Journal
BIORESOURCE TECHNOLOGY
Volume 351, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.biortech.2022.127023
Keywords
Physics Informed Machine Learning; Neural Network; Microbial Oil Recovery; Biosurfactant; Feature Importance
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Screening and predicting oil recovery in Microbial Enhanced Oil Recovery (MEOR) technique is essential for success. This study developed a Physics Informed Machine Learning (PIML) framework to achieve these goals in a shorter time and at a lesser cost.
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 (R-2 asymptotic to 0.99 ).
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