Publications Scientifiques

Now showing 1 - 4 of 4

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
Pure Co2-Oil system minimum miscibility pressure prediction using optimized artificial neural network by differential evolution
(2018) Nait Amar, Menad; Zeraibi, Noureddine; Redouane, Kheireddine
Miscible 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 reliability
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
Optimization of WAG process using dynamic proxy, genetic algorithm and ant colony optimization
(Springer, 2018) Nait Amar, Menad; Zeraibi, Noureddine; Redouane, Kheireddine

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