This repository contains the mains functions to perform foam model adjustment to experimental data. The considered models are the Newtonian and non-Newtonian variants implemented in CMG-STARS. It was originally developed in Python 3.7.4 The code is tested with the next libraries:
- PyMC3 version 3.8
- Theano version 1.0.5
You can find here the necessary data-files to perform model calibration. We have included three demo files. The Core sample properties are listed in input_par_Alvarez2001.dat. The experimental records for foam quality and apparent viscosity are available for two datasets Synthetic and Smooth.
Andres Valdez, email: arvaldez@psu.edu Bernardo Rocha, email: bernardomartinsrocha@ice.ufjf.br
If you find this library useful, cite any of the following papers:
@article{Valdez2020B, title = {Uncertainty quantification and sensitivity analysis for relative permeability models of two-phase flow in porous media}, author = {A. R. Valdez and B M. Rocha and G. Chapiro and R. W. dos Santos}, journal={Journal of Petroleum Science and Engineering}, year={2020}, doi={https://doi.org/10.1016/j.petrol.2020.107297}, publisher={Elsevier} }
@article{Valdez2020C, title = {Foam assisted water-gas flow parameters: from core-flood experiment to uncertainty quantification and sensitivity analysis}, author = {A. Valdez and B. Rocha and A. Pérez-Gramatges and J. Façanha and A. de Souza and G. Chapiro and R. dos Santos }, journal={Transport in Porous Media}, year={2021}, doi={10.1007/s11242-021-01550-0}, publisher={Springer International Publishing} }
@phdthesis{ThesisARValdez, author = {Valdez, Andrés Ricardo}, pages = {136}, school = {PGMC-UFJF}, title = {Inverse and forward uncertainty quantification of models for foam-assisted enhanced oil recovery}, type = {Ph.D. Thesis}, year = {2021} }
@article{Valdez2021, title = {Assessing uncertainties and identifiability of foam displacement models employing different objective functions for parameter estimation}, author = {A. R. Valdez and B M. Rocha and G. Chapiro and R. W. dos Santos}, journal={Journal of Petroleum Science and Engineering}, year={2022}, publisher={Elsevier} }