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 Study of Pore Level Influences on Reservoir Quality Based on Rock Typing: Case Study of Quartzite El Hamra, Algeria(Springer Nature, 2024) Nettari, Ferhat; Doghmane, Mohamed Zinelabidine; Aliouane, Leila; Ouadfeul, Sid-AliOne of the biggest challenges facing Geoscientists and reservoir modelers is how to improve the descriptive understanding of the hydrocarbons reservoir, and therefore, define the best representative reservoir properties (e.g., fluid flow capacity) in the simulation model, whereas poorly described reservoir characteristics can lead to a significant impact on reservoir performance predictions and its future production behaviors. In order to master the Quartzite El Hamra reservoir in Southern part of Hassi Messaoud field in Algeria, this study was dedicated to characterize the petrophysical properties by using rock typing and flow unit techniques (Winland R35 and FZI). The main objective was to evaluate the pore level’s influences on reservoir quality and log response and to study the relationships between the composition of pore geometry and reservoir quality. This allowed us to understand the factors that control the quality of the reservoir and the fluids’ flow characteristics. Moreover, this study was based on detailed description and laboratory tests on cores and thin sections and the integration of this information with geological, Petrophysical, and engineering data. Furthermore, appropriate set of reservoir properties (i.e., porosity—permeability ratio, R35, storage percentage, and percent flow) are well defined for six identified Hydraulic Flow Units (HFUs). The obtained results can improve reservoir simulation studies for performance prediction, history matching, and future development decisions in the field.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 Classification of ordovician tight reservoir facies in Algeria by using Neuro-Fuzzy algorithm(Springer, 2022) Doghmane, Mohamed Zinelabidine; Ouadfeul, Sid-Ali; Benaissa, Z.; Eladj, S.The Tight reservoirs in Algeria are generally characterized by their complex nature and their degree of heterogeneity. Wherein, the quantitative evaluation of such type of reservoirs necessitate the determination of facies in order to estimate the in-situ hydrocarbons and their nature. However, the classical methods of determining facies are essentially based on core data and carrots, which are not always technically available. Artificial neural network (ANN) is one of the recent developed methods being used to provide facies classification with a minimum available core data and by using well logs. Even though, the ANN results are acceptable, it determines only the dominant facies at each depth point off logs, no information can be provided for the secondary facies. For that reason, the main objective of this study is to develop a Neuro-fuzzy algorithm that allows the determination of secondary facies in addition to dominant facies. Indeed, the algorithm has been trained by using core data at wells’ scale in the Ordovician reservoir located in an Algerian southern Petroleum field. Moreover, the Neuro-fuzzy classifier has been tested in near wells, for which, the obtained results has demonstrated the effectiveness of the proposed approach to improve tight reservoir characterization in the studied field. Hence, the designed algorithm is highly recommended for other petroleum systems in Middle East and North Africa regionItem 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, Leila