computational efficiency

Fluid flow simulation and solid phase deformations in porous media are important steps in management and development of oil production of reservoirs. Since fluid simulation in all scales has severe computational cost, multiscale models have been developed recently. Moreover, the role of solid phase deformation in fluid flow and in oil production could not be neglected. In this research, the new mixed multiscale-multiphysics model has been developed. .the new model of mixed multiscale Multiphysics has been developed.  In the present model, not only could the role of elastic deformation in the fluid flow be considered, but also, plastic deformation affecting in the oil production rate could be regarded. The development of the base geotechnical model is performed with respect to the yield surface criterion and none associated flow role theory with the aid of implicit integrating and return mapping. The results were compared with other numerical method and also experimental results and reasonable agreement were achieved. Moreover, the computational efficiency of the present model is proved through the precise analytical calculation. The results confirm that the new model could simulate an oil production rate in an appropriate manner with the high computational efficiency.

The leak detection parameters in the inverse transient analysis (ITA) are obtained in an inverse approach by solving a nonlinear programming problem using metaheuristic algorithms such as the genetic algorithm (GA). Despite its high capability in deriving the leak detection parameters, the ITA method is computationally complex and costly as it uses the (GA). The reason can be attributed to the random behavior and gradual evolution of this algorithm inspired by the nature. This study aimed to increase the computational efficiency by employing surrogate models in the optimization process of the ITA method. The surrogate model is in fact a simulated sample of the main model capable of approximately calculating the objective function in a fraction of a second. The way these models are integrated into the optimization model highly affects their success or failure. To this end, two algorithms incorporating population-based surrogate models, namely (Pre-selection Strategy) PS and (Best Strategy) BS, were presented. To evaluate and compare the results, a distribution network was used to identify the leak detection parameters. The results indicated an increase in the computational efficiency compared to the ITA method integrated with the (GA). The PS algorithm demonstrated the highest performance by reducing the objective function and time complexity by 58% and 78%, respectively.

The inverse transient analysis (ITA) method is amongst the successful leak detection methods in water distribution networks. However, determining the unknown leakage parameters such as number, location, and area of leakages is computationally time-consuming and costly due to applying metaheuristic algorithms, like the genetic algorithm (GA). This study aimed to present a novel approach to resolve this issue in order to enhance the accuracy and speed of the ITA method while maintaining its computational structure. In this research, surrogate models were incorporated in the optimization process of the ITA method. Mimicking the behavior of the objective function, surrogate models attempt to represent the most similar behavior at a low computational cost. In this regard, a new optimization algorithm based on the Kriging surrogate model, called GA-Kriging was proposed. In this algorithm, according to the structural characteristics of the Kriging surrogate model, an EI index was presented to modify the offspring selection scheme in GA. In order to evaluate the GA-Kriging algorithm and compare its performance with the conventional GA, a reference water distribution network was considered for leak detection. The accuracy and computational efficiency of the results in the GA-Kriging algorithm were 52% and 75% higher than those of the conventional GA, respectively. The present study concluded that appropriate incorporation of surrogate models in the optimization process can make the computations more intelligent, reduce repeated computations and, ultimately, increase computational efficiency.