Application of PSO-ELM Algorithm in Porosity Prediction of Tuffaceous Sandstone Reservoir
LI-YING XU, Xiuwen Mo, Qiang Zhang, Xiao Li · DEStech Transactions on Environment Energy and Earth Science · 2017
Aiming at the problem that the conventional logging data processing method has low accuracy and large error for complex reservoirs, this paper presents a PSO-ELM algorithm based on particle swarm optimization for reservoir porosity prediction. The prediction model is established by the limit learning machine (ELM). The output weight of ELM is optimized by particle swarm optimization algorithm, and the upper and lower limits of the optimal prediction interval are obtained, and the advantages of ELM learning speed and generalization ability are fully utilized to forecast the reservoir porosity. The PSO-ELM algorithm is used to test the tuff sandstone reservoirs in a certain area, and the results are in good agreement with the core data.