Browsing by Author "Nait Amar, Menad"

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Adaptive surrogate modeling with evolutionary algorithm for well placement optimization in fractured reservoirs
(Elsevier, 2019) Redouane, Kheireddine; Zeraibi, Noureddine; Nait Amar, Menad
Well placement optimization is a decisive task for the reliable design of field development plans. The use of optimization routines coupled to reservoir simulation models as an automatic tool is a popular practice, which could improve the decision-making process on well placement problems. However, despite the various automatic techniques developed, there is still a lack of robust computer-added optimization tool, which can solve the well placement problem with high accuracy in reasonable time while handling the technical constraints properly. In this paper, a hybrid intelligent system is proposed to deal with a real well placement problem with arbitrary well trajectories, complex model grids, and linear and nonlinear constraints. In this intelligent approach, a Genetic Algorithm (GA) combined with a hybrid constraint-handling strategy is applied in conjunction with a constrained space-filling sampling design, Gaussian Process (GP) surrogate model, and one proposed adaptive sampling routine. This self-adaptive framework allows to consecutively augment the quality of surrogate, enhance the accuracy of the process, and thus guide the optimization rapidly into the optimal solution. To demonstrate the efficiency of the developed method, a full-field reservoir case is considered. This case covers a real well placement project in a fractured unconventional reservoir of El Gassi, which is a mature field located in Hassi-Massoud, Algeria. The obtained results highlighted the effectiveness of the proposed approach for solving the real well placement problem with high accuracy in reasonable CPU-time. These auspicious features make it a reliable tool to be used on other real optimization projects
Application of gene expression programming for predicting density of binary and ternary mixtures of ionic liquids and molecular solvents
(ELSEVIER, 2020) Nait Amar, Menad; Ghriga, Mohammed Abdelfetah; Hemmati-Sarapardeh, Abdolhossein
Ionic Liquids (ILs) have received increased attention across a number of disciplines in recent years. This noticeable importance of ILs is attributed to their attractive proprieties. Precise evaluation of the thermophysical properties of ionic liquids and their mixtures with molecular solvents is essential for distinct multidisciplinary applications. In this study, a rigorous white-box intelligent technique, viz. gene expression programming (GEP) was implemented for establishing new correlations for accurate prediction of density of binary and ternary mixtures of ILs and molecular solvents. The newly suggested correlations were developed using a comprehensive experimental database with 1985 real measurements under a variety of operational conditions. The obtained results revealed that the newly established GEP-based correlations can predict the density of binary and ternary mixtures of ILs and molecular solvents with a high degree of integrity. The GEP-based correlations exhibited overall average absolute relative error (AARE) values of 0.5621% and 0.2128% for binary and ternary cases, respectively. Besides, it was found that our proposed explicit correlations followed the expected tendency with respect to the considered variables. Furthermore, the superiority and the reliability of the GEP-based correlations was testified against the best-existing approaches in the literature. Finally, the leverage approach was performed and the statistical validity of the correlations and the experimental data was testified.
