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致密油藏压裂水平井非稳态产能评价方法研究 被引量:3

Unsteady deliverability evaluation method of fractured horizontal wells in tight oil reservoirs
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摘要 针对致密油藏压裂水平井的非稳态生产过程复杂,其产能评价的研究不够深入,考虑流体微可压缩性、人工缝有限导流能力等因素影响,建立致密油藏压裂水平井非稳态产能评价数学模型。建立并耦合外区基质、内区基质、人工缝、水平井筒的非稳态渗流方程,应用Laplace变换和Stehfest数值反演获得了产能模型的半解析解。绘制致密油藏压裂水平井典型产能曲线,并进行正交试验,分析初始产能和采收率的主控因素。结果表明,外边界封闭时的非稳态生产过程可划分为5个流动阶段,初始产能的主控因素为人工裂缝间距、人工裂缝半长、人工裂缝导流能力;而采收率则主要依赖于人工裂缝半长。本文的研究为准确评价其产能变化,掌握油井生产动态提供了依据。 The unsteady production process of fractured horizontal wells in tight oil reservoirs is complicated,and the research on its production evaluation is not deep enough.Considering influences of relative factors,such as fluid micro compressibility,finite conductivity of artificial fractures and so on,the unsteady deliverability mathematical model of fractured horizontal wells on tight oil reservoirs is developed successfully.At the same time,the seepage equations of outer and inner matrix,artificial fractures and horizontal wellhole are established and coupled together,and then Laplace transformation and Stehfest numerical algorithm are applied to get semi-analytical solution of deliverability model.Typical deliverability curves of fractured horizontal wells in tight oil reservoir are plotted and orthogonal test is carried out to analyze the main factors of initial production and oil recovery.The results show that the unsteady production process can be divided into five flow stages when the outer boundary is closed.The main controlling factors of the initial production capacity are artificial fracture spacing,artificial fracture half-length and artificial fracture conductivity.However,oil recovery is mainly dependent on artificial fracture half-length.The research of this paper provides the basis for accurate evaluation of production capacity and mastering the oil well production performance.
出处 《中国科技论文》 北大核心 2017年第9期1006-1010,共5页 China Sciencepaper
基金 国家自然科学基金资助项目(41672132)
关键词 致密油藏 产能评价 非稳态 压裂水平井 主控因素 tight oil reservoir deliverability evaluation unsteady flow fractured horizontal well major factors
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