期刊文献+

不确定性页岩油气产量递减预测方法 被引量:7

UNCERTAIN PRODUCTION DECLINE FORECASTING METHOD OF SHALE OIL AND GAS
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摘要 由于页岩储层的特殊性及长水平井多段压裂效果差异化特点,通常采用针对一个区块(区域)范围给出一条代表期望产量平均值的产量递减曲线的方法,来预测评价区块(区域)页岩油气的产能。此方法简单、快捷,无需分析单井产量差异,但只能给出确定的产量预测,存在着一定的经济评价风险。为此针对区块具有充分的生产动态数据和没有生产数据两种情况,分别研究了不确定性页岩油气产量递减预测方法。当区块拥有充分的生产动态数据时,预测方法建立在各井典型曲线参数的概率分布形式分析的基础上,可获得最终可采储量(EUR)的概率分布规律;而在没有生产数据时,预测方法建立在获得地质、油藏、工程参数概率分布基础上。该不确定性页岩油气产量递减预测方法应用于实际生产数据分析、评价,效果较好。 Due to the particularity of shale reservoirs and effect difference of multistage hydraulic fracturing in long horizontal wells, we usually adopt a method in which a production decline curve representing the average expected output is provided for a block, to forecast and evaluate productivity. This is a simple and efficient method, with which no production difference of single well needs to be analyzed. However, it can only provide certain production forecast, resulting in economic evaluation risks. We study an uncertain production decline forecasting method of shale oil and gas for two situations:(1) a block with sufficient production dynamic data, in this situation, the method obtains the probability distribution of the estimated ultimately reserve(EUR)based on the analysis of the probability distribution of typical curve parameters;(2) in another situation of a block without production data, the method is based on analyzing the probability distributions geology, reservoir and engineering parameters. The application of this method to production data analysis and evaluation shows a good effect.
机构地区 中海油研究总院
出处 《天然气勘探与开发》 2016年第3期45-48,7,共4页 Natural Gas Exploration and Development
基金 中海石油(中国)有限公司综合科研项目(YXKY-2016-ZY-03)"海外页岩油气产能评价技术与方法研究"资助
关键词 动态数据 页岩油气 产量递减 典型曲线 不确定性 预测方法 dynamic data shale oil and gas production decline typical curve uncertainty forecasting method
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参考文献13

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二级参考文献18

  • 1M.,K.,Fisher(著),李雅琴,(译),张建国(译),陈钦保,(校).综合裂缝绘图技术优化Barnett页岩气藏增产措施[J].国外油气地质信息,2006(1):80-86. 被引量:4
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  • 10FAN L,THOMPSON J W,ROBINSON J R. Understanding gas production mechanism and effectiveness of well stimulation in the Haynesville shale through reservoir simulation[C]. CSUG/SPE 136696, 2010.

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