| 基于粒子群优化相关向量机的岩层可钻性预测 |
| 投稿时间：2016-01-13 修订日期：2016-01-13 点此下载全文 |
| 引用本文：韩丽丽.基于粒子群优化相关向量机的岩层可钻性预测[J].钻探工程,2016,43(3):23-26.HAN Li-li. Drillability of Rock Formations Assessment by Relevance Vector Machine Based on Particle Swarm Optimization[J]. Drilling Engineering, 2016,43(3):23-26. |
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| 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”（编号：12120113016900） |
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| 中文摘要:本文提出了一种基于粒子群优化相关向量机（PSO-RVM）的岩层可钻性预测方法。该方法选取岩层埋深H、声波时差AC、电阻率ρd、岩层密度ρ和泥质含量Vsh等5个参数作为评价岩层可钻性的基本参数。以某油田Du4钻井为例，采用PSO-RVM方法、多元回归方法和RVM方法对岩层可钻性进行评价。计算结果表明，PSO-RVM模型的预测结果与实测数据非常接近，其预测精度明显高于多元回归方法和RVM方法，说明本文提出的方法具有一定的优越性和较高的精度，可以较好地应用于钻井工程中岩层可钻性预测。 |
| 中文关键词:粒子群算法 支持向量机算法 岩层可钻性 |
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| Drillability of Rock Formations Assessment by Relevance Vector Machine Based on Particle Swarm Optimization |
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| Abstract:This paper presents a relevance vector machine based on particle swarm optimization method (PSO-RVM) for assessing 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. |
| keywords:particle swarm optimization support vector machine algorithm rock drillability |
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