In this paper, we design and develop a new software tool that helps automatic lithofacies segmentation from geological data. Lithofacies is a crucial problem in reservoir characterization, and our study intends to prove that soft computing techniques like Wavelet transform modulus maxima lines (WTMM) and Detrended fluctuation analysis (DFA) approaches allow a geological lithology segmentation from differed well logging. On one hand, WTMM proves to be useful for delimitation of each layer. We based on its sensitivity on the presence of more than one layer, On the other hand, DFA is used to enhance the estimation if the roughness coefficient of each lithology. We use them jointly to segment the lithofacies of boreholes located in the Algerian Sahara. Obtained results are encouraging to publish this method, because the principal benefit is economic.

The delimitation of the very fines lithologies from seismic data is a crucial problem in geophysics, indeed the presence of the noise in seismic traces can deteriorate information and hide important hydrocarbons accumulations. For that we have to try in this paper to use a recent technique developed by A.Arneodo and his collaborators which is the wavelet transform modulus maxima lines (WTMM) combined with the discrete wavelet transform (DWT), to denoising traces and characterize each amplitude in the seismic trace by an exponent of Holder. In order to separate information that is of a significant geological lithology variation with the various noises. Our application at VSP data shows that this technique is a powerful tool of processing.

Since twenty years wavelet transform was recognized as a privileged tool for analysis of the fractals objects. We exploited the self-similarity of the wavelet transform to detect singularity which is a fractal signal characterization .In a first part, we use the wavelet transform modulus maxima lines (WTMM) as a tool for analysis of synthetics fractals signals. In second part we applied this technique to the data of wells located in the Algerian Sahara, we proposed then an automatic algorithm of segmentation which is applied thereafter to a simple resolution well-logs data another with a high resolution. We demonstrate the potentialities of the method in the segmentation of different geological formations. We finalize this work by planning a multilayer perceptron neuronal machine. We used the precedents results as information able to detect the lithology and the nature of the pores fluid . Keywords: Wavelet transform, WTMM, Segmentation, simple resolution, High resolution.

Made available in DSpace on 2015-03-03T15:16:22Z (GMT). No. of bitstreams: 1 EdemersonSBM.pdf: 812078 bytes, checksum: 167690407a20b9462083f00be2b0a159 (MD5) Previous issue date: 2007-12-28 / Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico / In this work, the study of some complex systems is done with use of two distinct procedures. In the first part, we have studied the usage of Wavelet transform on analysis and characterization of (multi)fractal time series. We have test the reliability of Wavelet Transform Modulus Maxima method (WTMM) in respect to the multifractal formalism, trough the calculation of the singularity spectrum of time series whose fractality is well known a priori. Next, we have use the Wavelet Transform Modulus Maxima method to study the fractality of lungs crackles sounds, a biological time series. Since the crackles sounds are due to the opening of a pulmonary airway bronchi, bronchioles and alveoli which was initially closed, we can get information on the phenomenon of the airway opening cascade of the whole lung. Once this phenomenon is associated with the pulmonar tree architecture, which displays fractal geometry, the analysis and fractal characterization of this noise may provide us with important parameters for comparison between healthy lungs and those affected by disorders that affect the geometry of the tree lung, such as the obstructive and parenchymal degenerative diseases, which occurs, for example, in pulmonary emphysema. In the second part, we study a site percolation model for square lattices, where the percolating cluster grows governed by a control rule, corresponding to a method of automatic search. In this model of percolation, which have characteristics of self-organized criticality, the method does not use the automated search on Leaths algorithm. It uses the following control rule: pt+1 = pt + k(Rc ? Rt), where p is the probability of percolation, k is a kinetic parameter where 0 < k < 1 and R is the fraction of percolating finite square lattices with side L, LxL. This rule provides a time series corresponding to the dynamical evolution of the system, in particular the likelihood of percolation p. We proceed an analysis of scaling of the signal obtained in this way. The model used here enables the study of the automatic search method used for site percolation in square lattices, evaluating the dynamics of their parameters when the system goes to the critical point. It shows that the scaling of , the time elapsed until the system reaches the critical point, and tcor, the time required for the system loses its correlations, are both inversely proportional to k, the kinetic parameter of the control rule. We verify yet that the system has two different time scales after: one in which the system shows noise of type 1 f , indicating to be strongly correlated. Another in which it shows white noise, indicating that the correlation is lost. For large intervals of time the dynamics of the system shows ergodicity / Neste trabalho, o estudo de alguns sistemas complexos ? feito com a utiliza??o de dois procedimentos distintos. Na primeira parte, estudamos a utiliza??o da transformada Wavelet na an?lise e caracteriza??o (multi)fractal de s?ries temporais. Testamos a confiabilidade do M?todo do M?ximo do M?dulo da Transformada Wavelet (MMTW) com rela??o ao formalismo multifractal, por meio da obten??o do espectro de singularidade de s?ries temporais cuja fractalidade ? bem conhecida a priori. A seguir, usamos o m?todo do m?ximo do m?dulo da transformada wavelet para estudar a fractalidade dos ru?dos de crepita??o pulmonar, uma s?rie temporal biol?gica. Uma vez que a crepita??o pulmonar se d? no momento da abertura de uma via a?rea ? br?nquios, bronqu?olos e alv?olos ? que estava inicialmente fechada, podemos obter informa??es sobre o fen?meno de abertura em cascata das vias a?reas de todo o pulm?o. Como este fen?meno est? associado ? arquitetura da ?rvore pulmonar, a qual apresenta geometria fractal, a an?lise e caracteriza??o da fractalidade desse ru?do pode nos fornecer importantes par?metros de compara??o entre pulm?es sadios e aqueles acometidos por patologias que alteram a geometria da ?rvore pulmonar, tais como as doen?as obstrutivas e as de degenera??o parenquimatosa, que ocorre, por exemplo, no enfisema pulmonar. Na segunda parte, estudamos um modelo de percola??o por s?tios em rede quadrada, onde o aglomerado de percola??o cresce governado por uma regra de controle, correspondendo a um m?todo de busca autom?tica. Neste modelo de percola??o, que apresenta caracter?sticas de criticalidade auto-organizada, o m?todo de busca autom?tica n?o usa o algoritmo de Leath. Usa-se a seguinte regra de controle: pt+1 = pt +k(Rc ?Rt), onde p ? a probabilidade de percola??o, k ? um par?metro cin?tico onde 0 < k < 1 e R ? a fra??o de redes quadradas finitas de lado L, LxL, percolantes. Esta regra fornece uma s?rie temporal correspondente ? evolu??o din?mica do sistema, em especial da probabilidade de percola??o p. ? feita uma an?lise de escalas do sinal assim obtido. O modelo aqui utilizado permite que o m?todo de busca autom?tica para a percola??o por s?tios em rede quadrada seja, per si, estudado, avaliando-se a din?mica dos seus par?metros quando o sistema se aproxima do ponto cr?tico. Verifica-se que os escalonamentos de ?, o tempo decorrido at? que o sistema chegue ao ponto cr?tico, e de tcor, o tempo necess?rio para que o sistema perca suas correla??es, s?o, ambos, inversamente proporcionais a k, o par?metro cin?tico da regra de controle. Verifica-se ainda que o sistema apresenta duas escalas temporais distintas depois de ? : uma em que o sistema mostra ru?do do tipo 1 f? , indicando ser fortemente correlacionado; outra em que aparece um ru?do branco, indicando que se perdeu a correla??o. Para grandes intervalos de tempo a din?mica do sistema mostra que ele se comporta como um sistema erg?dico