基于神经网络的液力马达提速效果预测研究

Research on evaluating of using hydraulic motor based on neural network

摘要:

钻井技术是深部资源勘探不可或缺的技术支撑，对钻井效率的预测是提升钻井工艺技术的重要途径。针对南海某区块钻井提速的需求，本文对现场采集的10口井数据进行了插值补全和标准化预处理。为消除数据间高相关性，基于因子分析将原始数据的43种不同参数进行降维，最终确定建模输入参数为相互无相关性的21种公共因子。以井号和深度为基准，结合10折交叉验证的方案，针对原始数据进行分层采样分组，优选单隐藏层15个神经元结构，分别建立了使用液力马达和不使用液力马达的神经网络模型，对比真实钻速数据，模型整体预测精度均超过96%。模型预测显示，在目标区块内含硅质不高的井段内使用液力马达能够有效提高钻进效率。同时模型针对高硅质井段钻进效果进行了预测，结果显示提速工具的使用将加剧钻具的磨损，引起钻速的下降。研究结果表明，基于神经网络建立的钻速预测模型能够有效消除不同钻井井眼之间的差异，高效预测提速工具的使用效果，提高钻进效率。

Abstract:

Drilling technology is an indispensable technical support for deep resource exploration， and the prediction of drilling efficiency is an important way to improve drilling technology. In response to the requirements for drilling speed-up in a certain block in the South China Sea， this paper collected actual drilling data from 10 wells， and these data were first interpolated and normalized. In order to eliminate the high correlation among different parameters， the initial 43 parameters were reduced to 21 common factors based on factor analysis， where there was no correlation between the 21 factors. Based on well number and depth， combining with a 10-fold cross-validation scheme， stratified sampling and grouping were performed on the original data. Through an optimized structure with a single hidden layer and 15 neurons， two neural network models were established on the basis of whether a hydraulic motor was used， and they both achieved an accuracy of over 96%. The model prediction shows that the use of speed-up tools in the target block with low silica content can effectively improve the drilling efficiency. At the same time， the model also predicts that for the high silicon content section， the use of speed-up drilling tools will increase wear on the drilling tools and cause a decrease in drilling speed. The results of the study show that the drilling speed prediction model based on a neural network can effectively make up for the differences among wellbores. Through accurate drilling speed prediction， it is possible to efficiently evaluate the effect of using speed-up tools and improve drilling efficiency.