Prédiction des Paramètres Physiques des Couches Pétrolifères par Analyse des Réseaux de Neurones et Analyse Faciologique.

Characterization of the shaly sand reservoirs by well log data is a practical way of reservoirdescriptions in the oil fields. During the last few years several studies were conducted in thefield of petroleum engineering by applying artificial intelligence. This work represents apetrophysical-based method that uses well loggings and core plug data to predict well log datarecorded at depth in shaly sand reservoir of the Triassic Formation in Hassi R’Mel field(Algerian Sahara). In the study of oil reservoirs, the prediction of absolute permeability is afundamental key in reservoir descriptions which has a direct impact on, amongst others,effective completion designs, successful water injection programs and more efficient reservoirmanagement. The Triassic Formations of the Hassi R’Mel field are composed of sandstonesand shaly sand with dolomite. Logs from the 10 wells are the starting point for the reservoircharacterization. This work presents a hybrid neuro-fuzzy model based on the use of well logdata in porosity and permeability estimation. A fuzzy logic approach is used to calibrate thecalculated permeability and core permeability and neural network was developed in thismodel based on data available in the field. Fuzzy analysis is based on fuzzy logic and is usedto get the best related well logs with core porosity and permeability data. Neural network isused as a nonlinear regression method to develop transformation between the selected welllogs and core measurements. Porosity and permeability are predicted in these wells using thelinear regression and multilayer perceptron models are constructed. Their reliabilities arecompared using regression coefficients for predictions in uncored sections. This method ofintelligent technique is used as a powerful tool for reservoir properties estimation from welllogs in oil and natural gas development projects.La caractérisation des réservoirs argilo-gréseux par les données de diagraphies est un moyenpratique de la description des réservoirs dans les champs pétroliers. Au cours des dernièresannées, plusieurs études ont été menées dans le domaine de l'ingénierie pétrolière enappliquant l'intelligence artificielle. Ce travail représente une méthode basée sur lapétrophysique qui utilise des diagraphies de puits et des données de modules de base pourprédire et enregistrer les données en profondeur dans les réservoirs argilo-gréseux de laformation du Trias dans le champ de Hassi R'Mel (Sahara algérien).Dans l'étude des gisements de pétrole, la prédiction de la perméabilité absolue et de laporosité est un élément fondamental dans les descriptions de réservoirs ayant un impact directsur les autres paramètres pétrophysiques, les programmes d'injection d'eau et la bonne gestionde réservoir d’une manière plus efficace. Les formations du Trias du champ de Hassi R’Melsont composées de grès et de sable schisteux avec de la dolomie. Les enregistrementsdiagraphiques de 10 puits de ce champ sont le point de départ pour la caractérisation de sonréservoir. Ce travail présente un modèle hybride "neuro-fuzzy" basé sur l'utilisation desdonnées de diagraphies pour l’estimation de la porosité et de la perméabilité. Une approche dela logique floue (fuzzy logic) est utilisée pour comparer la perméabilité carotte et laperméabilité calculée à partir des réseaux de neurones ainsi que celles de la porosité,développées dans ce modèle sur la base des données disponibles au niveau des puits. Lalogique floue est utilisée pour le choix des meilleurs rapports de forage associés à la porositéet la base de données de perméabilité. Le réseau neuronal est utilisé comme méthode derégression non linéaire pour développer une transformation entre diagraphies de puitssélectionnés et mesures de porosité et de perméabilité. Cette technique de méthodeintelligente est utilisée comme un outil puissant pour l’estimation des propriétés des réservoirsd’après les paramètres diagraphiques et dans les projets de développement pétrolier et de gaznature

Characterization and estimation of gas-bearing properties of Devonian coals using well log data from five Illizi Basin wells (Algeria)

     In Algeria, wells drilled in the Illizi Basin suggest the presence of a significant areal trend of Devonian coal seams with the thickest coal seams penetrated in the Lower Devonian stratigraphic unit F6. This makes them some of the oldest thick coal seams encountered. These coals exist between approximately 1500 and 4000 meters below surface. In particular, numerous coals in these formations drilled in the Oudoume field have recorded gas shows while drilling. A study of basic well log data from five wells penetrating Illizi Basin coals is conducted to characterize their distribution and provisionally evaluate their gas-bearing potential using petrophysical analysis coupled with machine learning. A simple multi-layer perceptron model (one hidden layer with four nodes) is used in a novel way to replicate estimates of gas saturation in the coal samples calculated approximately with the modified Kim equation. It does so by considering three commonly measured well-log variables: gamma ray, sonic travel time, deep resistivity (307 data records from the five wells studied). The log-calculated approximations (modified Kim equation) can be matched to better than plus or minus 1 scf/ton by the multi-layer perceptron model. The results and analysis presented provide preliminary encouragement that suggests the presence of a potentially extensive gas-bearing Devonian coal trend in the Illizi Basin that is worthy of further exploration. Future work is required to integrate data from additional wells and laboratory analysis of core samples to verify the extent of that coal trend and to quantify its gas concentrations.Cited as: Baouche, R., Wood, D.A. Characterization and estimation of gas-bearing properties of Devonian coals using well log data from five Illizi Basin wells (Algeria). Advances in Geo-Energy Research, 2020, 4(4), 356-371, doi: 10.46690/ager.2020.04.0

