Occurrence of jarosite within Quaternary coastal sabkha sediments in Kuwait,Arabian Gulf

X-ray diffractometer analysis and SEM investigation confirmed the occurrence of jarosite and alunogen minerals in the Quaternary siliciclastic sabkha sediments of northern Kuwait within the Bahrah oilfield area, Arabian Gulf. Jarosite is relatively abundant in a near-surface whitish sticky tidal muddy sand layer about 60 cm thick that overlies the ferruginous sandstone of the Oligo-Miocene Ghar Formation. Jarosite occurs as clusters of euhedral pseudo-cubes of about 1 μm in size and as agglomerated nanoglobules of 250 nm in size. A hypogenetic origin related to the reaction of sulfuric acid produced by the oxidation of H₂S associated with hydrocarbon gas seepages with K and Fe leachates is suggested. The restricted occurrence of jarosite within near-surface sabkha sediments may be attributed to limited tidal inundation and prevalence of arid climatic conditions.     

3D geomechanical modeling and estimating the compaction and subsidence of Fahlian reservoir formation (X-field in SW of Iran)

A geomechanical model can reveal the mechanical behavior of rocks and be used to manage the reservoir programs in a better mode. Fluid pressure will be reduced during hydrocarbon production from a reservoir. This reduction of pressure will increase the effective stress due to overburden sediments and will cause porous media compaction and surface subsidence. In some oil fields, the compacting reservoir can support oil and gas production. However, the phenomena can also cause the loss of wells and reduced production and also cause irreparable damage to the surface structures and affect the surrounding environment. For a detailed study of the geomechanical behavior of a hydrocarbon field, a 3D numerical model to describe the reservoir geomechanical characteristics is essential. During this study, using available data and information, a coupled fluid flow-geomechanic model of Fahlian reservoir formation in X-field in SW of Iran was constructed to estimate the amount of land subsidence. According to the prepared model, in this field, the maximum amount of the vertical stress is 110 MPa and the maximum amount of the horizontal stress is 94 MPa. At last, this model is used for the prediction of reservoir compaction and subsidence of the surface. The maximum value of estimated ground subsidence in the study equals to 29 mm. It is considered that according to the obtained values of horizontal and vertical movement in the wall of different wells, those movements are not problematic for casing and well production and also the surrounding environment.     

Review of water and soil contamination in and around Salihli geothermal field (Manisa,Turkey)

The town of Salihli is situated in Gediz Graben in the western Anatolia. This region is important in terms of industry, mining, geothermal energy, water sources, and agricultural production. Geothermal flow and anthropogenic activities in Salihli threaten the surrounding environment due to the contamination of cold groundwater, surface water, and soil. The goal of the present study is to determine the environmental effects of the geothermal and anthropogenic activities in Salihli on soil, stream sediments, and water. Stream sediments and farm soil have been contaminated by substances derived from geothermal and industrial effluents. To this end, the quality review of the water was completed and the heavy metal levels in stream sediment samples were measured to determine the extent of contamination. The elements As, B, Br, Fe, and Ni are the major contaminants present in surface water and groundwater in the study area. The concentrations of these elements excess tolerance limits of international water standards. Gibbsite, K-mica, kaolinite, sepiolite, halite, sulfur, willemite, and Pb(OH)₂ might be precipitated as scales at low temperatures on the soil; this could be interpreted as a resultant from soil contamination. The concentrations of 17 elements (As, Ba, B, Cd, Co, Cr, Cu, Fe, Hg, Li, Mo, Mn, Ni, Pb, Sb, Sr, and Zn) were measured in samples from stream sediments and surface soils. In the study area, especially geothermal and anthropogenic activities give rise to environmental pollution.     

Evaluation of the physico-mechanical parameters affecting the deterioration rate of Ahlat ignimbrites (Bitlis,Turkey)

The paper principally focuses on the durability assessment of various stratigraphic levels of Ahlat ignimbrites collected from the eastern region of Turkey. A total of four different ignimbrite types with dissimilar color, texture and particularly welding degree were tested in laboratory. The laboratory tests performed on the ignimbrite specimens indicate that the welding degree as well as the lithic material content mainly controls the strength and capillarity properties of the ignimbrites. In addition, the durability of highly porous ignimbrites strongly depends upon the degree of welding. The effect of several weathering agents on the ignimbrites was evaluated on the basis of decay constant parameter. Accordingly, salt and ice crystallization pressures are a couple of major destructive agents acting within the micropores of the ignimbrites. Conversely, the investigated specimens are relatively durable against cyclic wetting–drying. Statistical evaluations reveal that the pore diameter is the major controlling factor on the deterioration rate of the ignimbrites after specifically recurrent freeze–thaw cycles. Moreover, the dry unit weight of the ignimbrites is more significant than the uniaxial compressive strength considering the deterioration rates during wetting–drying and salt crystallization. A less significant relationship was obtained between pore diameter and salt crystallization decay constant.     

