Publications Internationales

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/13

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Subject: search.filters.subject.Prediction

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Now showing 1 - 10 of 11
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    On the capability of multilayer perceptron to predict total organic carbon and elemental capture spectroscopy data in unconventional hydrocarbon reservoirs: the case of the Barnett Shale and Bakken oil field
    (Obstetrics & Gynecology Science, 2025) Aliouane , L.; Ouadfeul, S.-A.
    This study explores the capability of a Multilayer Perceptron (MLP) neural network (NN) machine to predict missing or expensive core rocks and well-log data measurements such as the Total Organic Carbon (TOC) and Elemental Capture Spectroscopy (ECS) measurements. Data of boreholes drilled in the Lower Barnett Shale and Bakken oil and gas fields, located in the USA, are used. TOC estimation is first addressed using the Schmoker method in the Barnett Shale gas and Bakken oil reservoirs, followed by the implementation of MLP NNs trained with various learning algorithms such as the Hidden Weight Optimisation, the Conjugate Gradient, and the Levenberg-Marquardt. Input data include standard well logs such as sonic, gamma ray, resistivity, and neutron porosity. The MLP models are validated and generalised using both horizontal and vertical well data. Furthermore, ECS data prediction is performed using MLPs trained on elementary analysis-derived log parameters, offering a cost-effective alternative to direct ECS logging. The results demonstrate that the efficiency and reliability of MLP-based approaches in enhancing geochemical and petrophysical characterisation of subsurface formations is conditioned by the choice of the learning algorithm, the reservoir complexity, number of wells, and their distribution
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    On the capability of multilayer perceptron to predict total organic carbon and elemental capture spectroscopy data in unconventional hydrocarbon reservoirs: the case of the Barnett Shale and Bakken oil field
    (Obstetrics & Gynecology Science, 2025) Aliouane, L.; Ouadfeul, S.-A.
    This study explores the capability of a Multilayer Perceptron (MLP) neural network (NN) machine to predict missing or expensive core rocks and well-log data measurements such as the Total Organic Carbon (TOC) and Elemental Capture Spectroscopy (ECS) measurements. Data of boreholes drilled in the Lower Barnett Shale and Bakken oil and gas fields, located in the USA, are used. TOC estimation is first addressed using the Schmoker method in the Barnett Shale gas and Bakken oil reservoirs, followed by the implementation of MLP NNs trained with various learning algorithms such as the Hidden Weight Optimisation, the Conjugate Gradient, and the Levenberg-Marquardt. Input data include standard well logs such as sonic, gamma ray, resistivity, and neutron porosity. The MLP models are validated and generalised using both horizontal and vertical well data. Furthermore, ECS data prediction is performed using MLPs trained on elementary analysis-derived log parameters, offering a cost-effective alternative to direct ECS logging. The results demonstrate that the efficiency and reliability of MLP-based approaches in enhancing geochemical and petrophysical characterisation of subsurface formations is conditioned by the choice of the learning algorithm, the reservoir complexity, number of wells, and their distribution
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    Failure Prediction of Laminated Composites: Simulation of the Nonlinear Behavior and Progressive Damage
    (Korean Fiber Society, 2023) Bensalem, Abdelhafid; Daoui, Abdelhakim; Cheriet, Abderrahmane; Lecheb, Samir; Chellil, Ahmed; Kebir, Hocine; Aissani, Linda
    The Hashin’s criteria are useful in composite structural applications because of their simple concept and their theoretical results are relatively close to that got in the experimental parts. In the present study, in the present study, the failure of the composite laminates under static loading have been developed predicted using Hashin’s Criterion. Nonhomogeneous stresses within a structure may induce a complicated failure scenario whereby one ply at a point can initiate failure and can affect also other plies at the same point or the same ply in different neighboring points. With neglecting the possibility of interlaminar failure, only in-plane loads are considered in this state. The results showed that the failure analysis was proposed to simulate the nonlinear laminate behavior and progressive damage of selected laminates under to their ultimate strength. In our approach, the finite element analysis is performed using MATLAB software to study the effect of tensile and compressive loading on the failure of epoxy resin laminate AS4/3501-6.
