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.     

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.     

Comparative study on the equivalent linear and the fully nonlinear site response analysis approaches

Seismic site effect has been a major issue in the field of earthquake engineering due to the large local amplification of the seismic motion. This paper presents the importance of an appropriate soil behavior model to simulate earthquake site response and gives an overview of the field of site response analysis. Some of the well-known site response analysis methods are discussed. The objective of this paper is to investigate the influences of nonlinearity on the site response analysis by means of a more precise numerical model. In this respect, site responses of four different types of one-layered soil deposit, based on various shear wave velocities with the assumption of linear and rigid base bedrock, were analyzed by using the equivalent linear and fully nonlinear approaches. Nonlinear analyses?? results were compared with those of the linear method, and both of the similarities and differences are discussed. It is concluded that in the case of nonlinearity of soil under strong ground motions, 1-D equivalent linear modeling overestimates the amplification patterns in terms of absolute amplification level, and cannot correctly account for resonant frequencies and hysteric soil behavior. Therefore, more practical and appropriate numerical techniques for ground response analysis should be surveyed.     

Structural elements of the Makran region, Oman sea and their potential relevance to tsunamigenisis

The character of convergence along the Arabian–Iranian plate boundary changes radically eastward from the Zagros ranges to the Makran region. This appears to be due to collision of continental crust in the west, in contrast to subduction of oceanic crust in the east. The Makran subduction zone with a length of about 900 km display progressively older and highly deformed sedimentary units northward from the coast, together with an increase in elevation of the ranges. North of the Makran ranges are large subsiding basins, flanked to the north by active volcanoes. Based on 2D seismic reflection data obtained in this study, the main structural provinces and elements in the Gulf of Oman include: (i) the structural elements on the northeastern part of the Arabian Plate and, (ii) the Offshore Makran Accretionary Complex. Based on detailed analysis of these data on the northeastern part of the Arabian Plate five structural provinces and elements—the Musendam High, the Musendam Peneplain, the Musendam Slope, the Dibba Zone, and the Abyssal Plain have been identified. Further, the Offshore Makran Accretionary Complex shown is to consist Accretionary Prism and the For-Arc Basin, while the Accretionary Prism has been subdivided into the Accretionary Wedge and the Accreted/Colored Mélange. Lastly, it is important to note that the Makran subduction zone lacks the trench. The identification of these structural elements should help in better understanding the seismicity of the Makran region in general and the subduction zone in particular. The 1945 magnitude 8.1 tsunamigenic earthquake of the Makran and some other historical events are illustrative of the coastal region’s vulnerability to future tsunami in the area, and such data should be of value to the developing Indian Ocean Tsunami Warning System.     

Probabilistic Analysis of Ground Surface Vibration Due to Train Movement, a Case Study on Tehran Metro Line 4

Ground surface vibration produced by moving train is one of the most important aspects in urban areas. The purpose of this study is the probabilistic analysis of ground surface vibration, which is generated by metro transportations. For this reason, Tehran metro line 4 is considered as a case study. In this paper, at first, a new procedure is used to simulate train dynamic load. In the second step, based on the variation of geomechanical properties and train characteristics in Tehran metro line 4, more than 60 numerical models are simulated. The results of numerical simulations are analyzed by multivariate statistical technique and an equation for prediction of peak particle velocity (PPV) in the ground surface is presented. In the next step, probabilistic analysis is done using Monte Carlo Simulation (MCS). Finally, sensitivity of input data on ground surface vibration is discussed and the impact of geomechanical properties and train characteristics on the surface vibration is considered. Based on the probabilistic analysis, PPV in the surface region of Tehran metro line 4 is <2.76?mm/s with 95?% probability.     

