Reservoir fluid characterization is critical to understanding the nature and phase behavior of reservoir fluids. This process has typically been undertaken using laboratory analyses, a time-intensive and costly process which also provides compositional data. Over time, correlations have been developed to predict the PVT properties of crude oil based on parameters such as solution gas-oil ratio, saturation pressure, viscosity, and density. These correlations have had shortcomings such as utilizing a leave-one-out approach, or recently, focused on non-inferable methods such as Neural Networks. This work utilizes compositional data, hitherto neglected in PVT correlations, as input into an inferable machine learning algorithm which can be used to predict PVT properties of crude oil from the Niger Delta basin.
Data containing bubble point pressure, solution gas-oil ratio, and oil formation volume factor alongside composition were obtained and used to develop models. Machine learning model training techniques such as data preprocessing, transformation and hyper-parameter tuning were undertaken. The elastic net regression algorithm utilizing a cross-validation approach was used to develop the models. This ensured an adequate bias-variance tradeoff.
The resulting models were compared with established correlations such as Standing & Katz. Upon statistical analyses performed comparably. The bubble point pressure model, solution gas-oil ratio, oil formation volume factor achieved R-squared value of 0.87, 0.95 and 0.84 respectively on the validation dataset. The models are expressed in the form of equations which can be used in petroleum engineering calculations or implemented in reservoir simulation software. By implementing this approach, a framework for utilizing machine learning for Petroleum Engineering problems which produces inferable results is established. Given potential discoveries in the Niger Delta, upon obtaining compositional data, these set of equations can be used to predict the reservoir crude oil PVT properties, leading to savings in time, cost, and effort, while obtaining actionable and accurate results.