Generalized dynamical fuzzy model for identification and prediction

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 result

Effect of stress-strain conditions on physical precursors and failure stages development in rock samples

Precursory stages of failure development in large rock samples were studied and simultaneous observations of the space-time variation of several physical fields were carried out under different stress-strain states. The failure process was studied in detail. A hierarchical structure of discreet rock medium was obtained after loading. It was found that the moisture reduced the rock strength, increased the microcrack distribution and influenced the shape of the failure physical precursors. The rise in temperature up to 400 °C affected the physical precursors at the intermediate and final stages of the failure. Significant variations were detected in the acoustic and electromagnetic emissions. The coalescence criterion was slightly depending on the rock moisture and temperature effect. The possibility of identifying the precursory stage of failure at different strain conditions by means of a complex parameter derived from the convolution of physical recorded data is shown. The obtained results point out the efficiency of the laboratory modelling of seismic processe

Prediction of permeability and porosity from well log data using the nonparametric regression with multivariate analysis and neural network, Hassi R’Mel Field, Algeria

AbstractMost commonly, to estimate permeability, we can use values of porosity, pore size distribution, and water saturation from logging data and established correlations. One benefit of using wireline log data to estimate permeability is that it can provide a continuous permeability profile throughout a particular interval.This study will focus on the evaluation of formation permeability for a sandstone reservoir in the reservoir formations of Hassi R’Mel Field Southern from well log data using the multivariate methods. In order to improve the permeability estimation in these reservoirs, several statistical regression techniques have already been tested in previous work to correlate permeability with different well logs. It has been shown that statistical regression for data correlation is quite promising. We propose a two-step approach to permeability prediction that utilizes non-parametric regression in conjunction with multivariate statistical analysis. First we classify the well log data into electrofacies types. A combination of principal component analysis, model-based cluster analysis and discriminant analysis is used to characterize and identify electrofacies types. Second, we apply non-parametric regression techniques to predict permeability using well logs within each electrofacies. Three non-parametric approaches are examined via alternating conditional expectations (ACE), generalized additive model (GAM) and neural networks (NNET) and the relative advantages and disadvantages are explored. The results are compared with three other approaches to permeability predictions that utilize data partitioning based on reservoir layering, lithofacies information and hydraulic flow units. An examination of the error rates associated with discriminant analysis for uncored wells indicates that data classification based on electrofacies characterization is more robust compared to other approaches.These methods are tested and compared at the heterogeneous reservoirs in Triassic formations of Hassi R’Mel. The results show that permeability prediction is improved by applying variable selection to non-parametric regression ACE while tree regression is unable to predict permeability.In comparing the relative predictive performance of the three regression methods, the alternating conditional expectations with ACE method appears to outperform the other two methods

Regularities in discrete hierarchy seismo-acoustic mode in a geophysical field

Some regularities in seismo acoustic mode have been studied during the preparation and development of dynamic events generated during the deformation at different scales in a geophysical field. The time-space behavior of certain auto similarity parameters: the slope of the recurrence plot c, the fractal dimension of the hypocenters set D, the relationship D-3c and the crack concentration parameter Ksr in laboratory and field experiments in Algerian seismoactive zone have been analyzed as well. Precursory stage and local failure evolution in rock samples and in natural conditions were studied. It is shown that the regularities in the behavior of the parameters under study do not qualitatively depend on the dimension of the object being loaded. The quoted examples above speak of inhomogeneity and self-similarity of seismicity distribution in space-time. This suggests that the evolution of cracking process at different scales, from rock samples to Earth crust, is controlled by the same physical mechanism. The further development of these studies consolidates the physical basis of the prediction of the dynamic events

Neuro-fuzzy system to predict permeability and porosity from well log data: A case study of Hassi R׳Mel gas field, Algeria

International audienceCharacterization of shaly sand reservoirs by well log data is a usual way of describing oil/gas field reservoirs. Over the last few years, several studies have been conducted in the field of petroleum engineering by applying artificial intelligence. This work represents a petrophysical-based method that uses well logs and core plug data to predict well log data recorded at depth in a shaly sand reservoir of Triassic Formation in Hassi R׳Mel field, Algeria. In the study of oil reservoirs, the prediction of absolute permeability is a fundamental key in reservoir descriptions and has a direct impact, in particular, on effective completion designs, successful water injection programs and more efficient reservoir management. The Triassic Formations of Hassi R׳Mel fields are composed of sandstones and shaly sands with dolomites. Logs from 10 wells are the starting point for the reservoir characterization. This paper presents a hybrid neuro-fuzzy model based on the use of data from four wells regarding porosity and permeability estimation. A fuzzy logic approach is used to calibrate the calculated permeability and core permeability; and a neural network was developed in this model, based on the data available from the field. Fuzzy analysis is based on fuzzy logic and is used to choose the best well logs with regard to core porosity and permeability data. A neural network is used as a nonlinear regression method to develop transformation between the selected well logs and core measurements. Porosity and permeability are predicted in these wells through linear regression; and back-propagation models are constructed and their reliabilities are compared according to the regression coefficients for predictions in un-cored sections. This investigative hybrid neuro-fuzzy method becomes a powerful tool for the estimation of reservoir properties from well logs in oil and natural gas development projects

Neuro-fuzzy system to predict permeability and porosity from well log data: A case study of Hassi R׳Mel gas field, Algeria

