Publications Scientifiques
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Item 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, TaharItem Intelligent methods for predicting nuclear magnetic resonance of porosity and permeability by conventional well-logs : a case study of Saharan field(Springer, 2017) Baouche, Rafik; Aïfa, TaharItem Magnetic susceptibility and its relation with fractures and petrophysical parameters in the tight sand oil reservoir of Hamra quartzites, southwest of the Hassi Messaoud oil field, Algeria(Elsevier, 2014) Aïfa, Tahar; Ali Zerrouki, Ahmed; Baddari, Kamel; Géraud, YvesItem Application of a radial basis function artificial neural network to seismic data inversion(Elsevier, 2009) Baddari, Kamel; Aïfa, Tahar; Djarfour, Noureddine; Ferahtia, JalalItem Seismic noise attenuation by means of an anisotropic non-linear diffusion filter(Elsevier, 2011) Baddari, Kamel; Ferahtia, Jalal; Aïfa, Tahar; Djarfour, NoureddineItem Prediction of natural fracture porosity from well log data by means of fuzzy ranking and an artificial neural network in Hassi Messaoud oil field, Algeria(Elsevier, 2014) Zerrouki, Ahmed Ali; Aïfa, Tahar; Baddari, KamelItem Image-based processing techniques applied to seismic data filtering(Elsevier, 2013) Ferahtia, J.; Aïfa, Tahar; Baddari, K.; Djarfour, Noureddine; Eladj, S.Item Application of feedback connection artificial neural network to seismic data filtering(Elsevier, 2008) Djarfour, Noureddine; Aïfa, Tahar; Baddari, K.; Mihoubi, A.; Ferahtia, J.The Elman artificial neural network (ANN) (feedback connection) was used for seismic data filtering. The recurrent connection that characterizes this network offers the advantage of storing values from the previous time step to be used in the current time step. The proposed structure has the advantage of training simplicity by a back-propagation algorithm (steepest descent). Several trials were addressed on synthetic (with 10% and 50% of random and Gaussian noise) and real seismic data using respectively 10 to 30 neurons and a minimum of 60 neurons in the hidden layer. Both an iteration number up to 4000 and arrest criteria were used to obtain satisfactory performances. Application of such networks on real data shows that the filtered seismic section was efficient. Adequate cross-validation test is done to ensure the performance of network on new data setsItem Acoustic impedance inversion by feedback artificial neural network(Elsevier, 2010) Baddari, K.; Djarfour, Noureddine; Aïfa, Tahar; Ferahtia, J.The determination of acoustic impedance distribution from the seismic data field measurement can be expressed as an ill-posed inverse problem. This work deals with the use of the Elman artificial neural network (ANN) (feedback connection) for the seismic data inversion. In the proposed structure the hidden neuron outputs from the previous time step are fed back to their inputs through time delay units; this enables them to process temporal behaviour and provide multi-step-ahead predictions. The ANN architectures and learning rules are presented to allow the best estimate of acoustic impedance from seismic data. The effects of network architectures using 5 to 60 neurons and 10 to 90 neurons in the hidden layer respectively for synthetic and real data on the rate of convergence and prediction accuracy of ANN models are discussed. The behaviour of networks observed on training data is very similar to the one observed on test data. The results obtained clearly prove the feasibility of the proposed method for seismic data inversion by feedback neural networks. Different tests indicate that the back-propagation conjugate gradient algorithm can easily train the proposed Elman ANN structure without getting stuck in local minima