Salinisation des nappes côtières : cas de la nappe nord du Sahel de Sfax,Tunisie

The intensive agricultural and economic activities induce the increase of the risk of groundwater quality degradation through high groundwater pumping rates. The salinization and contamination are the main sources of this pollution, especially in coastal aquifers. The explanation of the origin of salinity for the shallow aquifer of Northern Sahel of Sfax was analysed by a chemical study of the groundwater main compounds. The partitioning of groundwaters into homogenous groups is undertaken by graphical techniques, including a Stiff pattern diagram, an expanded Durov diagram and several binary diagrams. The study indicates the presence of various salinization processes. In the recharge area, salinization is the result of dissolution/precipitation of the aquifer formation material (group I). The irrigation water return and the intensive pumping have been identified as major sources of salinization in the south by direct cation exchange and mixing reactions (groups II and III). The anomaly of high groundwater salinity observed near the Hazeg zone was explained by the presence of a seawater intrusion in this area. This hypothesis is related to the high chloride concentration, to the presence of inverse cation exchange reactions (group IV), and to the piezometric level inferior to sea level. To cite this article: R. Trabelsi et al., C. R. Geoscience 337 (2005).     

Hydrogeological investigations of thermal waters in the Sfax Basin (Tunisia)

The Sfax Basin in eastern Tunisia is bounded to the east by the Mediterranean Sea. Thermal waters of the Sfax area have measured temperatures of 23–36°C, and electrical conductivities of 3,200 and 14,980 μS/cm. Most of the thermal waters are characterized as Na–Cl type although there are a few Na–SO₄–Cl waters. They issue from Miocene units which are made up sands and sandstones interbedded with clay. The Quaternary sediments cap the system. The heat source is high geothermal gradient which are determined downhole temperature measurements caused by graben tectonics of the area. The results of mineral equilibrium modeling indicate that the thermal waters of the Sfax Basin are undersaturated with respect to gypsum, anhydrite and fluorite, oversaturated with respect to kaolinite, dolomite, calcite, microcline, quartz, chalcedony, and muscovite. Assessments from various chemical geothermometers, Na–K–Mg ternary and mineral equilibrium diagrams suggest that the reservoir temperature of the Sfax area can reach up to 120°C. According to δ¹⁸O and δ²H values, all thermal and cold groundwater is of meteoric origin.     

Role of γ-ray spectrometry in detecting potassic alteration associated with Um Ba’anib granitic gneiss and metasediments, G. Meatiq area, Central Eastern Desert, Egypt

The radiometric responses of the Gebel (G., which means mountain in Arabic) Meatiq area display the overall high radiation of the high grade metamorphic Um Ba’anib granite gneiss, metasediments, as well as Arieki adamellite rocks. Whereas, the low grade metamorphic ophiolitic nappes country reveal the lowest radiometric response. The eU, eTh, and K contents tend to increase with the youthfulness of the plutons with a maximum amounts in the more alkali varieties, e.g., Arieki adamellite (580 Ma), then the high grade metamorphic rocks of the younger Meatiqian orogeny (626?±?2 Ma). Also, these rocks reveal that the major radiometric anomaly with exposure rates ≈139 nGy/h, more than double of the global terrestrial values. While, the low grade metamorphic ophiolitic rocks reveal the lowest average exposure rates ≈46.8 nGy/h. The areas of high gamma ray values of F-parameter of Efimov (K × U/Th), ternary composite map, K map, K/eTh, and K/eU ratios maps are related to K enrichment conditions during formation (diagenesis) or deformation of the high grade metamorphic rocks and the Arieki adamellite intrusion. From the geochemical point of view, these areas are associated with rocks that are characterized by high-K calc-alkaline, calc-alkaline affinity, and enriched in REE.     