Prédiction des Paramètres Physiques des Couches Pétrolifères par Analyse des Réseaux de Neurones et Analyse Faciologique.

Characterization of the shaly sand reservoirs by well log data is a practical way of reservoirdescriptions in the oil fields. During the last few years several studies were conducted in thefield of petroleum engineering by applying artificial intelligence. This work represents apetrophysical-based method that uses well loggings and core plug data to predict well log datarecorded at depth in shaly sand reservoir of the Triassic Formation in Hassi R’Mel field(Algerian Sahara). In the study of oil reservoirs, the prediction of absolute permeability is afundamental key in reservoir descriptions which has a direct impact on, amongst others,effective completion designs, successful water injection programs and more efficient reservoirmanagement. The Triassic Formations of the Hassi R’Mel field are composed of sandstonesand shaly sand with dolomite. Logs from the 10 wells are the starting point for the reservoircharacterization. This work presents a hybrid neuro-fuzzy model based on the use of well logdata in porosity and permeability estimation. A fuzzy logic approach is used to calibrate thecalculated permeability and core permeability and neural network was developed in thismodel based on data available in the field. Fuzzy analysis is based on fuzzy logic and is usedto get the best related well logs with core porosity and permeability data. Neural network isused as a nonlinear regression method to develop transformation between the selected welllogs and core measurements. Porosity and permeability are predicted in these wells using thelinear regression and multilayer perceptron models are constructed. Their reliabilities arecompared using regression coefficients for predictions in uncored sections. This method ofintelligent technique is used as a powerful tool for reservoir properties estimation from welllogs in oil and natural gas development projects.La caractérisation des réservoirs argilo-gréseux par les données de diagraphies est un moyenpratique de la description des réservoirs dans les champs pétroliers. Au cours des dernièresannées, plusieurs études ont été menées dans le domaine de l'ingénierie pétrolière enappliquant l'intelligence artificielle. Ce travail représente une méthode basée sur lapétrophysique qui utilise des diagraphies de puits et des données de modules de base pourprédire et enregistrer les données en profondeur dans les réservoirs argilo-gréseux de laformation du Trias dans le champ de Hassi R'Mel (Sahara algérien).Dans l'étude des gisements de pétrole, la prédiction de la perméabilité absolue et de laporosité est un élément fondamental dans les descriptions de réservoirs ayant un impact directsur les autres paramètres pétrophysiques, les programmes d'injection d'eau et la bonne gestionde réservoir d’une manière plus efficace. Les formations du Trias du champ de Hassi R’Melsont composées de grès et de sable schisteux avec de la dolomie. Les enregistrementsdiagraphiques de 10 puits de ce champ sont le point de départ pour la caractérisation de sonréservoir. Ce travail présente un modèle hybride "neuro-fuzzy" basé sur l'utilisation desdonnées de diagraphies pour l’estimation de la porosité et de la perméabilité. Une approche dela logique floue (fuzzy logic) est utilisée pour comparer la perméabilité carotte et laperméabilité calculée à partir des réseaux de neurones ainsi que celles de la porosité,développées dans ce modèle sur la base des données disponibles au niveau des puits. Lalogique floue est utilisée pour le choix des meilleurs rapports de forage associés à la porositéet la base de données de perméabilité. Le réseau neuronal est utilisé comme méthode derégression non linéaire pour développer une transformation entre diagraphies de puitssélectionnés et mesures de porosité et de perméabilité. Cette technique de méthodeintelligente est utilisée comme un outil puissant pour l’estimation des propriétés des réservoirsd’après les paramètres diagraphiques et dans les projets de développement pétrolier et de gaznature

Prédiction des Paramètres Physiques des Couches Pétrolifères par Analyse des Réseaux de Neurones et Analyse Faciologique.

