Effects of global warming on animal distribution and performance become visible in many marine ecosystems. The present study was designed to develop a concept for a cause and effect understanding with respect to temperature changes and to explain ecological findings based on physiological processes. The concept is based on a wide comparison of invertebrate and fish species with a special focus on recent data obtained in two model species of fish. These fish species are both characterized by northern and southern distribution limits in the North Atlantic: eelpout (Zoarces viviparus), as a typical non-migrating inhabitant of the coastal zone and the cod (Gadus morhua), as a typical inhabitant of the continental shelf with a high importance for fisheries.Mathematical modelling demonstrates a clear significant correlation between climate induced temperature fluctuations and the recruitment of cod stocks. Growth performance in cod is optimal at temperatures close to 101C, regardless of the population investigated in a latitudinal cline. However, temperature specific growth rates decrease at higher latitudes. Also, fecundity is less in White Sea than in North and Baltic Sea cod or eelpout populations. These findings suggest that a cold-induced shift in energy budget occurs which is unfavorable for growth performance and fecundity. Thermal tolerance limits shift depending on latitude and are characterized by oxygen limitation at both low or high temperatures. Oxygen supply to tissues is optimized at low temperature by a shift in hemoglobin isoforms and oxygen binding properties to lower affinities and higher unloading potential. Protective stimulation of heat shock protein synthesis was not observed.According to a recent model of thermal tolerance the downward shift of tolerance limits during cold adaptation is associated with rising mitochondrial densities and, thus, aerobic capacity and performance in the cold, especially in eurythermal species. At the same time the costs of mitochondrial maintenance reflected by mitochondrial proton leakage should rise leaving a lower energy fraction for growth and reproduction. The preliminary conclusion can be drawn that warming will cause a northern shift of distribution limits for both species with a rise in growth performance and fecundity larger than expected from the Q 10 effect in the north and lower growth or even extinction of the species in the south. Such a shift may heavily affect fishing activities in the North Sea. r

Summary The use of ensemble Kalman filter techniques for continuous updating of reservoir model is demonstrated. The ensemble Kalman filter technique is introduced, and thereafter applied to a simplified 2-D field model, which are generated by using a single horizontal layer from a North Sea field model. By assimilating measured production data, the reservoir model is continuously updated. The updated models give improved forecasts and the forecasts improve as more data is included. Both dynamic variables, such as pressure and saturations, and static variables, such as the permeability, are updated in the reservoir model. Introduction In the management of reservoirs, it is important to utilize all available data in order to make accurate forecasts. For short time forecasts, in particular, it is important that the initial values are consistent with recent measurements. The ensemble Kalman filter1 is a Monte Carlo approach, which is promising with respect to achieving this goal through continuous model updating and reservoir monitoring. In this paper, the ensemble Kalman filter is utilized to update both static parameters, such as the permeability, and dynamic variables, such as the pressure and saturation of the reservoir model. The filter computations are based on an ensemble of realizations of the reservoir model, and when new measurements are available, new updates are obtained by combining the model predictions with the new measurements. Statistics about the model uncertainty is built from the ensemble. When new measurements become available, the filter is used to update all the realizations of the reservoir model. This means that an ensemble of updated realizations of the reservoir model is always available. The ensemble Kalman filter has previously been successfully applied for large-scale nonlinear models in oceanography2 and hydrology3. In those applications, only dynamic variables were tuned. Tuning of model parameters and dynamic variables was done simultaneously in a well flow model used for underbalanced drilling4. In two previous papers5,6, the filter has been used to update static parameters in near-well reservoir models, by tuning the permeability field. In this paper, the filter has been further developed to tune the permeability for simplified real field reservoir simulation models. We present results from a synthetic, simplified real field model. The measurements are well bottom-hole pressures, water cuts and gas/oil ratios. A synthetic model gives the possibility of comparing the solution obtained by the filter to the true solution, and the performance of the filter can be evaluated. It is shown how the reservoir model is updated as new measurements becomes available, and that good forecasts are obtained. The convergence of the reservoir properties to the true solution as more measurements becomes available is investigated. Since the members of the ensemble are updated independently of each other, the method is very suitable for parallel processing. It is also conceptually straightforward to extend the methodology to update other reservoir properties than the permeability. Based on the updated ensemble of models, production forecasts and reservoir management studies may be performed on a single "average" model, which is always consistent with the latest measurements. Alternatively, the entire ensemble may be applied to estimate the uncertainties in the forecasts. Updating reservoir models with ensemble Kalman filter The Kalman filter was originally developed to update the states of linear systems to take into account available measurements7. In our case, the system is a reservoir model, using black oil, and three phases (water, oil and gas).For this model, the solution variables of the system are the pressure and the water saturation, in addition to a third solution variable that depends on the oil and gas saturation. If the gas saturation is zero, the third solution variable becomes the solution gas/oil ratio, if the oil saturation is zero it becomes the vapor oil/gas ratio. Otherwise the third solution variable is the gas saturation. The states of this system are the values of the solution variables for each grid block of the simulation model. This model is non-linear.