Automated design of a new integrated intelligent computing paradigm for constructing a constitutive model applicable to predicting rock fractures
(Elsevier, 2020) Peng, Kang; Nait Amar, Menad; Ouaer, Hocine; Motahari, Mohammed Reza; Hasanipanah, Mahdi
Making a relation between strains and stresses is an important subject in the rock engineering field. Shear behaviors of rock fractures have been extensively investigated by different researchers. Literature mostly consists of constitutive models in the form of empirical functions that represent experimental data using mathematical regression techniques. As an alternative, this study aims to present a new integrated intelligent computing paradigm to form a constitutive model applicable to rock fractures. To this end, an RBFNN-GWO model is presented, which integrates the radial basis function neural network (RBFNN) with grey wolf optimization (GWO). In the proposed model, the hyperparameters and weights of RBFNN were tuned using the GWO algorithm. The efficiency of the designed RBFNN-GWO was examined comparing it with the RBFNN-GA model (a combination of RBFNN and the Genetic Algorithm). The proposed models were trained based on the results of a systematic set of 84 direct shear tests gathered from the literature. The finding of the current study demonstrated the efficiency of both the RBFNN-GA and RBFNN-GWO models in predicting the dilation angle, peak shear displacement, and stress as the rock fracture properties. Among the two models proposed in this study, the statistical results revealed the superiority of RBFNN-GWO over RBFNN-GA in terms of prediction accuracy
Bottom hole pressure estimation using hybridization neural networks and grey wolves optimization
(KeAi, 2018) Nait Amar, Menad; Zeraibi, Noureddine; Redouane, Kheireddine
An effective design and optimum production strategies of a well depend on the accurate prediction of its bottom hole pressure (BHP) which may be calculated or determined by several methods. However, it is not practical technically or economically to apply for a well test or to deploy a permanent pressure gauge in the bottom hole to predict the BHP. Consequently, several correlations and mechanistic models based on the known surface measurements have been developed. Unfortunately, all these tools (correlations & mechanistic models) are limited to some conditions and intervals of application. Therefore, establish a global model that ensures a large coverage of conditions with a reduced cost and high accuracy becomes a necessity. In this study, we propose new models for estimating bottom hole pressure of vertical wells with multiphase flow. First, Artificial Neural Network (ANN) based on back propagation training (BP-ANN) with 12 neurons in its hidden layer is established using trial and error. The next methods correspond to optimized or evolved neural networks (optimize the weights and thresholds of the neural networks) with Grey Wolves Optimization (GWO), and then its accuracy to reach the global optima is compared with 2 other naturally inspired algorithms which are the most used in the optimization field: Genetic Algorithm (GA) and Particle Swarms Optimization (PSO). The models were developed and tested using 100 field data collected from Algerian fields and covering a wide range of variables. The obtained results demonstrate the superiority of the hybridization ANN-GWO compared with the 2 other hybridizations or with the BP learning alone. Furthermore, the evolved neural networks with these global optimization algorithms are strongly shown to be highly effective to improve the performance of the neural networks to estimate flowing BHP over existing approaches and correlations
An efficient methodology for multi-objective optimization of water alternating CO2 EOR process
(Elsevier, 2019) Nait Amar, Menad; Zeraibi, Noureddine
Elaboration et développement des modèles proxy pour l’étude et l’optimisation de WAG
(2018) Nait Amar, Menad
L’estimation des paramètres optimaux des processus WAG (water alternating gas) est un problème complexe nécessitant un nombre important de simulation numérique qui sont chronophages et couteuses. Le but de cette thèse est d’élaborer des modèles proxy dynamiques robustes basés sur des méthodes d’intelligences artificielles pour substituer les simulateurs numériques dans les études d’optimisation des processus du WAG, tout en palliant le calcul intensif demandé et en gardant la précision recherchée. Vu l’efficacité des proxy et le fait que l’optimisation des paramètres du WAG peut se formuler comme un problème mono ou multi objectif avec ou sans contraintes, l’hybridation de ces proxy avec des algorithmes évolutionnistes et méta heuristiques assure une optimisation adéquate et appréciable de ces paramètres. Les résultats obtenus montrent que les hybridations proposées sont très efficaces, précises et rapides
Evolving support vector regression using Grey Wolf optimization; forecasting the geomechanical properties of rock
(Springer link, 2020) Xu, Chuanhua; Nait Amar, Menad; Ghriga, Mohammed Abdelfetah; Ouaer, Hocine; Zhang, Xiliang; Hasanipanah, Mahdi
