PhD Studentship in Uncertainty Quantification project
Geological realism in reservoir models: uncertain data integration
Improvement of geological realism in reservoir models is one of the ultimate goals in assessing uncertainty of production predictions. One of the ways to characterize realistic geology is to describe model components and relations in a form of prior distributions, which are based on relevant geological knowledge and data. For instance, a relation between curvature and widths for fluvial channels is supported by observation of contemporary river systems.
The PhD work will be focused on integration of geo-data from different fields – geology, hydrology, paleo-geology, geography – into sub-surface reservoir models. More efficient use of relevant data as informative priors aims to improve realism and prediction capability of the petroleum reservoir forecasting model. Uncertain forecast can be improved by using realistic quantification of model unknowns described by statistical priors, elicited from geological prior knowledge. Use of informative prior distribution in prediction modelling requires self-consistent description of complex dependencies hidden in data, which are subject to vast uncertainties and usually cannot be well represented analytically.
The candidate will join a dynamic and diverse team of post-docs and PhDs lead by Prof. Mike Christie. The research carried out by the team addresses aspects of uncertainty quantification including stochastic optimisation methods, geostatistics, machine learning, and employs high-level scientific computation (including a 84 node Linux cluster). The research is funded by a consortium of oil companies, and the skills acquired in while studying for the PhD are likely to be applicable to a wide range of areas, including the oil industry.
The successful candidate must have a degree in geology, strong numeric geological knowledge and understanding of natural systems behaviour and data acquisition. Experience in subsurface geology, paleo-geology, hydro-geology and ground water modelling will be beneficial. Knowledge of computational methods and petroleum engineering is desirable.
To apply send a CV to Dr V Demyanov, vasily.demyanov[ at ]pet.hw.ac.uk
Closing date: 31st July 2009.
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Disclaimer: Every effort has been made to ensure the above information is current and correct. However, applicants should contact the appropriate administering body before making an application, as details do change frequently.