in porous media. Our approach addresses two key elements: (a) the spatial variability of the effective diffusion coefficient which is typically documented in the presence of a sediment-fluid interface and (b) the need to provide a model that can yield the complete distribution of the concentration probability density function, not being limited only to the mean concentration value and thus fully addressing solute mixing. Our work is motivated by the importance of solute transport processes in the hyporheic zone, which can have strong implications in natural attenuation of pollutants. Our approach combines Lagrangian schemes to address transport and mixing in the presence of spatial variability of effective diffusion. An exemplary scenario we consider targets a setup constituted by a homogeneous (fully saturated) porous medium underlying a clear water column where turbulent flow is generated. Solute concentration histories obtained through a model based solely on diffusive transport are benchmarked against an analytical solution. These are then compared against the results obtained by modeling the combined effects of diffusion and mixing. A rigorous sensitivity analysis is performed to evaluate the influence of model parameters on solute concentrations and mixing, the latter being quantified in terms of the scalar dissipation rate
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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