The geomechanical properties of rock, including shear strength (SS) and uniaxial compressive strength (UCS), are very important parameters in designing rock structures. To improve the accuracy of SS and UCS prediction, this study presented an evolving support vector regression (SVR) using Grey Wolf optimization (GWO). To examine the feasibility and applicability of the SVR-GWO model, the differential evolution (DE) and artificial bee colony (ABC) algorithms were also used. In other words, the SVR hyperparameters were tuned using the GWO, DE, and ABC algorithms. To implement the proposed models, a comprehensive database accessible in an open-source was used in this study. Finally, the comparative experiments such as root mean square error (RMSE) were conducted to show the superiority of the proposed models. The SVR-GWO model predicted the SS and UCS with RMSE of 0.460 and 3.208, respectively, while, the SVR-DE model predicted the SS and UCS with RMSE of 0.542 and 5.4, respectively. Furthermore, the SVR-ABC model predicted the SS and UCS with RMSE of 0.855 and 5.033, respectively. The aforementioned results clearly exhibited the applicability as well as the usability of the proposed SVR-GWO model in the prediction of both SS and UCS parameters. Accordingly, the SVR-GWO model can be also applied to solving various complex systems, especially in geotechnical and mining fields
Hybrid soft computational approaches for modeling the maximum ultimate bond strength between the corroded steel reinforcement and surrounding concrete
(Springer, 2020) Ben Seghier, Mohamed El Amine; Ouaer, Hocine; Ghriga, Mohammed Abdelfetah; Nait Amar, Menad; Duc-Kien, Thai
The capacity efficiency of load carrying with the accurate serviceability performances of reinforced concrete (RC) structure is an important aspect, which is mainly dependent on the values of the ultimate bond strength between the corroded steel reinforcements and the surrounding concrete. Therefore, the precise determination of the ultimate bond strength degradation is of paramount importance for maintaining the safety levels of RC structures affected by corrosion. In this regard, hybrid intelligence and machine learning techniques are proposed to build a new framework to predict the ultimate bond strength in between the corroded steel reinforcements and the surrounding concrete. The proposed computational techniques include the multilayer perceptron (MLP), the radial basis function neural network and the genetic expression programming methods. In addition to that, the Levenberg–Marquardt (LM) deterministic approach and two meta-heuristic optimization approaches, namely the artificial bee colony algorithm and the particle swarm optimization algorithm, are employed in order to guarantee an optimum selection of the hyper-parameters of the proposed techniques. The latter were implemented based on an experimental published database consisted of 218 experimental tests, which cover various factors related to the ultimate bond strength, such as compressive strength of the concrete, concrete cover, the type steel, steel bar diameter, length of the bond and the level of corrosion. Based on their performance evaluation through several statistical assessment tools, the proposed models were shown to predict the ultimate bond strength accurately; outperforming the existing hybrid artificial intelligence models developed based on the same collected database. More precisely, the MLP-LM model was, by far, the best model with a determination coefficient (R2) as high as 0.97 and 0.96 for the training and the overall data, respectively.
Modeling temperature-based oil-water relative permeability by integrating advanced intelligent models with grey wolf optimization : application to thermal enhanced oil recovery processes
(Elsevier, 2019) Nait Amar, Menad; Zeraibi, Noureddine; Abdolhossein, Hemmati-Sarapardeh; Shahaboddin, Shamshirband
Modeling viscosity of CO 2 at high temperature and pressure conditions
(Elsevier, 2020) Nait Amar, Menad; Ghriga, Mohammed Abdelfetah; Ouaer, Hocine; Ben Seghier, Mohamed El Amine; Thai Pham, Binh
The present work aims at applying Machine Learning approaches to predict CO2 viscosity at different thermodynamical conditions. Various data-driven techniques including multilayer perceptron (MLP), gene expression programming (GEP) and group method of data handling (GMDH) were implemented using 1124 experimental points covering temperature from 220 to 673 K and pressure from 0.1 to 7960 MPa. Viscosity was modelled as function of temperature and density measured at the stated conditions. Four backpropagation-based techniques were considered in the MLP training phase; Levenberg-Marquardt (LM), bayesian regularization (BR), scaled conjugate gradient (SCG) and resilient backpropagation (RB). MLP-LM was the most fit of the proposed models with an overall root mean square error (RMSE) of 0.0012 mPa s and coefficient of determination (R2) of 0.9999. A comparison showed that our MLP-LM model outperformed the best preexisting Machine Learning CO2 viscosity models, and that our GEP correlation was superior to preexisting explicit correlations.
