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.
Bioresour Technol
; 351: 127023, 2022 May.
Article
em En
| MEDLINE
| ID: mdl-35307523
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).
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Petróleo
Tipo de estudo:
Diagnostic_studies
/
Prognostic_studies
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Risk_factors_studies
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Screening_studies
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article