Distribution of gold in different size fractions of stream sediments as a guide to bedrock gold mineralisation along the Shyok Suture Zone and adjacent areas of the Kohistan Island Arc,Pakistan

Arabian Journal of Geosciences - This paper describes a new exploration methodology using stream sediments in remote and glaciated regions of northern Pakistan. Gold concentrations in 268 samples...     

New impact equation using barrier Froude number for the design of dual rigid barriers against debris flows

Landslides - In the design of multiple rigid barriers, the height of the first barrier governs the impact dynamics of debris flow on the next barrier in a channel. However, current design...     

Spatiotemporal modelling of rainfall-induced landslides using machine learning

Landslides - Natural terrain landslides are mainly triggered by rainstorms in Hong Kong, which pose great threats to life and property. To mitigate landslide risk, building a prediction model which...     

Flood susceptibility prediction using four machine learning techniques and comparison of their performance at Wadi Qena Basin,Egypt

Natural Hazards - Floods represent catastrophic environmental hazards that have a significant impact on the environment and human life and their activities. Environmental and water management in...     

Short and Long-term Behaviours of Bored Tunnels in Port-Said Clay

Geotechnical and Geological Engineering - This study investigates the behaviour of bored tunnels in the Port-Said very soft to firm clay deposit in El-Tina Plain, north-eastern Egypt. Recent...     

Biostratigraphy,Depositional and Diagenetic Processes in Carbonate Rocks from Southern Lebanon: Impact on Porosity and Permeability

Carbonate rocks contain prolific hydrocarbon reserves all over the world, particularly in the Middle East. For exploration and production strategies, it is essential to understand carbonate reservoirs in terms of their internal characteristics, depositional environment, relative age, diagenetic processes and impact on petrophysical properties. This study has been performed on exposed Cretaceous, Paleocene and Miocene marine carbonate sedimentary sequences in two localities (Maghdoucheh and Qennarit) near the city of Sidon (Southern Lebanon). It represents the first comprehensive study that takes into consideration the carbonate reservoir facies, diagenetic history and reservoir quality in the area. Rocks at Maghdoucheh are mainly dominated by limestone beds showing sedimentary structures and erosive bases alternating with microfossil-rich silty marls, related to a shallowing upward sequence in a restricted marine platform environment. Shells of benthic foraminifera and mollusks dominate the fossil assemblage extracted from the studied rocks. The microfossil and nannofossil assemblage detected in the Maghdoucheh sections indicates a middle Miocene age. Rocks at Qennarit are composed of mudstone/wackestone limestone beds rich in planktonic foraminifera and nannofossils related to open marine conditions. Based on the nannofossil content, rocks from Qennarit 1 and 2 are Paleocene and Cretaceous in age, respectively. Four main types of microfacies have been identified, i.e. (1) microbioclastic peloidal calcisiltite, (2) pelagic lime mudstone and wackestone with planktonic microfossils, (3) grainstone/packstone with abundant foraminifera and (4) fenestral bindstones, mudstones and packstones with porostromate microstructures. The porosity-permeability (poro-perm) analysis of representative samples reveals moderate to good porosity but very low permeability. This is mainly due to the presence of large moldic pores that are isolated in nature. The diagenetic features are dominated by micritization and dissolution (both fabric selective and non-fabric selective). Among all diagenetic features, dissolution in both localities contributes to porosity enhancement, while micritization, cementation, compaction and the filled fractures have negative impacts on permeability; hence the overall reservoir quality.     

Conditioning generative adversarial networks on nonlinear data for subsurface flow model calibration and uncertainty quantification

Conditioning complex subsurface flow models on nonlinear data is complicated by the need to preserve the expected geological connectivity patterns to maintain solution plausibility. Generative adversarial networks (GANs) have recently been proposed as a promising approach for low-dimensional representation of complex high-dimensional images. The method has also been adopted for low-rank parameterization of complex geologic models to facilitate uncertainty quantification workflows. A difficulty in adopting these methods for subsurface flow modeling is the complexity associated with nonlinear flow data conditioning. While conditional GAN (CGAN) can condition simulated images on labels, application to subsurface problems requires efficient conditioning workflows for nonlinear data, which is far more complex. We present two approaches for generating flow-conditioned models with complex spatial patterns using GAN. The first method is through conditional GAN, whereby a production response label is used as an auxiliary input during the training stage of GAN. The production label is derived from clustering of the flow responses of the prior model realizations (i.e., training data). The underlying assumption of this approach is that GAN can learn the association between the spatial features corresponding to the production responses within each cluster. An alternative method is to use a subset of samples from the training data that are within a certain distance from the observed flow responses and use them as training data within GAN to generate new model realizations. In this case, GAN is not required to learn the nonlinear relation between production responses and spatial patterns. Instead, it is tasked to learn the patterns in the selected realizations that provide a close match to the observed data. The conditional low-dimensional parameterization for complex geologic models with diverse spatial features (i.e., when multiple geologic scenarios are plausible) performed by GAN allows for exploring the spatial variability in the conditional realizations, which can be critical for decision-making. We present and discuss the important properties of GAN for data conditioning using several examples with increasing complexity.

     

Smooth particle hydrodynamics modelling of liquid-sediment system and coastal wave breaker

Ocean Dynamics - In this paper, a two-phase coastal liquid-sediment system and a wave breaker were numerically simulated using a particle-based smooth particle hydrodynamics (SPH). To verify the...