Modeling wax disappearance temperature using advanced intelligent frameworks
(American Chemical Society, 2019) Benamara, Chahrazed; Nait Amar, Menad; Gharbi, Kheira; Hamada, Boudjema
The deposition of wax is one of the most potential problems that disturbs the flow assurance during production processes of hydrocarbon fluids. In this study, wax disappearance temperature (WDT) that is recognized as a vital parameter in such circumstances is modeled using advanced machine learning techniques, namely, radial basis function neural network (RBFNN) coupled with genetic algorithm (GA) and artificial bee colony (ABC). Besides, an accurate and user-friendly correlation was established by implementing the group method of data handling. Results revealed the high reliability of the proposed hybrid models and the established correlation. Moreover, RBFNN coupled with ABC (RBFNN-ABC) was found to be the best paradigm with an overall average absolute relative error value of 0.5402% and a total coefficient of determination (R2) of 0.9706. Furthermore, the performance comparison showed that RBFNN-ABC and the established explicit correlation outperform the prior intelligent and thermodynamic models. Finally, by performing the outlier detection, the quality of the utilized database was assessed, the applicability realm of the best model was delineated, and only one point was found as doubtful
Modeling wax disappearance temperature using robust white-box machine learning
(Elsevier Ltd, 2024) Nait Amar, Menad; Zeraibi, Noureddine; Benamara, Chahrazed; Djema, Hakim; Saifi, Redha; Gareche, Mourad
Wax deposition is one of the major operational problems encountered in the upstream petroleum production system. The deposition of this undesirable scale can cause a variety of challenging problems. In order to avoid the latter, numerous parameters associated with the mechanism of wax deposition should be determined precisely. In this study, a new smart correlation was proposed for the accurate prediction of Wax disappearance temperature (WDT) using a robust explicit-based machine learning (ML) approach, namely gene expression programming (GEP). The correlation was developed using comprehensive experimental measurements. The obtained results revealed the promising degree of accuracy of the suggested GEP-based correlations. In this context, the newly-introduced correlations provided excellent statistical metrics (R2 = 0.9647 and AARD = 0.5963 %). Furthermore, performance of the developed correlation outperformed that of many existing approaches for predicting WDT. In addition, the trend analysis performed on the outcomes of the proposed GEP-based correlations divulged their physical validity and consistency. Lastly, the findings of this study provide a promising benefit, as the newly developed correlations can notably improve the adequate estimation of WDT, thus facilitating the simulation of wax deposition-related phenomena. In this context, the proposed correlations can supply the effective management of the production facilities and improvement of project economics since the provided correlation is a simple-to-use decision-making tool for production and chemical engineers engaged in the management of organic deposit-related issues.
On the evaluation of solubility of hydrogen sulfide in ionic liquids using advanced committee machine intelligent systems
(Elsevier, 2021) Nait Amar, Menad; Ghriga, Mohammed Abdelfetah; Ouaer, Hocine
Ionic Liquids (ILs) are increasingly emerging as new innovating green solvents with great importance from academic, industrial, and environmental perspectives. This surge of interest in considering ILs in various applications is owed to their attractive properties. Involvements in the gas sweetening and the reduction of the amounts of sour and acid gasses are among the most promising applications of ILs. In this study, new advanced committee machine intelligent systems (CMIS) were introduced for predicting the solubility of hydrogen sulfide (H2S) in various ILs. The implemented CMIS models were gained by linking robust data-driven techniques, namely multilayer perceptron (MLP) and cascaded forward neural network (CFNN) beneath rigorous schemes using group method of data handling (GMDH) and genetic programming (GP). The proposed paradigms were developed using an extensive database encompassing 1243 measurements of H2S solubility in 33 ILs. The performed comprehensive error investigation revealed that the newly implemented paradigms yielded very satisfactory prediction performance. Besides, it was found that CMIS-GP provided more accurate estimations of H2S solubility in ILs compared with both the other intelligent models and the best-prior paradigms. In this regard, the developed CMIS-GP exhibited overall average absolute relative deviation (AARD) and coefficient of determination (R2) values of 2.3767% and 0.9990, respectively. Lastly, the trend analyses demonstrated that the tendencies of CMIS-GP predictions were in excellent accordance with the real variations of H2S solubility in ILs with respect to pressure and temperature
