Earthquake acceleration analysis using wavelet method

There are several methods for analyzing the acceleration of an earthquake.In this research,a discrete wavelet theory based on the Mallat method was employed to analyze the acceleration of earthquake records.For this purpose,first,the acceleration of the main earthquake was determined using the method of banding,filtering and correction of a filtered wave.Then,the acceleration of the earthquake up to five stages was decomposed using discrete wavelet theory.In this method,in which the Down-Sampling rule is utilized in each step,the number of earthquake record points is half past.Each of the waveforms was based on the acceleration of the maximum original earthquake,and the maximum acceleration in all the waves was identical.For each of the five waves obtained from wavelet decomposition,the velocity curve and ground acceleration are obtained and compared with each other.Finally,a structure was analyzed using the main wave of the earthquake and each of the waveforms was analyzed in five stages and their dynamic response curves were compared.The results showed that until the third stage of the wavelet decomposition,the error was insignificant and the dynamic response to the magnitude of the earthquake was small.The analysis time is about 10% of the analysis time with the main wave,and the error is less than 6%.     

Reservoir-induced seismicity in Karun III dam (Southwestern Iran)

Statistical analyses of the Karun III reservoir seismicity reveal a remarkable correlation between seismicity rate and water-level harmonic changes. It seems that seismicity in this dam depends on rapid water-level changes. The three biggest earthquakes of Karun III, measuring 4, 4.1, and 4.3 on the Richter scale (ML), occurred after two stages of rapid filling of the dam on March 22, 2005 and May 12, 2006. These earthquakes happened when the water reached the maximum operational level. Since the beginning of filling the reservoir on November 8, 2004 until March, 2006, most reservoir-induced seismicity has been localized in three main clusters. The majority of the earthquakes occurred in the frontal anticline of Keyf Malek; the second and third clusters happened near Karun Blind Fault (KBF) and Mountain Front Fault (MFF), respectively. Filling Karun III reservoir immediately led to an increase in the occurrence of earthquakes. Further, following abrupt water-level changes, a considerable increase in the number of earthquakes is observed. Finally, in terms of seismicity rate, vertical and horizontal migration, magnitude, and distance, the earthquakes of Karun III behave differently.     

Calculation of fracture parameters and their effect on porosity and permeability using image logs and petrophysical data in carbonate Asmari reservoir,SW Iran

Evaluation of fractures and their parameters, such as aperture and density, is necessary in the optimization of oil production and field development. The purpose of this study is the calculation of fracture parameters in the Asmari reservoir using two electrical image logs (FMI, EMI), and the determination of fracture parameters’ effect on the porosity and permeability using thin sections and velocity deviation log (VDL). The results indicate that production in the Asmari reservoir is a combination of fractures and rock matrix. Fracture aperture (VAH) and fracture porosity (VPA) are only measurable with core and image logs directly. However, regarding core limitations, the image log has been recognized as the best method for fracture parameter determination due to their high resolution (2.5 mm). In this study, VDL log and thin sections have been used as auxiliary methods which may be available in all wells. The VDL log provides a tool to obtain downhole information about the predominant pore type in carbonates. Results indicate that between fracture parameters, VAH is considered as the most important parameter for determining permeability. For well No. 3, VAH ranges from minimum 51 × 10^?5 mm to maximum 0. 047 mm and VPA changes from min 10^?5% to maximum 0.02056%. For well No. 6, VAH varies from 5 × 10^?4 to 0.0695 mm and VPA varies from 10^?5 to 0.015%. Therefore, due to high fracture density and fracture aperture, it seems that most of effective porosity originates from fractures especially in well No. 3. However, VDL for well No. 6 indicates that intercrystalline and vuggy porosity are the dominant porosity. This result may be an indication for fracture set diversity in the two studied wells. While in well No. 3, they related to the folding and active faults, in well No. 6 they are only of folding type. Furthermore, results indicate the high capability for both of EMI and FMI image logs for calculation of fracture and vug parameters in the carbonate reservoirs.     

