الفهرس 
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Abstract
Gas hydrate formation is a well-known problem that forms an “ice plugs” in the production pipelines
under certain temperature and pressure conditions.These ice plugs in most cases results in dropping down
the production rates or even shutting down the operating facilities. Before the evolution of the Wireless
sensor networks (WSN) technology, many production wells in the oil and gas industry were suffering
from the gas hydration formation process, as most of them were remotely located away from the host
location, making the traditional monitoring systems an impossible option to rely on. By taking the
advantage of the WSN technology, it is possible now to monitor and predict the critical conditions at
which hydration will form by using any computerized model. In fact,most of the developed
computerized models are based on the twowell-known hand calculation methods which are the
Specific gravity and K-Factor methods. In this research, the proposed work is divided into two phases;
the first phase carries out the development of a three computerized prediction models using the Neural
Network algorithms (ANN) based on the specific gravity charts, the K-Factor method and the
production rates of the flowing gas mixture in the process pipelines. While in the second phase, two
WSN prototype models are designed and implemented using National Instruments WSN hardware
devices. Power analysis is carried out on the designed prototypes and regression models are developed
to give a relation between the sensing nodes (SN) consumedcurrent, Node-to-Gateway distance and
the operating link quality.The prototypes controller is interfaced with a GSM module and connected
to a web server to be monitored via mobile and internet networks. All the developed models are tested
and results demonstrate a good agreement between the target data and the developed ANN models
results.