Artificial Intelligence-based Modeling of Interfacial Tension for Carbon Dioxide Storage
Publish place: Gas Processing، Vol: 8، Issue: 1
Publish Year: 1399
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_GPJU-8-1_006
تاریخ نمایه سازی: 23 مرداد 1400
Abstract:
A key variable for determining carbon dioxide (CO۲) storage capacity in sub-surface reservoirs is the interfacial tension (IFT) between formation water (brine) and injected gas. Establishing efficient and precise models for estimating CO۲ – brine IFT from measurements of independent variables is essential. This is the case, because laboratory techniques for determining IFT are time-consuming, costly and require complex interpretation methods. For the datasets used in the current study, correlation coefficients between the input variables and measured IFT suggests that CO۲ density and pressure are the most influential variables, whereas brine density is the least influential. Six artificial neural network configurations are developed and evaluated to determine their relative accuracy in predicting CO۲ – brine IFT. Three models involve multilayer perceptron (MLP) tuned with Levenberg-Marquardt, Bayesian regularization and scaled conjugate gradient back-propagation algorithms, respectively. Three models involve the radial basis function (RBF) trained with particle swarm optimization, differential evolution and farmland fertility optimization algorithms, respectively. The six models all generate CO۲ – brine IFT predictions with high accuracy (RMSE
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Authors
Amir Hossein Hosseini
Petroleum Department, Semnan University, Semnan, Iran
Hossein Ghadery-Fahliyany
Petroleum Department, Shahid-Bahonar University, Kerman, Iran
David Wood
DWA Energy Limited, Lincoln, United Kingdom
Abouzar Choubineh
Petroleum Department, Petroleum University of Technology, Ahwaz, Iran
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