Experimental Study on Engineering Properties and Microstructure of Expansive Soils Treated by Lime Containing Silica Nanoparticles Under Various Temperatures

Geotechnical and Geological Engineering - In this study, improvement rate of engineering properties of expansive soils caused by the effect of varying curing temperature together with the addition...     

Recent trends in regional air temperature and precipitation and links to global climate change in the Maharlo watershed,Southwestern Iran

Trends in air temperature and precipitation data are investigated for linkages to global warming and climate change. After checking for serial correlation with trend-free pre-whitening procedure, the Mann–Kendall test is used to detect monotonic trends and the Mann–Whitney test is used for trend step change. The case study is Maharlo watershed, Southwestern Iran, representing a semi-arid environment. Data are for the 1951–2011 period, from four temperature sites and seven precipitation sites. A homogeneity test investigates regional similarity of the time series data. The results include mean annual, mean annual maximum and minimum and seasonal analysis of air temperature and precipitation data. Mean annual temperature results indicate an increasing trend, while a non-significant trend in precipitation is observed in all the stations. Furthermore, significant phase change was detected in mean annual air temperature trend of Shiraz station in 1977, indicating decreasing trend during 1951–1976 and increasing trend during 1977–2011. The annual precipitation analysis for Shiraz shows a non-significant decrease during 1951–1976 and 1977–2011. The result of homogeneity test reveals that the studied stations form one homogeneous region. While air temperature trends appear as regional linkage to global warming/global climate change, more definite outcome requires analysis of longer time series data on precipitation and air temperature.     

A new approach for free vibration analysis of a system of elastically interconnected similar rectangular plates

A new procedure is proposed to ease the analyses of the free vibration of an elastically connected identical plates system with respect to Kirchhoff plate theory. A structure of n parallel, elastically connected rectangular plates is of concern, whereby the motion is explained by a set of n coupled partial differential equations. The method involves a new change in variables to uncouple equations and form an equal system of n decoupled plates, while each is assumed to be elastically connected to...     

Acceleration of thermodynamic computations in fluid flow applications

Reservoir simulators model the highly nonlinear partial differential equations that represent flows in heterogeneous porous media. The system is made up of conservation equations for each thermodynamic species, flash equilibrium equations and some constraints. With advances in Field Development Planning (FDP) strategies, clients need to model highly complex Improved Oil Recovery processes such as gas re-injection and CO2 injection, which requires multi-component simulation models. The operating range of these simulation models is usually around the mixture critical point and this can be very difficult to simulate due to phase mislabeling and poor nonlinear convergence. We present a Machine Learning (ML) based approach that significantly accelerates such simulation models. One of the most important physical parameters required in order to simulate complex fluids in the subsurface is the critical temperature (T_crit). There are advanced iterative methods to compute the critical point such as the algorithm proposed by Heidemann and Khalil (AIChE J 26,769–799, 1980) but, because these methods are too expensive, they are usually replaced by cheaper and less accurate methods such as the Li-correlation (Reid and Sherwood 1966). In this work we use a ML workflow that is based on two interacting fully connected neural networks, one a classifier and the other a regressor, that are used to replace physical algorithms for single phase labelling and improve the convergence of the simulator. We generate real time compositional training data using a linear mixing rule between the injected and the in-situ fluid compositions that can exhibit temporal evolution. In many complicated scenarios, a physical critical temperature does not exist and the iterative sequence fails to converge. We train the classifier to identify, a-priori, if a sequence of iterations will diverge. The regressor is then trained to predict an accurate value of T_crit. A framework is developed inside the simulator based on TensorFlow that aids real time machine learning applications. The training data is generated within the simulator at the beginning of the simulation run and the ML models are trained on this data while the simulator is running. All the run-times presented in this paper include the time taken to generate the training data and train the models. Applying this ML workflow to real field gas re-injection cases suffering from severe convergence issues has resulted in a 10-fold reduction of the nonlinear iterations in the examples shown in this paper, with the overall run time reduced 2- to 10-fold, thus making complex FDP workflows several times faster. Such models are usually run many times in history matching and optimization workflows, which results in compounded computational savings. The workflow also results in more accurate prediction of the oil in place due to better single phase labelling.