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Collaborating Authors
Shams, M.
ABSTRACT Numerical and analytical reservoir models are constructed to predict the reservoir response under different prediction scenarios. These reservoir models can be only trusted after good calibration with actual historical data. The model is considered calibrated if it is able to reproduce the historical data of the reservoir it represents. This calibration process is called history matching and this is the most time consuming phase in any reservoir study. Traditional history matching is carried out through a trial and error approach of adjusting model parameters until a satisfactory match is obtained, which makes the process ill-posed due to the non-uniqueness solution issues and as a consequence the assisted history matching technique has been arisen. In assisted history matching technique, the simulated data is compared to the historical data by means of a misfit function, objective function. The objective function is minimized using appropriate optimization algorithm and thus the results are the model parameters that best reproduce the fluid rates and pressure data recorded during the reservoir life time. The objective of this paper is to introduce a very recent optimization algorithm, Firefly Optimization Algorithm (FFO), which is not used before in the area of the assisted history matching. Firefly Algorithm is a meta-heuristic algorithm for global optimization, which is inspired by flashing behavior of firefly insects. In addition, this paper provides a comparison of firefly algorithm with two other nature inspired algorithms; genetic algorithms (GA) and particle swarm optimization algorithm (PSO). Different reservoir models are used as testing problems to be solved by the different studied optimization algorithms to obtain conclusions. Solution times required by each algorithm to solve the problems are also taken into consideration as a comparative parameter. The results of this work indicate that the firefly algorithm is superior to the other two most common used nature inspired algorithms.
ABSTRACT Estimates of hydrocarbon reserves and the optimal scenario of their future recovery directly determine the profitability of the field development plan. The uncertainty in reservoir recovery predictions has been always considered as a major concern. Studying reservoir uncertainties should provide us with information about how "incorrect" a proposed scenario is. One effective approach to quantify reservoir uncertainties is to apply the concept of Experimental Design. As the name implies, experimental design is the technique used to guide the choice of the experiments to be conducted in an efficient way. Samples are chosen in the design space of the uncertain parameter in order to get the maximum amount of information through the lower number of experiments. Several experimental design techniques are introduced in literature, some are useful and effective and some are not. The objective of this paper is to introduce an efficient experimental design technique called Sobol sequence to the arena of petroleum reservoir uncertainty quantification. Sobol sequence technique is a space filling experimental design technique that uses one base Van Der Corput sequence for all design space dimensions and a different permutation of the vector elements for each dimension. To show the potentiality and efficiency of Sobol sequence technique, different reservoir analytical models are used as testing problems to compare between Sobol sequence performance in solving assisted history matching problems and the performance of the most popular experimental design technique, Latin hypercube. A performance indicator is developed to quantify how each response surface model created using the two different techniques approaches the correct solution of the testing problems. The results of this work indicate that the Sobol sequence technique is superior to the Latin hypercube method and hence provide improved reservoir uncertainty quantification.