Optimization of WAG in real geological field using rigorous soft computing techniques and nature-inspired algorithms
(Elsevier, 2021) Nait Amar, Menad; Jahanbani Ghahfarokhi, Ashkan; Ng, Cuthbert Shang Wui; Zeraibi, Noureddine
To meet the ever-increasing global energy demands, it is more necessary than ever to ensure increments in the recovery factors (RF) associated with oil reservoirs. Owing to this challenge, enhanced oil recovery (EOR) techniques are increasingly gaining more significance as robust strategies for producing more oil volumes from mature reservoirs. Water alternating gas (WAG) injection is an EOR method intended at improving the microscopic and macroscopic displacement efficiencies. To handle and implement successfully this technique, it is of vital importance to optimize its operating parameters. This study targeted at implementing robust proxy paradigms for investigating the suitable design parameters of a WAG project applied to real field data from “Gullfaks” in the North Sea. The proxy models aimed at reducing significantly the rum-time related to the commercial simulators without scarifying the accuracy. To this end, machine learning (ML) approaches, including multi-layer perceptron (MLP) and radial basis function neural network (RBFNN) were implemented for estimating the needed parameters for the formulated optimization problem. To improve the reliability of these ML methods, they were evolved using optimization algorithms, namely Levenberg–Marquardt (LM) for MLP, and ant colony optimization (ACO) and grey wolf optimization (GWO) for RBFNN. The performance analysis of the proxy models revealed that MLP-LMA has better prediction ability than the other two proxy paradigms. In this context, the highest average absolute relative deviation noticed per runs by MLP-LMA was lower than 3.60%. Besides, the best-implemented proxy was coupled with ACO and GWO for resolving the studied WAG optimization problem. The findings revealed that the suggested proxies are cheap, accurate, and practical in emulating the performance of numerical reservoir model. In addition, the results demonstrated the effectiveness of ACO and GWO in optimizing the parameters of WAG process for the real field data used in this study
Optimization of WAG process using dynamic proxy, genetic algorithm and ant colony optimization
(Springer, 2018) Nait Amar, Menad; Zeraibi, Noureddine; Redouane, Kheireddine
Optimization of WAG Process Using Dynamic Proxy, Genetic Algorithm and Ant Colony Optimization
(Springer, 2018) Nait Amar, Menad; Zeraibi, Noureddine; Kheireddine, Redouane
The optimization of water alternating gas injection (WAG) process is a complex problem, which requires a significant number of numerical simulations that are time-consuming. Therefore, developing a fast and accurate replacing method becomes a necessity. Proxy models that are light mathematical models have a high ability to identify very complex and non-straightforward problems such as the answers of numerical simulators in brief deadlines. Different static proxy models have been used to date, where a predefined model is employed to approximate the outputs of numerical simulators such as field oil production total (FOPT) or net present value, at a given time and not as functions of time. This study demonstrates the application of time-dependent multi Artificial Neural Networks as a dynamic proxy to the optimization of a WAG process in a synthetic field. Latin hypercube design is used to select the database employed in the training phase. By coupling the established proxy with genetic algorithm (GA) and ant colony optimization (ACO), the optimum WAG parameters, namely gas and water injection rates, gas and water injection half-cycle, WAG ratio and slug size, which maximize FOPT subject to some time-depending constraints, are investigated. The problem is formulated as a nonlinear optimization problem with bound and nonlinear constraints. The results show that the established proxy is found to be robust and an efficient alternative for mimicking the numerical simulator performances in the optimization of the WAG. Both GA and ACO are strongly shown to be highly effective in the combinatorial optimization of the WAG process.
