Publications Scientifiques
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Item Geothermal Energy in Algeria and the Contribution of Geophysics(MDPI, 2023) Aliouane, Leila; Ouadfeul, Sid-AliGeothermal energy is one of the cleanest, most accessible and cheapest alternative energies in the whole world. It is a renewable energy designating an inexhaustible source at a human scale that can be renewed (energy culture). Geothermal energy comes from the disintegration of radioactive elements present in rocks and the Earth’s core. These generate heat flow to the surface. This heat increases with depth on average by 30 ◦C/km [1]. In Algeria, this gradient varies from 25 ◦C/km in the north to 60 ◦C/km in the south [2]. In 2006, Madlnés published a world map showing the geothermal potential on all continental plates. North Africa has geothermal potential, which explains why many geothermal studies have been carried out in the north of Algeria (Figure 1). Figure 2 shows the geothermal areas in Algeria where the reservoir rocks are the Jurassic limestone in the north and Albian sandstone in the south. This renewable energy is used in multiple areas: fish farming, greenhouse heating or district heat networks, balneotherapy, and electricity production. Currently, only a tiny fraction of the world’s geothermal resources are used. Certain technological improvements and a better recognition of the true value of geothermal energy could lead to a strong development of this clean and reliable energy for the majority of the countries of the world. Algeria, which has about 200 thermal springs, has the possibility of being among the leaders in this field. In this presentation, we cite the characteristics of geothermal energy, the Algerian thermal springs and the possibilities of their uses according to the temperatures using the Lindal diagram, as well as the role of geophysics or the Earth’s physics in the exploration of geothermal sources before drilling where most of the techniques are the same as those used in petroleum exploration and reservoir characterization exploiting new technological development such as artificial intelligence from seismic and well-logs data [3].Item Unconventional tight-sand reservoir characterization by geomechanical study from well logs data in illizi basin(2021) Cherana, Amina; Aliouane, Leila; Keci, Naima; Ouadfeul, Sid-AliUnconventional Tight sand reservoirs require stimulation methods in order to make them produce; hydraulic fracturing is one of these methods. A geomechanical study is therefore necessary and its main objective is to determine an area favourable to hydraulic fracturing in order to optimize production. For this, the results of the petrophysical evaluation are exploited and the various dynamic and static geomechanical parameters (Young's modulus and the Poisson's ratio) are subsequently calculated. Finally, the pore pressure was also calculated in order to obtain the Effective Minimum Stress which helps determine the area favourable to fracturing. An application has been carried out realized exploiting well-logs of Ordovician Tight-sand reservoir of one well from the Illizi BasinItem Fuzzy clustering algorithm for Lithofacies classification of Ordovician unconventional Tight-sand reservoir from well-logs data (Algerian Sahara)(2019) Cherana, Amina; Aliouane, Leila; Doghmane, Mohamed Zinelabidine; Ouadfeul, Sid-AliIn this paper we present an approach based on a fuzzy clustering algorithm applied for lithofacies classification in an unconventional tight-sand reservoir from well-logs data. In some cases, these kinds of reservoirs are ra-dioactive due to the presence of non clayey radioactive minerals. However, conventional methods can give bad results. For that, artificial intelligence such as Fuzzy logic, can be suitable to solve the problem. Fuzzy clustering is an unsupervised machine learning technique where a given set of data is classified into groups. Hence, fuzzy logic is a more general logic then classical logic because it does not ignore uncertainties and accepts the implicit consideration of the inherited error associated with any physical measurement. This techniquet has been applied to real data of one well in an unconventional tight-sand reservoir in the Algerian Sahara. Predicted results are compared to lithofacies obtained from conventional methods and spectral mineralogical well-logs data Keywords: Well-log, Unconventional Tight reservoir, Lithofacies, Fuzzy logic, ClusteringItem Ionospheric data prediction of DEMETER Satellite using Levenberg Marquardt neural network model. application to ISL instrument(2015) Ouadfeul, Sid-Ali; Tourtchine, Victor; Aliouane, LeilaItem Structural edges delimitation from gravity data using the wavelet transform(2013) Ouadfeul, Sid-Ali; Aliouane, LeilaItem Lithofacies classification using the multilayer perceptron and the Self-organizing neural networks(Springer, 2012) Ouadfeul, Sid-Ali; Aliouane, LeilaItem Lithofacies classification using the multilayer perceptron and the Self-organizing neural networks(Springer, 2012) Ouadfeul, Sid-Ali; Aliouane, LeilaItem Total organic carbon prediction in shale gas reservoirs using the radial basis function neural network(2015) Ouadfeul, Sid-Ali; Aliouane, LeilaItem Solar geomagnetic activity prediction using the fractal analysis and neural network(2010) Ouadfeul, Sid-Ali; Aliouane, LeilaItem Aeoromagnetic data analysis using the 2D continuous wavelet transform(2011) Aliouane, Leila; Ouadfeul, Sid-Ali; Boudella, Amar