排序方式： 共有47条查询结果，搜索用时 15 毫秒
41.
Simulating search behaviour of fish towards bait 总被引：2，自引：0，他引：2
Vabo Rune; Huse Geir; Ferno Anders; Jorgensen Terje; Lokkeborg Svein; Skaret Georg 《ICES Journal of Marine Science》2004,61(7):1224-1232
42.
Einar Svendsen Morten Skogen Paul Budgell Geir Huse Jan Erik Stiansen Bjrn dlandsvik Frode Vikeb Lars Asplin Svein Sundby 《Deep Sea Research Part II: Topical Studies in Oceanography》2007,54(23-26):2810
The Norwegian Ecological Model (NORWECOM) biophysical model system implemented with the ROMS ocean circulation model has been run to simulate conditions over the last 25 years for the North Atlantic. Modeled time series of water volume fluxes, primary production, and drift of cod larvae through their modeled ambient temperature fields have been analyzed in conjunction with VPA estimated time series of 3-year-old cod recruits in the Barents Sea. Individual time series account for less than 50% of the recruitment variability; however, a combination of simulated flow of Atlantic water into the Barents Sea and local primary production accounts for 70% of the variability with a 3-year lead. The associated regression predicts increased recruitment between 2007 and 2008 from about 450–700 million individuals with a standard error of nearly 150 million. 相似文献
43.
Discontinuous Galerkin methods for advective transport in single-continuum models of fractured media
Birgitte Eikemo Knut-Andreas Lie Geir Terje Eigestad Helge K. Dahle 《Advances in water resources》2009
Accurate simulation of flow and transport processes in fractured rocks requires that flow in fractures and shear zones to be coupled with flow in the porous rock matrix. To this end, we will herein consider a single-continuum approach in which both fractures and the porous rock are represented as volumetric objects, i.e., as cells in an unstructured triangular grid with a permeability and a porosity value associated with each cell. Hence, from a numerical point of view, there is no distinction between flow in the fractures and the rock matrix. This enables modelling of realistic cases with very complex structures. To compute single-phase advective transport in such a model, we propose to use a family of higher-order discontinuous Galerkin methods. Single-phase transport equations are hyperbolic and have an inherent causality in the sense that information propagates along streamlines. This causality is preserved in our discontinuous Galerkin discretization. We can therefore use a simple topological sort of the graph of discrete fluxes to reorder the degrees-of-freedom such that the discretized linear system gets a lower block-triangular form, from which the solution can be computed very efficiently using a single-pass forward block substitution. The accuracy and utility of the resulting transport solver is illustrated through several numerical experiments. 相似文献
44.
Waterflooding using closed-loop control 总被引：2，自引：0，他引：2
To fully exploit the possibilities of “smart” wells containing both measurement and control equipment, one can envision a
system where the measurements are used for frequent updating of a reservoir model, and an optimal control strategy is computed
based on this continuously updated model. We developed such a closed-loop control approach using an ensemble Kalman filter
to obtain frequent updates of a reservoir model. Based on the most recent update of the reservoir model, the optimal control
strategy is computed with the aid of an adjoint formulation. The objective is to maximize the economic value over the life
of the reservoir. We demonstrate the methodology on a simple waterflooding example using one injector and one producer, each
equipped with several individually controllable inflow control valves (ICVs). The parameters (permeabilities) and dynamic
states (pressures and saturations) of the reservoir model are updated from pressure measurements in the wells. The control
of the ICVs is rate-constrained, but the methodology is also applicable to a pressure-constrained situation. Furthermore,
the methodology is not restricted to use with “smart” wells with down-hole control, but could also be used for flooding control
with conventional wells, provided the wells are equipped with controllable chokes and with sensors for measurement of (wellhead
or down hole) pressures and total flow rates. As the ensemble Kalman filter is a Monte Carlo approach, the final results will
vary for each run. We studied the robustness of the methodology, starting from different initial ensembles. Moreover, we made
a comparison of a case with low measurement noise to one with significantly higher measurement noise. In all examples considered,
the resulting ultimate recovery was significantly higher than for the case of waterflooding using conventional wells. Furthermore,
the results obtained using closed-loop control, starting from an unknown permeability field, were almost as good as those
obtained assuming a priori knowledge of the permeability field. 相似文献
45.
