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Durham University

Department of Earth Sciences

Postgraduate Students

Mr Maximilian Franzel

Personal web page

PhD Student in the Department of Earth Sciences
Room number: Open Plan Area

Contact Mr Maximilian Franzel (email at maximilian.franzel@durham.ac.uk)

Biography

2017 - present: PhD student, Durham University

2014 - 2017: MSc in Applied Geosciences, RWTH Aachen University, Germany

2011 - 2014: BSc in Geosciences, University of Cologne, Germany

PhD Project

Statistical characterisation of fluvial sand bodies: Implications for complex reservoir models

Three-dimensional geometry of fluvial channel sand bodies has received considerably less attention than their internal sedimentology, despite the importance of sandstone body geometry for subsurface reservoir modelling. The aspect ratio (width/thickness, W:T) of fluvial channels is widely used to characterise the geometry of channel sand bodies, with end members of 'ribbon' and 'sheet' sands. However, these approaches do not typically provide a full characterization of fluvial sand body shape, as many different W:T can create different channel geometries. Over- or underestimating the cross-section area of a sand body can have significant implications for reservoir models and hydrocarbon volume predictions. Thus, there is a clear need for the generation of versatile, quantitative, statistical-based models for sand body shape.

The main aim of this PhD project is to develop a new statistical-based approach to provide quantitative data, derived from outcrop analogues, to fully constrain stochastic fluvial reservoir models for mature basins and those in challenging environments (e.g. HPHT).

The research will focus on the collection of terrestrial laser scanning (LIDAR) data, RTK GPS , field data (e.g. palaeocurrents, graphic logs and detailed sedimentary architecture) and photogrammetry from excellent fluvial exposures of i) the Precambrian Torridonian, Scotland and ii) the Permo-Triassic, Central Iberian Basin, Spain. Data collection will focus on fluvial channel sand bodies and their cross-sectional geometries and will provide points (polylines) from the LIDAR , RTK GPS and photogrammetry scans for all the sand bodies in the multivariate space, to which statistical analysis and model building can be applied. Application of rigorous probabilistic models, such as Gaussian mixture model, will lead to statistically meaningful clusters, and hence to classifications of sand body shape, together with probabilities of class membership describing the uncertainty in the classification. The research will develop a predictive model to enable forecasting of reservoir channel sand body geometries and shapes that can be built into existing reservoir models.

Is supervised by

Publications

Journal Article