基于粒子群优化的融合特征选择钻速预测模型研究

Research on a Drill Rate Prediction Model Based on Feature Selection Integrated with Particle Swarm Optimization

摘要:

传统的钻速预测模型经常受到数据维度过高和特征相关性等问题的制约,导致钻速预测效率和精度受限。为了解决这些问题,本文提出了一种基于粒子群优化(PSO)的融合特征选择钻速预测算法模型。在数据预处理的基础上,首先以三个关键参数threshold_1、threshold_2和threshold_3为优化目标,通过结合历史数据和粒子群优化算法构建适应度函数,从而建立钻速预测模型。接着,使用实际钻井数据对所提出的钻速预测方法进行验证,并与传统的机器学习算法模型进行对比实验。实验结果表明,所提出的粒子群优化融合特征选择算法在特征选择方面具有更高的效率和准确性,使用优化后的融合特征优选结果所训练的4个机器学习钻速预测模型精度相较于优化前分别提升了59%、1%、3%和1%,相较于使用全部特征所训练的模型分别提升了24%、2%、4%和3%。本文为钻井工程中提取到的特征参数过多时提供了一种有效的特征选择方法,并对特征选择算法在工程领域的实际应用具有一定的指导意义。

Abstract:

Traditional drilling speed prediction models have often been constrained by issues such as high data dimensionality and feature correlation, resulting in limited efficiency and accuracy of drilling speed prediction. To address these issues, a drilling speed prediction algorithm model based on Particle Swarm Optimization (PSO) with integrated feature selection has been proposed in this paper. Based on data preprocessing, three key parameters, threshold_1, threshold_2, and threshold_3, have been chosen as optimization targets, and a fitness function has been constructed by combining historical data and the Particle Swarm Optimization algorithm, thereby establishing the drilling speed prediction model. Subsequently, the proposed drilling speed prediction method has been validated using actual drilling data and compared with traditional machine learning algorithm models. Experimental results have shown that the proposed Particle Swarm Optimization-based integrated feature selection algorithm has achieved higher efficiency and accuracy in feature selection. The accuracy of the four machine learning drilling speed prediction models trained using the optimized integrated feature selection results has been improved by 59%, 1%, 3%, and 1%, respectively, compared to before optimization, and by 24%, 2%, 4%, and 3%, respectively, compared to models trained using all features. This paper has provided an effective feature selection method for cases where too many feature parameters have been extracted in drilling engineering, and it offers significant guidance for the practical application of feature selection algorithms in the engineering field.