Detrital zircon provenance of Neoproterozoic to Cenozoic deposits in Iran: Implications for chronostratigraphy and collisional tectonics

Ion-microprobe U–Pb analyses of 589 detrital zircon grains from 14 sandstones of the Alborz mountains, Zagros mountains, and central Iranian plateau provide an initial framework for understanding the Neoproterozoic to Cenozoic provenance history of Iran. The results place improved chronological constraints on the age of earliest sediment accumulation during Neoproterozoic–Cambrian time, the timing of the Mesozoic Iran–Eurasia collision and Cenozoic Arabia–Eurasia collision, and the contribution of various sediment sources of Gondwanan and Eurasian affinity during opening and closure of the Paleotethys and Neotethys oceans. The zircon age populations suggest that deposition of the extensive ~ 1 km-thick clastic sequence at the base of the cover succession commenced in latest Neoproterozoic and terminated by Middle Cambrian time. Comparison of the geochronological data with detrital zircon ages for northern Gondwana reveals that sediment principally derived from the East African orogen covered a vast region encompassing northern Africa and the Middle East. Although most previous studies propose a simple passive-margin setting for Paleozoic Iran, detrital zircon age spectra indicate Late Devonian–Early Permian and Cambrian–Ordovician magmatism. These data suggest that Iran was affiliated with Eurasian magmatic arcs or that rift-related magmatic activity during opening of Paleotethys and Neotethys was more pronounced than thought along the northern Gondwanan passive-margin. For a Triassic–Jurassic clastic overlap assemblage (Shemshak Formation) in the Alborz mountains, U–Pb zircon ages provide chronostratigraphic age control requiring collision of Iran with Eurasia by late Carnian–early Norian time (220–210 Ma). Finally, Cenozoic strata yield abundant zircons of Eocene age, consistent with derivation from arc magmatic rocks related to late-stage subduction and/or breakoff of the Neotethys slab. Together with the timing of foreland basin sedimentation in the Zagros, these detrital zircon ages help bracket the onset of the Arabia–Eurasia collision in Iran between middle Eocene and late Oligocene time.     

Intra-survey reservoir fluctuations – implications for quantitative 4D seismic analysis

During the time taken for seismic data to be acquired, reservoir pressure may fluctuate as a consequence of field production and operational procedures and fluid fronts may move significantly. These variations prevent accurate quantitative measurement of the reservoir change using 4D seismic data. Modelling studies on the Norne field simulation model using acquisition data from ocean-bottom seismometer and towed streamer systems indicate that the pre-stack intra-survey reservoir fluctuations are important and cannot be neglected. Similarly, the time-lapse seismic image in the post-stack domain does not represent a difference between two states of the reservoir at a unique base and monitor time, but is a mixed version of reality that depends on the sequence and timing of seismic shooting. The outcome is a lack of accuracy in the measurement of reservoir changes using the resulting processed and stacked 4D seismic data. Even for perfect spatial repeatability between surveys, a spatially variant noise floor is still anticipated to remain. For our particular North Sea acquisition data, we find that towed streamer data are more affected than the ocean-bottom seismometer data. We think that this may be typical for towed streamers due to their restricted aperture compared to ocean-bottom seismometer acquisitions, even for a favourable time sequence of shooting and spatial repeatability. Importantly, the pressure signals on the near and far offset stacks commonly used in quantitative 4D seismic inversion are found to be inconsistent due to the acquisition timestamp. Saturation changes at the boundaries of fluid fronts appear to show a similar inconsistency across sub-stacks. We recommend that 4D data are shot in a consistent manner to optimize aerial time coverage, and that additionally, the timestamp of the acquisition should be used to optimize pre-stack quantitative reservoir analysis.     

Biostratigraphy and seismic data analysis to detect the sequence stratigraphic depositional environment of the Miocene succession: Gulf of Suez (Egypt)

This work comprises a study of the sequence stratigraphy, seismic-facies analysis, biostratigraphy and depositional environments of the northern part of the Gulf of Suez, Egypt, using a set of 24 3D seismic profiles, composite logs and sonic logs from ten wells. The syn-rift formations in the studied ten wells are described lithologically and interpreted based on investigating two seismic profiles. Biostratigraphically, the Miocene fossils are identified to correlate the five planktonic foraminiferal biozones in the examined boreholes (RB-A1, RB-B1, RB-B3, EE85-2 and RB-C1). The sequence stratigraphic analysis suggests that the Miocene succession can be subdivided into two major third order depositional sequences (S1 and S2) separated by the three major sequence boundaries (DSB1, DSB2 and DSB3).     

