Simulation of multiphase flow in fractured reservoirs using a fracture-only model with transfer functions

We present a fracture-only reservoir simulator for multiphase flow: the fracture geometry is modeled explicitly, while fluid movement between fracture and matrix is accommodated using empirical transfer functions. This is a hybrid between discrete fracture discrete matrix modeling where both the fracture and matrix are gridded and dual-porosity or dual-permeability simulation where both fracture and matrix continua are upscaled. The advantage of this approach is that the complex fracture geometry that controls the main flow paths is retained. The use of transfer functions, however, simplifies meshing and makes the simulation method considerably more efficient than discrete fracture discrete matrix models. The transfer functions accommodate capillary- and gravity-mediated flow between fracture and matrix and have been shown to be accurate for simple fracture geometries, capturing both the early- and late-time average behavior. We verify our simulator by comparing its predictions with simulation results where the fracture and matrix are explicitly modeled. We then show the utility of the approach by simulating multiphase flow in a geologically realistic fracture network. Waterflooding runs reveal the fraction of the fracture–matrix interface area that is infiltrated by water so that matrix imbibition can occur. The evolving fraction of the fracture–matrix interface area turns out to be an important characteristic of any particular fracture system to be used as a scaling parameter for capillary driven fracture–matrix transfer.     

Reconstruction of the differentiated long-term exhumation history of Fuerteventura, Canary Islands, Spain, through fission-track and (U-Th–Sm)/He data

Miocene Intrusives and Lower Cretaceous siliciclastic sedimentary rocks from the Basal Complex in western-Fuerteventura were analyzed with low-temperature thermochronometric methods such as fission-track, and (U–Th–Sm)/He dating, in order to reveal the evolution of the island’s exhumation history. The obtained thermochronometric data yields a very slow rate of cooling in the order of 1.5–3°C/Myr from ~50 to 20 Ma for the Early Cretaceous siliciclastic rocks. These sedimentary units have never been heated significantly above 240°C after deposition and still record the submarine onset of the island’s formation in the Eocene. Intrusive bodies associated with the early Miocene magmatic activity of the central volcanic complex of the island show rapid initial cooling rates of 50–70°C/Myr from ~20 to 14 Ma. Contemporaneous with the intrusions the cooling rate of the Cretaceous sedimentary units increased to 25–35°C/Myr and it is inferred that this increase is associated with enhanced uplift and erosion of the Central Volcanic Complex. After ~14 Ma rates slowed down to 3–6°C/Myr. Palaeosols overlying the sedimentary units are themselves covered by Pliocene basalt flows and reveal that the sedimentary rocks reached the surface before ~5 Ma. The thermochronometric data obtained in this study for central Fuerteventura is difficult to reconcile with the cooling history derived from previously obtained fission-track and K–Ar data from the north-western part of the island. This inconsistency is likely to indicate that the exhumation history of Fuerteventura is more complex and regionally subdivided than previously believed.     

Probabilistic Marcinkiewicz–Zygmund Inequalities on the Rotation Group

Approximation problems on the rotation group SO(3) naturally arise in various fields, like crystallography, chemistry, and biology. For example, in crystallographic texture analysis one is confronted with the problem of evaluating so-called orientation density functions (ODFs). In many situations one only has a finite number of measurements at scattered sampling nodes. In order to reconstruct ODFs over all rotations, so-called Marcinkiewicz–Zygmund inequalities on the rotation group are an important tool. These inequalities provide norm equivalences between polynomials on SO(3) and their sample values. Recently shown equivalences depend on a density parameter of the sampling set and the proven inequalities hold true for polynomials on SO(3) whose degree does not exceed an upper bound which is determined by this density parameter. In this paper, we show that we can enlarge this upper bound for the polynomial degree significantly if we are satisfied by such norm equivalences that hold with a given probability only. Moreover, we show that there are fixed sampling sets for which we get probabilistic Marcinkiewicz–Zygmund inequalities that hold for polynomials on SO(3) of all degrees.     

