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页岩气解析模型产量预测技术优化方案 被引量:3

Optimization of Production Forecasting Technique Using Analytical Models for Shale Gas Wells
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摘要 页岩超低渗、强非均质性以及复杂缝网特征使得解析模型法产量预测参数输入不确定性大,生产历史拟合多解性强,产量预测难度大。基于页岩气井生产长时间段呈线性流动的特点,开展线性流分析和解析模型产量预测工作,提出首先通过线性流分析解释确定解析模型参数初值和范围、再进行历史拟合和产量预测的思路和方法流程。研究表明:根据页岩气流动阶段诊断可以初选解析模型;运用流动物质平衡方法可确定动用储量和气藏尺寸;采用线性流不确定性分析可以确定渗透率和裂缝半长等参数范围。该方法可提高解析模型历史拟合和产量预测精度,同时提高了工作效率,经8口井应用测试结果表明:方法提高单井历史拟合速率40%以上,提高产量预测精度18.1%。 As a result of ultra-low permeability,anisotropy of shale rocks and complex fracture network,production forecasting using the analytical model for shale gas requires many input parameters which are hard to obtain and always are uncertain.That makes the history matching have a lot of confusing solutions and it is difficult to forecast the production correctly.Based on the characteristic of the long-term linear flow regimes of shale gas wells,the linear flow analysis and production forecast were studied,and a new procedure of production forecasting was established which suggested doing the linear flow analysis first,and followed by the history matching and production forecast.It is concluded that the diagnosis of flow regimes help to choose the suitable analytical model primary.The controlled reserve and drainage area can be estimated by flowing material balance method.The initial values and their ranges of input parameters such as permeability and fracture half-length are determined by linear flow uncertainty analysis.This procedure not only improves the accuracy of history matching and production forecasting,but also increases the working efficiency.It is applied in 8 wells and shows that the average single-well history matching time has decreased by 40%,the production forecast accuracy increases by 18.1%.
作者 徐兵祥 白玉湖 陈岭 陈桂华 XU Bing-xiang;BAI Yu-hu;CHEN Ling;CHEN Gui-hua(CNOOC Research Institute Co.,Ltd.,Beijing 100028,China)
出处 《科学技术与工程》 北大核心 2021年第9期3571-3575,共5页 Science Technology and Engineering
基金 国家重点研发计划(2018YFF0213802)。
关键词 页岩气 线性流 解析法 历史拟合 产量预测 shale gas linear flow analytical method history matching production forecasting
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