An adaptive finite element model for wellbore breakout simulation

A finite element formulation is proposed and implemented for analysing the stability of excavated wells using the DiMaggio-Sandler constitutive elastoplastic model with a typical carbonate reservoir configuration. The quality of the finite element approximation is ensured by applying smooth curved elements adapted to the wellbore geometry, and h − p adaptive finite element meshes in the plastic zone. General purpose procedures are defined to transfer the elastoplastic deformation history to newly created integration points. A breakout damage criterion is proposed based on the second invariant of the deviatoric plastic deformation tensor. This damage criterion is used to apply a mesh movement algorithm to represent material collapse. The automatic successive application of the breakout damage criterion results in elliptical realistically looking geometries obtained in experiments reported in the literature.     

Analytical models of contaminant transport in coastal aquifers

The Henry formulation, which couples subsurface flow and salt transport via a variable-density flow formulation, can be used to evaluate the extent of sea water intrusion into coastal aquifers. The coupling gives rise to nontrivial flow patterns that are very different from those observed in inland aquifers. We investigate the influence of these flow patterns on the transport of conservative contaminants in a coastal aquifer. The flow is characterized by two dimensionless parameters: the Péclet number, which compares the relative effects of advective and dispersive transport mechanisms, and a coupling parameter, which describes the importance of the salt water boundary on the flow. We focus our attention on two regimes – low and intermediate Péclet number flows. Two transport scenarios are solved analytically by means of a perturbation analysis. The first, a natural attenuation scenario, describes the flushing of a contaminant from a coastal aquifer by clean fresh water, while the second, a contaminant spill scenario, considers an isolated point source.     

The influence of land use/land cover variability and rainfall intensity in triggering landslides: a back-analysis study via physically based models

Natural Hazards - The objective of this study was to use physically based models to carry out a back-analysis of the set of factors that may have influenced slope instability and the consequent...     

Geochemistry and geothermometry of volcanic rocks from Serra Branca, Iberian Pyrite Belt, Portugal

Volcanic rocks from Serra Branca, Iberian Pyrite Belt, Portugal, consist of calc-alkaline felsic and intermediate rocks. The latter are massive andesites, whereas the former include four dacitic to rhyolitic lithologies, distinguishable on spiderdiagrams and binary plots of immobile elements. Zircon thermometry indicates that two felsic suites may have formed from different magmas produced at distinct temperatures, with only limited fractionation within each suite. Alternatively, all the felsic rocks can be related through fractionation of a single magma if the lower zircon saturation temperature obtained for one suite merely results from Zr dilution, mostly reflecting silicification.

The relatively high magma temperatures at Serra Branca ease the classification of felsic rocks based on their HFSE contents and also indicate volcanogenic massive sulfide deposit favorability. This contrasts with other areas of the Belt that register lower magma temperatures and are subsequently barren. However, magma temperatures may have not been high enough to cause complete melting of refractory phases in which HFSE reside during crustal fusion of an amphibolite protolith, implying difficult discrimination of tectonic environments for the felsic rocks. The intermediate rocks were possibly formed by mixing between basaltic magmas and crustal material, compatible with volcanism in an attenuated continental lithosphere setting.     

Using an inverse modelling approach with equifinality control to investigate the dominant controls on snowmelt nutrient export

There is great interest in modelling the export of nitrogen (N) and phosphorus (P) from agricultural fields because of ongoing challenges of eutrophication. However, the use of existing hydrochemistry models can be problematic in cold regions because models frequently employ incomplete or conceptually incorrect representations of the dominant cold regions hydrological processes and are overparameterized, often with insufficient data for validation. Here, a process‐based N model, WINTRA, which is coupled to a physically based cold regions hydrological model, was expanded to simulate P and account for overwinter soil nutrient biochemical cycling. An inverse modelling approach, using this model with consideration of parameter equifinality, was applied to an intensively monitored agricultural basin in Manitoba, Canada, to help identify the main climate, soil, and anthropogenic controls on nutrient export. Consistent with observations, the model results suggest that snow water equivalent, melt rate, snow cover depletion rate, and contributing area for run‐off generation determine the opportunity time and surface area for run‐off–soil interaction. These physical controls have not been addressed in existing models. Results also show that the time lag between the start of snowmelt and the arrival of peak nutrient concentration in run‐off increased with decreasing antecedent soil moisture content, highlighting potential implications of frozen soils on run‐off processes and hydrochemistry. The simulations showed TDP concentration peaks generally arriving earlier than NO₃ but also decreasing faster afterwards, which suggests a significant contribution of plant residue Total dissolved Phosphorus (TDP) to early snowmelt run‐off. Antecedent fall tillage and fertilizer application increased TDP concentrations in spring snowmelt run‐off but did not consistently affect NO₃ run‐off. In this case, the antecedent soil moisture content seemed to have had a dominant effect on overwinter soil N biogeochemical processes such as mineralization, which are often ignored in models. This work demonstrates both the need for better representation of cold regions processes in hydrochemical models and the model improvements that are possible if these are included.     

