Publications Scientifiques

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Author: search.filters.author.Zeraibi, Noureddine

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    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.
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    Toward robust models for predicting carbon dioxide absorption by nanofluids
    (John Wiley and Sons Inc, 2022) Nait Amar, Menad; Djema, Hakim; Belhaouari, Samir Brahim; Zeraibi, Noureddine; https://doi.org/10.1002/ghg.2166
    The application of nanofluids has received increased attention across a number of disciplines in recent years. Carbon dioxide (CO2) absorption by using nanofluids as the solvents for the capture of CO2 is among the attractive applications, which have recently gained high popularity in various industrial aspects. In this work, two robust explicit-based machine learning (ML) methods, namely group method of data handling (GMDH) and genetic programming (GP) were implemented for establishing accurate correlations that can estimate the absorption of CO2 by nanofluids. The correlations were developed using a comprehensive database that involved 230 experimental measurements. The obtained results revealed that the proposed ML-based correlations can predict the absorption of CO2 by nanofluids with high accuracy. Besides, it was found that the GP-based correlation yielded more precise predictions compared to the GMDH-based correlation. The GP-based correlation has an overall coefficient of determination of 0.9914 and an overall average absolute relative deviation of 3.732%. Lastly, the carried-out trend analysis confirmed the compatibility of the proposed GP-based correlation with the real physical tendency of CO2 absorption by nanofluids
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    Chemical characterization of asphaltenes deposits from Hassi Messaoud field
    (Elsevier, 2022) Behnous, Dounya; Bouhadda, Youcef; Moffatt, Brian; Zeraibi, Noureddine; Coutinho, João A.P.
    The precipitation and deposition of asphaltenes are complex phenomena that reduce the efficiency in oil production operations. In this study, spectroscopic and thermal methods were used for the characterization of asphaltene samples extracted from deposits belonging to different locations in the Hassi Messaoud field. Structural parameters and the chemical structure of the studied asphaltenes were determined using 13C solid nuclear magnetic resonance (NMR), X-ray diffraction and Raman spectroscopy. The thermal behavior of the asphaltenes studied was examined using differential scanning calorimetry. The results obtained suggest that island is the predominent architecture for the asphaltenes studied with an average of 7 to 8 fused rings and aliphatic length chain of about 3–4 carbons. The number of aromatic sheets in a stacked cluster (N) is between 7 and 8 sheets. The aromatic sheet diameter of the four samples ranges from 12.18 to 15.52 Å with an average interlayer distance between aromatic sheets of 3.52 Å and an average interchain layer distance of 4.48 Å
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    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
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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
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    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.
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    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 analyzed
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    Heat exchanges intensification through a flat plat solar collector by using nanofluids as working fluid
    (2018) Maouassi, Ammar; Baghidja, Abdelhadi; Douad, Salima; Zeraibi, Noureddine
    This paper illustrates how practical application of nanofluids as working fluid to enhance solar flat plate collector efficiency. A numerical investigation of laminar convective heat transfer flow throw a solar collector is conducted, by using CuO-water nanofluids. The effectiveness of these nanofluids is compared to conventional working fluid (water), wherein Reynolds number and nanoparticle volume concentration in the ranges of 25–900 and 0–10 % respectively. The effects of Reynolds number and nanoparticles concentration on the skin-friction and heat transfer coefficients are presented and discussed later in this paper. Results show that the heat transfer increases with increasing both nanoparticles concentration and Reynolds number, where nanofluid CuO-water gives best improvement in terms of heat transfer
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    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
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    An efficient methodology for multi-objective optimization of water alternating CO2 EOR process
    (Elsevier, 2019) Nait Amar, Menad; Zeraibi, Noureddine