Application of optimization and parameterization algorithms for the integration of seismic and well logging data in the process of building and updating lithofacies models

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Abstract

In this research, integration of well logging and 2D/3D seismic data in the reservoir lithofacies modeling process has been considered. For this purpose, two methods from the so-called seismic matching loop class have been used. In the first method, the particle swarm optimization (PSO) algorithm is implemented to find the optimal value of the probability perturbation method (PPM) deformation parameter. The PPM is used to convert an N-parameter optimization problem to a problem with one parameter. In the second method, in the absence of parametrization methods, the problem of updating lithofacies models will be considered as an optimization problem with the N-unknown parameter. Obviously as the number of optimization unknown parameters increases, the optimization algorithms ability in finding the optimum solution decreases. One way to overcome this problem is to design optimization algorithms with higher capabilities. In the second method, an attempt has been made to establish a proper balance between the exploration and exploitation capabilities of the optimization algorithm. In this research, the crossover and mutation operators of the genetic algorithm (GA) optimization method have been used to improve the exploration and exploitation capabilities of the PSO and artificial bee colony (ABC) algorithms. To evaluate the performance of the proposed methods, a 3D synthetic reservoir model (reference model) has been used. The obtained results show that reservoir lithofacies models generated by "PPM-PSO", "PSO-GA" and "ABC-GA" methods have 6.65%, 10.44%, and 0.99% mismatches compared with the reference lithofacies model, respectively. To highlight the ability of the proposed algorithms in generating and updating the reservoir lithofacies models, two traditional geostatistical methods have also been applied to the specified problem. The results indicate that using the "PPM-PSO", "PSO-GA" and "ABC-GA" algorithms, respectively, leads to 18.8%, 15.27%, and 24.46% improvement on mismatch values compared to the traditional geostatistical methods. Finally, the performance of "ABC-GA" method has been evaluated on two larger and more complex synthetic reservoir models.

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References

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Volume 8, Issue 2
January 0
Pages 121-142

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