Characterization of the shaly sand reservoirs by well log data is a practical way of reservoirdescriptions in the oil fields. During the last few years several studies were conducted in thefield of petroleum engineering by applying artificial intelligence. This work represents apetrophysical-based method that uses well loggings and core plug data to predict well log datarecorded at depth in shaly sand reservoir of the Triassic Formation in Hassi R’Mel field(Algerian Sahara). In the study of oil reservoirs, the prediction of absolute permeability is afundamental key in reservoir descriptions which has a direct impact on, amongst others,effective completion designs, successful water injection programs and more efficient reservoirmanagement. The Triassic Formations of the Hassi R’Mel field are composed of sandstonesand shaly sand with dolomite. Logs from the 10 wells are the starting point for the reservoircharacterization. This work presents a hybrid neuro-fuzzy model based on the use of well logdata in porosity and permeability estimation. A fuzzy logic approach is used to calibrate thecalculated permeability and core permeability and neural network was developed in thismodel based on data available in the field. Fuzzy analysis is based on fuzzy logic and is usedto get the best related well logs with core porosity and permeability data. Neural network isused as a nonlinear regression method to develop transformation between the selected welllogs and core measurements. Porosity and permeability are predicted in these wells using thelinear regression and multilayer perceptron models are constructed. Their reliabilities arecompared using regression coefficients for predictions in uncored sections. This method ofintelligent technique is used as a powerful tool for reservoir properties estimation from welllogs in oil and natural gas development projects.La caractérisation des réservoirs argilo-gréseux par les données de diagraphies est un moyenpratique de la description des réservoirs dans les champs pétroliers. Au cours des dernièresannées, plusieurs études ont été menées dans le domaine de l'ingénierie pétrolière enappliquant l'intelligence artificielle. Ce travail représente une méthode basée sur lapétrophysique qui utilise des diagraphies de puits et des données de modules de base pourprédire et enregistrer les données en profondeur dans les réservoirs argilo-gréseux de laformation du Trias dans le champ de Hassi R'Mel (Sahara algérien).Dans l'étude des gisements de pétrole, la prédiction de la perméabilité absolue et de laporosité est un élément fondamental dans les descriptions de réservoirs ayant un impact directsur les autres paramètres pétrophysiques, les programmes d'injection d'eau et la bonne gestionde réservoir d’une manière plus efficace. Les formations du Trias du champ de Hassi R’Melsont composées de grès et de sable schisteux avec de la dolomie. Les enregistrementsdiagraphiques de 10 puits de ce champ sont le point de départ pour la caractérisation de sonréservoir. Ce travail présente un modèle hybride "neuro-fuzzy" basé sur l'utilisation desdonnées de diagraphies pour l’estimation de la porosité et de la perméabilité. Une approche dela logique floue (fuzzy logic) est utilisée pour comparer la perméabilité carotte et laperméabilité calculée à partir des réseaux de neurones ainsi que celles de la porosité,développées dans ce modèle sur la base des données disponibles au niveau des puits. Lalogique floue est utilisée pour le choix des meilleurs rapports de forage associés à la porositéet la base de données de perméabilité. Le réseau neuronal est utilisé comme méthode derégression non linéaire pour développer une transformation entre diagraphies de puitssélectionnés et mesures de porosité et de perméabilité. Cette technique de méthodeintelligente est utilisée comme un outil puissant pour l’estimation des propriétés des réservoirsd’après les paramètres diagraphiques et dans les projets de développement pétrolier et de gaznature

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

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

Intelligent methods for predicting Nuclear Magnetic Resonance of porosity and permeability by conventional well-logs: case study of Saharan Field

International audienceIn the well-log data processing, the principal advantage of the nuclear magnetic resonance (NMR) method is the measurement of fluid volume and pore size distribution without resorting to parameters such as rock resistivity. Preliminary processing of the well-log data allowed first to have the petrophysical parameters and then to evaluate the performances of the transverse relaxation time T 2 NMR. Petrophysical parameters such as the porosity of the formation as well as the effective permeability can be estimated without having recourse the fluid type. The well-log data of five wells were completed during the construction of intelligent models in the Saharan oil field Oued Mya Basin in order to assess the reliability of the developed models. Data processing of NMR combined with conventional well data was performed by artificial intelligence. First, the support vector regression method was applied to a sandy clay reservoir with a model based on the prediction of porosity and permeability. NMR parameters estimated using intelligent systems, i.e., fuzzy logic (FL) model, back propagation neural network (BP-NN), and support vector machine, with conventional well-log data are combined with those of NMR, resulting in a good estimation of porosity and permeability. The results obtained during the processing are then compared to the FL and NN regression models performed by the regression method during the validation stage. They show that the correlation coefficients R 2 estimated vary between 0.959 and 0.964, corresponding to the root mean square error values of 0.20 and 0.15

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