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Advanced Deconvolution Technique for Analyzing Multirate Well Test Data

机译:用于分析多速率试井数据的高级去卷积技术

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Deconvolution allows the test analyst to estimate the constant-rate transient pressure response of a reservoir-well system, and assists us in system identification and parameter estimation. Unfortunately, deconvolution amplifies the noise contained in data. Often, we cannot identify the reservoir system from deconvolved results owing to solution instability caused by noise in measured data. We previously presented a deconvolution technique based on the fast Fourier transform that we applied to a single buildup or drawdown period. In this paper, we extend our previous work and apply the deconvolution technique based on the fast Fourier transform to arbitrarily changing rate profiles such as multirate tests. The deconvolution results, which represent a constant-rate pressure drawdown response spanning the entire duration of the test, can provide helpful insight into the correct reservoir description. We have improved our original deconvolution method in number of ways, particularly with the introduction of an iterative algorithm that produces stable deconvolution results. We demonstrate application of our deconvolution method to analysis of synthetic and field examples, including both flow and shut-in periods. Our deconvolution method can efficiently reproduce the characteristic responses of the reservoir-well system and increase our confidence in parameter estimates.
机译:反卷积可以使测试分析人员估算储油井系统的恒定速率瞬态压力响应,并帮助我们进行系统识别和参数估算。不幸的是,反卷积会放大数据中包含的噪声。通常,由于测量数据中的噪声导致解决方案不稳定,我们无法根据反卷积结果识别储层系统。先前,我们介绍了基于快速傅立叶变换的反卷积技术,该技术已应用于单个累积或缩编周期。在本文中,我们扩展了以前的工作,并将基于快速傅立叶变换的反卷积技术应用于任意变化的速率配置文件,例如多速率测试。解卷积结果代表了贯穿测试整个过程的恒定速率的压力下降响应,可以为正确的储层描述提供有用的见识。我们以多种方式改进了原始的反卷积方法,特别是引入了可产生稳定反卷积结果的迭代算法。我们展示了我们的解卷积方法在合成和现场实例分析中的应用,包括流动期和关闭期。我们的反卷积方法可以有效地再现油藏井系统的特征响应,并增加我们对参数估计的信心。

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