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首页> 外文期刊>Brazilian journal of chemical engineering >TOWARD PREDICTIVE MODELS FOR ESTIMATION OF BUBBLE-POINT PRESSURE AND FORMATION VOLUME FACTOR OF CRUDE OIL USING AN INTELLIGENT APPROACH
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TOWARD PREDICTIVE MODELS FOR ESTIMATION OF BUBBLE-POINT PRESSURE AND FORMATION VOLUME FACTOR OF CRUDE OIL USING AN INTELLIGENT APPROACH

机译：智能化方法的原油浊点压力和地层体积因子预测模型的建立

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摘要

Accurate estimation of reservoirs fluid properties, as vital tools of reservoir behavior simulation and reservoir economic investigations, seems to be necessary. In this study, two important properties of crude oil, bubble point pressure (P b ) and formation volume factor (B ob ), were modelled on the basis of a number of basic oil properties: temperature, gas solubility, oil API gravity and gas specific gravity. Genetic programming, as a powerful method, was implemented on a set of 137 crude oil data and acceptable correlations were achieved. In order to evaluate models, two test datasets (17 data for P b and 12 data for B ob ) were used. The squared correlation coefficient (R 2 ) and average absolute relative deviation (AARD %) over the total dataset (training + test) are 0.9675 and 8.22% for P b and 0.9436 and 2.004% for B ob , respectively. Simplicity and high accuracy are the advantages of the obtained models.
机译：作为储层行为模拟和储层经济研究的重要工具，准确估算储层流体性质似乎是必要的。在这项研究中，原油的两个重要特性，即泡点压力（P b）和地层体积因子（B ob），是根据许多基本石油特性建模的：温度，气体溶解度，石油API重力和天然气比重。遗传编程作为一种有效的方法，在一组137个原油数据上得到了实现，并获得了可接受的相关性。为了评估模型，使用了两个测试数据集（P b的17个数据和B ob的12个数据）。整个数据集（训练+测试）的平方相关系数（R 2）和平均绝对相对偏差（AARD％）对于P b分别为0.9675和8.22％，对于B ob分别为0.9436和2.004％。简单性和高精度是所获得模型的优点。

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