Fracture data are used to populate geological models which can inform decision making on reservoir properties, rock strength, seal integrity, and anticipated fluid flow. Failure to recognise and account for uncertainties in fracture data can limit model outcomes, with significant ramifications for the management of the environment. Despite this, current approaches to fracture data collection and interpretation rarely account appropriately for the multiple sources of uncertainties such as the resolution of the tools that we use to capture the presence and geometry of faults and fractures, to the range of cognitive and physical biases that affect and limit the data we collect (Andrews et al. 2019; Shipton et al. 2019). Such uncertainties affect fracture data observed using lab and field approaches, and at a range of scales of enquiry.
The PhD student will collect and interpret field data to investigate how approaches in capturing, modelling, and mitigating uncertainties in fracture data representation influence uncertainties in models derived from those data. Specifically, the student will:
a) collect and interpret new fracture datasets from outcrop field sites and tunnels (dm- to m-scale), remote sensing (km-scale), and X-CT scans (micron- to mm-scale);
b) design and conduct group workshops to collect empirical data on the interpretation of the same datasets by a wide range of geoscientists;
c) explore the consequences of these uncertainties in the resulting fluid flow model outcomes and their applications;
d) explore the sources of uncertainty in fracture data collection, and approaches to mitigate biases and reduce uncertainties, including protocols for ‘crowdsourcing’ data collection/interpretation within teams.
Field sites will be selected to inform geoenergy applications, for example, granites or sedimentary aquifers to inform geothermal systems or in caprock/overburden units to inform geological storage of hydrogen and CO2. Workshop participants will include geoscientists from both academia and industry (we will approach the CDT’s industry partners), and at least one of these workshops will be delivered as part of a short course for the CDT on bias in geological data collection and interpretation.
This studentship is part of the GeoNetZero CDT - the Centre for Doctoral Training in Geoscience and the Low Carbon Energy. For more about the CDT, or to see the advert for this PhD on the CDT webpages see: https://geo-net-zero.hw.ac.uk/phd-opportunities/
The ideal candidate should have a desire to work in an interdisciplinary, applications-focused field of recognised international importance in geoscience. They will be a practical self-motivated person who will lead the development and direction of their project. Applicants should hold (or expect to get) a minimum of an upper second-class honours degree or an MSc with distinction in physical sciences, maths, or a related field. They should have some programming experience (for example in analytical languages such as MATLAB or R) and an interest in developing these skills further.
This PhD comes with a UKRI level fully-funded studentship, including fees and stipend. The studentship is due to commence 01 October 2020. The fees and stipend can only be awarded to UK and EU students (and not to EEA or International students).
For further information on the studentship, including details of how to apply, please contact Dr Jen Roberts.
Andrews, B. J., Roberts, J. J., Shipton, Z. K., Bigi, S., Tartarello, M. C., and Johnson, G. (2019) How do we see fractures? Quantifying subjective bias in fracture data collection, Solid Earth, 10, 487–516, https://doi.org/10.5194/se-10-487-2019
Z. K. Shipton, J. J. Roberts, E. L. Comrie, Y. Kremer, R. J. Lunn and J. S. Caine. (2019) Fault fictions: systematic biases in the conceptualization of fault-zone architecture, Geological Society, London, Special Publications, 496, https://doi.org/10.1144/SP496-2018-161