Deep neural network application for 4D seismic inversion to changes in pressure and saturation: Optimizing the use of synthetic training datasets

In this work, we tackle the challenge of quantitative estimation of reservoir dynamic property variations during a period of production, directly from four-dimensional seismic data in the amplitude domain. We employ a deep neural network to invert four-dimensional seismic amplitude maps to the simultaneous changes in pressure, water and gas saturations. The method is applied to a real field data case, where, as is common in such applications, the data measured at the wells are insufficient for properly training deep neural networks, thus, the network is trained on synthetic data. Training on synthetic data offers much freedom in designing a training dataset, therefore, it is important to understand the impact of the data distribution on the inversion results. To define the best way to construct a synthetic training dataset, we perform a study on four different approaches to populating the training set making remarks on data sizes, network generality and the impact of physics-based constraints. Using the results of a reservoir simulation model to populate our training datasets, we demonstrate the benefits of restricting training samples to fluid flow consistent combinations in the dynamic reservoir property domain. With this the network learns the physical correlations present in the training set, incorporating this information into the inference process, which allows it to make inferences on properties to which the seismic data are most uncertain. Additionally, we demonstrate the importance of applying regularization techniques such as adding noise to the synthetic data for training and show a possibility of estimating uncertainties in the inversion results by training multiple networks.     

Fractional Flow Speed-Up from Porous Windbreaks for Enhanced Wind-Turbine Power

The potential for porous windbreaks to enhance wind-turbine power production is studied using linearized theory and wind-tunnel experiments. Results suggest that windbreaks have the potential to substantially increase power production, while lowering mean shear, and leading to negligible changes in turbulence intensity. The fractional increase in turbine power output is found to vary roughly linearly with windbreak height, where a windbreak 10% the height of the turbine hub increases power by around 10%. Wind-tunnel experiments with a windbreak imposed beneath a turbulent boundary layer show the linearized predictions to be in good agreement with particle-image-velocimetry data. Power measurements from a model turbine further corroborate predictions in power increase. Moreover, the wake of the windbreak showed a significant interaction with the turbine wake, which may inform windbreak use in large wind farms. Power measurements from a second turbine downwind of the first with its own windbreak show that the net effect for multiple turbines is dependent on windbreak height.     

A Neuro-Genetic Network for Predicting Uniaxial Compressive Strength of Rocks

Uniaxial Compressive Strength (UCS) is considered as one of the most important parameters in designing rock structures. Determination of this parameter requires preparation of rock samples which is costly and time consuming. Moreover discrepancy of laboratory test results is often observed. To overcome the drawbacks of traditional method of UCS measurement, in this paper, predictive models based on neuro-genetic approach and multivariable regression analysis have been developed for predicting compressive strength of different type of rocks. Coefficient of determinatoin (R2) and the Mean Square Error (MSE) were calculated for comparison of the models’ efficiency. It was observed that accuracy of the neuro-genetic model is significantly better than regression model. For the neuro-genetic and regression models, R2 and MSE were equal to 95.89 % and 0.0045 and 77.4 % and 1.61, respectively. According to sensitivity analysis for neuro-genetic model, Schmidt rebound number is the most effective parameter in predicting UCS.     

Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation

Flyrock arising from blasting operations is one of the crucial and complex problems in mining industry and its prediction plays an important role in the minimization of related hazards. In past years, various empirical methods were developed for the prediction of flyrock distance using statistical analysis techniques, which have very low predictive capacity. Artificial intelligence (AI) techniques are now being used as alternate statistical techniques. In this paper, two predictive models were developed by using AI techniques to predict flyrock distance in Sungun copper mine of Iran. One of the models employed artificial neural network (ANN), and another, fuzzy logic. The results showed that both models were useful and efficient whereas the fuzzy model exhibited high performance than ANN model for predicting flyrock distance. The performance of the models showed that the AI is a good tool for minimizing the uncertainties in the blasting operations.