Multiobjective Optimization With Application to Model Validation and
Uncertainty Quantification

Schulze-Riegert, Ralf; Krosche, Markus; Fahimuddin, Abul; Ghedan, Shawket G.

doi:10.2523/105313-ms

Cited by 20 publications

(6 citation statements)

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“…The purpose of the multi-objective history matching was to predict the future production probabilistically by generating multiple trade-off petrophysical reservoir models [7,[115][116][117][118][119][120]. Each objective function was quantified in terms of the sum of the data mismatch between the production obtained from the reservoir and obtained from the reservoir models.…”

Section: Discussionmentioning

confidence: 99%

Development of Pareto-based evolutionary model integrated with dynamic goal programming and successive linear objective reduction

Min

Park

Jang

et al. 2015

Applied Soft Computing

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Section: Discussionmentioning

confidence: 99%

Development of Pareto-based evolutionary model integrated with dynamic goal programming and successive linear objective reduction

Min

Park

Jang

et al. 2015

Applied Soft Computing

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“…An early attempt to apply a multiobjective optimization algorithm to the history matching problem was made by Schulze-Riegert [et al 2007]. They applied Strength Pareto Evolutionary Algorithm (SPEA) for finding Pareto front solutions for history matching problem.…”

Section: Application Of Multiobjective Optimization In Petroleum Engimentioning

confidence: 99%

Towards Multiobjective History Matching: Faster Convergence and Uncertainty Quantification

Hajizadeh¹,

Christie²,

Demyanov³

2011

SPE Reservoir Simulation Symposium

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Today we have 50 years of research in assisted history matching that bring us many fascinating frameworks to obtain multiple history matched models. Recently the evolutionary optimization algorithms for history matching have enjoyed an increasing popularity in our community. However these methods are often criticized for their high computational demands. We are looking to improve the convergence speed of these algorithms while maintaining their ability to obtain multiple history-matched models to have realistic uncertainty quantification.In this paper, we discuss a new approach to history matching and uncertainty quantification using multiobjective stochastic population-based optimization. The current practice in industry is to sum the individual match results from wells and minimize a global misfit value in order to get a history matched model. This work proposes a methodology where we can handle different objectives of history matching separately. We extend original differential evolution (DE) algorithm to optimize multiple objectives. The new algorithm is called differential evolution for multiobjective optimization using Pareto ranking (DEMOPR). We couple this algorithm with Bayesian uncertainty quantification framework to estimate the uncertainty in future recovery.The DEMOPR algorithm is tested for history matching and uncertainty quantification of the PUNQ-S3 reservoir. We have discussed the benefits of new multiobjective approach in fulfilling the targets of PUNQ-S3 problem: obtaining good history matching results and accurate prediction of the ultimate oil recovery. Results are very promising and show better final misfit values and simultaneously, two times increase in the speed of history matching in comparison with the original DE algorithm. We also study the number of models required to reach a stable Bayesian credible interval in quantifying the production uncertainty. We show that multiobjective approach stabilizes faster than original DE which means we need fewer simulations to have a reliable uncertainty estimate in the proposed workflow.

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“…For history matching, the multiobjective approach provides improved overall match of the data than the single objective as it avoids arbitrary weighting factors between matching variables(Ferraro and Verga, 2009;Sayyafzadeh et al, 2012). The approach preserves variation among the population and provides more reliable uncertainty quantification than the singleobjective approach(Hajizadeh et al, 2011;Han et al, 2010;Mohamed et al, 2011;Park et al, 2013;Schulze-Riegert et al, 2007). For example, the trapping efficiency of CO 2 storage in the saline aquifer can be optimized using the concept of Pareto optimality(Nghiem et al, 2009).…”

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confidence: 99%

Multiobjective design and optimization of polymer flood performance

Ekkawong

Han

Olalotiti-Lawal

et al. 2017

Journal of Petroleum Science and Engineering

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The multiobjective genetic algorithm can be used to optimize two conflicting objectives, oil production and polymer utility factor in polymer flood design. This approach provides a set of optimal solutions which can be considered as trade-off curve (Pareto front) to maximize oil production while preserving polymer performance. Then an optimal polymer flood design can be considered from post-optimization analysis. A 2D synthetic example, and a 3D field-scale application, accounting for geologic uncertainty, showed that beyond the optimal design, a relatively minor increase in oil production requires much more polymer injection and the polymer utility factor increases substantially. iii DEDICATION To my parents for their love, care and support iv ACKNOWLEDGEMENTS I would like to thank my academic advisor, Dr. Akhil Datta-Gupta for his valuable guidance throughout the course of this research. Also I want to thank my committee members, Dr. King and Dr. Mohanty for his valuable feedback and questions that have shaped the work in this research.I would like to thanks Fulbright for financial support and this valuable experience to be part of the family, PTTEP for their support and working experience which is very useful for my study.Special thanks to my colleagues at MCERI group; Satyajit for his inspiring ideas,

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Multiobjective Optimization With Application to Model Validation and Uncertainty Quantification

Cited by 20 publications

References 0 publications

Development of Pareto-based evolutionary model integrated with dynamic goal programming and successive linear objective reduction

Development of Pareto-based evolutionary model integrated with dynamic goal programming and successive linear objective reduction

Towards Multiobjective History Matching: Faster Convergence and Uncertainty Quantification

Multiobjective design and optimization of polymer flood performance

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