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

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

doi:10.2118/154488-ms

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…Fig. 7 shows map of Set-2 model parameterisation for each layer in PUNQ-S3 (modified after Abdollahzadeh et al 2012b). In the permeability-porosity relation, Set-2 parameterisation uses variable values in the crossplot that also will be samples in the history matching, while in Set-1 parameterisation uses a fix constant value.…”

Section: Set-2 Parameterisationmentioning

confidence: 99%

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

Hutahaean¹,

Demyanow²,

Christie³

2015

SPE/IATMI Asia Pacific Oil &Amp; Gas Conference and Exhibition

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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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Section: Set-2 Parameterisationmentioning

confidence: 99%

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

Hutahaean¹,

Demyanow²,

Christie³

2015

SPE/IATMI Asia Pacific Oil &Amp; Gas Conference and Exhibition

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Gaussian-based Estimation of Distribution Algorithms for History Matching

Abdollahzadeh¹,

Christie²,

Corne³

2012

Abu Dhabi International Petroleum Conference and Exhibition

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Numerical reservoir models are used to predict, optimise and improve production performance of the oil and gas reservoirs. History matching is required to calibrate reservoir models to dynamic behaviour of the reservoir. On the one hand, historymatching does not have a unique solution and multiple models can fit observation data, on the other hand, history-matching is a tedious and time-consuming trial and error process as it involves numerous reservoir simulation runs. Modern history matching techniques use optimisation algorithms aim at providing a set of good fitting models in an efficient time. Many optimisation algorithms are applied in history-matching. Of them, Evolutionary Algorithms (EAs), inspired by natural evolution, do not use gradient information from the optimisation problem and only require the fitness function, usually defined as the sum of squares root deviation of model response from the observation data. Estimation of distribution algorithms (EDAs) are a novel class of EAs developed as a natural alternative to genetic algorithms in the last decade. To date, many EDAs are introduced which differ in the probabilistic model that guides the search process. Most of the EDAs are designed for discrete problems and require discretisation of search space when used for continuous problems, e.g. in history matching. In some cases, discretization error can be significant and deteriorate the search process. Gaussian-based EDAs use characteristics of Gaussian distribution for multivariate continuous problems. i.e. they make use of mean and covariance matrix of the variables in the promising solutions to generate new solutions which fit better the observation data. In this paper, we introduce and for the first time apply four Gaussian-based EDAs to assisted historymatching of a standard synthetic case. We show our proposed algorithms may produce results more accurately and more efficiently for the continuous problems.

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An Adaptive Evolutionary Algorithm for History-Matching

Abdollahzadeh¹,

Christie²,

Corne³

et al. 2013

EAGE Annual Conference &Amp; Exhibition Incorporating SPE Europec

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Efficient history matching of highly uncertain reservoir models is important in many applications of the reservoir engineering area, such as reservoir management, production prediction, and development optimisation. History matching has commonly been done manually by a tedious trial and error approach in which global and local adjustments are done to the model properties until a best model is obtained which honours production response of the reservoir model. With recent advances in computer science and hardware, assisted history matching techniques can improve the quality of the match within less time and effort. Stochastic evolutionary algorithms are popular methods in history matching and have been widely used to explore and search the global parameter search space and find multiple good fitting models. They are quite easy to implement, and if well applied, able to be globally convergent even in complex problems such as history matching. General critiques of these algorithms include high computational demands and demand for the tuning of the algorithm’s control parameters. Adaptation methods for EAs adjust algorithm’s control parameters during the evolution to improve the search quality and accelerate the convergence by guiding search toward appropriate regions of the search space. This insures a balance between exploration and exploitation properties of the algorithm. In this paper, we introduce a novel adaptive scheme for EAs, which intelligently adapts the control parameter that affects the diversity of the population. The adapted control parameter is number of selected to generated solutions in each generation, which controls the balance between exploration and exploitation. We apply proposed algorithm to optimisation problem of two test functions and history matching problem of a well-known synthetic reservoir simulation model. Our results show that, compared with the original EAs, adaptive EA is able to find better spread of fitting models with better convergence to the minimum misfit, thus more effective and efficient history matching is achieved.

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

Cited by 4 publications

References 14 publications

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

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

Gaussian-based Estimation of Distribution Algorithms for History Matching

An Adaptive Evolutionary Algorithm for History-Matching

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