1,720,964 research outputs found
Spatial Patterns Emerging from a Stochastic Process Near Criticality
Full text linkThere is mounting empirical evidence that many communities of living organisms display key features which closely resemble those of physical systems at criticality. We here introduce a minimal model framework for the dynamics of a community of individuals which undergoes local birth-death, immigration, and local jumps on a regular lattice. We study its properties when the system is close to its critical point. Even if this model violates detailed balance, within a physically relevant regime dominated by fluctuations, it is possible to calculate analytically the probability density function of the number of individuals living in a given volume, which captures the close-to-critical behavior of the community across spatial scales. We find that the resulting distribution satisfies an equation where spatial effects are encoded in appropriate functions of space, which we calculate explicitly. The validity of the analytical formulae is confirmed by simulations in the expected regimes. We finally discuss how this model in the critical-like regime is in agreement with several biodiversity patterns observed in tropical rain forests
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
Seismic reservoir characterization in offshore Nile Delta. Part II: probabilistic petrophysical-seismic inversion.
No full textReservoir characterization plays an essential role in integrated exploration and reservoir studies, as it provides an optimal understanding of the reservoir internal architecture and properties. In reservoir characterization studies seismic reflection data are often used to derive petrophysical rock properties (water saturation, porosity, shale content) from elastic parameters (seismic velocities, rock density or impedances). The rock-physics model is the link between elastic properties and such petrophysical parameters and it can be based on theoretical rock-physics equations or on empirical set of equations derived from available information (well-log or core data) and valid for the specific case of interest.
The inverse problem of estimating petrophysical properties from seismic reflection data is multidimensional, ill-posed and it is strongly affected by noise and measurements errors. Therefore, it is not a surprise that the statistical approach to seismic reservoir characterization has become the most popular approach as it is able to take into account the uncertainties associated with the simplified rock-physics model, the error in the seismic data, and the natural variability of the petrophysical properties in the subsurface. The goal of this approach is to predict the probability of petrophysical variables when seismic velocities or impedances and density are assigned, and to capture the heterogeneity and complexity of the rocks and the uncertainty associated with the rock-physics model. For many examples of applications of this approach to reservoir characterization studies constrained by seismic and well-log data see for example Avseth et al. (2005).
In this paper we apply a two-step procedure to seismic reservoir characterization. The first step is a Bayesian linearized amplitude versus angle inversion (AVA) in which, on the line of Buland and Omre (2003) and Chiappa and Mazzotti (2009), we derive the elastic properties of the subsurface and their associated uncertainties assuming Gaussian-distributed errors and Gaussian-distributed elastic characteristics. The second step is a petrophysical inversion that uses the outcomes of AVA inversion, the previously defined rock-physics model, their associated uncertainties and the prior distribution of the petrophysical variables, to derive the probability distributions of the petrophysical properties in the target zone. The derivation and the calibration of different rock-physics models is the topic of the companion paper titled “Seismic reservoir characterization in offshore Nile Delta. Part I: Comparing different methods to derive a reliable rock-physics model”. In that paper the empirical, linear, rock-physics model derived with a multilinear stepwise regression (named SR in the companion paper) and the theoretical rock-physics model (named TRPM in the companion paper) demonstrated to be the most reliable in predicting the elastic characteristics from the petrophysical properties. Then, these two rock-physics models are applied in the petrophysical inversion described here. In the context of petrophysical inversion the main difference of applying a linear or a non-linear rock-physics model lies in the fact that the former allows the joint distribution of petrophysical and elastic properties to be analytically computed, while the latter requires a Monte Carlo simulation to derive such joint distribution.
We start with a brief theoretical description of the method and with a synthetic example based on actual well-log measurements. This test aims to demonstrate the applicability of the inversion method and to illustrate and compare the different results obtained by considering the empirical and the theoretical rock-physics models. Moreover, this synthetic test allows us to check the applicability and the reliability of the two rock-physics models in the specific case under examination. Then, a field case inversion is discussed. This inversion is performed for a single CMP location where well-control is available to validate the results
Seismic reservoir characterization in offshore Nile Delta. Part I: comparing different methods to derive a reliable rock-physics model.
No full textSeismic-reflection data are used in reservoir characterization not only for obtaining a geometric description of the main subsurface structures but also for estimating properties like lithologies and fluid contents of the target levels of interest. To this end, a rock-physics model (RPM) is incorporated into a seismic inversion scheme, such as amplitude versus angle (AVA) inversion (Grana and Della Rossa, 2011) or full-waveform inversion (Bacharach, 2006), to directly derive petrophysical rock properties from pre-stack seismic data. The outcomes of petrophysical-seismic inversion provide reservoir property maps to reservoir engineers for field appraisal, selection of optimal well location, and production enhancement (Bosh et al. 2010). A rock-physics model is a generic transformation (fRPM) that can be expressed as follow:
The RPM relates rock properties (which typically are porosity - φ -, water saturation - Sw - , shale content - Sh -) and the depth (z), that can be easily related to the pressure conditions, to elastic attributes (such as P-wave and S-wave velocities - Vp, Vs - and density). A rock-physics model can be based on theoretical equations (Avseth et al. 2005), or on empirical set of equations derived from available information (e.g. well-log or core measurements) for the specific case of interest (Mazzotti and Zamboni, 2003). In the last case, either a linear or a non-linear model can be considered (Eberhart-Phillips et al. 1989). In case of a non-linear approach many methods can be used to derive such rock-physics model. Among the non-linear approaches neural networks (Saggaf et al. 2003) and stochastic optimizations (Aleardi, 2015) have received great attention. Anyway, independently from the method used, there is no doubt that the quality and the reliability of available well-log data and/or core measurements play an essential role in defining a solid RPM.