A modelling framework to simulate field‐scale nitrate response and transport during snowmelt: The WINTRA model

Modelling nutrient transport during snowmelt in cold regions remains a major scientific challenge. A key limitation of existing nutrient models for application in cold regions is the inadequate representation of snowmelt, including hydrological and biogeochemical processes. This brief period can account for more than 80% of the total annual surface runoff in the Canadian Prairies and Northern Canada and processes such as atmospheric deposition, overwinter redistribution of snow, ion exclusion from snow crystals, frozen soils, and snow‐covered area depletion during melt influence the distribution and release of snow and soil nutrients, thus affecting the timing and magnitude of snowmelt runoff nutrient concentrations. Research in cold regions suggests that nitrate (NO₃) runoff at the field‐scale can be divided into 5 phases during snowmelt. In the first phase, water and ions originating from ion‐rich snow layers travel and diffuse through the snowpack. This process causes ion concentrations in runoff to gradually increase. The second phase occurs when this snow ion meltwater front has reached the bottom of the snowpack and forms runoff to the edge‐of‐the‐field. During the third and fourth phases, the main source of NO₃ transitions from the snowpack to the soil. Finally, the fifth and last phase occurs when the snow has completely melted, and the thawing soil becomes the main source of NO₃ to the stream. In this research, a process‐based model was developed to simulate hourly export based on this 5‐phase approach. Results from an application in the Red River Basin of southern Manitoba, Canada, shows that the model can adequately capture the dynamics and rapid changes of NO₃ concentrations during this period at relevant temporal resolutions. This is a significant achievement to advance the current nutrient modelling paradigm in cold climates, which is generally limited to satisfactory results at monthly or annual resolutions. The approach can inform catchment‐scale nutrient models to improve simulation of this critical snowmelt period.     

Making decisions to conserve species under climate change

Severe impacts on biodiversity are predicted to arise from climate change. These impacts may not be adequately addressed by conventional approaches to conservation. As a result, additional management actions are now being considered. However, there is currently limited guidance to help decision makers choose which set of actions (and in what order) is most appropriate for species that are considered to be vulnerable. Here, we provide a decision framework for the full complement of actions aimed at conserving species under climate change from ongoing conservation in existing refugia through various forms of mobility enhancement to ex situ conservation outside the natural environment. We explicitly recognize that allocation of conservation resources toward particular actions may be governed by factors such as the likelihood of success, cost and likely co-benefits to non-target species in addition to perceived vulnerability of individual species. As such, we use expert judgment of probable tradeoffs in resource allocation to inform the sequential evaluation of proposed management interventions.     

Paleomagnetism, geochemistry and Ar/Ar dating of the North-eastern Paraná Magmatic Province: tectonic implications

Seventy sites of sills, flows and dikes from Northeastern Paraná Magmatic Province (PMP), were submitted to paleomagnetic, chemical and radiometric analyses. The rocks are high in TiO₂ content, and similar in composition to the rocks from the northern region of PMP. The sills intrude mainly Paleozoic sediments, and can be subdivided into two domains; the northern being characterized by sills showing reversed polarities, and the southern essentially by sills of normal polarities. ⁴⁰Ar/³⁹Ar dating of three distinct sills gave plateau ages (129.9 ± 0.1, 130.3 ± 0.1 and 131.9 ± 0.4 Ma) that are similar to surface-outcropping flows of the Northern Paraná Basin, and the Ponta Grossa dikes. The new paleomagnetic data combined with existing data from the northern PMP allowed the calculation of a paleomagnetic pole at 71.4° E and 83.0° S (N = 92; α₉₅=2.4°; k = 39). This pole is in good agreement with poles for central and southern PMP, which are slightly older than the northern PMP, as well as for the contemporaneous Central Alkaline Province (Paraguay) on the western side of PMP. In contrast, the coeval pole for the Ponta Grossa dikes (eastern border of PMP), however, is slightly displaced from that group of poles, suggesting that dikes in that area may have undergone some tectonic tilting.     

Multiple imputation framework for data assignment in truncated pluri-Gaussian simulation

Truncated pluri-Gaussian simulation (TPGS) is suitable for the simulation of categorical variables that show natural ordering as the TPGS technique can consider transition probabilities. The TPGS assumes that categorical variables are the result of the truncation of underlying latent variables. In practice, only the categorical variables are observed. This translates the practical application of TPGS into a missing data problem in which all latent variables are missing. Latent variables are required at data locations in order to condition categorical realizations to observed categorical data. The imputation of missing latent variables at data locations is often achieved by either assigning constant values or spatially simulating latent variables subject to categorical observations. Realizations of latent variables can be used to condition all model realizations. Using a single realization or a constant value to condition all realizations is the same as assuming that latent variables are known at the data locations and this assumption affects uncertainty near data locations. The techniques for imputation of latent variables in TPGS framework are investigated in this article and their impact on uncertainty of simulated categorical models and possible effects on factors affecting decision making are explored. It is shown that the use of single realization of latent variables leads to underestimation of uncertainty and overestimation of measured resources while the use constant values for latent variables may lead to considerable over or underestimation of measured resources. The results highlight the importance of multiple data imputation in the context of TPGS.