Mapping the hydraulic connection between a coalbed and adjacent aquifer: example of the coal-seam gas resource area,north Galilee Basin,Australia

Coal-seam gas production requires groundwater extraction from coal-bearing formations to reduce the hydraulic pressure and improve gas recovery. In layered sedimentary basins, the coalbeds are often separated from freshwater aquifers by low-permeability aquitards. However, hydraulic connection between the coalbed and aquifers is possible due to the heterogeneity in the aquitard such as the existence of conductive faults or sandy channel deposits. For coal-seam gas extraction operations, it is desirable to identify areas in a basin where the probability of hydraulic connection between the coalbed and aquifers is low in order to avoid unnecessary loss of groundwater from aquifers and gas production problems. A connection indicator, the groundwater age indictor (GAI), is proposed, to quantify the degree of hydraulic connection. The spatial distribution of GAI can indicate the optimum positions for gas/water extraction in the coalbed. Depressurizing the coalbed at locations with a low GAI would result in little or no interaction with the aquifer when compared to the other positions. The concept of GAI is validated on synthetic cases and is then applied to the north Galilee Basin, Australia, to assess the degree of hydraulic connection between the Aramac Coal Measure and the water-bearing formations in the Great Artesian Basin, which are separated by an aquitard, the Betts Creek Beds. It is found that the GAI is higher in the western part of the basin, indicating a higher risk to depressurization of the coalbed in this region due to the strong hydraulic connection between the coalbed and the overlying aquifer.     

Improved regional groundwater flow modeling using drainage features: a case study of the central northern karst aquifer system of Puerto Rico (USA)

In northern Puerto Rico (USA), subsurface conduit networks with unknown characteristics, and surface features such as springs, rivers, lagoons and wetlands, drain the coastal karst aquifers. In this study, drain lines connecting sinkholes and springs are used to improve the developed regional model by simulating the drainage effects of conduit networks. Implemented in an equivalent porous media (EPM) approach, the model with drains is able to roughly reproduce the spring discharge hydrographs in response to rainfall. Hydraulic conductivities are found to be scale dependent and significantly increase with higher test radius, indicating scale dependency of the EPM approach. Similar to other karst regions in the world, hydraulic gradients are steeper where the transmissivity is lower approaching the coastline. This study enhances current understanding of the complex flow patterns in karst aquifers and suggests that using a drainage feature improves modeling results where available data on conduit characteristics are minimal.     

Rescaling of Access and Property Relations in a Frontier Landscape: Insights from Jambi,Indonesia

The recent expansion of protected areas and oil palm plantations in Jambi (Sumatra), Indonesia, has been notably disruptive. This article investigates the scalar dimensions of land conflicts within a privately managed conservation area. We built on qualitative research focusing on struggles related to the formation of two informal settlements within the conservation area. Results indicate that, especially in the context of rapid rescaling (e.g., decentralization), new power constellations emerge, thereby altering preexisting property relations. We argue that competing scales of meaning and regulation are structuring the ability of actors to access land and, consequently, reflect tensions between structure and agency.     

Contrasting sources of Late Paleozoic rhyolite magma in the Polish Lowlands: evidence from U–Pb ages and Hf and O isotope composition in zircon

International Journal of Earth Sciences - The Polish Lowlands, located southwest of the Teisseyre–Tornquist Zone, within Trans-European Suture Zone, were affected by bimodal, but dominantly...     

Upscaling permeability for three-dimensional fractured porous rocks with the multiple boundary method

Upscaling permeability of grid blocks is crucial for groundwater models. A novel upscaling method for three-dimensional fractured porous rocks is presented. The objective of the study was to compare this method with the commonly used Oda upscaling method and the volume averaging method. First, the multiple boundary method and its computational framework were defined for three-dimensional stochastic fracture networks. Then, the different upscaling methods were compared for a set of rotated fractures, for tortuous fractures, and for two discrete fracture networks. The results computed by the multiple boundary method are comparable with those of the other two methods and fit best the analytical solution for a set of rotated fractures. The errors in flow rate of the equivalent fracture model decrease when using the multiple boundary method. Furthermore, the errors of the equivalent fracture models increase from well-connected fracture networks to poorly connected ones. Finally, the diagonal components of the equivalent permeability tensors tend to follow a normal or log-normal distribution for the well-connected fracture network model with infinite fracture size. By contrast, they exhibit a power-law distribution for the poorly connected fracture network with multiple scale fractures. The study demonstrates the accuracy and the flexibility of the multiple boundary upscaling concept. This makes it attractive for being incorporated into any existing flow-based upscaling procedures, which helps in reducing the uncertainty of groundwater models.     

A review on missing hydrological data processing

Like almost all fields of science, hydrology has benefited to a large extent from the tremendous improvements in scientific instruments that are able to collect long-time data series and an increase in available computational power and storage capabilities over the last decades. Many model applications and statistical analyses (e.g., extreme value analysis) are based on these time series. Consequently, the quality and the completeness of these time series are essential. Preprocessing of raw data sets by filling data gaps is thus a necessary procedure. Several interpolation techniques with different complexity are available ranging from rather simple to extremely challenging approaches. In this paper, various imputation methods available to the hydrological researchers are reviewed with regard to their suitability for filling gaps in the context of solving hydrological questions. The methodological approaches include arithmetic mean imputation, principal component analysis, regression-based methods and multiple imputation methods. In particular, autoregressive conditional heteroscedasticity (ARCH) models which originate from finance and econometrics will be discussed regarding their applicability to data series characterized by non-constant volatility and heteroscedasticity in hydrological contexts. The review shows that methodological advances driven by other fields of research bear relevance for a more intensive use of these methods in hydrology. Up to now, the hydrological community has paid little attention to the imputation ability of time series models in general and ARCH models in particular.     

Porous Structure Reconstruction Using Convolutional Neural Networks

The three-dimensional high-resolution imaging of rock samples is the basis for pore-scale characterization of reservoirs. Micro X-ray computed tomography (µ-CT) is considered the most direct means of obtaining the three-dimensional inner structure of porous media without deconstruction. The micrometer resolution of µ-CT, however, limits its application in the detection of small structures such as nanochannels, which are critical for fluid transportation. An effective strategy for solving this problem is applying numerical reconstruction methods to improve the resolution of the µ-CT images. In this paper, a convolutional neural network reconstruction method is introduced to reconstruct high-resolution porous structures based on low-resolution µ-CT images and high-resolution scanning electron microscope (SEM) images. The proposed method involves four steps. First, a three-dimensional low-resolution tomographic image of a rock sample is obtained by µ-CT scanning. Next, one or more sections in the rock sample are selected for scanning by SEM to obtain high-resolution two-dimensional images. The high-resolution segmented SEM images and their corresponding low-resolution µ-CT slices are then applied to train a convolutional neural network (CNN) model. Finally, the trained CNN model is used to reconstruct the entire low-resolution three-dimensional µ-CT image. Because the SEM images are segmented and have a higher resolution than the µ-CT image, this algorithm integrates the super-resolution and segmentation processes. The input data are low-resolution µ-CT images, and the output data are high-resolution segmented porous structures. The experimental results show that the proposed method can achieve state-of-the-art performance.