Bayesian Optimization Algorithm Applied to Uncertainty Quantification

Abdollahzadeh, Asaad; Reynolds, Alan; Christie, Michael; Corne, David; Davies, Brian J.; Williams, Glyn

doi:10.2118/143290-pa

Cited by 20 publications

(5 citation statements)

References 24 publications

(27 reference statements)

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“…Generally these optimization algorithms have been coupled with a commercial simulator. The reported numbers of simulation runs for history matching the PUNQ-S3 reservoir model are in the order of thousands realizations Abdollahzadeh, et al, 2011). In this study, the required simulation runs to create and validate the SRM (eleven runs) are extremely less.…”

Section: Discussion and Concluding Remarksmentioning

confidence: 93%

“…PUNQ, which stands for Production forecasting with Uncertainty Quantification, was a mutual study supported by European Union and conducted by 10 European companies, universities, and research centers (Floris, et al, 2001). The reservoir model is based on a real field operated by Elf Exploration and Production (Floris, et al, 2001;Barker, et al, 2001) and is used widely as a standard synthetic test case to investigate the cability of different methods of history matching and uncertainty quantification) (Floris, et al, 2001;Barker, et al, 2001;Gu, et al, 2005;Gao, et al, 2005;Abdollahzadeh, et al, 2011). …”

Section: Punq-s3 Reservoir Modelmentioning

confidence: 99%

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Assisted History Matching Using Pattern Recognition Technology

Shahkarami¹,

Mohaghegh

Hajizadeh

2015

SPE Digital Energy Conference and Exhibition

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This study aims to examine the application of pattern recognition technologies to improve the time and efforts required for completing a successful history matching project. The pattern recognition capabilities of artificial intelligence and data mining techniques are used to develop a Surrogate Reservoir Model (SRM) and use it to perform the assisted history matching process. A well-known standard reservoir model, PUNQ-S3, was selected to examine the potentials of SRM in an assisted history matching process.SRM is a prototype of full field reservoir simulation model that runs in a matter of seconds. SRMs are built based on a spatiotemporal database. The database includes different types of data extracted from a few realizations of the simulation model. In this study, the SRM was developed using ten geological realizations of PUNQ-S3 reservoir simulation model. The uncertain properties are distributions of porosity, horizontal, and vertical permeability. The SRM requires low development cost and has high implementation pace. The SRM was coupled with the Differential Evolution (DE) optimization method to construct an automated history matching workflow. This workflow is able to produce multiple realizations of the reservoir, which match the past performance.The developed SRM showed a high accuracy in mimicking the behavior of reservoir simulation model. Once we select the best performing cases during history matching, we were able to also obtain relaiable future forecasts for the model. The results of this study prove the cability of SRMs in assisting history matching process using population-based sampling algorithms and other computationally intensive operations in reservoir management workflows.

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Section: Discussion and Concluding Remarksmentioning

confidence: 93%

Section: Punq-s3 Reservoir Modelmentioning

confidence: 99%

Assisted History Matching Using Pattern Recognition Technology

Shahkarami¹,

Mohaghegh

Hajizadeh

2015

SPE Digital Energy Conference and Exhibition

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“…These algorithms are all stochastic, thus we repeated each tuned algorithm 10 times and averaged for the misfit and diversity measures to minimize the effects of randomness in the experiments. For more details on BHEDA and BOA refer to Abdollahzadeh et al (2011b), on PSO and hybrid BOA/PSO to Reynolds et al (2011). We used 3,000 function evaluations (simulation runs) in 60 generations where 50 new solutions were created in each generation.…”

Section: Punq-s3 Resultsmentioning

confidence: 99%

On Population Diversity Measures of the Evolutionary Algorithms used in History Matching

