尼日利亚原油新地层体积因子相关性研究

Aneel Jordan Atthi, A. Sulaimon, Oluwatoyin Kunle Akinsete
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引用次数: 1

摘要

油藏流体性质的全面描述对于制定解决方案和解决油藏工程问题至关重要。油藏体积因子β 0是油藏工程计算中不可缺少的储层流体性质。在这项研究中,我们使用了来自1840个石油样本的11040个数据点,专门为尼日利亚原油开发了新的βo相关性,并为其他地区(本文中称为全球原油)开发了另一组相关性。利用线性回归(LR)、多元线性回归(MLR)、多元非线性回归(MNLR)、神经网络(NN)、支持向量机(SVM)和数据处理分组方法(GMDH)技术建立了若干相关性。结果表明,GMDH方法的相关性最好,而MNLR方法的相关性最差。尼日利亚和全球相关性的均方根误差(RMSE)分别为0.0033和0.0256。这两种相关性在准确性方面比现有相关性可靠地更好。新的相关性将有助于更准确地描述油藏特征,并可靠地设计地面设备。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
New Oil Formation Volume Factor Correlation for Nigerian Crude Oils
A comprehensive description of reservoir fluid properties is critical in developing solutions and resolving reservoir engineering issues. The oil formation volume factor, βo, is an indispensable reservoir fluid property in reservoir engineering calculations. In this study, we used a total of 11040 data points from 1840 oil samples to develop new βo correlations for the Nigerian crude oils specifically, and another set of correlations for the other regions herein referred to as the global crude oils. Linear regression (LR), multiple linear regression (MLR), multiple non-linear regression (MNLR), neural network (NN), support vector machine (SVM), and the group method of data handling (GMDH) techniques were used to develop several correlations. Results show that the GMDH method yielded the best correlation while the MNLR is the least accurate. The root means square error (RMSE) for the Nigerian, and Global correlations are 0.0033, and 0.0256 respectively. The two correlations are reliably better in terms of accuracy than the existing correlations. The new correlations would facilitate a more accurate reservoir characterization, and reliable design of surface equipment.
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