Shrinkage Properties of Soft Clay Treated with Cement and Geofibers

In this study, effects of two types of geofibers, namely polypropylene and recycled carpet, on three dimensional shrinkage properties of cement treated kaolinite and bentonite clays are investigated. Cement treated clay specimens were prepared with cement contents of 5, 10, and 15 % by weight of dry soil for kaolinite samples, and 30, 40 and 50 % for bentonite samples. To investigate and understand the influence of different fiber types and contents, three different percentages of fiber content (i.e. 0.1, 0.2 and 0.5 % polypropylene fibers; and 0.5, 0.75 and 1 % carpet fibers) were adopted. The results of shrinkage tests on 126 cylindrical samples of cement treated clay with various cement and fiber contents were analysed to understand the relationships between these parameters and the shrinkage percentage of treated soil. Results of this study indicate that combination of cement and fiber is effective in reducing the volume change of clayey soils undergoing drying process. In the applied ranges of cement and fiber contents, the influence of cement addition on the shrinkage reduction is more significant than the addition of fibers for the treated kaolinite. However, addition of fibers in curtailing the shrinkage of bentonite clay is more significant than the cement addition.     

Evaluation of climate change effects in a coastal aquifer in Morocco using a density-dependent numerical model

A density-dependent numerical groundwater model was applied to study the climate change impact in a shallow aquifer in the Mediterranean coast of Morocco, the Saïdia aquifer. The stresses imposed to the model were derived from the IPCC emission scenarios and included recharge variation and sea level rise. The main effect of the climate change in the Saïdia aquifer will be a decrease in renewable resources, which in the worst-case scenario may decrease to 50–60% of present-day values, due to the decline in recharge and to a reduced inflow from the adjacent Triffa aquifer. The water quality will be affected mostly in the area immediately adjacent to the seashore, where salinity may increase up to 30 g/l. Localised areas may see a decrease in salinity due to the induced freshwater recharge from Oued Moulouya River and diminished inflow from high-salinity springs.     

The Sabzevar blueschists of the North-Central Iranian micro-continent as remnants of the Neotethys-related oceanic crust subduction

The Sabzevar ophiolites mark the Neotethys suture in east-north-central Iran. The Sabzevar metamorphic rocks, as part of the Cretaceous Sabzevar ophiolitic complex, consist of blueschist, amphibolite and greenschist. The Sabzevar blueschists contain sodic amphibole, epidote, phengite, calcite ± omphacite ± quartz. The epidote amphibolite is composed of sodic-calcic amphibole, epidote, albite, phengite, quartz ± omphacite, ilmenite and titanite. The greenschist contains chlorite, plagioclase and pyrite, as main minerals. Thermobarometry of a blueschist yields a pressure of 13–15.5 kbar at temperatures of 420–500 °C. Peak metamorphic temperature/depth ratios were low (~12 °C/km), consistent with metamorphism in a subduction zone. The presence of epidote in the blueschist shows that the rocks were metamorphosed entirely within the epidote stability field. Amphibole schist samples experienced pressures of 5–7 kbar and temperatures between 450 and 550 °C. The presence of chlorite, actinolite, biotite and titanite indicate greenschist facies metamorphism. Chlorite, albite and biotite replacing garnet or glaucophane suggests temperatures of >300 °C for greenschist facies. The formation of high-pressure metamorphic rocks is related to north-east-dipping subduction of the Neotethys oceanic crust and subsequent closure during lower Eocene between the Central Iranian Micro-continent and Eurasia (North Iran).     

Soil temperature modeling at different depths using neuro-fuzzy,neural network,and genetic programming techniques

The applicability of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and genetic programming (GP) techniques in estimating soil temperatures (ST) at different depths is investigated in this study. Weather data from two stations, Mersin and Adana, Turkey, were used as inputs to the applied models in order to model monthly STs. The first part of the study focused on comparison of ANN, ANFIS, and GP models in modeling ST of two stations at the depths of 10, 50, and 100 cm. GP was found to perform better than the ANN and ANFIS-SC in estimating monthly ST. The effect of periodicity (month of the year) on models’ accuracy was also investigated. Including periodicity component in models’ inputs considerably increased their accuracies. The root mean square error (RMSE) of ANN models was respectively decreased by 34 and 27 % for the depths of 10 and 100 cm adding the periodicity input. In the second part of the study, the accuracies of the ANN, ANFIS, and GP models were compared in estimating ST of Mersin Station using the climatic data of Adana Station. The ANN models generally performed better than the ANFIS-SC and GP in modeling ST of Mersin Station without local climatic inputs.     

