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A New Pressure/Rate-Deconvolution Algorithm To Analyze Wireline- Formation-Tester and Well-Test Data

机译:一种新的压力/速率反褶积算法,用于分析电缆地层测试仪和试井数据

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Reconstructing constant-rate drawdown-pressure response and its logarithmic time (or Bourdet) derivative by deconvolution from multirate pressure-transient data is very important for wellbore-/ reservoir-system identification and interpretation. In recent years, the use of pressure/rate deconvolution has increased considerably because of significant improvement of the algorithms. In this paper, we present a new deconvolution algorithm based on a weighted Euclidean norm in the Tikhonov (1963) regularized objective function so that one can assign weights to individual pressure- and rate-measurement points, and, thus, define different error estimates for different sections of the data. Incorporating such features into the deconvolution algorithm is very useful to mitigate the effects of unreliable pressure and rate measurements and the sections of the data not obviously consistent with the wellbore/reservoir model.rnWe present two applications of the new algorithm using real field pressure/rate transient data sets. In addition to conventional drillstem-test (DST) well-test data, we apply the algorithm to wireline formation-tester (WFT) pressure transients, which are usually also referred to as interval pressure-transient tests (IPTTs). The results show that the new deconvolution algorithm presented in this paper is useful in interpreting pressure/rate transient data from both formation and well tests.
机译:通过从多速率压力瞬变数据中解卷积来重建恒速降压响应及其对数时间(或Bourdet)导数,对井眼/储层系统的识别和解释非常重要。近年来,由于算法的显着改进,压力/速率反褶积的使用已大大增加。在本文中,我们在Tikhonov(1963)正则化目标函数中提出了一种基于加权欧几里得范数的新反卷积算法,以便可以将权重分配给各个压力和速率测量点,从而定义不同的误差估计数据的不同部分。将这些特征整合到反褶积算法中对于缓解不可靠的压力和速率测量结果以及数据段与井眼/储层模型明显不一致的影响非常有用。我们在此介绍了使用实地压力/速率的新算法的两个应用瞬态数据集。除了常规的钻杆测试(DST)试井数据外,我们还将算法应用于电缆地层测试仪(WFT)压力瞬变,通常也称为间隔压力瞬变测试(IPTT)。结果表明,本文提出的新反褶积算法可用于解释来自地层和试井的压力/速率瞬变数据。

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