The aim of this work is derive a reliable RPM to be used in conjunction with an AVA inversion for the characterization of a clastic reservoir located in offshore Nile delta. To derive the RPM both theoretical and empirical approaches are employed. For what concerns the empirical approaches we use both a linear and two non-linear methods to define different rock-physics models. The linear model is obtained by applying a multilinear stepwise regression, whereas neural networks and genetic algorithms are used to derive non-linear transformations from petrophysical to elastic properties. The main difference among neural networks and genetic algorithms is that the former is a gradient-based method while the latter is a global, stochastic, optimization method.
We start by introducing the different methods used to derive the theoretical and the empirical rock-physics models. Then, the RPMs resulting from theoretical and empirical approaches are analyzed in detail to define the benefits and the limits of each method. Moreover, in the empirical approaches we focus our attention on discussing the differences between linear and non-linear methods for the specific case under examination and on analyzing the drawbacks that characterize the neural network technique. The simplicity and the reliability of the empirical rock-physics model derived by applying multilinear stepwise regression and the optimal prediction capability of the theoretical rock-physics model enable us to consider these two RPMs in the petrophysical AVA inversion that is discussed in the companion paper titled “Seismic reservoir characterization in offshore Nile Delta. Part II: Probabilistic petrophysical-seismic inversion”
Comparison of different classification methods for litho-fluid facies identification in offshore Nile Delta.
No full textAmplitude Versus Angle (AVA) inversion is usually applied to derive the elastic properties of the subsurface from pre-stack seismic data. Seismic reservoir characterization often uses the outcomes of AVA inversion to infer the litho-fluid facies around the target zone. In this work we test different classification methods for litho-fluid facies identification in a clastic reservoir located in offshore Nile Delta. The reservoir zone is gas saturated and is hosted in sands channels surrounded by shale sequences. This characteristic leads us to consider three different facies in the classification that are shales, brine sands and gas sands, while the available well log data enable us to separate the different facies in terms of petrophysical properties (water saturation, shaliness and porosity) and elastic properties (seismic impedances and density). The classification is performed on the feature space defined by the P- and S-wave impedances that are derived from the observed seismic data by means of a Bayesian linearized AVA inversion (Buland and Omre, 2003).The analyzed case is particularly challenging due to the significant overlap between the elastic characteristics of brine and gas sands. The classification methods we consider can be conveniently divided in two main categories: the methods that do not require any a-priori information about the overall proportions of the litho-fluid facies or about their vertical continuity in the investigated area and those methods that require such information. To the first group belong the quadratic discriminant analysis and the neural network approach, whereas to the second group belong the standard Bayesian approach and the Bayesian approach which include a 1D Markov chain prior model to constrain the vertical continuity of litho-fluid facies.
The quadratic discriminant analysis (DA; Avseth et al. 2005) considers that the different classes are divided by quadratic discriminant surfaces in the feature space, while the neural network (NN) method is able to discriminate classes divided by highly non linear discriminant surfaces. This difference allows us to check if the assumption of quadratic discriminant surfaces yields a reliable classification in the investigated area or if more complicated non linear surfaces are required. These two methods return a deterministic classification in which each time sample is classified to one class or to another class without giving an idea on the probability that the sample effectively belongs to that class. Conversely, the two Bayesian classification methods exploit the seismic likelihood function and a set of a-priori information (derived from well log data) to produce a posterior probability that describes the probability that each given sample belongs to a particular litho-fluid class. The first Bayesian method we consider is what we call the standard Bayesian (SB) approach in which only the overall proportions of facies in the target interval is given as a-priori information (Avseth et al. 2005). If we consider a 1D vertical profile this approach classifies each given input sample independently from the adjacent classified samples. In the second Bayesian approach (that we indicate with the acronym MC) a 1D Markov chain prior model (in the form of a transition probability matrix) is given as additional prior information in order to constrain the vertical continuity of the litho-fluid facies along the vertical profile (Larsen et al. 2006).
The ultimate goal of this study is to find an optimal classification method for the area under examination and to this end we first analyze the performances of the four classification algorithms in a synthetic AVA inversion in which the seismic data are computed on the basis of the available well log information, then the results obtained in the field data classification 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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