TY - JOUR ID - 87471 TI - Reservoir characterization and porosity classification using probabilistic neural network (PNN) based on single and multi-smoothing parameters JO - International Journal of Mining and Geo-Engineering JA - IJMGE LA - en SN - 2345-6930 AU - Lashkari Ahangarani, Masood AU - Mojeddifar, Saeed AU - Hemmati Chegeni, Mohsen AD - Mining Engineering Department, Arak University of Technology, Arak, Iran Y1 - 2022 PY - 2022 VL - 56 IS - 4 SP - 383 EP - 390 KW - probabilistic neural network KW - Smoothing Parameter KW - model-based optimization KW - Particle Swarm Optimization DO - 10.22059/ijmge.2022.287780.594822 N2 - A probabilistic neural network (PNN) is a feed-forward neural network using a smoothing parameter. We used the PNN algorithm based on single and multi-smoothing parameters for multi-dimensional data classification. Using multi-smoothing parameters, we implemented an improved probabilistic neural network (PNN) to estimate the porosity distribution of a gas reservoir in the North Sea. Comparing the results of implementing smoothing parameters obtained from model-based optimization and particle swarm optimization (PSO) indicated the efficiency of PNN in characterizing the gas. Also, results showed that while the PSO algorithm was able to specify smoothing parameters with more precision, about 9%, it was very time-consuming. Finally, multi PNN based on PSO was applied to estimate the porosity distribution of the F3 reservoir. The results validated the main fracture or gas chimney of the F3 reservoir with higher porosity. Also, gas-bearing layers were highlighted by energy and similarity attributes. UR - https://ijmge.ut.ac.ir/article_87471.html L1 - https://ijmge.ut.ac.ir/article_87471_c8b621de36c8096a01d1bdf09eb40fa3.pdf ER -