Permeability prediction and construction of 3D geological model: application of neural networks and stochastic approaches in an Iranian gas reservoir

Determination of petrophysical parameters by using available data has a specific importance in exploration and production studies for oil and gas industries. Modeling of corrected permeability as a petrophysical parameter can help in decision making processes. The objective of this study is to construct a comprehensive and quantitative characterization of a carbonate gas reservoir in marine gas field. Artificial neural network is applied for prediction of permeability in accordance with other petrophysical parameters at well location. Correlation coefficient for this method is 84 %. In the study, the geological reservoir model is developed in two steps: First, the structure skeleton of the field is constructed, and then, reservoir property is distributed within it by applying new stochastic methods. Permeability is modeled by three techniques: kriging, sequential Gaussian simulation (SGS) and collocated co-simulation using modeled effective porosity as 3D secondary variable. This paper enhances the characterization of the reservoir by improving the modeling of permeability through a new algorithm called collocated co-simulation. Kriging is very simple in modeling the reservoir permeability, and also, original distribution of the data changes considerably in this model. In addition, the SGS model is noisy and heterogeneous, but it retains the original distribution of the data. However, the addition of a 3D secondary variable in third method resulted in a much more reliable model of permeability.     

A hybrid fuzzy-ontology based intelligent system to determine level of severity and treatment recommendation for Benign Prostatic Hyperplasia

This paper deals with application of fuzzy intelligent systems in diagnosing severity level and recommending appropriate therapies for patients having Benign Prostatic Hyperplasia. Such an intelligent system can have remarkable impacts on correct diagnosis of the disease and reducing risk of mortality. This system captures various factors from the patients using two modules. The first module determines severity level of the Benign Prostatic Hyperplasia and the second module, which is a decision making unit, obtains output of the first module accompanied by some external knowledge and makes an appropriate treatment decision based on its ontology model and a fuzzy type-1 system. In order to validate efficiency and accuracy of the developed system, a case study is conducted by 44 participants. Then the results are compared with the recommendations of a panel of experts on the experimental data. Then precision and accuracy of the results were investigated based on a statistical analysis.     

Numerical study of the solution of the Burgers and coupled Burgers equations by a differential transformation method

In this paper, the Differential Transformation Method (DTM) is employed to obtain the numerical/analytical solutions of the Burgers and coupled Burgers equations. We begin by showing how the differential transformation method applies to the linear and nonlinear parts of any PDE and give some examples to illustrate the sufficiency of the method for solving such nonlinear partial differential equations. We also compare it against three famous methods, namely the homotopy perturbation method, the homotopy analysis method and the variational iteration method. These results show that the technique introduced here is accurate and easy to apply.     

On the cooperative MIMO communication for energy-efficient cluster-to-cluster transmission at wireless sensor network

Energy-efficient data transmission is one of the key factors for energy-efficient wireless sensor networks (WSN). Cooperative multiple input multiple output (MIMO) explores the wireless communication schemes between multiple sensors emphasizing the MIMO structure. In this paper, an energy-efficient cooperative technique is proposed for a WSN where selected numbers of sensors at the transmitting end are used to form a MIMO structure wirelessly connected with a selected number of sensors at the receiving end. The selection of nodes in the transmitting end is based on a selection function, which is a combination of channel condition, residual energy, inter-sensor distance in a cluster, and geographical location, whereas the selection in the receiving side is performed on the basis of channel condition. Data are sent by the sensors in a cluster to a data-gathering node (DGN) using a multihop transmission. We are concentrating our design on the intermediate hop, where sensors in a cluster transmit their data to the sensors in another cluster with MIMO communication. Energy models are evaluated for both correlated and uncorrelated scenarios. The delay model of the proposed cooperative MIMO is also derived. Experimental results show that the selected MIMO structure outperforms the unselected MIMO in terms of total energy consumption. They also show energy-efficient performance by around 20% over unselected MIMO when they are compared with single-input-single-output structure. Also, the proposed approach takes around 50 more rounds than the geographically selected approach before dying at distance d?>?20 m.     

An advanced reactive ion etching process for very high aspect-ratio sub-micron wide trenches in silicon

This paper reports on a practical modification of the two-step time-multiplexed plasma etching recipe (also known as the Bosch process) to achieve high aspect-ratio sub-micron wide trenches in silicon. Mixed argon and oxygen plasma depassivation steps are introduced in between the passivation and etching phases to promote the anisotropic removal of the passivation layer at the base of the trench. Argon does not chemically react with polymers and silicon and removes the passivation layer only by physical sputtering. Therefore, it results in a highly anisotropic polymer etching process. This recipe can be easily integrated on conventional ICP equipment and the scalloping on the trench sidewall can potentially be reduced in size to less than 50 nm. To clean up all the passivation residues, a short oxygen plasma step is also added at the end of the cycle that effectively improves the uniformity of the etching profile over various opening sizes. Excellent anisotropy of the inserted argon depassivation step facilitates narrow trenches down to 130 nm wide and gap aspect-ratios as high as 40:1, extending the application of deep reactive ion etching (DRIE) processes into a new broad regime.     

