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As large carnivores recover from over-exploitation, managers often lack evidence-based information on species habitat requirements and the efficacy of management practices, particularly where species repopulate areas from which they have long been extirpated. We investigated the movement and habitat use by 2 semi-aquatic carnivores (Australian fur seals Arctocephalus pusillus doriferus and New Zealand fur seals A. forsteri) at the northern end of their distributions in Australia, where after a long absence both are recolonising their historic range. We also assessed male fur seal habitat use overlap with terrestrial and marine protected areas (PAs). While at the margin of the range during winter and early spring, the males remained inshore close to terrestrial sites and where interactions with humans often occur. From early spring, the males from the range margin showed uniform movement toward colonies in the core of the species’ range prior to their breeding seasons. This contrasts with males tracked from the core of the species’ range that returned periodically to colonies during the year, and highlights the importance of range-wide monitoring of a species to inform conservation planning. Habitat use by some males included over 90% of a marine PA at the margin of the species’ range. Most terrestrial haul-outs used were within terrestrial PAs, while sites not protected were on the margin of the range. Despite wide-ranging habits, their dependence on coastal sites, where human access and activities can be regulated and more readily enforced, suggests that terrestrial and marine PAs will continue to play an important role in managing the recovery of these fur seals.

In recent years, the military establishments of the US and the UK have produced a series of reports that attempt to ‘think the unthinkable in imagining future threats to the security of the West. A new genre of military futurology has emerged which owes as much to apocalyptic Hollywood movies as it does to the cold war tradition of ‘scenario planning . Often outlandish and bizarre in its prophecies, and always dystopian, this new military futurism sees threats to the western way of life emanating not only from rogue states, weapons of mass destruction and terrorism but also from resurgent nationalism, conflicts over dwindling resources, migration, disease, organised crime, abrupt climate change and the emergence of‘failed cities where social disorder is rife. This article provides a survey of the genre, showing how the grim predictions of the military futurists provide a justification for endless global war against enemies that may never exist.

The `global war on terror' is often represented as a struggle between incompatible opposites, of good versus evil, terror versus democracy and civilisation versus barbarism. The deployment of such dichotomies was part of the background to the onslaught on Fallujah in 2004, serving to provide the US military with the appearance of moral legitimacy, as it turned the city to rubble in order to `save' it. In the US media, the arrogant assumption that the US is civilisationally superior both to the `barbarians' its armies were fighting in the city and to the broader mass of the Iraqi population, was a recurring theme among neo-conservative and pro-war liberal ideologues. Yet, with the city's destruction presented as a moral imperative on behalf of civilised values, there has been scant examination of the allegations that US forces were guilty of war crimes. Moreover, the attack on Fallujah shows that civilisation and barbarism are not diametrically opposed concepts in a `global war on terror' which continues to cause more death and destruction than the violence it is supposedly intended to eliminate.

In recent years, an increasingly influential intellectual consensus on both sides of the Atlantic has presented Europe as a doomed and decadent continent that is being transformed into an Islamic colony called ‘Eurabia’. The term was originally coined by the British-Swiss historian Bat Ye’or to describe what she identified as a secret project between European politicians and the Arab world for the ‘Islamicisation’ of Europe. What began as an outlandish conspiracy theory has become a dangerous Islamophobic fantasy that has moved ever closer towards mainstream respectability, as conservative historians and newspaper columnists, right-wing Zionists and European neofascists find common cause in the threat to ‘Judeo-Christian’ civilisation from Muslim immigrants with supposedly incompatible cultural values.

Dichotomy in geophysical remote sensing is both relative and absolute: while the seismic reflection relates to the impedance contrast, the reservoir properties, such as porosity, relate to the absolute value of the impedance. One way of interpreting the relative in terms of the absolute is to perturb the absolute and calculate the corresponding relative. To accomplish this task in geophysics, the velocity and density curves from an existing prototype well are perturbed according to a likely situation at a different location. A new pseudowell thus created is used to generate synthetic full-waveform seismograms which are then compared to real data at the location, with the expectation that the similarity in the seismic response reflects the similarity in the reservoir properties.

We present a new method for calibrating a classified 3D seismic volume. The classification process employs a Kohonen self-organizing map, a type of unsupervised artificial neural network; the subsequent calibration is performed using one or more suites of well logs. Kohonen self-organizing maps and other unsupervised clustering methods generate classes of data based on the identification of various discriminating features. These methods seek an organization in a dataset and form relational organized clusters. However, these clusters may or may not have any physical analogues in the real world. In order to relate them to the real world, we must develop a calibration method that not only defines the relationship between the clusters and real physical properties, but also provides an estimate of the validity of these relationships. With the development of this relationship, the whole dataset can then be calibrated. The clustering step reduces the multi-dimensional data into logically smaller groups. Each original data point defined by multiple attributes is reduced to a one- or two-dimensional relational group. This establishes some logical clustering and reduces the complexity of the classification problem. Furthermore, calibration should be more successful since it will have to consider less variability in the data. In this paper, we present a simple calibration method that employs Bayesian logic to provide the relationship between cluster centres and the real world. The output will give the most probable calibration between each self-organized map node and wellboremeasured parameters such as lithology, porosity and fluid saturation. The second part of the output comprises the calibration probability. The method is described in detail, and a case study is briefly presented using data acquired in the Orange River Basin, South Africa. The method shows promise as an alternative to current techniques for integrating seismic and log data during reservoir characterization.

