Estimation of minimum miscibility pressure (MMP) in impure/pure N2 based enhanced oil recovery process: A comparative study of statistical and machine learning algorithms
This work addresses the need for accurate MMP estimation in oil recovery processes, but it appears incremental as it compares existing methods without introducing a new paradigm.
The study tackled the problem of predicting minimum miscibility pressure (MMP) in nitrogen-based enhanced oil recovery by comparing statistical and machine learning methods, resulting in most models outperforming existing correlation and predictive models from literature.
Minimum miscibility pressure (MMP) prediction plays an important role in design and operation of nitrogen based enhanced oil recovery processes. In this work, a comparative study of statistical and machine learning methods used for MMP estimation is carried out. Most of the predictive models developed in this study exhibited superior performance over correlation and predictive models reported in literature.