Abstract
Petroleum reservoir models are vital tools to help engineers in making field development decisions. Uncertainty of reservoir models in predicting future performance of a field needs to be quantified for risk management practices. Rigorous optimisation and uncertainty quantification of the reservoir simulation models are the two important steps in any reservoir engineering study. These steps facilitate decision making and have a direct impact on technical and financial performance of oil and gas companies.
Optimisation of reservoir models to match past petroleum production data – history matching – entails tuning the simulation model parameters to reproduce dynamic data profiles observed at the production wells. History matching is an inverse problem with non-unique solution; thus, different combinations of reservoir model parameters can provide a good match to the data. Multiple history matched reservoir models are used to quantify uncertainty of future hydrocarbon production from a field.
Recently application of evolutionary and swarm intelligence algorithms to history matching problems has become very popular. Stochastic sampling algorithms are used to explore the model parameter space and to find good fitting models. Exploration/exploitation of the search space is essential to obtain a diverse set of history matched reservoir models. Diverse solutions from different regions of the search space represent different possible realizations of the reservoir model and are essential for realistic uncertainty quantification of reservoir performance in the future.
This chapter compares the application of four recent stochastic optimisation methods: Ant Colony Optimisation, Differential Evolution, Particle Swarm Optimisation and the Neighbourhood Algorithm for the problem of history matching. The algorithms are integrated within a Bayesian framework to quantify uncertainty of the predictions.
Two petroleum reservoir examples illustrate different aspects of the comparative study. The Teal South case study is a real reservoir with a simple structure and a single producing well. History matching of this model is a low dimensional problem with eight parameters. The second case study – PUNQ-S3 reservoir – is a synthetic benchmark problem in petroleum industry. The PUNQ-S3 model has a more complex geological structure than Teal South model, which entails solving a high dimensional optimisation problem. This model is fitted to multivariate production data coming from multiple wells.
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Hajizadeh, Y., Demyanov, V., Mohamed, L., Christie, M. (2011). Comparison of Evolutionary and Swarm Intelligence Methods for History Matching and Uncertainty Quantification in Petroleum Reservoir Models. In: Köppen, M., Schaefer, G., Abraham, A. (eds) Intelligent Computational Optimization in Engineering. Studies in Computational Intelligence, vol 366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21705-0_8
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