Status of sedimentation in King Talal Dam,case study from Jordan

This paper focuses on characterizing the current status of physical and chemical characteristics of King Talal Dam (KTD) sediments. The quality of sediments was investigated based on several international sediment quality guidelines. The volume of sediment and sedimentation rate in (KTD) was also estimated. Five types of granulometric textural facies were observed for the sediments of KTD reservoir bed, with an average grain size distribution of gravel (1.38%), sand (32.38%), silt (32.35%) and clay (32.38%). The fraction of <63 micron showed that the total mean size was 8.0 micron. Other sediment parameters in KTD were 7.0% for total organic matter (TOM), 7.3 for pH, 35.9% for CaCO₃ and 100.5 meq/100 g for cation exchange capacity, with dominant mineralogical constituents of quartz, calcite, dolomite, clay minerals and minor feldspar. The total concentrations of F, Cl, NO₂, NO₃, PO₄ and SO₄ in sediment samples were lower inward for KTD. The vast majority of trace metals in sediments were generally within the standard limits recommended for soils. TOM and grain size of sediment are major factors governing the trace metal concentrations. The calculated geoaccumulation index (I _geo) and enrichment factor of metals in bottom sediments showed that the metals were in the following order: Cd > Pb > Zn > Co > Cr > Ni > Fe > Cu > Sr > Mn and Cd > Zn > Pb > Co > Cr > Ni > Cu > Sr > Mn, respectively. Based on the sonic survey of KTD reservoir, the annual sedimentation rate was about 0.4061 MCM/year, in which about 18.66% of the reservoir initial storage capacity was lost to sediments and a projected lifespan of about 198 years.     

Uncertainty assessment of the multilayer perceptron (MLP) neural network model with implementation of the novel hybrid MLP-FFA method for prediction of biochemical oxygen demand and dissolved oxygen: a case study of Langat River

Accurate prediction of the chemical constituents in major river systems is a necessary task for water quality management, aquatic life well-being and the overall healthcare planning of river systems. In this study, the capability of a newly proposed hybrid forecasting model based on the firefly algorithm (FFA) as a metaheuristic optimizer, integrated with the multilayer perceptron (MLP-FFA), is investigated for the prediction of monthly water quality in Langat River basin, Malaysia. The predictive ability of the MLP-FFA model is assessed against the MLP-based model. To validate the proposed MLP-FFA model, monthly water quality data over a 10-year duration (2001–2010) for two different hydrological stations (1L04 and 1L05) provided by the Irrigation and Drainage Ministry of Malaysia are used to predict the biochemical oxygen demand (BOD) and dissolved oxygen (DO). The input variables are the chemical oxygen demand (COD), total phosphate (PO₄), total solids, potassium (K), sodium (Na), chloride (Cl), electrical conductivity (EC), pH and ammonia nitrogen (NH₄-N). The proposed hybrid model is then evaluated in accordance with statistical metrics such as the correlation coefficient (r), root-mean-square error, % root-mean-square error and Willmott’s index of agreement. Analysis of the results shows that MLP-FFA outperforms the equivalent MLP model. Also, in this research, the uncertainty of a MLP neural network model is analyzed in relation to the predictive ability of the MLP model. To assess the uncertainties within the MLP model, the percentage of observed data bracketed by 95 percent predicted uncertainties (95PPU) and the band width of 95 percent confidence intervals (d-factors) are selected. The effect of input variables on BOD and DO prediction is also investigated through sensitivity analysis. The obtained values bracketed by 95PPU show about 77.7%, 72.2% of data for BOD and 72.2%, 91.6% of data for DO related to the 1L04 and 1L05 stations, respectively. The d-factors have a value of 1.648, 2.269 for BOD and 1.892, 3.480 for DO related to the 1L04 and 1L05 stations, respectively. Based on the values in both stations for the 95PPU and d-factor, it is concluded that the neural network model has an acceptably low degree of uncertainty applied for BOD and DO simulations. The findings of this study can have important implications for error assessment in artificial intelligence-based predictive models applied for water resources management and the assessment of the overall health in major river systems.     

