Heavy oil is classified as unconventional oil resource because of its difficulty to recover in its natural state, difficulties in transport and difficulties in marketing it. Upgrading solution to the heavy oil has positive impact technically and economically specially when it will be a competitive with conventional oils from the marketing prospective. Developing Qaiyarah heavy oil field was neglected in the last five decades, the main reason was due to the low quality of the crude oil resulted in the high viscosity and density of the crude oil in the field which was and still a major challenge putting them on the major stream line of production in Iraq. The low quality of the crude properties led to lower oil prices in the global markets

The instant global trend towards developing tight reservoir is great; however, development can be very challenging due to stress and geomechanical properties effect in horizontal well placement and hydraulic fracturing design. Many parameters are known to be important to determine the suitable layer for locating horizontal well such as petrophysical and geomechanical properties. In the present study, permeability sensitivity to stress is also considered in the best layer selection for well placement. The permeability sensitivity to the stress of the layers was investigated using measurements of 27 core sample at different confining stress values. 1-D mechanical earth model (MEM) was built and converted to a 3-D full-field geomechanical mode

In this research the results of applying Artificial Neural Networks with modified activation function to
perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance
Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of
identification strategy consists of a feed-forward neural network with a modified activation function that
operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have
been trained online and offline have been used, without requiring any previous knowledge about the
system to be identified. The activation function that is used in the hidden layer in FFNN is a modified
version of the wavelet func

The current study aimed to adopt a method for inducing callus cells and regenerating the important common red bean using different types of growth regulators such as N6-benzylaminopurine (BAP), Naphthalene acetic acid (NAA), and Thidiazuron (TDZ). Different types of common bean pinto cultivar explants, such as internodes, cotyledons and roots, were inoculated on Murashige and Skoog medium (MS) provided with different combinations of plant growth regulators, including 1- BAP (5 mg/l) 2-BAP (4.5 mg/l) NAA (0.5 mg/l), 3- BAP (4.5 mg/l), and TDZ (0.1mg/l). Callus was initiated on MS culture medium supplied with 5 mg/l BAP for all explants (internodes, cotyledons, and roots) at 50, 20, and 10% respectively, while adding NAA with 0.5mg/l showed

     This research develops a new method based on spectral indices and random forest classifier to detect paddy rice areas and then assess their distributions regarding to urban areas. The classification will be conducted on Landsat OLI images and Landsat OLI/Sentinel 1 SAR data. Consequently, developing a new spectral index by analyzing the relative importance of Landsat bands will be calculated by the random forest. The new spectral index has improved depending on the most three important bands, then two additional indices including the normalized difference vegetation index (NDVI), and standardized difference built-up index (NDBI) have been used to extract paddy rice fields from the data. Several experiments being

Wireless sensor networks (WSNs) are emerging in various application like military, area monitoring, health monitoring, industry monitoring and many more. The challenges of the successful WSN application are the energy consumption problem. since the small, portable batteries integrated into the sensor chips cannot be re-charged easily from an economical point of view. This work focusses on prolonging the network lifetime of WSNs by reducing and balancing energy consumption during routing process from hop number point of view. In this paper, performance simulation was done between two types of protocols LEACH that uses single hop path and MODLEACH that uses multi hop path by using Intel Care i3 CPU (2.13GHz) laptop with MATLAB (R2014a). Th

The Electric Discharge (EDM) method is a novel thermoelectric manufacturing technique in which materials are removed by a controlled spark erosion process between two electrodes immersed in a dielectric medium. Because of the difficulties of EDM, determining the optimum cutting parameters to improve cutting performance is extremely tough. As a result, optimizing operating parameters is a critical processing step, particularly for non-traditional machining process like EDM. Adequate selection of processing parameters for the EDM process does not provide ideal conditions, due to the unpredictable processing time required for a given function. Models of Multiple Regression and Genetic Algorithm are considered as effective methods for determ

In this research the results of applying Artificial Neural Networks with modified activation function to perform the online and offline identification of four Degrees of Freedom (4-DOF) Selective Compliance Assembly Robot Arm (SCARA) manipulator robot will be described. The proposed model of identification strategy consists of a feed-forward neural network with a modified activation function that operates in parallel with the SCARA robot model. Feed-Forward Neural Networks (FFNN) which have been trained online and offline have been used, without requiring any previous knowledge about the system to be identified. The activation function that is used in the hidden layer in FFNN is a modified version of the wavelet function. This approach ha

Wireless sensor applications are susceptible to energy constraints. Most of the energy is consumed in communication between wireless nodes. Clustering and data aggregation are the two widely used strategies for reducing energy usage and increasing the lifetime of wireless sensor networks. In target tracking applications, large amount of redundant data is produced regularly. Hence, deployment of effective data aggregation schemes is vital to eliminate data redundancy. This work aims to conduct a comparative study of various research approaches that employ clustering techniques for efficiently aggregating data in target tracking applications as selection of an appropriate clustering algorithm may reflect positive results in the data aggregati