Investigation on the Effect of Geometrical and Geotechnical Parameters on Elongated Offshore Piles Using Fuzzy Inference Systems

Among numerous offshore structures used in oil extraction, jacket platforms are still the most favorable ones in shallow waters. In such structures, log piles are used to pin the substructure of the platform to the seabed. The pile’s geometrical and geotechnical properties are considered as the main parameters in designing these structures. In this study, ANSYS was used as the FE modeling software to study the geometrical and geotechnical properties of the offshore piles and their effects on supporting jacket platforms. For this purpose, the FE analysis has been done to provide the preliminary data for the fuzzy-logic post-process. The resulting data were implemented to create Fuzzy Inference System (FIS) classifications. The resultant data of the sensitivity analysis suggested that the orientation degree is the main factor in the pile’s geometrical behavior because piles which had the optimal operational degree of about 5° are more sustained. Finally, the results showed that the related fuzzified data supported the FE model and provided an insight for extended offshore pile designs.     

Application of the coral health chart to determine bleaching status of <Emphasis Type="Italic">Acropora downingi</Emphasis> in a subtropical coral reef

The ‘Coral Health Chart’ has become a popular tool for monitoring coral bleaching worldwide. The scleractinian coral Acropora downingi (Wallace 1999) is highly vulnerable to temperature anomalies in the Persian Gulf. Our study tested the reliability of Coral Health Chart scores for the assessment of bleaching-related changes in the mitotic index (MI) and density of zooxanthellae cells in A. downingi in Qeshm Island, the Persian Gulf. The results revealed that, at least under severe conditions, it can be used as an effective proxy for detecting changes in the density of normal, transparent, or degraded zooxanthellae and MI. However, its ability to discern changes in pigment concentration and total zooxanthellae density should be viewed with some caution in the Gulf region, probably because the high levels of environmental variability in this region result in inherent variations in the characteristics of zooxanthellae among “healthy” looking corals.     

Modeling core-scale permeability anisotropy in highly bioturbated “tight oil” reservoir rocks

A high-resolution simulation model of a heterogeneous low-permeability rock sample is used to investigate the effects of physical and biogenic sedimentary structures on scaling and anisotropy of absolute permeability at the core scale. Several simulation sub-samples with random locations and volumes were also selected for evaluation of the effects of scale and lithological composition on the calculated permeability. Vertical and horizontal permeability values (from whole core simulation) are in good agreement with routine core analysis (RCA) measurements from offsetting cores. Despite relatively good reservoir quality associated with geobodies of biogenic and relic bedding structures, results from the full diameter core simulation demonstrate that their limited volumetric abundance and restricted connectivity prevent these features from controlling fluid flow in these rocks. In fact, permeability seems to be dominated by the tighter encasing matrix, which exhibits average permeability values very close to those reported from RCA. Geometric averaging offers a better representation for the upscaling of horizontal permeability datasets; whereas, both geometric and harmonic averaging work similarly well for the vertical measurements. The methodology used in this work is particularly applicable to the detailed characterization of reservoir rocks with a high degree of heterogeneity caused by biological reworking and diagenesis.     

An integrated object-based image analysis and CA-Markov model approach for modeling land use/land cover trends in the Sarab plain

The present paper is an attempt to integrate a semi-automated object-based image analysis (OBIA) classification framework and a cellular automata-Markov model to study land use/land cover (LULC) changes. Land use maps for the Sarab plain in Iran for the years 2000, 2006, and 2014 were created from Landsat satellite data, by applying an OBIA classification using the normalized difference vegetation index, salinity index, moisture stress index, soil-adjusted vegetation index, and elevation and slope indicators. The classifications yielded overall accuracies of 91, 93, and 94% for 2000, 2006, and 2014, respectively. Finally, using the transition matrix, the spatial distribution of land use was simulated for 2020. The results of the study revealed that the number of orchards with irrigated agriculture and dry-farm agriculture in the Sarab plain is increasing, while the amount of bare land is decreasing. The results of this research are of great importance for regional authorities and decision makers in strategic land use planning.     

Geochemistry and origin of Dehoo manganese deposit,south Zahedan,southeastern Iran

Dehoo manganese deposit is located 52 km to the south of Zahedan in Sistan and Baluchestan Province, southeastern Iran. This deposit that lies in the central part of the Iranian Flysch Zone is lenticular in shape and lies above the micritic limestone-radiolarite cherts of the upper Cretaceous ophiolite unit. It is hosted within the reddish to brown radiolarite cherts and in places interlinks with them, so that the radiolarite chert packages play a key role for Mn mineralization in the region. Investigated ore-paragenetic successions and the geochemical characteristics of the Dehoo deposit were studied by means of major oxide, trace, and rare earth element (REE) contents that provide information as to the mineral origin. Strong positive correlations were found between major oxides and trace elements (Al₂O₃-TiO₂, r = 0.95; TiO₂-MgO, r = 0.94; Fe₂O₃-Al₂O₃, r = 0.90; MgO-Al₂O₃, r = 0.84; MgO-Fe₂O₃, r = 0.88; Fe₂O₃-TiO₂, r = 0.91; Fe₂O₃-K₂O, r = 0.74; Al₂O₃-K₂O, r = 0.69; Al₂O₃-V, r = 0.72; TiO₂-V, r = 0.73, and MgO-V, r = 0.69) that testify to the contribution of mafic terrigenous detrital material to the deposit. Chondrite-normalized REE patterns of all ore samples are characterized by negative Ce (0.06–0.15, average 0.10) and slightly positive Eu (0.29–0.45, average 0.36) anomalies. Based on ratios of Mn/Fe (average 56.23), Co/Ni (average 0.33), Co/Zn (average 0.38), U/Th (average 3.40), La/Ce (average 1.45), La_n/Nd_n (average 2.16), Dy_n/Yb_n (average 0.33), and light REE/heavy REE (average 8.40; LREE > HREE), as well as Ba (average 920 ppm) and total REE contents (average 6.96 ppm) negative Ce and positive Eu anomalies, Dehoo could be considered a predominantly submarine hydrothermal Mn deposit complemented by terrigenous detrital mafic material.     

