Prediction of CO<sub>2</sub>‐Oil Minimum Miscibility Pressure Using Soft Computing Methods
Amir Hossein Saeedi Dehaghani, Reza Soleimani
Abstract
Abstract Searching for computational approaches for determination of the minimum miscibility pressure ( MMP ) is highly requested during the miscible gas injection process. New models, namely, the stochastic gradient boosting (SGB) algorithm and two distinct hybrid artificial neural network (ANN) models were used to predict CO 2 MMP as a function of reservoir temperature, mole percent of volatile oil components, mole percent of intermediate oil components, molecular weight of pentane‐plus fraction in the oil phase, mole percentage of CO 2 in injected gas, volatile components, and intermediate components in the injected gas based on 144 published data points. The SGB model was found to provide the better performance. The reservoir temperature turned out to be the most important factor.