Phd Studentship: Machine Learning In Biostratigraphy

Quantitative or semi-quantitative biostratigraphic data (microfossil occurrences in well and outcrop samples) are routinely collected within the hydrocarbon industry, but the interpretation of the data in terms of age/biozone and palaeoenvironment (which then feeds into other aspects of the exploration process) can be laborious, requiring access to specialist knowledge. Many companies no longer have sufficient specialist knowledge in-house and historical biostratigraphic data has become an underutilized resource. A great deal of value could be released from this data if the process of interpretation was automated, allowing geoscientists to rapidly synthesise the results into depositional environment maps, for example. The digital form of most biostratigraphic data makes it suitable for machine learning techniques, using training datasets where the interpretations are already verified.

We propose to use data sets from BGS (British Geological Survey) and IODP (International Ocean Drilling Program). Additionally, discussions are ongoing for the release of suitable training data from major oil companies. The aim of the project would be to enable the characterization of the microfossil assemblage in a sample in terms of palaeoenvironment and age/biozone with confidence and range of uncertainty in the interpretation expressed. Further implications for sequence stratigraphic interpretations and the identification of reworking and caving is desirable (i.e. automation of cleaning of the data pre-processing).

It is worth noting that this type of data has been captured by operators for many decades. As a consequence, there is a vast back catalogue of unstructured data that could be processed, cleaned and interrogated with the techniques to lead to new insights.

See CENTA web page for information on how to apply and general information (http://www.birmingham.ac.uk/generic/centa). Contact supervisors for specific information on this project Dr Ian Boomer (i.boomer@bham.ac.uk), Dr Mike Simmons (mike.simmons@halliburton.com), Helen Smyth (helen.smyth@halliburton.com)

Funding Notes

CENTA studentships are for 3.5 years and are funded by NERC. In addition to the full payment of their tuition fees, successful candidates will receive the following financial support:

Annual stipend, set at £14,777 for 2018/19
Research training support grant (RTSG) of £8,000

 

References

Hammer, O. & Harper, D. 2006. Paleontological Data Analysis. Blackwell Publishing, 351pp.
O'Neil, M.A. & Denos, M. 2017. Automating biostratigraphy in oil and gas exploration: Introducing GeoDAISY. Journal of Petroleum Science and Engineering, 149, 851—859.
Peters, S.E., Zhang, C., Livny, M., & Ré, C. 2014. A machine reading system for assembling synthetic paleontological databases. PLoS one, 9, e113523.