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    Prediction of Lattice Constant of A 2 XY 6 Cubic Crystals Using Gene Expression Programming
    (2020) Nait Amar, Menad; Ghriga, Mohammed Abdelfetah; Ben Seghier, Mohamed El Amine; Ouaer, Hocine
    Lattice constant is one of the paramount parameters that mark the quality of thin film fabrication. Numerous research efforts have been made to calculate and measure lattice constant, including experimental and empirical approaches. Not withstanding these efforts, a reliable and simple-to-use model is still needed to predict accurately this vital parameter. In this study, gene expression programming (GEP) approach was implemented to establish trustworthy model for prediction of the lattice constant of A2XY6 (A = K, Cs, Rb, TI; X = tetravalent cation; and Y = F, Cl, Br, I) cubic crystals based on a comprehensive experimental database. The obtained results showed that the proposed GEP correlation provides excellent prediction performance with an overall average absolute relative deviation (AARD%) of 0.3596% and a coefficient of determination (R2) of 0.9965. Moreover, the comparison of the performance between the newly proposed correlation and the best pre-existing paradigms demonstrated that the established GEP correlation is more robust, reliable, and efficient than the prior models for prediction of lattice constant of A2XY6 cubic crystals
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    Wavelet-based fractal analysis of interMagnet observatories data
    (2013) Ouadfeul, Sid-Ali; Aliouane, Leila
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    Probabilistic model to forecast earthquakes in the Zemmouri (Algeria) seismoactive area on the basis of moment magnitude scale distribution functions
    (2013) Baddari, Kamel; Makdeche, Said; Bellalem, Fouzi
    Based on the moment magnitude scale, a probabilistic model was developed to predict the occurrences of strong earthquakes in the seismoactive area of Zemmouri, Algeria. Firstly, the distributions of earthquake magnitudes M i were described using the distribution function F 0(m), which adjusts the magnitudes considered as independent random variables. Secondly, the obtained result, i.e., the distribution function F 0(m) of the variables M i was used to deduce the distribution functions G(x) and H(y) of the variables Y i = Log M 0,i and Z i = M 0,i , where (Y i)i and (Z i)i are independent. Thirdly, some forecast for moments of the future earthquakes in the studied area is given
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    Generalized dynamical fuzzy model for identification and prediction
    (2014) Saad Saoud, Lyes; Rahmoune, Fayçal; Tourtchine, Victor; Baddari, Kamel
    In this paper, the development of an improved Takagi Sugeno (TS) fuzzy model for identification and chaotic time series prediction of nonlinear dynamical systems is proposed. This model combines the advantages of fuzzy systems and Infinite Impulse Response (IIR) filters, which are autoregressive moving average models, to create internal dynamics with just the control input. The structure of Fuzzy Infinite Impulse Response (FIIR) is presented, and its learning algorithm is described. In the proposed model, the Butterworth analogue prototype filters are estimated using the obtained membership functions. Based on the founding orders of the analogue filters, the IIR filters could be constructed. The IIR filters are introduced to each TS fuzzy rule which produces local dynamics. Gustafson-Kessel (GK) clustering algorithm is used to generate the clusters which will be used to find the number of the IIR parameters for each rule. The hybrid genetic algorithm and simplex method are used to identify the consequence parameters. The stability of the obtained model is studied. To demonstrate the performance of this modeling method, three examples have been chosen. Comparative results between the FIIR model on one hand, and the traditional TS fuzzy model, the neural networks and the neuro-fuzzy network on the other hand. The results show that the proposed method provides promising identification results
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    Boundary problems for stochastic equations and stochastic markovian fields
    (2009) Khaldi, Khaled; Bouzaghti, Yamina