Predicting performance of in-situ microbial enhanced oil recovery process and screening of suitable microbe-nutrient combination from limited experimental data using physics informed machine learning approach

Abstract

Screening of suitable microbe-nutrient combination and prediction of oil recovery at the initial stage is essential for the success of Microbial Enhanced Oil Recovery (MEOR) technique. However, experimental and physics-based modelling approaches are expensive and time-consuming. In this study, Physics Informed Machine Learning (PIML) framework was developed to screen and predict oil recovery at a relatively lesser time and cost with limited experimental data. The screening was done by quantifying the influence of parameters on oil recovery from correlation and feature importance studies. Results revealed that microbial kinetic, operational and reservoir parameters influenced the oil recovery by 50%, 32.6% and 17.4%, respectively. Higher oil recovery is attained by selecting a microbe-nutrient combination having a higher ratio of value between biosurfactant yield and microbial yield parameters, as they combinedly influence the oil recovery by 27%. Neural Network is the best ML model for MEOR application to predict oil recovery (R²≈0.99).

Keywords: Biosurfactant; Feature Importance; Microbial Oil Recovery; Neural Network; Physics Informed Machine Learning.