dispersal and reception in northern italy comparing systems along the brenner route

In the last decades, policy restrictions and practices at national and local levels have curtailed the rights of seekers and holders of international protection, thus impacting on their lives and on the territories they transit through. This is particularly evident in border contexts. Various border areas have gradually transformed into internal hotspots, with increasing border enforcement. This includes Brenner, situated at the border between Italy and Austria. In the wider Brenner route area, particularly in the nearby Italian cities of Verona, Trento and Bolzano, "spaces of transit" have emerged and both public and humanitarian actors have been "forced" to deal with it. This chapter draws upon the work of the multilevel governance of migration (Caponio and Borkert 2010), and on the proliferation of borders (Mezzadra and Neilson 2016), to present a comparative analysis of the reception scenario in these three cities. By building on qualitative data analysis (legal analysis of policy documents, content analysis of interviews and newspaper articles), it discusses to what extent and how the respective local systems of reception have managed to cater for migrants that transit through them. Similarities and differences are pointed out, as well as the relevance of factors such as geographical proximity in influencing the respective approaches

Application of a Markov Chain Monte Carlo-AVA inversion algorithm for reservoir characterization in offshore Nile Delta

We propose a formulation of Amplitude Versus Angle (AVA) inversion in terms of a Markov Chain Monte Carlo (MCMC) algorithm, and we show its application for reservoir characterization and litho-fluid facies prediction in a gas-saturated reservoir in offshore Nile Delta. A linear empirical rock physics model is used to link the petrophysical characteristics (porosity, water saturation and shaliness) to the elastic attributes (P-wave velocity, S-wave velocity and density), whereas the non-linear exact Zoeppritz equations are used to relate such elastic properties to the observed AVA responses. The exact Zoeppritz equations allow us to take advantage of the long offset seismic acquisition and thus to consider a wide range of incidence angles (between 0 and 60 degrees) in the inversion. The proposed algorithm, at the expense of a relatively high computational cost, reliably estimates the posterior probability distributions of the sought parameters, taking into consideration the uncertainties in the prior information, the uncertainties in the estimated rock-physics model and the errors affecting the observed AVA responses. The match between the predicted properties and the well log information demonstrates the applicability of the proposed method and the reliability of the results

Fourier reconstruction minimum norm

Frmn (Fourier Reconstruction Minimum Norm) è un applicativo per la regolarizzazione di dati sismici 2D campionati irregolarmente nella direzione spaziale. L'algoritmo utilizza un metodo di regolarizzazione basato sulla stima dello spettro fk ottimale tramite un approccio di inversione probabilistica (Tarantola1987; Duijndam, 1999) che considera come informazione a priori sul modello la trasformata di Fourier non uniforme (NDFT o sommatoria di Riemann) (Lugano, Mazzotti, Stucchi, 2008). Una volta stimato lo spettro fk ottimale, il dato viene ricostruito lungo un profilo regolare, nel dominio tempi-offset, calcolando l'antitrasformata di Fourier 2D

Seismic reservoir characterization in offshore Nile Delta. Part II: probabilistic petrophysical-seismic inversion.

Reservoir 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

Comparison of different classification methods for litho-fluid facies identification in offshore Nile Delta.

Amplitude 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

Seismic reservoir characterization in offshore Nile Delta. Part I: comparing different methods to derive a reliable rock-physics model.

Seismic-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”

Extended Elastic Impedance Inversion Applied to On-Shore and Off-Shore Seismic Data for Reservoir Characterization

We apply an extended elastic impedance (EEI) inversion for quantitative reservoir characterization. The EEI approach is applied to both on-shore and off-shore seismic data where target reservoirs are gas-bearing sands located in sand-shale sequences. The workflow we adopt can be divided in three phases. The starting point is a petrophysical analysis in which the relationships between petrophysical and elastic properties are studied. The second step of extended elastic impedance (EEI) analysis uses a cross-correlation procedure to determine the best chi (?) projection angles for the petrophysical parameters of interest (i.e. porosity, water saturation and shaliness). In the final step, pre-stack seismic data are simultaneously inverted into P-wave velocity, acoustic, and gradient impedances, and the last two elastic volumes are finally projected to ? angles corresponding to the target petrophysical parameters. The estimated porosity, water saturation, and shaliness values reveal a proper match at blind well locations. This work shows that extended elastic impedance is an effective way for lithology and fluid differentiation in clastic reservoirs. The output of this work can be beneficial for static reservoir model building and volumetric calculation and can be also used to determine new potential drilling locations

Target-oriented, AVA-petrophysical Inversion through Anisotropic Markov Random Field

We implement an AVA-petrophysical inversion that uses an anisotropic Markov random field to model the lateral variability of petrophysical properties. A Bayesian framework is adopted for transforming pre-stack data to the maximum-a-posteriori solution of reservoir properties. The lateral heterogeneity of the investigated reservoir is reasonably modeled by the Huber energy function. For computational feasibility reasons, we limit our attention to a target-oriented inversion that uses the amplitude versus angle (AVA) responses extracted along a time slice representing the top reflection of the investigated reservoir, to infer the petrophysical properties of interest for the reservoir layer. The implemented AVA-petrophysical inversion uses a previously defined linear rock-physics model to rewrite the linear Aki and Richards AVA equation for P-P waves in terms of contrasts in the petrophysical properties at the reflecting interface. This reformulation allows us to directly derive the petrophysical properties around the target zone from AVA data. We applied this method to 3D onshore seismic data for the characterization of a clastic, gas-saturated, reservoir. A comparison with the outcomes of a more standard laterally unconstrained AVA-petrophysical inversion is used to demonstrate the antinoise and the imaging ability of the implemented approach