利用概率密度函数分析非常规油藏产量递减的新方法

Hamzeh Alimohammadi, Mehdi Sadeghi, Shengnan Chen
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引用次数: 2

摘要

在过去的几十年里,传统的递减曲线分析作为一种快速、简便、有效的储量估计和产量预测方法得到了广泛的应用。自2000年以来,人们提出了一些新的模型来解决传统的页岩和致密储层递减模型的局限性,特别是多流态和生产剖面的长尾行为,这些模型会导致传统模型对储量的高估。这些新提出的递减曲线分析(DCA)模型是保守的,提供了悲观的储量估计。这项工作的主要目的是评估六个重尾概率密度函数(pdf)在页岩和致密储层产量近似中的应用。利用实际生产数据和模拟生产数据,研究了一种适用于捕捉页岩和致密储层产量递减趋势的新型DCA模型。实践证明,该DCA可以更准确地预测致密和页岩储层的产量,特别是在生产历史不足几个月的油井数据有限的情况下。
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A Novel Procedure for Analyzing Production Decline in Unconventional Reservoirs Using Probability Density Functions
In the past several decades, traditional decline curve analyses have been widely used as a quick and simple yet efficient method for reserve estimation and production forecasting. Several new models have been proposed since 2000s to address limitations of traditional decline models in shale and tight reservoirs especially multiple flow regimes and long-tail behavior of production profile which results in overestimating the reserve by the traditional models. Several of these newly proposed decline curve analysis (DCA) models are conservative and provide pessimistic reserve estimates. The main purpose of this work is to evaluate the application of six heavy-tailed probability density functions (PDFs) to approximate production in shale and tight reservoirs. A new class of DCA model suitable to capture the production decline trend in shale and tight reservoirs is examined using real and simulated production data. The proposed class of DCA has been demonstrated to predict production more accurately in tight and shale reservoirs especially when only limited data are available from wells with less than a few months of production history.
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