Publications Scientifiques

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Author: search.filters.author.Aliouane, Leila

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    Automatic fault tracking from 3D seismic data using the 2D Continuous Wavelet Transform combined with a Convolutional Neural Network
    (Istituto Nazionale di Oceanografia e di Geofisica Sperimentale, 2024) Ouadfeul, Sid Ali; Aliouane, Leila
    The aim of this work is to propose a new technique for automatic fault tracking from 3D seismic data using the 2D Continuous Wavelet Transform (CWT) method combined with artificial intelligence. Time slices of the variance attribute, derived from the 3D seismic data and chosen by the user, are analysed using the 2D CWT with the 2D Mexican Hat as an analysing wavelet, and the maxima of the modulus of the 2D CWT are mapped for the full range of scales. The ensemble of mapped maxima for the set of time slices is filtered using a Convolutional Neural Network machine. Machine training is performed with a supervised mode using the manually tracked faults as a desired output. Application to real data shows the efficiency and robustness of the proposed method, which can greatly help seismic interpreters in avoiding manual fault tracking, a difficult and time-consuming task.
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    Artificial Intelligence Technique in earth sciences for porosity prediction in shaly petroleum reservoir from geophysical well-logs data. Application to Hassi R'mel field, Algeria
    (MEACSE Publication, 2024) Aliouane, Leila; Ouadfeul, Sid-ali
    Machine learning techniques are becoming very popular in earth sciences, mainly in petroleum exploration and exploitation. Reservoir characterization using geophysical well-logs data analysis is commonly conducted and plays a central role in formation evaluation in petroleum domain. The most petrophysical parameters that describe the reservoir are the porosity, the permeability and the water saturation where the porosity is the main key. Using conventional methods, the estimation of the porosity is very difficult, mainly in shaly reservoirs where the presence of clay affects considerably, the porosity and the permeability. For that, we propose to accurately predict the porosity from geophysical recordings crossed the formation of wells using machine learning methods such as multi-layer neural network. The input layer are constituted by the petrophysical well-logs data and the output layer presented by one neuron corresponding to the predicted porosity. The training step of neural network machine (NNM) is processed using core data (CORPOR) by minimizing the root mean square error using Radial Basis Function algorithm (RBF). Once trained, the model is then applied to the target wells to predict porosity (PORRBF). The predicted porosity match the core values with good accuracy. This approach provides significantly a robust computation method and reduces dependency on prior domain knowledge
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    Geothermal Energy in Algeria and the Contribution of Geophysics
    (MDPI, 2023) Aliouane, Leila; Ouadfeul, Sid-Ali
    Geothermal 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].
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    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-Ali
    One 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.
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    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, Leila
    Static 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 industr
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    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, Leila
    The 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 characterisation
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    Unconventional tight-sand reservoir characterization by geomechanical study from well logs data in illizi basin
    (2021) Cherana, Amina; Aliouane, Leila; Keci, Naima; Ouadfeul, Sid-Ali
    Unconventional 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 Basin
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    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-Ali
    In 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, Clustering
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    Structural edges delimitation from gravity data using the wavelet transform
    (2013) Ouadfeul, Sid-Ali; Aliouane, Leila