Development of functionalized carrageenan,chitosan and alginate as polymeric chelating ligands for water softening

Chitosan, carrageenan and alginate are among the most abundant biopolymers in nature. They were prepared in uniform beads shape with a diameter of 2 mm ± 10%, using the encapsulator for removal of calcium, magnesium and iron cations from hard water. Solutions of 100–500 mg/L were prepared from each cation, and the detection of cations was carried out using atomic absorption spectrometer. Carrageenan and chitosan were able to chelate the three cations without further modification. However, alginate beads succeeded to chelate iron and magnesium and failed to chelate any calcium ions; in contrast, it increased the initial calcium concentration! That could be due to the pre-cross-linking of alginate beads using calcium chloride solution, which may be leaked back to the solution. However, grafting the alginate beads with polyethyleneimine and bromoacetic acid rectified this problem and the new functional group, –COOH, has been proved using the FT-IR. Optimization of the results in terms of beads weight (0.25–3.0 g) and cations concentrations (100–500 mg/L) has shown that most biopolymeric beads can chelate 85–100% of the cations in concentrations up to 500 mg/L. According to our finding, we came up with the recommendation to use chitosan for chelation of calcium and iron as it showed 100% chelation efficiency of both cations, whereas carrageenan is highly recommended for chelation of iron and magnesium, as it showed 100 and 98% chelation efficiency, respectively. Further work can be done on the reusability of the beads and scale up for the industrial use.     

Temporal change of glaciers area and geomorphometric parameters in Parbati valley,Himachal Pradesh,India

Temporal change in the glacier coverage is analyzed for the period between 1962 and 2003 in Parbati valley, Himachal Pradesh. It is observed that the total glacier cover has been decreased by 17% ranging between 8 and 100% for individual glacier. The pattern of de-glaciation shows a high degree of shrinkage in outer zone of Parbati valley, while least shrinkage is observed in the inner valley. The present study is conducted to establish relationship between glacio-geomorphic parameters and glaciers shrinkage pattern to predict the future glacier cover in warming scenario. A systematic change is observed for glacio-geomorphic parameters associated with temporal change in glacier cover. It is observed that mean and minimum elevation, slope, relief and duration of insolation have changed substantially. Maximum elevation, plan/profile curvatures and aspect have shown less change from 1962 to 2003. A correlation matrix between glacio-geomorphic parameters for glaciers between 1962 and 2003 shows that the recent glaciers are much more controlled by terrain characteristics than that in the recent past.     

Applying an optimized proxy-based workflow for fast history matching

History matching is still one of the main challenging parts of reservoir study especially in giant brown oil fields with lots of wells. In these cases, history matching with conventional manual technique needs many runs and takes months to get a match. In this work, an innovative approach was suggested for fast history matching in a real brown field. The workflow was employed based on an optimized proxy model for history matching of a field consisting of 14 active wells with multiple responses (which are production rate and pressure data) in the south part of Iran. The main important features of the proposed algorithm were defining a proxy model which is response surface method in which 21 model parameters were incorporated based on cubic centered face method. The proxy model was then optimized by one of the most famous algorithms which is genetic algorithm. Proxy model was successfully performed using 256 samples leading into p- value of 0.531 and R ² of 0.91 dataset. As a result, the proposed workflow and algorithm showed good and acceptable results for history matching of studied real model.     

Landfill site selection using combination of fuzzy logic and multi-attribute decision-making approach

During the last decades, growth of urbanization and industrialization led to an increase in solid waste generation. Landfilling is the most prevalent ultimate disposal method for the municipal solid wastes in developing countries. The rapid municipal solid waste generation in Markazi province (central part of Iran) causes the need for precision in finding a suitable landfill site selection. In the present study, 12 factors (environmental and socioeconomic factors) have been applied to select the landfill site in Markazi province, Iran. The different methods including the analytic network process (ANP) combined with fuzzy linguistic quantifier, ordered weighted average (OWA), and weighted linear combination (WLC) approach in geographic information system was applied to find an appropriate landfill site. The OWA operator function permits the evaluation of the wide spectrum of consequences (with different scenario) obtained from different management strategies. Results revealed that integration of fuzzy logic, ANP, and OWA provides flexible and better ideas compared to the Boolean logic and WLC to select a suitable landfill site.