Webinar: Machine learning based oil family classification above 62° North – method and some observations

Summary

Unsupervised and supervised machine learning algorithms were applied on two large geochemical data sets from the Norwegian Sea (N = 253) and from the Barents Sea (N = 86). The unsupervised approach allows the algorithm to find similarities among the samples without giving pre-defined attributes. Two different approaches were tested for the unsupervised method.  1) All available geochemical data i.e. light hydrocarbons, aromatics, biomarkers and fraction isotopes were combined in one single dataset and used without pre-screening, and 2) the datasets were carefully examined with regards to sample type (oil vs. condensate), contamination and data integrity. Based on this pre-screening, it was decided to divide the data in 3 compound classes (light hydrocarbon, C₁₀-C₁₆ aromatics, C₂₀₊ biomarker range compounds). It could be shown that by combining all available data, the algorithm classifies the samples based on large scale processes (biodegradation, oil based mud contamination, oil vs. condensate), rather than reveal sample classes based on i.e. source rock depositional environment, type of organic matter supply or maturity. For the supervised approach – which uses pre-defined labels for classification – we established a workflow that enables an expert to infer oil types and source rock correlation in an immature (low confidence) area given available samples in a mature (high confidence) area. We emphasize through examples the importance of data quality and data consistency when using machine learning for automatic oil typing. The results demonstrate that both unsupervised and supervised machine learning can be used to differentiate between different petroleum provinces in a very consistent manner, and furthermore give additional insight into outlier samples.

Benedikt currently works as a consultant at Aker BP focusing on petroleum system analysis offshore Mid-Norway. He received his diploma degree (equivalent M.Sc.) in geology from the University of Kiel (Germany) and holds a Ph.D. in petroleum geochemistry from the University of Oslo. After his Ph.D. he spent two years of postdoctoral research in Oslo, before starting in Aker BP in 2018.

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