Publications Scientifiques
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Item Application of local optimisation with Steepest Ascent Algorithm for the residual static corrections in a southern Algeria geophysical survey(2022) Bansir, Fateh; Eladj, S.; Harrouchi, Lakhdar; Doghmane, M. Z.; Aliouane, LeilaStatic corrections in the seismic data processing sequence are one of the most sensitive steps in seismic exploration undertaken in areas with complex topography and geology. Using the stack energy as an objective function for the inversion problem, static corrections can be performed without using the cross-correlation of all traces of a Common Depth Point (CDP) with all other CDP traces. This step is a time-consuming operation and requires huge computer memory capacities. The pre-calculation step of the crosscorrelation table can provide greater processing efficiency in practice; by either a local optimisation algorithm such as Steepest Ascent applied to the traces, or a global search method such as genetic algorithms. The sudden change of the topography and the signal/ noise (S/N) ratio decrease can cause failure in residual static (RS) corrections operations; consequently, it may lead to poor quality of the seismic section. In this study, we firstly created a synthetic seismic section (synthetic stack), which describes a geological model. Then, the Steepest Ascent Algorithm (SAA) method is used to estimate RS corrections and evaluate its performance, in order that the encountered problems in the field will be overcome. The generated synthetic stack, with a two-layer tabular geological model, has been disturbed by introducing wrong static corrections and random noise. Thus, the model became a noised stack with low S/N ratio and poor synthetic horizons continuity. After 110 iterations, the SAA estimated the appropriate corrections and eliminated disturbances introduced earlier. Moreover, it improves the quality of the stack and the continuity of synthetic horizons. Therefore, we have applied this algorithm using the same methodology for calculating the RS corrections of real data of seismic prospection in southern Algeria; the input data has poor quality caused by near-surface anomalies. We found that our proposed methodology has improved the RS corrections in comparison to currently used conventional methods in the seismic processing in Algerian industrItem Levenberg-Marquardt algorithm neural network for clay volume estimation from well-log data in an unconventional tight sand gas reservoir of Ahnet basin (Algerian Sahara)(Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, 2022) Aliouane, LeilaThe main goal of this paper is to show the contribution of artificial intelligence, namely a neural network, in reservoir characterisation to predict the clay volume in an unconventional tight sand gas reservoir. Clay volume is usually estimated using the natural gamma ray log, which can give bad results if non-clayey radioactive minerals are present in the reservoir. Our purpose is to implement a multilayer perceptron neural network machine to predict the clay volume using the conventional well-log data as an input and the measured mineralogical component, as desired output with a Levenberg-Marquardt algorithm. Application to two Ordovician reservoir intervals of a borehole located in the Ahnet basin in the Algerian Sahara shows the contribution and the efficacy of the implemented neural network machine in unconventional tight sand reservoirs characterisationItem 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 Identification and evolution of clay minerals in the sand-shale reservoirs of the Berkine basin (Algeria)(HAL, 2010) Boudella, Amar; Aliouane, Leila; Bounif, Abdallah; Benaïssa, Zahia; Benaissa, Abdelkader; Bentellis, Abdelhakim; Aïfa, Tahar