A multi-tracer approach to delineate groundwater dynamics in the Rio Actopan Basin,Veracruz State,Mexico

Geochemistry and environmental tracers were used to understand groundwater resources, recharge processes, and potential sources of contamination in the Rio Actopan Basin, Veracruz State, Mexico. Total dissolved solids are lower in wells and springs located in the basin uplands compared with those closer to the coast, likely associated with rock/water interaction. Geochemical results also indicate some saltwater intrusion near the coast and increased nitrate near urban centers. Stable isotopes show that precipitation is the source of recharge to the groundwater system. Interestingly, some high-elevation springs are more isotopically enriched than average annual precipitation at higher elevations, indicating preferential recharge during the drier but cooler winter months when evapotranspiration is reduced. In contrast, groundwater below 1,200 m elevation is more isotopically depleted than average precipitation, indicating recharge occurring at much higher elevation than the sampling site. Relatively cool recharge temperatures, derived from noble gas measurements at four sites (11–20 °C), also suggest higher elevation recharge. Environmental tracers indicate that groundwater residence time in the basin ranges from 12,000 years to modern. While this large range shows varying groundwater flowpaths and travel times, ages using different tracer methods (¹⁴C, ³H/³He, CFCs) were generally consistent. Comparing multiple tracers such as CFC-12 with CFC-113 indicates piston-flow to some discharge points, yet binary mixing of young and older groundwater at other points. In summary, groundwater within the Rio Actopan Basin watershed is relatively young (Holocene) and the majority of recharge occurs in the basin uplands and moves towards the coast.     

Identifying influence areas with connectivity analysis – application to the local perturbation of heterogeneity distribution for history matching

Numerical representations of a target reservoir can help to assess the potential of different development plans. To be as predictive as possible, these representations or models must reproduce the data (static, dynamic) collected on the field. However, constraining reservoir models to dynamic data – the history-matching process – can be very time consuming. Many uncertain parameters need to be taken into account, such as the spatial distribution of petrophysical properties. This distribution is mostly unknown and usually represented by millions of values populating the reservoir grid. Dedicated parameterization techniques make it possible to investigate many spatial distributions from a small number of parameters. The efficiency of the matching process can be improved from the perturbation of specific regions of the reservoir. Distinct approaches can be considered to define such regions. For instance, one can refer to streamlines. The leading idea is to identify areas that influence the production behavior where the data are poorly reproduced. Here, we propose alternative methods based on connectivity analysis to easily provide approximate influence areas for any fluid-flow simulation. The reservoir is viewed as a set of nodes connected by weighted links that characterize the distance between two nodes. The path between nodes (or grid blocks) with the lowest cumulative weight yields an approximate flow path used to define influence areas. The potential of the approach is demonstrated on the basis of 2D synthetic cases for the joint integration of production and 4D saturation data, considering several formulations for the weights attributed to the links.     

Crustal melting and recycling: geochronology and sources of Variscan syn-kinematic anatectic granitoids of the Tormes Dome (Central Iberian Zone). A U–Pb LA-ICP-MS study

International Journal of Earth Sciences - In this study, we report U–Pb Laser Ablation ICP-MS zircon and ID-TIMS monazite ages for peraluminous granitoid plutons...     

Rainfall thresholds for the triggering of landslides considering previous soil moisture conditions (Asturias,NW Spain)

Given its geological and climatic conditions and its rugged orography, Asturias is one of the most landslide prone areas in the North of Spain. Most of the landslides occur during intense rainfall episodes. Thus, precipitation is considered the main triggering factor in the study area, reaching average annual values of 960 mm. Two main precipitation patterns are frequent: (i) long-lasting periods of moderate rainfall during autumn and winter and (ii) heavy short rainfall episodes during spring and early summer. In the present work, soil moisture conditions in the locations of 84 landslides are analysed during two rainfall episodes, which represent the most common precipitation patterns: October–November 2008 and June 2010. Empirical data allowed the definition of available water capacity percentages of 99–100% as critical soil moisture conditions for the landslide triggering. Intensity-duration rainfall thresholds were calculated for each episode, considering the periods with sustained high soil moisture levels before the occurrence of each analysed landslide event. For this purpose, data from daily water balance models and weather stations were used. An inverse relationship between the duration of the precipitation and its intensity, consistent with published intensity-duration thresholds, was observed, showing relevant seasonal differences.     

Exploratory Factor Analysis of Wireline Logs Using a Float-Encoded Genetic Algorithm

In the paper, a novel inversion approach is used for the solution of the problem of factor analysis. The float-encoded genetic algorithm as a global optimization method is implemented to extract factor variables using open-hole logging data. The suggested statistical workflow is used to give a reliable estimate for not only the factors but also the related petrophysical properties in hydrocarbon formations. In the first step, the factor loadings and scores are estimated by Jöreskog’s fast approximate method, which are gradually improved by the genetic algorithm. The forward problem is solved to calculate wireline logs directly from the factor scores. In each generation, the observed and calculated well logs are compared to update the factor population. During the genetic algorithm run, the average fitness of factor populations is maximized to give the best fit between the observed and theoretical data. By using the empirical relation between the first factor and formation shaliness, the shale volume is estimated along the borehole. Permeability as a derived quantity also correlates with the first factor, which allows its determination from an independent source. The estimation results agree well with those of independent deterministic modeling and core measurements. Case studies from Hungary and the USA demonstrate the feasibility of the global optimization based factor analysis, which provides a useful tool for improved reservoir characterization.