Application of a continuum numerical model for pile driving analysis and comparison with a real case

This paper presents the results of a so-called continuum numerical model for wave propagation analysis and soil-pile dynamic response during pile driving. An axisymmetric finite difference numerical model is developed having solid elements for both pile structure and the soil media surrounding and below the pile. Interface elements are used between the pile shaft and the soil to facilitate the sliding between the two media. The performance of the developed model is verified in two stages. First, a simple rod is subjected to a half sine-wave force function at the rod head and the corresponding reflections of force and velocity (multiplied by impedance) are presented for different boundary conditions at the rod tip. The model is then used for signal matching analysis of a real driven pile for which complete information of soil layering, dynamic test signals, and static load test results are available. The signal matching analysis was performed successfully and comparison between several other predicted and measured parameters proved the reasonably good performance of the developed continuum model.     

Strange quark star in dilaton gravity

In this work, we first obtain the hydrostatic equilibrium equation in dilaton gravity. Then, we examine some of the structural characteristics of a strange quark star in dilaton gravity in the context of Einstein gravity. We show that the variations of dilaton parameter do not affect the maximum mass, but variations in the cosmological constant lead to changes in the structural characteristics of the quark star. We investigate the stability of strange quark stars by applying the MIT bag model with dilaton gravity. We also provide limiting values for the dilaton field parameter and cosmological constant. We also study the effects of dilaton gravity on the other properties of a quark star such as the mean density and gravitational redshift.We conclude that the last reported value for the cosmological constant does not affect the maximum mass of a strange quark star.     

Comparison of split window algorithms to derive land surface temperature from satellite TIRS data

Remote sensing data can be used as the basis for meteorological data. Due to the limitations of meteorological stations on the Earth, derivation of land surface temperature is one of the most important aspects of the remote sensing application in climatology studies. In the present study, Landsat-8 thermal infrared sensor data of the scene located over Khuzestan province with row/path of 165/38 were used to derive land surface temperature (LST). Normalized difference vegetation index (NDVI), fraction of vegetation cover, satellite brightness temperature, and land surface emissivity were calculated as the vital criteria to derive LSTs using the split window algorithms. LST determination was performed by nine different split window algorithms. Eventually, LST products were evaluated using ground-based measurements at the meteorological stations of the study area. The results showed that algorithm of Coll and Casselles had a highest accuracy with RMSE 1.97 °C, and Vidal’s method presented the lowest accuracy to derive LST with RMSE 4.11 °C. According to the results, regions with high density of vegetation and water resources have lowest diurnal temperature and regions with bare soils and low density of vegetation have a highest diurnal temperature. Results of the study indicated that LST algorithm accuracy is an important factor in the environmental and climate change studies.     

Improved normalization of time‐lapse seismic data using normalized root mean square repeatability data to improve automatic production and seismic history matching in the Nelson field

Updating of reservoir models by history matching of 4D seismic data along with production data gives us a better understanding of changes to the reservoir, reduces risk in forecasting and leads to better management decisions. This process of seismic history matching requires an accurate representation of predicted and observed data so that they can be compared quantitatively when using automated inversion. Observed seismic data is often obtained as a relative measure of the reservoir state or its change, however. The data, usually attribute maps, need to be calibrated to be compared to predictions. In this paper we describe an alternative approach where we normalize the data by scaling to the model data in regions where predictions are good. To remove measurements of high uncertainty and make normalization more effective, we use a measure of repeatability of the monitor surveys to filter the observed time‐lapse data. We apply this approach to the Nelson field. We normalize the 4D signature based on deriving a least squares regression equation between the observed and synthetic data which consist of attributes representing measured acoustic impedances and predictions from the model. Two regression equations are derived as part of the analysis. For one, the whole 4D signature map of the reservoir is used while in the second, 4D seismic data is used from the vicinity of wells with a good production match. The repeatability of time‐lapse seismic data is assessed using the normalized root mean square of measurements outside of the reservoir. Where normalized root mean square is high, observations and predictions are ignored. Net: gross and permeability are modified to improve the match. The best results are obtained by using the normalized root mean square filtered maps of the 4D signature which better constrain normalization. The misfit of the first six years of history data is reduced by 55 per cent while the forecast of the following three years is reduced by 29 per cent. The well based normalization uses fewer data when repeatability is used as a filter and the result is poorer. The value of seismic data is demonstrated from production matching only where the history and forecast misfit reductions are 45% and 20% respectively while the seismic misfit increases by 5%. In the best case using seismic data, it dropped by 6%. We conclude that normalization with repeatability based filtering is a useful approach in the absence of full calibration and improves the reliability of seismic data.