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Development of New Gas Viscosity Correlations

机译:开发新的气体粘度相关性

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Most of the existing correlations for estimating gas viscosity were developed in mid 60's and 70's of the last century. Limited number of data was used to develop them and their accuracies are questionable. Predicting accurate gas viscosity is extremely important in the oil and gas industry as it has a major impact on reservoir recovery, fluid flow, deliverability, and well storage. In this study, a new correlation has been introduced. This correlation is simpler, features higher accuracy, and uses fewer coefficients compared with the existing correlations. Its application covers a wider range of gas specific gravity without jeopardizing the accuracy of the correlation. Another model was built using Artificial Neural Networks, ANN in order to compare its results with those obtained from the new correlation. The existing correlations were studied and analyzed using the same, large set of measured data used for this study. Most of these correlations suffered from high errors and thus were optimized using the linear and non-linear regressions. New set of coefficients for these correlations are recalculated for which the accuracy has significantly improved. In spite of such an improvement, the new correlation and new ANN model outperform the existing correlations.
机译:大多数估算气体粘度的相关性在上世纪60年代和70年代中期开发。使用有限数量的数据来发展它们,它们的准确性是可疑的。预测精确的气体粘度在石油和天然气工业中非常重要,因为它对储层恢复,流体流动,可交换性和储存储存具有重大影响。在这项研究中,已经介绍了一种新的相关性。这种相关性更简单,具有更高的精度,与现有相关性相比,使用较少的系数。其应用涵盖了更广泛的气体特异性重力,而不会危及相关性的准确性。另一种模型是利用人工神经网络,ANN建造的,以便将其结果与从新相关者获得的结果进行比较。使用该研究的相同大量测量数据研究和分析了现有的相关性和分析。这些相关性的大多数相关性来自高误差,因此使用线性和非线性回归进行了优化。重新计算这些相关性的新系数集合,精度显着提高。尽管这样的改进,新的相关性和新的神经网络模型相对强于现有的相关性。

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