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Atlantic cod have been a primary target for marine stock enhancement since the 1880s. In the early part of this period, hatched larvae were released in Norway, the USA and Canada. The last larval releases were conducted in Norway in 1971, and a century of cod larvae releases were halted without any clear evidence of benefit. Since the early 1980s, the focus has been on production of larger, more viable juvenile cod. Emphasis has been given to the design of tag–release programmes involving large‐scale releases and ecosystem analysis in selected ecosystems. Most of this research has been carried out in Norway, where more than one million tagged juvenile cod have been released. Smaller stocking experiments have also been performed in Denmark, Sweden, the Faroe Islands and the USA. This paper reviews the major findings from these programmes. We include summaries and evaluations of rearing techniques for juvenile cod, methods of tagging and recapture, experimental fishing, migration, mortality and growth rates in the different habitats, genetic analysis, and ecosystem studies that have tried to describe the variation in the cod carrying capacity of selected release areas. Despite relatively large variation in environmental conditions, in cod production and in fishing mortality along the Norwegian coast, results indicate that, under the conditions experienced during the 1980s and 1990s, releases of juvenile cod did not significantly increase cod production and catches. The biological limitations and future prospects of Atlantic cod stock enhancement are addressed.

The nonlinear filtering problem occurs in many scientific areas. Sequential Monte Carlo solutions with the correct asymptotic behavior such as particle filters exist, but they are computationally too expensive when working with high-dimensional systems. The ensemble Kalman filter (EnKF) is a more robust method that has shown promising results with a small sample size, but the samples are not guaranteed to come from the true posterior distribution. By approximating the model error with a Gaussian distribution, one may represent the posterior distribution as a sum of Gaussian kernels. The resulting Gaussian mixture filter has the advantage of both a local Kalman type correction and the weighting/resampling step of a particle filter. The Gaussian mixture approximation relies on a bandwidth parameter which often has to be kept quite large in order to avoid a weight collapse in high dimensions. As a result, the Kalman correction is too large to capture highly non-Gaussian posterior distributions. In this paper, we have extended the Gaussian mixture filter (Hoteit et al., Mon Weather Rev 136:317-334, 2008) and also made the connection to particle filters more transparent. In particular, we introduce a tuning parameter for the importance weights. In the last part of the paper, we have performed a simulation experiment with the Lorenz40 model where our method has been A. S. Stordal (B) · G. Naevdal · B. Vallès IRIS, compared to the EnKF and a full implementation of a particle filter. The results clearly indicate that the new method has advantages compared to the standard EnKF.

The focus of this work is on an alternative implementation of the iterative ensemble smoother (iES). We show that iteration formulae similar to those used in [3,6] can be derived by adopting a regularized Levenberg-Marquardt (RLM) algorithm [14] to approximately solve a minimum-average-cost (MAC) problem. This not only leads to an alternative theoretical tool in understanding and analyzing the behaviour of the aforementioned iES, but also provides insights and guidelines for further developments of the smoothing algorithms. For illustration, we compare the performance of an implementation of the RLM-MAC algorithm to that of the approximate iES used in [3] in three numerical examples: an initial condition estimation problem in a strongly nonlinear system, a facies estimation problem in a 2D reservoir and the history matching problem in the Brugge field case. In these three specific cases, the RLM-MAC algorithm exhibits comparable or even better performance, especially in the strongly nonlinear system.

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A large variety of severe medical conditions involve alterations in microvascular circulation. Hence, measurements or simulation of circulation and perfusion has considerable clinical value and can be used for diagnostics, evaluation of treatment efficacy, and for surgical planning. However, the accuracy of traditional tracer kinetic one-compartment models is limited due to scale dependency. As a remedy, we propose a scale invariant mathematical framework for simulating whole brain perfusion. The suggested framework is based on a segmentation of anatomical geometry down to imaging voxel resolution. Large vessels in the arterial and venous network are identified from time-of-flight (ToF) and quantitative susceptibility mapping (QSM). Macro-scale flow in the large-vessel-network is accurately modelled using the Hagen-Poiseuille equation, whereas capillary flow is treated as two-compartment porous media flow. Macro-scale flow is coupled with micro-scale flow by a spatially distributing support function in the terminal endings. Perfusion is defined as the transition of fluid from the arterial to the venous compartment. We demonstrate a whole brain simulation of tracer propagation on a realistic geometric model of the human brain, where the model comprises distinct areas of grey and white matter, as well as large vessels in the arterial and venous vascular network. Our proposed framework is an accurate and viable alternative to traditional compartment models, with high relevance for simulation of brain perfusion and also for restoration of field parameters in clinical brain perfusion applications.