Vertical resolution enhancement of petrophysical Nuclear Magnetic Resonance (NMR) log using ordinary kriging

Nuclear Magnetic Resonance (NMR) logging provides priceless information about hydrocarbon bearing intervals such as free fluid porosity and permeability. This study focuses on using geostatistics from NMR logging instruments at high depths of investigation to enhance vertical resolution for better understanding of reservoirs. In this study, a NMR log was used such that half of its midpoint data was used for geostatistical model construction using an ordinary kriging technique and the rest of the data points were used for assessing the performance of the constructed model. This strategy enhances the resolution of NMR logging by twofold. Results indicated that the correlation coefficient between measured and predicted permeability and free fluid porosity is equal to 0.976 and 0.970, respectively. This means that geostatistical modeling is capable of enhancing the vertical resolution of NMR logging. This study was successfully applied to carbonate reservoir rocks of the South Pars Gas Field.     

Oil formation volume factor modeling: Traditional vs. Stochastically optimized neural networks

Oil formation volume factor (FVF) is considered as relative change in oil volume between reservoir condition and standard surface condition. FVF, always greater than one, is dominated by reservoir temperature, amount of dissolved gas in oil, and specific gravity of oil and dissolved gas. In addition to limitations on reliable sampling, experimental determination of FVF is associated with high costs and time-consumption. Therefore, this study proposes a novel approach based on hybrid genetic algorithm-pattern search (GA-PS) optimized neural network (NN) for fast, accurate, and cheap determination of oil FVF from available measured pressure-volume-temperature (PVT) data. Contrasting to traditional neural network which is in danger of sticking in local minima, GA-PS optimized NN is in charge of escaping from local minima and converging to global minimum. A group of 342 data points were used for model construction and a group of 219 data points were employed for model assessment. Results indicated superiority of GA-PS optimized NN to traditional NN. Oil FVF values, determined by GA-PS optimized NN were in good agreement with reality.     

Noise Reduction in Radon Monitoring Data Using Kalman Filter and Application of Results in Earthquake Precursory Process Research

Monitoring the concentration of radon gas is an established method for geophysical analyses and research, particularly in earthquake studies. A continuous radon monitoring station was implemented in Jooshan hotspring, Kerman province, south east Iran. The location was carefully chosen as a widely reported earthquake-prone zone. A common issue during monitoring of radon gas concentration is the possibility of noise disturbance by different environmental and instrumental parameters. A systematic mathematical analysis aiming at reducing such noises from data is reported here; for the first time, the Kalman filter (KF) has been used for radon gas concentration monitoring. The filtering is incorporated based on several seismic parameters of the area under study. A novel anomaly defined as “radon concentration spike crossing” is also introduced and successfully used in the study. Furthermore, for the first time, a mathematical pattern of a relationship between the radius of potential precursory phenomena and the distance between epicenter and the monitoring station is reported and statistically analyzed.     

Hydraulic conductivity prediction based on grain-size distribution using M5 model tree

The hydraulic conductivity, K_s, is one of the most important hydraulic properties which controls the water and solute movement into the soil. It is measured on soil specimens in the laboratory. On the other hand, sometimes it is obtained by tests carried out in the field by a number of researchers. Therefore, several experimental formulas have developed to predict it. Recently, soft computing tools have been used to evaluate the hydraulic conductivity. However, these tools are not as transparent as empirical formulas. In this study, another soft computing approach, i.e. model trees, have been used for predicting the hydraulic conductivity. The main advantage of model trees is that, unlike the other data learning tools, they are easier to use and represent understandable mathematical rules more clearly. In this paper, a new formula that includes some parameters is derived to estimate the hydraulic conductivity. To develop the new formulas, experimental data sets of hydraulic conductivity were used. A comparison is made between the estimated hydraulic conductivity by this new formula and formulas given by other’s researches.     

Prediction of blasting-induced fragmentation in Meydook copper mine using empirical,statistical, and mutual information models

One of the most important aims of blasting in open pit mines is to reach desirable size of fragmentation. Prediction of fragmentation has great importance in an attempt to prevent economic drawbacks. In this study, blasting data from Meydook mine were used to study the effect of different parameters on fragmentation; 30 blast cycles performed in Meydook mine were selected to predict fragmentation where six more blast cycles are used to validate the results of developed models. In this research, mutual information (MI) method was employed to predict fragmentation. Ten parameters were considered as primary ones in the model. For the sake of comparison, Kuz-Ram empirical model and statistical modeling were also used. Coefficient of determination (R ²), root mean square error (RMSE), and mean absolute error (MAE) were then used to compare the models. Results show that MI model with values of R ², RMSE, and MAE equals 0.81, 10.71, and 9.02, respectively, is found to have more accuracy with better performance comparing to Kuz-Ram and statistical models.     