Predicting Methane Hydrate Formation Temperature in the Presence of Diverse Brines Using Explainable Artificial Intelligence
(American Chemical Society, 2025) Nait Amar, Menad; Zeraibi, Noureddine; Alqahtani, Fahd Mohamad; Djema, Hakim; Benamara, Chahrazed; Saifi, Redha; Gareche, Mourad; Ghasemi, Mohammad; Merzoug, Ahmed
Thisstudy presents three advanced techniques, includingthe leastsquares support vector machine (LSSVM), categorical boosting (CatBoost),and cascaded forward neural network (CFNN), to model methane hydrateformation temperature (MHFT) across various brines under a wide pressurerange. Utilizing a comprehensive data set of nearly 1000 samples,the models underwent rigorous training and testing phases. Graphicalanalyses and statistical assessment confirmed the high accuracy ofthe implemented models, with the CFNN scheme outperforming the others,achieving a total root-mean-square error (RMSE) of 0.3569 and an R2 of 0.9977. Comparison with existing modelsfurther highlighted the CFNN model’s superior performance.Additionally, the Shapley Additive exPlanning (SHAP) method was employedto enhance the aspects related to predictions’ explainabilityby assessing the impact of different inputs on the outcomes. Lastly,the proposed model holds significant potential for advancing industrialand academic applications related to hydrate phenomena
Predicting solubility of nitrous oxide in ionic liquids using machine learning techniques and gene expression programming
(Elsevier, 2021) Nait Amar, Menad; Ghriga, MMohammed Abdelfetah; Ben Seghier, Mohamed El Amine; Ouaer, Hocine
Background: - Nitrous oxide (N2O), as a potent greenhouse gas, is increasingly becoming a major multidisciplinary concern in recent years. Therefore, the removal of N2O using powerful green solvents such as ionic liquids (ILs) has turned into an attractive way to reduce the amount of N2O in the atmosphere. Methods: -The aim of this study was to establish rigorous models that can predict the solubility of N2O in various ILs. To achieve this, three advanced soft-computing methods, viz. cascaded forward neural network (CFNN), radial basis function neural network (RBFNN), and gene expression programming (GEP) were trained and tested using comprehensive experimental measurements. Significant Findings: - The obtained results demonstrated that the newly implemented models can predict the solubility of N2O in ILs with high accuracy. Besides, it was found that the CFNN model optimized using Levenberg-Marquardt (LM) algorithm was the best predictive paradigm (R2=0.9994 and RMSE=0.0047). Lastly, the Leverage technique was carried out, and the statistical validity of the newly implemented model was documented as more than 96% of data were located in the applicability realm of this paradigm. © 2021 Taiwan Institute of Chemical Engineers
Predicting thermal conductivity of carbon dioxide using group of data-driven models
(Elsevier, 2020) Nait Amar, Menad; AshkanJahanbani, Ghahfarokhi; Zeraibi, Noureddine
Thermal conductivity of carbon dioxide (CO2) is a vital thermophysical parameter that significantly affects the heat transfer modeling related to CO2 transportation, pipelines design and associated process industries. The current study lays emphasis on implementing powerful soft computing approaches to develop novel paradigms for estimation of CO2 thermal conductivity. To achieve this, a massive database including 5893 experimental datapoints was acquired from the experimental investigations. The collected data, covering pressure values from 0.097 to 209.763 MPa and temperature between 217.931 and 961.05 K, were employed for establishing various models based on multilayer perceptron (MLP) optimized by different back-propagation algorithms, and radial basis function neural network (RBFNN) coupled with particle swarm optimization (PSO). Then, the two best found models were linked under two committee machine intelligent systems (CMIS) using weighted averaging and group method of data handling (GMDH). The obtained results showed that CMIS-GMDH is the most accurate paradigm with an overall AARD% and R2 values of 0.8379% and 0.9997, respectively. In addition, CMIS-GMDH outperforms the best prior explicit models. Finally, the leverage technique confirmed the validity of the model and more than 96% of the data are within its applicability realm
Prediction of Lattice Constant of A 2 XY 6 Cubic Crystals Using Gene Expression Programming
(2020) Nait Amar, Menad; Ghriga, Mohammed Abdelfetah; Ben Seghier, Mohamed El Amine; Ouaer, Hocine
Lattice constant is one of the paramount parameters that mark the quality of thin film fabrication. Numerous research efforts have been made to calculate and measure lattice constant, including experimental and empirical approaches. Not withstanding these efforts, a reliable and simple-to-use model is still needed to predict accurately this vital parameter. In this study, gene expression programming (GEP) approach was implemented to establish trustworthy model for prediction of the lattice constant of A2XY6 (A = K, Cs, Rb, TI; X = tetravalent cation; and Y = F, Cl, Br, I) cubic crystals based on a comprehensive experimental database. The obtained results showed that the proposed GEP correlation provides excellent prediction performance with an overall average absolute relative deviation (AARD%) of 0.3596% and a coefficient of determination (R2) of 0.9965. Moreover, the comparison of the performance between the newly proposed correlation and the best pre-existing paradigms demonstrated that the established GEP correlation is more robust, reliable, and efficient than the prior models for prediction of lattice constant of A2XY6 cubic crystals