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

被引：14

作者：

Pavan, P. S. ^{[1
]}

Arvind, K. ^{[2
]}

Nikhil, B. ^{[2
]}

Sivasankar, P. ^{[1
]}

机构：

[1] Indian Inst Petr & Energy IIPE, Dept Petr Engn & Earth Sci, Geoenergy Modelling & Simulat Lab, Visakhapatnam 530003, Andhra Pradesh, India

[2] OsloMet Univ, Dept Mech Chem & Elect Engn, Oslo, Norway

来源：

BIORESOURCE TECHNOLOGY | 2022年 / 351卷

关键词：

Physics Informed Machine Learning; Neural Network; Microbial Oil Recovery; Biosurfactant; Feature Importance; MULTISPECIES REACTIVE TRANSPORT; PSEUDOMONAS-PUTIDA; BIOSURFACTANT; INJECTION; MODEL;

D O I：

10.1016/j.biortech.2022.127023

中图分类号：

S2 [农业工程];

学科分类号：

0828 ;

摘要：

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 ).

引用

页数：11