An adaptive latency mitigation scheme for massively multiuser virtual environments

As massively multiuser virtual environments (MMVEs) expand in terms of size and user population, they tend toward using P2P architectures as a way to provide scalability without the need for large centralized resources. Distributed hash table (DHT)-based networks have been introduced as a promising option for overlay-based distributed massively multiuser virtual environment applications. However, overlay latency stretch seriously affects MMVE performance where QoS is crucial for real-time user collaboration. This work includes a series of efforts in the alleviation of such undesired latency. Our approach to latency mitigation consists of two phases. First, we propose a position-based ID assignment approach to minimize message hop-count by exploiting the clustered pattern of traffic exchange among MMVE users. Second, we introduce a new ant-based distributed neighbor selection scheme that can be used by MMVE users to select the best neighbors within their areas of interest. In order to evaluate the performance of this heuristic approach, we model the neighbor selection problem in the form of a network flow problem and use its solution as an optimality bound to compare the results. Simulation results demonstrate that the proposed algorithms will compensate for DHT latency stretch to a high extent and the performance of the resulting system would closely follow the optimal bound while communication overhead is negligible.     

Numerical investigation of flow field configuration and contact resistance for PEM fuel cell performance

A steady-state three-dimensional non-isothermal computational fluid dynamics (CFD) model of a proton exchange membrane fuel cell is presented. Conservation of mass, momentum, species, energy, and charge, as well as electrochemical kinetics are considered. In this model, the effect of interfacial contact resistance is also included. The numerical solution is based on a finite-volume method. In this study the effects of flow channel dimensions on the cell performance are investigated. Simulation results indicate that increasing the channel width will improve the limiting current density. However, it is observed that an optimum shoulder size of the flow channels exists for which the cell performance is the highest. Polarization curves are obtained for different operating conditions which, in general, compare favorably with the corresponding experimental data. Such a CFD model can be used as a tool in the development and optimization of PEM fuel cells.     

Effect of corona discharge treatment on indigo dyed cotton fabric

The present study proposes new conditions for achieving the aged-look effect of denim. Indigo dyed cotton fabrics were treated by corona discharge at different power levels and number of passages. Colorimetric parameters of corona treatment were assessed by the CIE L*a*b* colorimetric system and the pH and colour fastness of samples were tested. The surfaces of dyed samples were studied by scanning electron microscopy and Fourier Transform–infrared/attenuated total reflectance (FTIR/ATR) analysis. Under the same conditions of corona treatment, the faded-look effect was reduced as the depth of dyeing was increased. Increasing the power and the number of passages increases L* and b * and decreases a * values. This indicates that samples become lighter and yellower but soaping can reduce yellowness. FTIR/ATR results show that indigo dye oxidises, and isatin and anthranilic acid are produced. The pH levels of corona-treated samples reduce as the power and number of passages increase. This acidification is in agreement with the results of FTIR/ATR analysis.     

Spectral analysis methods for vehicle interior vibro-acoustics identification

Noise has various effects on comfort, performance and health of human. Sound are analysed by human brain based on the frequencies and amplitudes. In a dynamic system, transmission of sound and vibrations depend on frequency and direction of the input motion and characteristics of the output. It is imperative that automotive manufacturers invest a lot of effort and money to improve and enhance the vibro-acoustics performance of their products. The enhancement effort may be very difficult and time-consuming if one relies only on ‘trial and error’ method without prior knowledge about the sources itself. Complex noise inside a vehicle cabin originated from various sources and travel through many pathways. First stage of sound quality refinement is to find the source. It is vital for automotive engineers to identify the dominant noise sources such as engine noise, exhaust noise and noise due to vibration transmission inside of vehicle. The purpose of this paper is to find the vibro-acoustical sources of noise in a passenger vehicle compartment. The implementation of spectral analysis method is much faster than the ‘trial and error’ methods in which, parts should be separated to measure the transfer functions. Also by using spectral analysis method, signals can be recorded in real operational conditions which conduce to more consistent results. A multi-channel analyser is utilised to measure and record the vibro-acoustical signals. Computational algorithms are also employed to identify contribution of various sources towards the measured interior signal. These achievements can be utilised to detect, control and optimise interior noise performance of road transport vehicles.     

Economical load distribution in power networks that include hybrid solar power plants

With respect to the growing share of renewable resources in secure provision of electrical energy, proper utilization of hybrid power plants is of great importance. Therefore, an optimal production planning for operation of these power plants is evidently necessary.