A Workflow for Static Reservoir Modeling Guided by Seismic Data in a Fluvial System

Realistic and accurate static geologic models are an essential element needed to predict the behavior of subsurface reservoirs and play an important role in petroleum engineering. Data used in the development of a static geologic model are gathered from various sources, such as seismic, log, and core data, each of them providing information on different physical properties of interest and with varying degrees of resolution. Compiling all data from various sources into a single representation of the subsurface formation of interest is a daily challenge for many petroleum geologists and engineers. This paper describes a framework to develop and select process-mimicking models that are consistent with available seismic attributes, namely impedance. Using a process-mimicking modeling package, 75 models of a fluvial meandering system are generated, one of which is chosen as the “true” model and masked thereafter. The implemented selection method relies on the degree of similarity in the histogram of representations of clusters of all possible patterns in the seismic impedance domain based on each process-mimicking model and that of the “true” model at several resolutions. The results demonstrate the effectiveness of the use of a weighted average divergence distance across multiple levels to select process-mimicking models that honor seismic data the best.     

Geochemical,isotopic and hydrological mass balance approaches to constrain the lake water–groundwater interaction in Dal Lake,Kashmir Valley

It is important to have qualitative as well as quantitative understanding of the hydraulic exchange between lake and groundwater for effective water resource management. Dal, a famous urban fresh water lake, plays a fundamental role in social, cultural and economic dynamics of the Kashmir Valley. In this paper geochemical, isotopic and hydrological mass balance approaches are used to constrain the lake water–groundwater interaction of Dal Lake and to identify the sources of lake water. Water samples of precipitation (n = 27), lake water (n = 18) and groundwater (n = 32) were collected across the lake and its catchment for the analysis of δ¹⁸O and δ²H. A total of 444 lake water samples and 440 groundwater samples (springs, tube wells and dug wells) were collected for the analysis of Ca²⁺, Mg²⁺, HCO₃ ^?, SO₄ ^2?, Cl^?, NO₃ ^?, Na⁺ and K⁺. Water table and lake water level were monitored at 40 observation locations in the catchment. Water table map including pH and EC values corroborate and verify the gaining nature of the Dal Lake. Stable isotopes of lake water in Boddal and Gagribal basins showed more deviation from the global meteoric water line than Hazratbal and Nigeen basins, indicating the evaporation of lake water. The isotopic and geochemical mass balance suggested that groundwater contributes a significant proportion (23–40%) to Dal Lake. The estimated average groundwater contribution to Dal Lake ranged from 31.2 × 10³ to 674 × 10³ m³ day^?1 with an average of 276 × 10³ m³ day^?1. The study will be useful to delineate the possible sources of nutrients and pollutants entering the lake and for the management of lake water resources for sustainable development.     

Benthic foraminiferal response to changes in mining pattern: a case study from the Zuari estuary,Goa, India

Being sensitive to environmental changes, foraminifera have been extensively used to monitor pollution level in the marine environment, including the effect of mining in coastal areas. In the Goa state of India, the rejects from opencast mining on land largely find their way to the estuaries, as washout during monsoon. Additionally, the Mormugao Port at the mouth of the Zuari estuary is the hub of activities due to the transport of ore from hinterland areas by barges and its subsequent loading for export. On the directive of the Supreme Court of India, all the mining-related activities abruptly stopped throughout India, including that in Goa in 2012, and got reinstated in 2015. Therefore, it provided a fit case to test the effectiveness of benthic foraminifera as an indicator of environmental impact due to mining activities. A total of ten surface sediment samples from five locations in Zuari estuary were collected from a depth range of 4.5–8.5 m in the years of 2013 and 2016 and were analyzed for both the living (stained) and dead benthic foraminifera. The year 2013 represents a time interval immediately after the closure of extensive mining activity, and the sampling during 2016 represents minimal mining. The living benthic foraminiferal abundance was higher (19–54/g sediment) during 2013 and decreased substantially during 2016 (3–22/g sediment), suggesting an adverse effect of activities associated with mine closure on benthic foraminifera. Additionally, the relative abundance of Ammonia was also significantly low during the year 2016. The temporal variation in dead foraminifera was, however, different than that of the living foraminifera. The differential response was attributed to the terrigenous dilution as a result of change in sedimentation rate. Therefore, we conclude that living foraminifera correctly incorporate the changes in mining pattern and may be used as an effective tool to monitor the impact of mining. We further suggest that the potential counter effect of terrigenous dilution on total and living benthic foraminiferal population should be considered while interpreting temporal variations in foraminiferal abundance in marginal marine settings.     

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.