An important requirement of reservoir management is to understand the properties of reservoir fluids and dependent phase behaviors. This makes it possible to determine the properties of reservoir fluids in laboratory pressure-volume-temperature (PVT) tests. Such laboratory tests are costly and time consuming, and reservoir data are sometimes not available. When estimating the in-place fluid volumes and / or designing enhanced recovery processes information on bubble point pressure (BPP) and oil formation volume factor (OFVF) is required. Predicting these two parameters is therefore one of the priorities of reservoir engineers. It is becoming increasingly beneficial to be able to predict BPP and OFVF with efficient machine-learning algorithms based on underlying variables that are more easily measured directly in the field. In this study, a dataset of 638 data records of published crude oil fluid samples from around the world is evaluated. After filtering the dataset for outliers, 591 data records for BPP and 599 datasets for OFVF are evaluated with efficient hybrid machine-learning algorithms (multi-layer extreme learning machine --MELM-- and least squares support vector machine -- LSSVM) optimized using a genetic algorithm –GA-- and a particle swarm optimizer –PSO-- to improve their prediction performance. Four underlying variables are considered for each data record: temperature (T), solution gas oil ratio (Rs), gas specific gravity (γ_g) and oil gravity (API). The PSO- MELM-PSO hybrid algorithm achieved the highest prediction accuracy measured in terms of root mean square errors (RMSE) of 33.5 psi for BPP and 0.0199 for OFVF. The four-hybrid machine-learning-optimization algorithms evaluated all outperform the empirical relationships used for many decades in the oil industry to predict BPP and OFVF.

储层管理的一项重要要求是了解储层流体的性质和相关的相行为。这样就可以在实验室压力-体积-温度（PVT）测试中确定储层流体的性质。这种实验室测试既昂贵又费时，并且有时无法获得储层数据。在估算就地流体量和/或设计提高采收率的过程时，需要有关起泡点压力（BPP）和油形成体积因数（OFVF）的信息。因此，预测这两个参数是储层工程师的工作重点之一。能够使用基于潜在变量的高效机器学习算法预测BPP和OFVF变得越来越有利，这些变量在现场更容易直接测量。在这个研究中，评估了来自世界各地的638个已发布原油流体样本的数据记录的数据集。在过滤了异常值的数据集之后，使用有效的混合机器学习算法（多层极限学习机-MELM-和最小二乘支持向量机-LSSVM）对BPP的591条数据记录和OFVF的599个数据集进行了评估遗传算法– GA –和粒子群优化程序– PSO –改善了预测性能。每个数据记录考虑四个基本变量：温度（T），溶液瓦斯油比（Rs），气体比重（γ 使用有效遗传算法–GA--优化的高效混合机器学习算法（多层极限学习机-MELM-和最小二乘支持向量机-LSSVM）评估了BPP的591条数据记录和OFVF的599个数据集以及粒子群优化器– PSO –以改善其预测性能。每个数据记录考虑四个基本变量：温度（T），溶液瓦斯油比（Rs），气体比重（γ 使用有效遗传算法–GA--优化的高效混合机器学习算法（多层极限学习机-MELM-和最小二乘支持向量机-LSSVM）评估了BPP的591条数据记录和OFVF的599个数据集以及粒子群优化器– PSO –以改善其预测性能。每个数据记录考虑四个基本变量：温度（T），溶液瓦斯油比（Rs），气体比重（γ_g）和油比重（API）。PSO-MELM-PSO混合算法以BPP的均方根误差（RMSE）和OFVF的0.0199均达到了最高的预测精度。四混合机器学习优化算法的评估均优于石油行业数十年来用来预测BPP和OFVF的经验关系。