基于GA-BPANN的钻井机械钻速预测模型

A Prediction Model for Drilling Speed of Drilling Machinery Based on GA-BPANN

在钻井过程中,优化钻井技术可以降低钻井成本和减少施工事故,而钻速预测是优化钻井的基础。为了提高机械钻速(ROP)预测模型的准确性,开发了一种遗传算法优化的BP人工神经网络(GA-BPANN)的ROP预测模型。首先,采用最大信息系数(MIC)方法进行特征选择降低模型冗余,并将数据进行标准化处理。其次,利用遗传算法(GA)对BPANN的初始权重和偏置进行优化,建立ROP预测新模型。最后,将新模型与BPANN、支持向量回归(SVR)模型进行对比分析。研究结果表明,GA-BPANN模型具有较高的预测精度,同时为钻井过程中提高ROP提供科学依据。

During the drilling process,optimizing drilling techniques can reduce drilling costs and construction accidents,and drilling speed prediction is the foundation of optimizing drilling.In order to improve the accuracy of the mechanical drilling speed(ROP) prediction model,this paper developed a genetic algorithm optimized BP artificial neural network(GA-BPANN) ROP prediction model.Firstly,the maximum information coefficient(MIC)method is used for feature selection to reduce model redundancy,and the data is standardized.Secondly,using genetic algorithm(GA) to optimize the initial weights and biases of BPANN,a new ROP prediction model is established.Finally,compare and analyze the new model with BPANN and Support Vector Regression(SVR) models.The research results indicate that the GA-BPANN model has high prediction accuracy and provides scientific basis for improving ROP during the drilling process.