Determination of bubble point pressure & oil formation volume factor of crude oils applying multiple hidden layers extreme learning machine algorithms

Abstract

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 (γg) 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.