摘要
文章不仅选择了重复压裂选井选层的影响因素,而且通过因子分析法对影响因素进行分析,用几个主因子来代替所有影响因素的信息,从而达到了降维、简化数据的作用。而通过因子解释将影响因素归类分析,能够更加明确影响因素与评价指标之间的关系。同时,提出以压后日增油量为压裂效果评价标准,运用改进的BP神经网络方法建立了预测模型、评价方法,克服了由于人的主观因素等方面的不足,达到了很好的效果。并通过实例分析验证了该分析方法的可行性,对现场采取压裂措施有一定的指导意义。
The paper probes into the influencing factors of seleeting well and formation in refracting and analyzes these factors through factor analysis which uses several principal factors to replace all information of influencing factors to reduce dimensions and simplify the data and applies factor interpretation to classify the influencing factors to make it dearer about the relationship between the influencing factors and the evaluation index. The paper also suggests that the oil production of single well per day be the evaluation criterion for the fracturing effect and ereates a prediction model and evaluation method through an improved BP neural network method, which overcomes the deficiency of subjective factors, thus, achieves a good effect. Moreover, this study, by using the example analysis, testifies the feasibility of the analysis method, which is of great significance to take fracturing measure on site.
出处
《内蒙古石油化工》
CAS
2009年第1期49-51,共3页
Inner Mongolia Petrochemical Industry
关键词
选井选层
压裂效果
因子分析
BP神经网络
Well and Layer Selection
Fracturing Effect
Faetor Analysis
BP Neural Network