International audienceCharacterization of shaly sand reservoirs by well log data is a usual way of describing oil/gas field reservoirs. Over the last few years, several studies have been conducted in the field of petroleum engineering by applying artificial intelligence. This work represents a petrophysical-based method that uses well logs and core plug data to predict well log data recorded at depth in a shaly sand reservoir of Triassic Formation in Hassi R׳Mel field, Algeria. In the study of oil reservoirs, the prediction of absolute permeability is a fundamental key in reservoir descriptions and has a direct impact, in particular, on effective completion designs, successful water injection programs and more efficient reservoir management. The Triassic Formations of Hassi R׳Mel fields are composed of sandstones and shaly sands with dolomites. Logs from 10 wells are the starting point for the reservoir characterization. This paper presents a hybrid neuro-fuzzy model based on the use of data from four wells regarding porosity and permeability estimation. A fuzzy logic approach is used to calibrate the calculated permeability and core permeability; and a neural network was developed in this model, based on the data available from the field. Fuzzy analysis is based on fuzzy logic and is used to choose the best well logs with regard to core porosity and permeability data. A neural network is used as a nonlinear regression method to develop transformation between the selected well logs and core measurements. Porosity and permeability are predicted in these wells through linear regression; and back-propagation models are constructed and their reliabilities are compared according to the regression coefficients for predictions in un-cored sections. This investigative hybrid neuro-fuzzy method becomes a powerful tool for the estimation of reservoir properties from well logs in oil and natural gas development projects

Probabilistic model to forecast earthquakes in the Zemmouri (Algeria) seismoactive area on the basis of moment magnitude scale distribution functions

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 give

ETUDE DE LA RELATION ENTRE LA SUSCEPTIBILITE MAGNETIQUE ET LES PARAMETRES PETROPHYSIQUES DANS LE RESERVOIR DEQUARTZITES DU HAMRA (SUD-OUEST DU CHAMP DE HASSI MESSAOUD)

International audienceCe travail rentre dans le cadre de la caractérisation des réservoirs non conventionnels, parmi lesquels seclassent les réservoirs compacts. Ces réservoirs posent de grands problèmes du point de vue exploitation àcause de la faible porosité et perméabilité. Les enregistrements diagraphiques dans les puits étudiésmontrent une saturation en hydrocarbure considérable, mais les résultats des tests de puits sont négatifs, cequi nécessite une caractérisation détaillée pour les exploiter de la meilleure façon possible.Nous focaliserons notre étude sur la formation des quartzites de Hamra située dans le sud-ouest du champde Hassi Messaoud, pour trouver une relation entre la susceptibilité magnétique et les paramètrespétrophysiques mesurés dans le puits en utilisant l’analyse des composantes principales et les réseaux deneurones artificiels. Ces techniques combinées sont utilisées pour la première fois pour étudier ce réservoirfracturé en Algérie.L’analyse des composantes principales montre que le premier axe explique une totalité des variances dedonnées de 37.54%. Cet axe représente pratiquement la porosité neutron et le gamma ray. La deuxièmecomposante principale explique une variance de 30.76%. Elle contient principalement la saturation enhydrocarbure qui est exprimée dans cet axe par un coefficient de corrélation égale à 0.729. La troisièmecomposante est reliée à la susceptibilité magnétique.Le meilleur résultat dans la phase de test du réseau de neurones artificiel est trouvé avec 25 neurones dansla couche cachée. Les valeurs de l’erreur quadratique moyenne et le coefficient de corrélation entre lesdonnées mesurées et prédites sont respectivement, 0.014 et 0.90. Ces résultats confirment que le réseau deneurones artificiel estime la susceptibilité magnétique à partir des données pétrophysiques, avec une forteprécision par rapport aux données mesurées

ETUDE DE LA RELATION ENTRE LA SUSCEPTIBILITE MAGNETIQUE ET LES PARAMETRES PETROPHYSIQUES DANS LE RESERVOIR DEQUARTZITES DU HAMRA (SUD-OUEST DU CHAMP DE HASSI MESSAOUD)

International audienceCe travail rentre dans le cadre de la caractérisation des réservoirs non conventionnels, parmi lesquels seclassent les réservoirs compacts. Ces réservoirs posent de grands problèmes du point de vue exploitation àcause de la faible porosité et perméabilité. Les enregistrements diagraphiques dans les puits étudiésmontrent une saturation en hydrocarbure considérable, mais les résultats des tests de puits sont négatifs, cequi nécessite une caractérisation détaillée pour les exploiter de la meilleure façon possible.Nous focaliserons notre étude sur la formation des quartzites de Hamra située dans le sud-ouest du champde Hassi Messaoud, pour trouver une relation entre la susceptibilité magnétique et les paramètrespétrophysiques mesurés dans le puits en utilisant l’analyse des composantes principales et les réseaux deneurones artificiels. Ces techniques combinées sont utilisées pour la première fois pour étudier ce réservoirfracturé en Algérie.L’analyse des composantes principales montre que le premier axe explique une totalité des variances dedonnées de 37.54%. Cet axe représente pratiquement la porosité neutron et le gamma ray. La deuxièmecomposante principale explique une variance de 30.76%. Elle contient principalement la saturation enhydrocarbure qui est exprimée dans cet axe par un coefficient de corrélation égale à 0.729. La troisièmecomposante est reliée à la susceptibilité magnétique.Le meilleur résultat dans la phase de test du réseau de neurones artificiel est trouvé avec 25 neurones dansla couche cachée. Les valeurs de l’erreur quadratique moyenne et le coefficient de corrélation entre lesdonnées mesurées et prédites sont respectivement, 0.014 et 0.90. Ces résultats confirment que le réseau deneurones artificiel estime la susceptibilité magnétique à partir des données pétrophysiques, avec une forteprécision par rapport aux données mesurées