Webinar: Traditional and machine learning techniques applied to oil-source correlation in the Norwegian Sea: Åre we able to end the Spekk-ulation?

Scope

Oil-source correlation traditionally focusses on integrating common isotopic and biomarker characteristics of rock extract and fluid samples in what can be regarded as a “bottom-up” approach. However, in data-rich areas we can begin to utilise these large databases by using a “top-down” approach, utilising advanced statistics and machine learning. Top-down modelling and machine learning are increasingly fashionable tools within relatively data-rich areas of exploration, helping to place information from traditional “bottom-up” modelling into a regional, big picture, spatial context.

Here we utilise a geochemical database of over 30,000 samples across the Norwegian Sea to explore variations in the source rock quality, maturity and depositional environment of the early Jurassic Åre Fm. and late Jurassic Spekk/Melke Fms. Integrating relatively simple machine learning techniques (PCA, Gaussian process regression & cluster analysis), we can begin to differentiate oils into several families based on the molecular and isotopic composition of gasoline and biomarker compounds, related to differential maturity, alteration and source rock(s). It is critical that this exploratory data analysis recognises challenges related to noisy and erroneous data, requiring an iterative approach in practice and an understanding of the robustness, or lack of robustness, of the methods employed.

Short bio:

David graduated with a Masters in Geology from the University of Southampton in 2011, joining IGI Ltd. shortly after as a Geochemist and Basin Modeller. Now working in a senior role (and having become Consultancy Manager in 2022), David has built experience working as a consultant and project manager in many tectonic settings worldwide, with a particular interest in passive margins, igneous-affected sedimentary basins and the application of radiogenic isotopes within petroleum geochemistry.

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