This study aimed to evaluate the reservoir petrophysical properties (porosity, water saturation, and permeability) for optimal flow unit assessment within the Sadi Formation. Utilizing open hole logging data from five wells, the Sadi formation was divided into two rock units. The upper unit (A) is 45-50 meters thick, mainly consisting of limestone, mainly consisting of shaly limestone at the lower part. The lower unit (B) has a thickness of approximately 75-80 meters and is primarily composed of limestone, further subdivided into three subunits (B1, B2, B3). The average water resistivity is 0.04 ohm-m, and the average mud filtrate resistivity is 0.06 ohm-m. The Pickett plot was utilized to determine Archie parameters (tortuosit

This paper includes an experimental study of hydrogen mass flow rate and inlet hydrogen pressure effect on the fuel cell performance. Depending on the experimental results, a model of fuel cell based on artificial neural networks is proposed. A back propagation learning rule with the log-sigmoid activation function is adopted to construct neural networks model. Experimental data resulting from 36 fuel cell tests are used as a learning data. The hydrogen mass flow rate, applied load and inlet hydrogen pressure are inputs to fuel cell model, while the current and voltage are outputs. Proposed model could successfully predict the fuel cell performance in good agreement with actual data. This work is extended to developed fuel cell feedback

Image Fusion Using A Convolutional Neural Network

In this research an Artificial Neural Network (ANN) technique was applied for the prediction of Ryznar Index (RI) of the flowing water from WTPs in Al-Karakh side (left side) in Baghdad city for year 2013. Three models (ANN1, ANN2 and ANN3) have been developed and tested using data from Baghdad Mayoralty (Amanat Baghdad) including drinking water quality for the period 2004 to 2013. The results indicate that it is quite possible to use an artificial neural networks in predicting the stability index (RI) with a good degree of accuracy. Where ANN 2 model could be used to predict RI for the effluents from Al-Karakh, Al-Qadisiya and Al-Karama WTPs as the highest correlation coefficient were obtained 92.4, 82.9 and 79.1% respe

The paper proposes a methodology for predicting packet flow at the data plane in smart SDN based on the intelligent controller of spike neural networks(SNN). This methodology is applied to predict the subsequent step of the packet flow, consequently reducing the overcrowding that might happen. The centralized controller acts as a reactive controller for managing the clustering head process in the Software Defined Network data layer in the proposed model. The simulation results show the capability of Spike Neural Network controller in SDN control layer to improve the (QoS) in the whole network in terms of minimizing the packet loss ratio and increased the buffer utilization ratio.

The petrophysical characteristics of five wells drilled into the Sa'di Formation in the Halfaya oil field were evaluated using IP software to determine a reservoir and explore hydrocarbon reserve zones. The lithology was evaluated using the M-N cross-plot method. The diagram showed that the Sa'di Formation was mainly composed of calcite (represented by the limestone region) is the main mineral in the Sa′di Reservoir. Using a density-neutron cross plot to identify the lithology showed that the formation mainly consists of limestone with minor shale. Gamma-ray logs were employed to calculate the shale quantity in each well. The porosity at weak hole intervals was calculated using a sonic log and neutron-density log at the reservoir