Health risk assessments due to nitrate levels in drinking water in villages of Azadshahr,northeastern Iran

Groundwater is the main water source used for drinking and cooking purposes globally. Nitrate level in most groundwater resources in arid and semi-arid areas has increased in the past several decades as a result of human activities and natural processes. This may exert a great impact on human health. To learn the contamination circumstances of groundwater nitrate in villages of Azadshahr, Iran and assess its probable risk to the health of adults, children and infants, fifty-eight groundwater samples were collected from wells and springs in 2018. Nitrate concentrations had a wide spatial variability in wells and springs of the studied villages, with values going from 1 up to 51 mg/L. Exceedances of the EPA standard value were limited to two village springs (villages Nili and Narab, with nitrate level of 51 and 46 mg/L, respectively). The hazard quotients (HQ) values for 41% of children and infants were above the safety level (i.e., HQ?>?1), suggesting that groundwater nitrate would have significant health effects on these age groups. Therefore, appropriate control measures and sanitation improvement programs should be put in place to protect the health of the residents in the contaminated villages.     

Women and strategic urban planning in Tehran: channeling women’s social capacity to prepare city development strategies

GeoJournal - City development strategy (CDS) is an urban strategic planning approach that has already been implemented in over 200 cities worldwide. Nevertheless, its preparation process is...     

Comparison of conventional and Monte Carlo simulation-based probabilistic seismic hazard analyses for Shiraz city,southern Iran

Estimation of ground-motion amplitudes of different hazard levels is of paramount importance in planning of urban development of any metropolis. Such estimation can be computed through a probabilistic seismic hazard analysis (PSHA). This paper concentrates on the PSHA of an area located in Shiraz city, southern Iran. The area includes whole of Shiraz city (i.e., one of the largest and most populous cities of Iran) and its outskirts. Conventional and Monte Carlo simulation-based approaches are utilized to perform the PSHA of the studied area. Two areal seismic source models are delineated, and thence seismicity parameters of all zones associated with their corresponding uncertainties are computed. Uncertainties in ground-motion prediction are accounted for via three ground-motion prediction equations (GMPEs) within the logic tree framework. These GMPEs are applied to estimate bedrock ground shaking (V_s30?=?760 m/s) for several return periods (i.e., 75, 475, 975, and 2475 years). In general, the results of the two abovementioned PSHA approaches show relatively similar results. However, the Monte Carlo simulation-based approach overpredicts bedrock spectral accelerations at periods of 0.4–2.5 s compared to the conventional PSHA approach for return periods of 475, 975, and 2475 years.     

Evidence of the formation of landslide-dammed lakes in the Zagros Mountains range,Iran

Alternation of high and low resistance sedimentary beds,active tectonics,large rivers,and slope erosion in valleys consequently resulted in landslide in dammed lakes within the Zagros range.This study presents the results in the analysis of geological and topographic data,satellite imageries,morphotectonics and hydrodynamics of drainage networks about the landslide dammed lakes.There are four landslides in central Zagros(Zagros FoldThrust Belt,ZFTB)which have formed five dammed lakes named,Seymareh,Jaidar,Shur,Shimbar,and Godar.According to the results,damming landslides occurred in the active-tectonic regions on the slopes of anticlines and in valleys with undercutting effects of rivers on their slopes consisting of alternations of loose and resistant beds.The studied landslide dams in narrow valleys are formed as a result of blocking river by sliding debris slopes and rock slides.This study also indicates the formation of Jaidar and Godar in one stage and the presence of Lake Terrace sequence in Seymareh,Shur and Shimbar lakes.The observed sequences of terrace formation in these lakes are caused by four Seymareh Landslides followed by the three-stage excavation of Shur andShimbar lakes.     

A Hybrid Particle Swarm Optimization and Genetic Algorithm for Model Updating of A Pier-Type Structure Using Experimental Modal Analysis

China Ocean Engineering - Conventional design of pier structures is based on the assumption of fully rigid joints. In practice, the real connections are semi-rigid that cause changes in dynamic...