Biomarker responses as indication of contaminant effects in blue mussel (Mytilus edulis) and female eelpout (Zoarces viviparus) from the southwestern Baltic Sea

During a field study performed in spring and autumn 2001 and 2002, blue mussels (Mytilus edulis) and female eelpout (Zoarces viviparus) were collected at three locations in the Wismar Bay (Baltic Sea), and several biomarkers of contaminant effects were analysed. Besides seasonal and inter-annual variations, biomarker signals were most pronounced at the location closest to Wismar Harbour (Wendorf) in both species. Lysosomal membrane stability (LMS) was lowest and acetylcholinesterase activity (AChE) was significantly reduced. Frequency of micronuclei (MN) was significantly higher (in blue mussels), indicating mutagenic effects. In eelpout elevated levels of DNA adducts, EROD induction and PAH-metabolites were measured. Metallothionein (MT), biomarker for trace metal exposure, showed a gradient only in spring. Organochlorine contaminant analyses (PCBs, DDTs) corresponded to the observed biomarker levels. The results obtained clearly demonstrate pollution effects in the southwestern Baltic Sea. Moreover, they show that a multibiomarker approach is also applicable in a brackish water environment.     

Climate change impacts on forest fire potential in boreal conditions in Finland

The aim of this work was to study the forest fire potential and frequency of forest fires under the projected climate change in Finland (N 60°–N 70°). Forest fire index, generally utilized in Finland, was used as an indicator for forest fire potential due to climatological parameters. Climatic scenarios were based on the A2 emission scenario. According to the results, the forest fire potential will have increased by the end of this century; as a result of increased evaporative demand, which will increase more than the rise in precipitation and especially in southern Finland. The annual number of forest fire alarm days is expected to increase in southern Finland to 96–160 days by the end of this century, compared to the current 60–100 days. In the north, the corresponding increase was from 30 to 36 days. The expected increase in the annual frequency of forest fires over the whole country was about 20% by the end of this century compared to the present day. The greatest increase in the frequency of fires, per 1,000 km², was in the southernmost part of the country, with six to nine fires expected annually per 1,000 km² at the end of this century, meaning a 24–29% increase compared to the present day frequencies.     

Mountain–valley precipitation differences in the northern Alps: an exemplary high-resolution modeling study

This paper investigates the dependence on environmental conditions of altitudinal precipitation differences in the northern Alps, based on high-resolution numerical simulations with the MM5 model for a selected region in the Bavarian Alps (Zugspitze mountain and surrounding valley stations). Three exemplary precipitation events representing climatological regimes with different orographic enhancement characteristics are selected. After validating the MM5 precipitation fields against the available surface observations, the model results are used to analyse the interactions of atmospheric dynamics and cloud microphysics with the local orography. The first two cases (19–22 March 1997, 05–09 February 1999) are characterized by a strong northwesterly or northerly flow, associated with large precipitation differences between the mountain and the surrounding valley stations. For these cases, the model results indicate a dominance of the classical seeder–feeder mechanism, with strong orographic lifting generating dense orographic clouds over each individual mountain ridge, which in turn intensify precipitation. The related surface precipitation maxima can be found near the mountain peaks or somewhat in the lee due to hydrometeor drifting. The third case (05–07 December 1992) represents conditions with relatively small (i.e. below climatological average) precipitation differences between the Zugspitze and the surrounding valley stations. For this event, the model results indicate that relatively weak ambient winds at and below Alpine crest level (700 hPa) were primarily responsible for the lack of substantial precipitation enhancement. Precipitation was nevertheless moderately intense because of strong frontal lifting at higher levels. In all three cases, the agreement between simulated and observed precipitation patterns is so high that there is good reason to expect that mountain–valley precipitation differences will be quantitatively predictable for nonconvective events once a sufficiently high model resolution is computationally affordable.     

Does temperature contain a stochastic trend? Evaluating conflicting statistical results

We evaluate the claim by Gay et al. (Clim Change 94:333–349, 2009) that “surface temperature can be better described as a trend stationary process with a one-time permanent shock” than efforts by Kaufmann et al. (Clim Change 77:249–278, 2006) to model surface temperature as a time series that contains a stochastic trend that is imparted by the time series for radiative forcing. We test this claim by comparing the in-sample forecast generated by the trend stationary model with a one-time permanent shock to the in-sample forecast generated by a cointegration/error correction model that is assumed to be stable over the 1870–2000 sample period. Results indicate that the in-sample forecast generated by the cointegration/error correction model is more accurate than the in-sample forecast generated by the trend stationary model with a one-time permanent shock. Furthermore, Monte Carlo simulations of the cointegration/error correction model generate time series for temperature that are consistent with the trend-stationary-with-a-break result generated by Gay et al. (Clim Change 94:333–349, 2009), while the time series for radiative forcing cannot be modeled as trend stationary with a one-time shock. Based on these results, we argue that modeling surface temperature as a time series that shares a stochastic trend with radiative forcing offers the possibility of greater insights regarding the potential causes of climate change and efforts to slow its progression.     

