Browsing by Author "Ouadfeul, S."

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3D seismic AVO data established by the wavelet transform modulus maxima lines to characterize reservoirs heterogeneities
(Society of Petroleum Engineers, 2010) Ouadfeul, S.; Aliouane, Leila
Fractal analysis based on the continuous wavelet transform and lithofacies classification from well-logs data using the self-organizing map neural network
(Springer-Verlag, 2013) Aliouane, Leila; Ouadfeul, S.; Boudella, A.
The main goal of this paper is to show that the fractal analysis based on the continuous wavelet transform is not able to improve lithofacies classification using the self-organizing map (SOM) neural network model from well-logs data. The proposed idea consists to inject many inputs in SOM neural network machines and to choose the best map. These inputs are: data set 1: the five raw well-logs data which are: the gamma ray, density, neutron porosity, photoelectric absorption coefficient and sonic well-log; data set 2: the estimated Hölder exponents using the continuous wavelet transform of the data set 1; data set 3: data set 1 and the three radioactive elements concentrations; data set 4: the estimated Hölder exponents of the data set 1 and the Hölder exponents of the radioactive elements concentrations; data set 5: the estimated Hölder exponents of the data set 1 and the three radioactive elements concentrations logs. Application of the proposed idea at two boreholes located in the Algerian Sahara shows that the Hölder exponents estimated with the continuous wavelet transform as an input of the SOM neural network are not able to give geological details. However, the raw well-logs as an input give more details and precision especially when they are enhanced with the natural gamma ray spectrometry data
Fractal analysis revisited by the continuous wavelet transform of AVO seismic data
(Springer-Verlag, 2012) Ouadfeul, S.; Alioaune, L.
The main goal of this paper is to establish reservoirs media heterogeneities by the wavelet transform modulus maxima lines. First, we gathered amplitude versus offsets (AVO) amplitudes at the top of the reservoirs, then we calculated the 2D wavelet transform after we calculated its maxima, and we estimated the Hölder exponent at each maxima. Variation of the Hölder exponent can give more information about lithology and fluid nature at any point. We applied the proposed idea at a 2D synthetic AVO intercept model, obtained results showed that the wavelet transform modulus maxima lines can be used as a seismic image processing tool. We suggest application of the proposed idea on real AVO seismic data and its attributes. It can give more ideas about reservoirs model
Multifractal analysis revisited by the continuous wavelet transform applied in lithofacies segmentation from well-logs data
(2011) Ouadfeul, S.; Aliouane, Leila
The main goal of this paper is to use the wavelet transform modulus maxima lines (WTMM) and the detrended fluctuations analysis (DFA) methods to establish a new technique of lithofacies segmentation from well logs data. The WTMM is used to delimitate lithoafacies boundaries and the DFA is used to provide an exact estimation of the roughness coefficient of lithofacies. Application of the proposed idea at the synthetic and real data of a borehole located in Berkine basin shows that the proposed technique can enhance reservoirs characterization
Shale Gas Lithofacies Classification and Gas Content Prediction Using Artificial Neural Network
(2016) Ouadfeul, S.; Aliouane, Leila
Here, we show the contribution of the artificial intelligence such as neural network to predict the lithofacies in the lower Barnett shale gas reservoir. The Multilayer Perceptron (MLP) neural network with Hidden Weight Optimization Algorithm is used. The input is raw well-logs data recorded in a horizontal well drilled in the Lower Barnett shale formation, however the output is the concentration of the Clay and the Quartz calculated using the ELAN model and confirmed with the core rock measurement. After training of the MLP machine weights of connection are calculated, the raw well-logs data of two other horizontal wells drilled in the same reservoir are propagated though the neural machine and an output is calculated. Comparison between the predicted and measured clay and Quartz concentrations in these two horizontal wells shows the ability of neural network to improve shale gas reservoirs characterization. The present paper is limited only to the lithofacies prediction however the MLP neural network will be used also for the prediction of the gas content form well-logs data in the Barnett shale gas reservoirs which is an extended work of the present research
Structural boundaries delimitation from geomagnetic data using the continuous wavelet transform. Application to Hoggar (Algeria)
(Springer-Verlag, 2012) Ouadfeul, S.; Eladj, S.; Aliouane, Leila
The main goal of the proposed work is to delineate structural boundaries in a very complex geology environment using the spatial and statistical properties of the potential field data. The analysis is performed using magnetic anomaly of the total field data over In Ouzzal, an Archaean north–south elongated block belonging to the Hoggar (Algeria). This region is geologically and geophysically very poorly known except some localized areas. The intrinsic properties of high-frequency signals and the related causative sources are explored, thanks to two-dimensional continuous wavelet transform. The obtained results, represented by spatial distribution of the maxima of the modulus of the wavelet transform at each scale, clearly show that the major magnetic singularities of the field may be related to geological features. Comparison with the Euler’s deconvolution solutions exhibits a very good correlation. Even though where geological structures are known, our method shows better resolution and accuracy. The proposed multiscale method proves to be more powerful, easy to use, and versatile where classical methods of potential field interpretation fail or are very constraining. However, work is still ongoing to try to better and fully characterize the causative sources of the potential fields