PhD: Computational innovation for the energy transition: Bayesian stratigraphic correlation of subsurface data

Understanding subsurface geology underpins the UK’s green energy transition by enabling technologies such as geothermal energy, carbon capture and storage, hydrogen containment, and the safe storage of nuclear waste. Our understanding of subterranean geological structure draws on geophysical, chemical and sedimentological data obtained from individual boreholes. Correlating geological formations between these boreholes traditionally uses visual methods. This subjective approach can struggle to integrate diverse datasets, and to quantify uncertainties. An objective and systematic approach to stratigraphic correlation that better predicts the structure and distribution of geological units will increase the feasibility of subsurface energy projects.

This project will expand the supervisory team’s novel correlation software StratoBayes, which uses a Bayesian algorithm to correlate geophysical and geochemical stratigraphic records. The successful student will extend the algorithm to broaden the range of stratigraphic data that can be used for geological interpretation, allowing the production of more reliable correlations based on large quantities of diverse data.

Starting from existing subsurface datasets held by the British Geological Survey, the student will develop a multi-step modelling framework that can consecutively update stratigraphic models with new data. This involves upgrading the underlying Markov Chain Monte Carlo algorithm in StratoBayes to handle large subsurface datasets such as well log data, which are essential for exploring carbon capture and storage and or geothermal energy potential. The new multi-step algorithm will be refined using Lower Jurassic data and validated against well-established ammonite biozones.

Further methodological innovations will include integrating additional information such as bio- and lithostratigraphy, and automated lithology identification based on geophysical signals. Model development will be driven by continuous testing with real-world data sets, and the improved software be used test and refine stratigraphic models of key formations.

By generating high-resolution, probabilistic stratigraphic models, this research will provide crucial insights into the stratigraphic framework of the UK. These subsurface models will ultimately contribute to the precise deployment of green subsurface technology, advancing the UK’s transition towards a net-zero economy.