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 optimization of artificial gas lift techniques plays a crucial role in the advancement of oil field development. This study focuses on investigating the impact of gas lift design and optimization on production outcomes within the Mishrif formation of the Halfaya oil field. A comprehensive production network nodal analysis model was formulated using a PIPESIM Optimizer-based Genetic Algorithm and meticulously calibrated utilizing field-collected data from a network comprising seven wells. This well group encompasses three directional wells currently employing gas lift and four naturally producing vertical wells. To augment productivity and optimize network performance, a novel gas lift design strategy was proposed. The optimization of

Prediction of daily rainfall is important for flood forecasting, reservoir operation, and many other hydrological applications. The artificial intelligence (AI) algorithm is generally used for stochastic forecasting rainfall which is not capable to simulate unseen extreme rainfall events which become common due to climate change. A new model is developed in this study for prediction of daily rainfall for different lead times based on sea level pressure (SLP) which is physically related to rainfall on land and thus able to predict unseen rainfall events. Daily rainfall of east coast of Peninsular Malaysia (PM) was predicted using SLP data over the climate domain. Five advanced AI algorithms such as extreme learning machine (ELM), Bay

Mishrif Formation regards one of the most important reservoirs in Iraq. Well logging represents one of the most important tool in the formation evaluation. According to the Petrophysical properties that have been gotten from well logging, Mishrif Formation in terms of reservoirs units, consist of several reservoirs units. Major reservoirs units divided into three reservoir units,MA,MB&MC. Each of these major units divided into minor reservoirs units (MB11,MB12,MC2&MC3).MB major reservoir units represent the best reservoir unit. These reservoirs units separated by cap rocks(mainly tight limestone)(CR1,CR2,CR3,CR4,CR5,CR6,and CR7).CPI were demonstrated for all wells.Hydrocarbon saturation vs.

The study is an attempt to predict reservoir characterization by improving the estimation of petro-physical properties (porosity), through integration of wells information and 3D seismic data in early cretaceous carbonate reservoir Yamama Formation of (Abu-Amoud) field in southern part of Iraq. Seismic inversion (MBI) was used on post- stack 3 dimensions seismic data to estimate the values of P-acoustic impedance of which the distribution of porosity values was estimated through Yamama Formation in the study area. EMERGE module on the Hampson Russel software was applied to create a relationship between inverted seismic data and well data at well location to construct a perception about the distribution of porosity on the level of all uni

Mishrif Formation is the main reservoir in Amara Oil Field. It is divided into three units (MA, TZ1, and MB12). Geological model is important to build reservoir model that was built by Petrel -2009. FZI method was used to determine relationship between porosity and permeability for core data and permeability values for the uncored interval for Mishrif formation. A reservoir simulation model was adopted in this study using Eclipse 100. In this model, production history matching executed by production data for (AM1, AM4) wells since 2001 to 2015. Four different prediction cases have been suggested in the future performance of Mishrif reservoir for ten years extending from June 2015 to June 2025. The comparison has been mad

In this paper, precision agriculture system is introduced based on Wireless Sensor Network (WSN). Soil moisture considered one of environment factors that effect on crop. The period of irrigation must be monitored. Neural network capable of learning the behavior of the agricultural soil in absence of mathematical model. This paper introduced modified type of neural network that is known as Spiking Neural Network (SNN). In this work, the precision agriculture system  is modeled, contains two SNNs which have been identified off-line based on logged data, one of these SNNs represents the monitor that located at sink where the period of irrigation is calculated and the other represents the soil. In addition, to reduce p