In this case study from a fractured, locally karstified, and hydrothermally leached carbonate platform we show how proper selection of prognostic seismic attributes significantly rejuvenates a mature play by reducing drilling risk. To optimize rewards from applied subsurface geophysical interpretation, it proves beneficial to place seismic attributes into their proper regional geological context. The complex interplay between physical, geometric, and elastic formation parameters may assist exploration/exploitation efforts in advance by high-grading attribute selection during prospecting. The field-tested exploration strategy presented here encapsulates all pertinent play characteristics into a viable geological model within which each dominant reservoir property is expressed as a risk parameter that in turn can be resolved by a seismic attribute. Seismic attribute analysis permits illumination of discrete reservoir properties, specifically: 1.) fracturing, 2.) lithology (dolomitization), and, 3.) porosity.

Abstract Seismic Reservoir Characterization, also known as reservoir geophysics, has evolved over the past several years into a multi-disciplinary, business-critical function in most ED&P organizations. Sheriff defines reservoir geophysics as "The use of geophysical methods to assist in delineating or describing a reservoir or monitoring the changes in a reservoir as it is produced." Reservoir geophysics is applied across a wide spectrum of the oilfield life cycle from discovery and early development to tertiary recovery. One critical part of this process is careful analysis and understanding of petrophysical properties from well logs and core data (seismic petrophysics). The purpose of this paper is to illustrate why seismic petrophysics is so important and to show how carefully constructed synthetic models can help the geoscientist interpret acoustic and elastic impedance inversion from seismic data. Introduction Well logs are sometimes viewed by geophysicists as "hard data" and not subjected to the same level of scrutiny as the seismic data. This can be a mistake because well logs are susceptible to errors from a number of sources. In this presentation we will examine some of the processes and procedures that allow well logs to be correctly used in Seismic Reservoir Characterization. The basic steps in seismic petrophysics analysis are:Collect and organize input dataPerform geophysical log interpretation for volume minerals, porosity, and fluidsDetermine fluid properties (oil API, brine salinity, etc.) and reservoir pressuretemperaturePerturb reservoir properties using rock physics effective medium models (pseudo-well modeling)Compute synthetic seismic tracesGenerate trend curves and crossplotsCreate graphics and digital output files. Geophysical Well Log Analysis Well log analysis for geophysics differs in several important ways from standard log analysis. In most cases well logs are obtained for the purpose of estimating recoverable hydrocarbon volumes. Therefore the zone of interest is mainly the producing interval(s). For geophysics, well logs form the basis for relating seismic properties to the reservoir. While we are still concerned about producing intervals, we also need good information about all of the rock through which the seismic waves have passed. Therefore our zone of interest is much larger and encompasses basically everything from the surface to total depth. In all cases the log data will require some editing, normalization, and interpretation before they can be used in a reservoir study. Several specific analysis steps will be followed:De-spike and filter to remove or correct anomalous data pointsNormalize logs from all of the selected wells to determine the appropriate ranges and cutoffs for porosity, clay content, water resistivity, etc.Compute the volumetric curves such as total porosity, Vclay, and SwCalibrate the volumetric curves to core data if availableCorrect sonic and density logs for mud filtrate invasion if neededCompute Vshear on all wells. Missing log curves can often be computed with a reasonable degree of certainty. There are two major ways this is done. The first is through application of modern rock physics principles.

In this paper we will review the use of Rock Physics Diagnostics applied to log data that illustrates a relational model between porosity, clay and saturation. We use these relations to estimate porosity from elastic impedance attributes. Using statistical fits may work locally around the property values experienced by a well for example but away from the well you need to employ some systematic approach to improve the confidence and reduce the risk associated with such estimations. Such a systematic approach is Rock Physics Diagnostics and we believe that this methodology is essential for extracting rock properties from seismic data.

Well-bore derived measurements provide a link through rock physics, to the seismic domain. Shear wave velocity log data is important for many aspects of seismic modeling, including offset synthetic seismogram generation, and half space modeling for AVO analysis. Due to the relatively recent advent of shear wave velocity logs (VS), VS data is in many instances missing or unreliable. However, as compressional velocity logs (VP) are widely available a local empirical transform relating VP to VS would be useful. Several authors have published methods for predicting shear wave velocities in the absence of measured data, including Greenberg and Castagna (1992) and Krief (1990). These published models require, P-wave velocity information, but also accurate porosity, mineralogical content, fluid content, and associated elastic parameters. Han (1986) related VP to VS using empirical regressions of ultrasonic velocities, of 80 well-consolidated Gulf Coast sandstones. Here in, we present a simple method for determining an empirical relationship between VP and VS, and apply that relationship to a regional study in the Norwegian Sea. Discussion Our overall objective is to improve the reliability and accuracy of seismic to well ties in the Norwegian Sea, as well as to evaluate the response of synthetic seismograms to the effect of varying potential reservoir fluids. A main component is a rational rock physics model that allows for the robust prediction of shear velocity. Proper preconditioning of the well log data through rigorous log analysis is necessary to build a log suite that can be used in rock physics modeling (i.e. fluid substitution or porosity modeling) and synthetic seismogram generation. Shear wave information is vital for both. If shear velocity information is missing, VS must first be predicted.