Communications Internationales

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/11

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Subject: search.filters.subject.Algorithm

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    Prediction of shear wave velocity in the williston basin using big data analysis and robust machine learning algorithms
    (2022) Laalam, A.; Mouedden, N.; Ouadi, H.; Chemmakh, A.; Merzoug, A.; Boualam, A.; Djezzar, S.; Aihar, A.; Berrehal, B. E.
    The shear velocity is one of the most critical parameters in determining the mechanical rock elastic properties, which serve as inputs for different studies such as wellbore stability, mechanical earth modeling, hydraulic fracturing, and reservoir characterization. However, the sonic log is not acquired in every drilled well. We analyzed the log data of more than 35000 wells in the Williston Basin, and we found that only very few wells had sonic logs. For this reason, several studies attempted to correlate the shear velocity (or slowness) to other easily accessible properties; these will be presented in the literature review, with their pros and cons. The focus of this paper is to apply machine learning algorithms to synthesize the shear slowness log. Our models are trained and tested with log data from 27 wells drilled in the Bakken petroleum system, Williston Basin. Logging data include Gamma Ray, Deep Resistivity, Density, Neutron Porosity, and Shear Slowness. Five different algorithms were developed and tested against blind data including Xtreme Gradient Booster, Random Forest Regressor, Linear Regression, Ada Boost Regression, and Bayesian Ridge Regression. Overall, the R2-score varied from 0.55 to 0.92, with the XGBoost outperforming the other algorithms
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    Finding the Boundary Nodes of a Wireless Sensor Network Without Conditions on the Starting Node
    (2016) Bounceur, Ahcène; Bezoui, Madani; Euler, Reinhardt; Sevaux, Marc
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    BROGO : a new low energy consumption algorithm for leader election in WSNs
    (2017) Bounceur, Ahcène; Bezoui, Madani; Euler, Reinhardt; Kadjouh, Nabil; Lalem, Farid
    The Leader Election in Wireless Sensor Networks depends on the nature of the application domain, the use case and the energy consumption. In the case of real time applications, the choice will be based on the speed of the election, and in the case where time is not important, the choice will be based on the energy consumption. The classical algorithm allowing to elect such a node is called the Minimum Finding Algorithm. In this algorithm, each node sends its value in a broadcast mode each time a better value is received. This process is very energy consuming and not reliable since it is subject to an important number of collisions and lost messages. In this paper, we propose a new algorithm called BROGO (Branch Optima to Global Optimum) where after finding a spanning tree of a WSN, each leaf will route a message through its branch to the root in order to find the leader in that branch. The root will then elect the global leader from the received branch leaders. This process is more reliable since there is a small number of broadcast messages and a reduced number of nodes that send broadcast messages at the same time. The obtained results show that the proposed algorithm reduces the energy consumption with rates that can exceed 95% when compared with the classical Minimum Finding Algorithm. Its message and time complexity is equal to O(n)
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    Measurement quality enhancement using digital filter in power grid integrating TCSC
    (IEEE, 2015) Zitouni, Abdelkader; Ouadi, Abderrahmane; Bentarzi, Hamid