Modelling European winter wind storm losses in current and future climate

Severe wind storms are one of the major natural hazards in the extratropics and inflict substantial economic damages and even casualties. Insured storm-related losses depend on (i) the frequency, nature and dynamics of storms, (ii) the vulnerability of the values at risk, (iii) the geographical distribution of these values, and (iv) the particular conditions of the risk transfer. It is thus of great importance to assess the impact of climate change on future storm losses. To this end, the current study employs—to our knowledge for the first time—a coupled approach, using output from high-resolution regional climate model scenarios for the European sector to drive an operational insurance loss model. An ensemble of coupled climate-damage scenarios is used to provide an estimate of the inherent uncertainties. Output of two state-of-the-art global climate models (HadAM3, ECHAM5) is used for present (1961–1990) and future climates (2071–2100, SRES A2 scenario). These serve as boundary data for two nested regional climate models with a sophisticated gust parametrizations (CLM, CHRM). For validation and calibration purposes, an additional simulation is undertaken with the CHRM driven by the ERA40 reanalysis. The operational insurance model (Swiss Re) uses a European-wide damage function, an average vulnerability curve for all risk types, and contains the actual value distribution of a complete European market portfolio. The coupling between climate and damage models is based on daily maxima of 10 m gust winds, and the strategy adopted consists of three main steps: (i) development and application of a pragmatic selection criterion to retrieve significant storm events, (ii) generation of a probabilistic event set using a Monte-Carlo approach in the hazard module of the insurance model, and (iii) calibration of the simulated annual expected losses with a historic loss data base. The climate models considered agree regarding an increase in the intensity of extreme storms in a band across central Europe (stretching from southern UK and northern France to Denmark, northern Germany into eastern Europe). This effect increases with event strength, and rare storms show the largest climate change sensitivity, but are also beset with the largest uncertainties. Wind gusts decrease over northern Scandinavia and Southern Europe. Highest intra-ensemble variability is simulated for Ireland, the UK, the Mediterranean, and parts of Eastern Europe. The resulting changes on European-wide losses over the 110-year period are positive for all layers and all model runs considered and amount to 44% (annual expected loss), 23% (10 years loss), 50% (30 years loss), and 104% (100 years loss). There is a disproportionate increase in losses for rare high-impact events. The changes result from increases in both severity and frequency of wind gusts. Considerable geographical variability of the expected losses exists, with Denmark and Germany experiencing the largest loss increases (116% and 114%, respectively). All countries considered except for Ireland (?22%) experience some loss increases. Some ramifications of these results for the socio-economic sector are discussed, and future avenues for research are highlighted. The technique introduced in this study and its application to realistic market portfolios offer exciting prospects for future research on the impact of climate change that is relevant for policy makers, scientists and economists.     

Bayesian multi-model projection of climate: bias assumptions and interannual variability

Current climate change projections are based on comprehensive multi-model ensembles of global and regional climate simulations. Application of this information to impact studies requires a combined probabilistic estimate taking into account the different models and their performance under current climatic conditions. Here we present a Bayesian statistical model for the distribution of seasonal mean surface temperatures for control and scenario periods. The model combines observational data for the control period with the output of regional climate models (RCMs) driven by different global climate models (GCMs). The proposed Bayesian methodology addresses seasonal mean temperatures and considers both changes in mean temperature and interannual variability. In addition, unlike previous studies, our methodology explicitly considers model biases that are allowed to be time-dependent (i.e. change between control and scenario period). More specifically, the model considers additive and multiplicative model biases for each RCM and introduces two plausible assumptions (“constant bias” and “constant relationship”) about extrapolating the biases from the control to the scenario period. The resulting identifiability problem is resolved by using informative priors for the bias changes. A sensitivity analysis illustrates the role of the informative prior. As an example, we present results for Alpine winter and summer temperatures for control (1961–1990) and scenario periods (2071–2100) under the SRES A2 greenhouse gas scenario. For winter, both bias assumptions yield a comparable mean warming of 3.5–3.6°C. For summer, the two different assumptions have a strong influence on the probabilistic prediction of mean warming, which amounts to 5.4°C and 3.4°C for the “constant bias” and “constant relation” assumptions, respectively. Analysis shows that the underlying reason for this large uncertainty is due to the overestimation of summer interannual variability in all models considered. Our results show the necessity to consider potential bias changes when projecting climate under an emission scenario. Further work is needed to determine how bias information can be exploited for this task.