Estimation of minimum miscibility pressure (MMP) in impure/pure N2 based enhanced oil recovery process: A comparative study of statistical and machine learning algorithms

TL;DR

This paper compares statistical and machine learning methods for predicting minimum miscibility pressure in nitrogen-based enhanced oil recovery, demonstrating improved performance over existing models.

Contribution

It introduces a comparative analysis of statistical and machine learning algorithms for MMP prediction, highlighting their effectiveness in EOR applications.

Findings

Most models outperformed existing correlations

Machine learning models showed higher accuracy

Statistical methods provided reliable estimates

Abstract

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.