A Productivity Prediction Method Based on Artificial Neural Networks and Particle Swarm Optimization for Shale-Gas Horizontal Wells  

作　　者：Bin Li 

机构地区：[1]China United Coalbed Methane Corporation,Ltd.,Beijing,100011,China

出　　处：《Fluid Dynamics & Materials Processing》2023年第10期2729-2748,共20页流体力学与材料加工（英文）

基　　金：This study was financially supported by China United Coalbed Methane Corporation,Ltd.(ZZGSSALFGR2021-581),Bin Li received the grant.

摘　　要：In order to overcome the deficiencies of current methods for the prediction of the productivity of shale gas hor-izontal wells after fracturing,a new sophisticated approach is proposed in this study.This new model stems from the combination several techniques,namely,artificial neural network(ANN),particle swarm optimization(PSO),Imperialist Competitive Algorithms(ICA),and Ant Clony Optimization(ACO).These are properly implemented by using the geological and engineering parameters collected from 317 wells.The results show that the optimum PSO-ANN model has a high accuracy,obtaining a R2 of 0.847 on the testing.The partial dependence plots(PDP)indicate that liquid consumption intensity and the proportion of quartz sand are the two most sensitive factors affecting the model’s performance.

关 键 词：Shale gas productivity prediction ANN meta-heuristic algorithm PDP 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]