Bottom-hole flowing pressure and pump intake pressure (PIP) are important parameters to optimize the performance of oil wells. In recent years, downhole sensors are becoming widely used in electrical submersible pump systems to measure these pressures. However, it is still rare to use downhole sensors in sucker rod and progressive cavity pumped wells. In this study, two correlations were developed to calculate bottom-hole flowing pressure and PIP from readily available field data. The two correlations do not require measurements of buildup tests, but they rely on measuring the dynamic fluid level and estimating fluid gradient correction factor using either (1) the tubing gas flow rate or (2) the annular gas flow rate. Then, the PIP is calculated by the summation of either (1) the tubing pressure plus the tubing gaseous liquid column pressure or (2) the casing pressure, annular gas column pressure and the annular gaseous liquid column pressure. The correlations were developed using 419 field data points (389 points for training and 30 for testing) of wide range for each input parameter. Using the training data, the mean absolute percentage deviation (MAPD) between the calculated and measured PIPs is 25% and 20% for the first and second correlations, respectively. However, the testing data showed MAPD of 33% and 12% for the first and second correlations, respectively. The accuracy of these correlations is significantly higher than that of the previously available methods, and the correlations require simpler input. Such study is an original contribution to calculate the PIP with improved accuracy and without downhole pressures sensors.

中文翻译：

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从抽水井中的简单表面数据计算井底压力的模型

井底流动压力和泵吸入压力（PIP）是优化油井性能的重要参数。近年来，井下传感器已被广泛用于潜水电泵系统中以测量这些压力。但是，仍然很少在抽油杆和渐进式腔抽井中使用井下传感器。在这项研究中，开发了两种相关性，可以根据现有的现场数据来计算井底流动压力和PIP。这两个相关性不需要测量堆积测试，但是它们依赖于使用（1）管道气体流速或（2）环形气体流速测量动态流体液位并估算流体梯度校正因子。然后，PIP的计算方式是：（1）管道压力加上管道气态液柱压力之和，或者（2）套管压力，环形气柱压力和环形气态液柱压力之和。使用每个输入参数的419个现场数据点（用于训练的389点和用于测试的30个点）开发了相关性。使用训练数据，对于第一和第二相关性，在计算出的PIP和测得的PIP之间的平均绝对百分比偏差（MAPD）分别为25％和20％。但是，测试数据显示，第一和第二相关性的MAPD分别为33％和12％。这些关联的准确性明显高于以前可用的方法，并且这些关联需要更简单的输入。