Evolution of groundwater quality in intensive agricultural zone: case of Chtouka-Massa Aquifer,Morocco

The Chtouka-Massa area in Southern Morocco has shown an increase in water scarcity during the last decades, caused mainly by withdrawal of water resources aggravated by agricultural intensification and climate change impacts. To better understand the changes of groundwater quality, a sampling campaign was conducted in many wells during March 2015 and compared to historical chemical data from the hydraulic basin agency, as well as previous studies performed at Ibn Zohr University. All data were used to assess the spatial-temporal evolution of nitrate and salinity relevant to the recent sampling. This paper describes the current state of groundwater quality in the Chtouka-Massa zone with an overview of different sources of water mineralization and the nitrate evolution in an agricultural area. Our results indicate a general increase in mineralization from the north to the south, and from the east to the west. The plain, dominated by farms, shows a relatively high conductivity (up to 2000 μS/cm), while in both costal area and Anti-Atlas Mountain the water salinity shows a gradient increase from the north to the south. However, the highest electrical conductivity is observed along the Massa River. The water type is bicarbonate, chloride, and sodium for farm samples, while from the other parts, it is mostly dominated by chloride and sodium. The spatial-temporal analysis of nitrates generally shows an increasing trend. However, the levels remain overall lower than the limit. The temporal evolution of control points set by the hydraulic agency shows a decreasing trend decline that can be explained by the improvement of agriculture practices, including the conversion towards drip irrigation mode. Different chemical tracers highlighted some processes involving the changes of mineralization of groundwater (e.g., irrigation water return, marine intrusion, and water/rock interaction). The results will be used to improve water management in this area showing water quality degradation.     

Monotone multiscale finite volume method

The MultiScale Finite Volume (MSFV) method is known to produce non-monotone solutions. The causes of the non-monotone solutions are identified and connected to the local flux across the boundaries of primal coarse cells induced by the basis functions. We propose a monotone MSFV (m-MSFV) method based on a local stencil-fix that guarantees monotonicity of the coarse-scale operator, and thus, the resulting approximate fine-scale solution. Detection of non-physical transmissibility coefficients that lead to non-monotone solutions is achieved using local information only and is performed algebraically. For these ‘critical’ primal coarse-grid interfaces, a monotone local flux approximation, specifically, a Two-Point Flux Approximation (TPFA), is employed. Alternatively, a local linear boundary condition can be used for the dual basis functions to reduce the degree of non-monotonicity. The local nature of the two strategies allows for ensuring monotonicity in local sub-regions, where the non-physical transmissibility occurs. For practical applications, an adaptive approach based on normalized positive off-diagonal coarse-scale transmissibility coefficients is developed. Based on the histogram of these normalized coefficients, one can remove the large peaks by applying the proposed modifications only for a small fraction of the primal coarse grids. Though the m-MSFV approach can guarantee monotonicity of the solutions to any desired level, numerical results illustrate that employing the m-MSFV modifications only for a small fraction of the domain can significantly reduce the non-monotonicity of the conservative MSFV solutions.     

Prediction of porosity and water saturation using pre-stack seismic attributes: a comparison of Bayesian inversion and computational intelligence methods

Rock physical parameters such as porosity and water saturation play an important role in the mechanical behavior of hydrocarbon reservoir rocks. A valid and reliable prediction of these parameters from seismic data is essential for reservoir characterization, management, and also geomechanical modeling. In this paper, the application of conventional methods such as Bayesian inversion and computational intelligence methods, namely support vector regression (SVR) optimized by particle swarm optimization (PSO) and adaptive network-based fuzzy inference system-subtractive clustering method (ANFIS-SCM), is demonstrated to predict porosity and water saturation. The prediction abilities offered by Bayesian inversion, SVR-PSO, and ANFIS-SCM were presented using a synthetic dataset and field data available from a gas carbonate reservoir in Iran. In these models, seismic pre-stack data and attributes were utilized as the input parameters, while the porosity and water saturation were the output parameters. Various statistical performance indexes were utilized to compare the performance of those estimation models. The results achieved indicate that the ANFIS-SCM model has strong potential for indirect estimation of porosity and water saturation with high degree of accuracy and robustness from seismic data and attributes in both synthetic and real cases of this study.