An approximate dynamic programming approachto decision making in the presence of uncertainty for surfactant-polymer flooding

Alkhatib, Ali; King, Peter R.

doi:10.1007/s10596-014-9406-2

Cited by 7 publications

(2 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The implementation of PCM was possible through utilization of a toolbox developed by Feinberg []. We refer to Alkhatib and King [2014a, 2014b, Appendix A] for details about the Legendre and Hermite polynomials.…”

Section: Pce With Pcm and Nisp For Uncertainty Quantificationmentioning

confidence: 99%

See 1 more Smart Citation

Robust optimization of well location to enhance hysteretical trapping of CO₂: Assessment of various uncertainty quantification methods and utilization of mixed response surface surrogates

Babaei

Pan

Alkhatib

2015

Water Resources Research

Self Cite

View full text Add to dashboard Cite

The paper aims to solve a robust optimization problem (optimization in presence of uncertainty) for finding the optimal locations of a number of CO 2 injection wells for geological sequestration of carbon dioxide in a saline aquifer. The parametric uncertainties are the interfacial tension between CO 2 and aquifer brine, the Land's trapping coefficient and the boundary aquifer's absolute permeability. The spatial uncertainties are due to the channelized permeability field which exhibits a binary channel-non-channel system. The objective function of the optimization is the amount of residually trapped CO 2 due to the hysteresis of the relative permeability curves. A risk-averse value derived from the cumulative density function of the distribution of the amount of trapped gas is chosen as the objective function value. In order to ensure that the uncertainties are effectively taken into account, Monte Carlo simulation and Polynomial Chaos Expansion (PCE)-based methods are used and compared with each other. For different cases of parametric and spatial uncertainties, the most accurate uncertainty quantification (UQ) method is chosen to be integrated within the optimization algorithm. While for parametric uncertainty cases of up to two uncertain variables, PCE-based methods computationally outperform Monte Carlo simulations, it is shown that for the multimodal distributions of the function of trapped gas occurring for the spatial uncertainty case, Monte Carlo simulations are more reliable than PCE-based UQ methods. For the discrete (integer) optimization problem, various mixed response surface surrogate models are tested and the robust optimization resulted in optimal CO 2 injection well locations. Key Points:The trapped CO 2 by hysteresis maximized in presence of uncertainty Parametric uncertainty dealt with by polynomial chaos expansion Monte Carlo simulation was more effective for spatial uncertainty case Correspondence to: M. Babaei, masoud.babaei@manchester.ac.uk Citation: Babaei, M., I. Pan, and A. Alkhatib (2015), Robust optimization of well location to enhance hysteretical trapping of CO 2 : Assessment of various uncertainty quantification methods and utilization of mixed response surface surrogates, Water Resour. Res., 51, 9402-9424,

show abstract

Section: Pce With Pcm and Nisp For Uncertainty Quantificationmentioning

confidence: 99%

“…[] have applied PCE's for closed‐loop optimal design, Li and Zhang [] and Li and Zhang [] for multiphase flows, Oladyshkin and Nowak [] for robust design, Laloy et al . [] for groundwater modeling, and Alkhatib and King [] and Alkhatib and King [] for surfactant‐polymer flooding.…”

Section: Introductionmentioning

confidence: 99%

Robust optimization of well location to enhance hysteretical trapping of CO₂: Assessment of various uncertainty quantification methods and utilization of mixed response surface surrogates

Babaei

Pan

Alkhatib

2015

Water Resources Research

Self Cite

View full text Add to dashboard Cite

show abstract

Robust optimization of subsurface flow using polynomial chaos and response surface surrogates

2015

View full text Add to dashboard Cite

Bayesian model calibration and optimization of surfactant-polymer flooding

et al. 2019

View full text Add to dashboard Cite

An approximate dynamic programming approachto decision making in the presence of uncertainty for surfactant-polymer flooding

Cited by 7 publications

References 47 publications

Robust optimization of well location to enhance hysteretical trapping of CO₂: Assessment of various uncertainty quantification methods and utilization of mixed response surface surrogates

Robust optimization of well location to enhance hysteretical trapping of CO₂: Assessment of various uncertainty quantification methods and utilization of mixed response surface surrogates

Robust optimization of subsurface flow using polynomial chaos and response surface surrogates

Bayesian model calibration and optimization of surfactant-polymer flooding

Contact Info

Product

Resources

About

An approximate dynamic programming approachto decision making in the presence of uncertainty for surfactant-polymer flooding

Cited by 7 publications

References 47 publications

Robust optimization of well location to enhance hysteretical trapping of CO2: Assessment of various uncertainty quantification methods and utilization of mixed response surface surrogates

Robust optimization of well location to enhance hysteretical trapping of CO2: Assessment of various uncertainty quantification methods and utilization of mixed response surface surrogates

Robust optimization of subsurface flow using polynomial chaos and response surface surrogates

Bayesian model calibration and optimization of surfactant-polymer flooding

Contact Info

Product

Resources

About

Robust optimization of well location to enhance hysteretical trapping of CO₂: Assessment of various uncertainty quantification methods and utilization of mixed response surface surrogates

Robust optimization of well location to enhance hysteretical trapping of CO₂: Assessment of various uncertainty quantification methods and utilization of mixed response surface surrogates