Use of electrical resistivity tomography in delineating zones of groundwater potential in arid regions: a case study from Diriyah region of Saudi Arabia

A detailed geophysical investigation in the form of electrical resistivity tomography (ERT) was carried out using the SYSCAL Pro Unit at two locations with a dipole?Cdipole configuration in one of the private farms in Diriyah area of Riyadh, Saudi Arabia with the purpose of delineating the approximate depth of the water-bearing formations. The survey helped in delineating a superficial moist zone at the first site which was mainly due to the seepage from surface water pipes used for irrigating the date palms. At the second site, a potential water-bearing zone was detected starting from a depth of 23 to 46?m. However, an already existing bore well in close vicinity of the site of the second survey showed a water table depth of 85?m, indicating the presence of a layered aquifer. Finally, it was concluded that a shallow bore well drilled up to a depth of 50?C60?meters would be able to tap the water resources of shallow water-bearing horizon discovered at the second site during the survey; however, the yield of the bore well might not be sustainable over a longer period of time. The survey at the first site did not show any promising groundwater potential up to the investigated depth which was around 70?m. The study confirms the usage of ERT surveys for exploring sources of freshwater supplies in arid regions.     

P–T evolution of meta-peridotite in the Penjwin ophiolite, northeastern Iraq

The Penjwin meta-peridotite rock represents one of the five main metamorphosed ultramafic bodies in Kurdistan region, Northwest Zagros Thrust Zone. It underwent at least two successively low-retrograde metamorphic events with one progressive one which all modified the original mineralogy and texture of primary dunite and harzburgite. The primary upper mantle mineral assemblage olivine?+?orthopyroxene?+?chromian spinel is replaced by olivine?+?tremolite–actiolite?+?anthopylite?+?talc?+?ferichromite?+?Cr-chlorite assemblage of amphibolite facies. The further retrograde metamorphic amphibolite facies assemblage is replaced by lizardite–chrysotile?+?Cr-chlorite?+?syn-serpentinization Cr-magnetite of lower greenschist facies. Later at the main Zagros thrust fault, low greenschist facies underwent progressive metamorphism due to the local effect of shear stress as a result of the exhumation and obduction of Penjwin ophiolite suite over Merga Red bed series during Tertiary. Lizardite–chrysotile transformed to antigorite and producing antigorite?+?carbonate?+?syn-serpentinization Cr-magnetite?+?Cr-chlorite assemblage of upper greenschist facies. Chromian spinel is concentrically zoned as a result of multi-stages retrogressive metamorphic events, in which the Cr # (Cr/(Cr?+?Al)) increases from core to rim (0.5 to 1). Three zones can be identified from core to rim: The core is primary Al-rich and mantled by ferrichromite of amphibolite facies. The most outer zone of chromian spinel grains is represented by syn-serpentinization Cr-magnetite of greenschist facies.     

Support vector machines and feed-forward neural networks for spatial modeling of groundwater qualitative parameters

The present study attempts to model the spatial variability of three groundwater qualitative parameters in Guilan Province, northern Iran, using artificial neural networks (ANNs) and support vector machines (SVMs). Data collected from 140 observation wells for the years 2002–2014 were used. Five variables, X and Y coordinates of the observation well, distance of the observation well from the shoreline, areal average 6-month rainfall depth, and groundwater level at the day of water quality sampling, were considered as primary input variables. In addition, nine qualitative variables were also considered as auxiliary input variables. Electrical conductivity (EC), sodium concentration (Na⁺), and sulfate concentration (SO₄ ^2?) of the groundwater in the region were estimated using ANNs and SVMs with different input combinations. The results showed that both ANNs and SVMs work well when the only primary input variable is the well location. The ANN yielded an RMSE of 1.03 mEq/l for SO₄ ^2?, 1.05 mEq/l for Na⁺, and 203.17 μS/cm for EC, using the X and Y coordinates of the observation wells in the study area. In the case of SVM, these values were, respectively, 0.87, 0.87, and 176.68. Considering the auxiliary input variables (pH, EC, and the concentrations of Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl^?, SO₄ ^2?, and HCO₃ ^?) resulted in a significant decrease in the RMSE of both ANNs (0.22, 0.30, and 33.04) and SVMs (0.26, 0.34, and 36.23). Comparing these RMSE values with those of cokriging interpolation technique (0.59, 0.98, and 177.59) indicated that ANNs and SVMs produced more accurate estimates of the three qualitative parameters. The relative importance of auxiliary input variables was also determined using Gamma test. The output uncertainty of ANNs and SVMs were determined using p-factor and d-factor. The results showed that SVMs have less uncertainty than ANNs.