论文部分内容阅读
为提高固井质量 ,实现固井质量的预测和跟踪分析 ,建立了一种基于人工神经网络的固井质量预测方法 ;采用SAS系统对影响固井质量的众多因素做相关分析 ,依据主要因素并通过人工神经网络建立了固井质量预测模型 .实际应用结果表明 ,该方法能够提高判断固井质量的精度 ,且特别适用于不确定或非结构化信息的处理 ,对固井中各种未知信息的预测有着较好的适用性 .
In order to improve cementing quality and realize cementing quality prediction and tracking analysis, a cementing quality prediction method based on artificial neural network was established. SAS system was used to make correlation analysis on many factors affecting cementing quality. According to the main factors The cementing quality prediction model is established by artificial neural network.The practical application shows that this method can improve the precision of cementing quality and is especially suitable for the processing of uncertain or unstructured information, Prediction has good applicability.