摘要
气井产能预测是气藏工程研究中用于指导气田合理生产的重要工作和任务,它在气田的整体评价和高效开发进程中具有很强的预见性和主动性。以测井解释结果为基础,引入近年来预测效果较好的支持向量回归机技术,建立了气井产能预测的基本模型,用来对无动态资料的气井进行产能预测。实例分析表明,该方法预测精度与传统的神经网络技术方法相比有明显提高,它是一种较为适用和可靠的气井产能预测评价方法。
The gas well productivity prediction is one of the main tasks to guide the reasonable production of a gas field in gas pool engineering study. It has intensely forecastive and initiative in the whole evaluation of a gas field and in the process of high efficiency development. Based on the well log interpretation, this paper introduces the support vector regression machine （SVRM） which has preferable forecasting effect to set up the forecasting model for gas well productivity, being suitably applied to prediction of the gas well productivity without dynamic data. The case study shows that this method has more accuracy than traditional nerve network and is a successful evaluating method for gas well productivity prediction. Meantime, it offers a new way for fast quantitative assessment of oil-gas reservoirs.
出处
《新疆石油地质》
CAS
CSCD
北大核心
2008年第3期382-384,共3页
Xinjiang Petroleum Geology
关键词
气井
产能预测
气藏
支持向量机
gas well
productivity prediction
gas pool
support vector machine
