基于地层成分和钻进参数的钻速预测模型

钻速预测是优化钻进施工过程中的重点难点。本文以实际采集的数据资料（包含4大类共28种不同的数据类型）出发，建立了以地层成分和钻进参数相结合为基本的钻速预测模型。为了提高模型的预测精度，将原始数据首先进行了标准化处理，消除了其量纲和数量级对模型预测可能产生的影响。由于原始数据种类过多，将原始数据先后进行了聚类分析和因子分析，提取其中有效信息最终将其缩减为11个输入参数和1个输出参数的预测模型。利用人工神经网络技术对这个模型进行了非线性拟合，结果显示本预测模型能够将误差控制在10%以内，具有一定的指导生产实践的能力。

ROP Prediction Model Based on Formation Composition and Drilling Parameters

Prediction is an important and difficult target during the drilling optimization process. Drawn upon the field data(comprising of a total of 28 types in 4 categories), a ROP prediction model is developed with combination of formation composition and drilling parameters. In order to increase the prediction accuracy and avoid the potential impact caused by its dimension and order of magnitude, the raw data is standardized. In addition, due to the excessive volume of the raw data, it is processed with polymer analysis and factor analysis to pick effective data, and then reduced to a prediction model of 11 input parameters and 1 output parameter. The technology of artificial neural network is used to perform the non-linearly fitting work, and the results indicate that the prediction error is less than 10%, which means the proposed prediction model can somehow provide guidance for field operations.