There are several issues to consider when we use ensemble smoothers to condition reservoir models on rate data. The values in a time series of rate data contain redundant information that may lead to poorly conditioned inversions and thereby influence the stability of the numerical computation of the update. A time series of rate data typically has correlated measurement errors in time, and negligence of the correlations leads to a too strong impact from conditioning on the rate data and possible ensemble collapse. The total number of rate data included in the smoother update will typically exceed the ensemble size, and special care needs to be taken to ensure numerically stable results. We force the reservoir model with production rate data derived from the observed production, and the further conditioning on the same rate data implies that we use the data twice. This paper discusses strategies for conditioning reservoir models on rate data using ensemble smoothers. In particular, a significant redundancy in the rate data makes it possible to subsample the rate data. The alternative to subsampling is to model the unknown measurement error correlations and specify the full measurement error covariance matrix. We demonstrate the proposed strategies using different ensemble smoothers with the Norne full-field reservoir model. 相似文献
46.
Geir Evensen 《Computational Geosciences》2018,22(3):885-908
This paper examines the properties of the Iterated Ensemble Smoother (IES) and the Multiple Data Assimilation Ensemble Smoother (ES–MDA) for solving the history matching problem. The iterative methods are compared with the standard Ensemble Smoother (ES) to improve the understanding of the similarities and differences between them. We derive the three smoothers from Bayes’ theorem for a scalar case which allows us to compare the equations solved by the three methods, and we can better understand which assumptions are applied and their consequences. When working with a scalar model, it is possible to use a vast ensemble size, and we can construct the sample distributions for both priors and posteriors, as well as intermediate iterates. For a linear model, all three methods give the same result. For a nonlinear model, the iterative methods improve on the ES result, but the two iterative methods converge to different solutions, and it is not clear which should be the preferred choice. It is clear that the ensemble of cost functions used to define the IES solution does not represent an exact sampling of the posterior-Bayes’ probability density function. Also, the use of an ensemble representation for the gradient in IES introduces an additional approximation compared to using an exact analytic gradient. For ES–MDA, the convergence, as a function of increasing number of uniform update steps, is studied for a huge ensemble size. We illustrate that ES–MDA converges to a solution that differs from the Bayesian posterior. The convergence is also examined using a realistic sample size to study the impact of the number of realizations relative to the number of update steps. We have run multiple ES–MDA experiments to examine the impact of using different schemes for choosing the lengths of the update steps, and we have tried to understand which properties of the inverse problem imply that a non-uniform update step length is beneficial. Finally, we have examined the smoother methods with a highly nonlinear model to examine their properties and limitations in more extreme situations. 相似文献
47.
In recent years, data assimilation techniques have been applied to an increasingly wider specter of problems. Monte Carlo
variants of the Kalman filter, in particular, the ensemble Kalman filter (EnKF), have gained significant popularity. EnKF
is used for a wide variety of applications, among them for updating reservoir simulation models. EnKF is a Monte Carlo method,
and its reliability depends on the actual size of the sample. In applications, a moderately sized sample (40–100 members)
is used for computational convenience. Problems due to the resulting Monte Carlo effects require a more thorough analysis
of the EnKF. Earlier we presented a method for the assessment of the error emerging at the EnKF update step (Kovalenko et
al., SIAM J Matrix Anal Appl, in press). A particular energy norm of the EnKF error after a single update step was studied.
The energy norm used to assess the error is hard to interpret. In this paper, we derive the distribution of the Euclidean
norm of the sampling error under the same assumptions as before, namely normality of the forecast distribution and negligibility
of the observation error. The distribution depends on the ensemble size, the number and spatial arrangement of the observations,
and the prior covariance. The distribution is used to study the error propagation in a single update step on several synthetic
examples. The examples illustrate the changes in reliability of the EnKF, when the parameters governing the error distribution
vary. 相似文献