Abdollahzadeh¹,

Reynolds²,

Christie³

et al. 2012

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In history matching, the aim is to generate multiple good-enough history-matched models with a limited number of simulations which will be used to efficiently predict reservoir performance. History matching is the process of the conditioning reservoir model to the observation data; is mathematically ill-posed, inverse problem and has no unique solution and several good solutions may occur.Numerous evolutionary algorithms are applied to history matching which operate differently in terms of population diversity in the search space throughout the evolution. Even different flavours of an algorithm behave differently and different values of an algorithm's control parameters result in different levels of diversity. These behaviours vary from explorative to exploitative.The need to measure population diversity arises from two bases. On the one hand maintaining population diversity in evolutionary algorithms is essential to detect and sample good history-matched ensemble models in parameter search space. On the other hand, since the objective function evaluations in history matching are computationally expensive, algorithms with fewer total number of reservoir simulations in result of a better convergence are much more favourable. Maintaining population's diversity is crucial for sampling algorithm to avoid premature convergence toward local optima and achieve a better match quality.In this paper, we introduce and use two measures of the population diversity in both genotypic and phenotypic space to monitor and compare performance of the algorithms. These measures include an entropy-based diversity from the genotypic measures and a moment of inertia based diversity from the phenotypic measures.The approach has been illustrated on a synthetic reservoir simulation model, PUNQ-S3, as well as on a real North Sea model with multiple wells. We demonstrate that introduced population diversity measures provide efficient criteria for tuning the control parameters of the population-based evolutionary algorithms as well as performance comparison of the different algorithms used in history matching.

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“…AHM, middle block in Fig. 1, powered by stochastic populationbased sampling algorithms have been growing in popularity to handle history matching task (Romero et al 2000;Schulze-Riegert et al 2001;Christie et al 2002;Mohamed et al 2010a;Hajizadeh et al 2010;Hajizadeh et al 2011a;Abdollahzadeh et al 2012a;. These algorithms minimize a misfit function and thus obtain the reservoir model that best approximates the dynamic variable data recorded during reservoir life.…”

Section: Introductionmentioning

confidence: 99%

“…These algorithms minimize a misfit function and thus obtain the reservoir model that best approximates the dynamic variable data recorded during reservoir life. Powered by stochastic optimiser, AHM can be approached by Single Objective (SO) (Schulze-Riegert et al 2001;Ferraro and Verga 2009;Hajizadeh et al 2010;Mohamed et al 2010a;Mohamed et al 2010b;Hajizadeh et al 2011a;Abdollahzadeh et al 2012a; or Multi-Objective (MO) (Schulze-Riegert et al 2007;Ferraro and Verga 2009;Hajizadeh et al 2011b;Mohamed et al 2012;Sayyafzadeh et al 2012;Christie et al 2013;Park et al 2013;Min, B., et al 2014). The last block in the workflow is quantifying uncertainty of reservoir performance prediction from ensemble of matched models.…”

Section: Introductionmentioning

confidence: 99%

Impact of Model Parameterisation and Objective Choices on Assisted History Matching and Reservoir Forecasting

Hutahaean¹,

Demyanow²,

Christie³

2015

All Days

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Assisted history matching (AHM) can be approached either by single objective (SO) or multi-objective (MO) optimisation. AHM generates multiple matched-models, which are then used to quantify uncertainty of the forecast. The goals in this framework are not only to produce matched-models in an efficient way but also to provide a reliable forecast under uncertainty. Choice of the model paramaterisation is one of the sources of uncertainty in reservoir modelling. This is usually done based on geological information and engineering knowledge.This paper explores the impact of different geological model parameterisations on AHM and its impact on the reservoir forecasting. It compares the performance of SO and MO AHM approaches under different model parameterisation. In the MO approach, different choices of objective grouping were studied. Three performance measures in AHM and forecasting will be discussed, namely: diversity, convergence, and forecast reliability. Diversity is defined as set of models that are different but still match history. Convergence is defined as how fast history matching achieves the desirable match. Reliability, for synthetic case study in the present paper, is defined as encapsulation of a simulated truth case into the probabilistic range of forecasting (P10 -P50 -P90).Study on a standard industry benchmark case in this paper shows that MO optimisation approach provides a more diverse set of matched-models, which leads to a better forecast. It also confirms that a MO approach is more robust and reliable in forecasting than SO under different model parameterisation. Different objective grouping choices in MO approach still give a reliable reservoir forecasting, although variability in convergence rate is seen.

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Bayesian Optimization Algorithm Applied to Uncertainty Quantification

Cited by 20 publications

References 24 publications

Assisted History Matching Using Pattern Recognition Technology

Assisted History Matching Using Pattern Recognition Technology

On Population Diversity Measures of the Evolutionary Algorithms used in History Matching

Impact of Model Parameterisation and Objective Choices on Assisted History Matching and Reservoir Forecasting

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