探矿工程-岩土钻掘工程2016，Vol.43Issue(3)：23-26,4.
基于粒子群优化相关向量机的岩层可钻性预测
Drillability of Rock Formations Assessment by Relevance Vector Machine Based on Particle Swarm Optimization
摘要
Abstract
This paper presents a relevance vector machine based on particle swarm optimization method ( PSO-RVM) for as-sessing the drillability of rock formations.Five parameters, including the depth of rock formations (H), acoustic travel time (AC), electrical resistivity (ρd), density of rock formations (ρ) and the shaliness of rock formations(Vsh), are selected as the basic parameters in the PSO-RVM model.The Du4 well drilling in an oil field is chosen as an example and the PSO-RVM method, multiple regression method and relevance vector machine ( RVM) method are used to assess the rock formation drillability of the well.The results suggest that the predict results of PSO-RVM method accord well with the measured data and the prediction accuracy is significantly higher than that of multiple regression method and the RVM method.It is shown that PSO-RVM method can be applied in the prediction of rock formation drillability with its advantages and high accuracy.关键词
粒子群算法/支持向量机算法/岩层可钻性Key words
particle swarm optimization/support vector machine algorithm/rock drillability分类
地质学引用本文复制引用
韩丽丽..基于粒子群优化相关向量机的岩层可钻性预测[J].探矿工程-岩土钻掘工程,2016,43(3):23-26,4.基金项目
中国地质调查局地质调查项目“鄂尔多斯盆地陇东严重缺水地区水文地质调查” （）
