Publications Scientifiques
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Item 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, MouradWax 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.Item Modeling asphaltene precipitation in Algerian oilfields with the CPA EoS(Elsevier B.V., 2020) Behnous, Dounya; Palma, A; Zeraibi, Noureddine; Coutinho, João A.P.One of the major flow assurance problems afflicting the oil industry is the asphaltene precipitation during the production, transportation and storage of oil. The precipitation of these heavy compounds is responsible for changes in crude oil properties, increases in oil viscosity, and formation of deposits that reduce oil production and disable equipment leading to significant operational costs. In Algeria, the deposition of asphaltene in reservoirs and pipelines is a severe problem. During production the depressurization of reservoir fluid and the variations of thermodynamic conditions create the need to frequently pig the lines and, in some cases, to inject solvents and dispersants to maintain the production. The understanding of the asphaltene behavior and the prediction of its deposition in flow conditions is crucial to implement appropriate strategies for the prevention or remediation, especially in the wellbore region. In this work we used the CPA EoS to describe the asphaltene phase envelope and predict the PT regions of stability for five Algerian live oils. The model provides a very good description of the experimental behavior of live oils without and with gas injection. The sensitivity to SARA analysis data and its effect on the asphaltene phase boundaries were also analyzedItem Predicting thermal conductivity of carbon dioxide using group of data-driven models(Elsevier, 2020) Nait Amar, Menad; AshkanJahanbani, Ghahfarokhi; Zeraibi, NoureddineThermal 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 realmItem An efficient methodology for multi-objective optimization of water alternating CO2 EOR process(Elsevier, 2019) Nait Amar, Menad; Zeraibi, NoureddineItem 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, ShamshirbandItem Pure Co2-Oil system minimum miscibility pressure prediction using optimized artificial neural network by differential evolution(2018) Nait Amar, Menad; Zeraibi, Noureddine; Redouane, KheireddineMiscible CO2 flooding is one of the most attractive enhanced oil recovery options thanks to its microscopic efficiency improvement. A successful implementation of this method depends mainly on the accurate estimation of minimum miscibility pressure (MMP) of the CO2-oil system. As the determination of MMP through experimental tests (slim tube, and rising bubble apparatus (RBA)) is very expensive and time consuming, many correlations have been developed. However, all these correlations are based on limited set of experimental data and specified range of conditions, thus making their accuracies questionable. In this research, we propose to build robust, fast and cheap approach to predict MMP for pure CO2-oil by applying hybridization of artificial neural networks with differential evolution (DE). DE is used to find best initial weights and biases of neural network. Four parameters that affecting the MMP are chosen as input variables: reservoir temperature, mole fraction of volatile-oil components, mole fraction of intermediate-oil components and molecular weight of components C5+. 105 MMP data covering wide range of conditions are considered from the published literature to establish the model. The obtained results demonstrate that our approach outperforms all the published correlations in term of accuracy and reliabilityItem Bottom hole pressure estimation using hybridization neural networks and grey wolves optimization(KeAi, 2018) Nait Amar, Menad; Zeraibi, Noureddine; Redouane, KheireddineAn 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 correlationsItem Optimization of WAG process using dynamic proxy, genetic algorithm and ant colony optimization(Springer, 2018) Nait Amar, Menad; Zeraibi, Noureddine; Redouane, KheireddineItem Numerical study of nanofluid heat transfer SiO2 through a solar flat plate collector(International Information and Engineering Technology Association, 2017) Maouassi, Ammar; Baghidja, Abdelhadi; Daoud, Said; Zeraibi, NoureddineThis paper illustrates how practical application of nanoparticles (SiO2) as working fluid to stimulate solar flat plate collector efficiency with heat transfer modification properties. A numerical study of nanofluids laminar forced convection, permanent and stationary (SiO2), is conducted in a solar flat plate collector. The effectiveness of these nanofluids are compared to conventional working fluid (water), wherein the dynamic and thermal properties are evaluated for four volume concentrations of nanoparticles (1%, 3%, 5% and 10%), and this done for Reynolds number from 25 to 900. Results from the application of those nonfluids are obtained versus average temperature; pressure drop coefficient and Nusselt number are discussed later in this paper. Finally, we concluded that heat transfer increases with increasing both nanoparticles concentration and Reynolds numberItem Relationship between the fractal structure with the shear complex modulus of montmorillonite suspensions(Elsevier, 2016) Gareche, Mourad; Allal, A.; Zeraibi, Noureddine; Roby, F.; Azril, N.; Saoudi, L.