Geological Insigths Gained from a Seismic Data Lake

Seismic data has been and continues to be the main tool for hydrocarbon exploration. Storing very large quantities of seismic data, as well as making it easily accessible and with machine learning functionality, is the way forward to gain regional and local understanding of petroleum systems. Seismic data has been made available as a streamed service through a web-based platform allowing seismic data access on the spot, from large datasets stored in the cloud. A data lake can be defined as transformed data used for tasks such as reporting, visualization, advanced analytics and machine learning. The global library of data has been deconstructed from the rigid flat file format traditionally associated with seismic and transformed into a distributed, scalable, big data store. This allows for rapid access, complex queries, and efficient use of computer power – fundamental criteria for enabling Big Data technologies such as deep learning.  

This data lake concept is already changing the way we access seismic data, enhancing the efficiency of gaining insights into any hydrocarbon basin. Examples include the identification of potentially prolific mixed turbidite/contourite systems in the Trujillo Basin offshore Peru, together with important implications of BSR-derived geothermal gradients, which are much higher than expected in a fore arc setting, opening new exploration opportunities. Another example is de-risking and ranking of offshore Malvinas Basin blocks by gaining new insights into areas until very recently considered to be non-prospective. Further de-risking was achieved by carrying out an in-depth source rock analysis in the Malvinas and conjugate southern South Africa Basins. Additionally, the data lake enabled the development of machine learning algorithms for channel recognition which were successfully applied to data offshore Australia and Norway.

“On demand” regional seismic dataset access is proving invaluable in our efforts to make hydrocarbon exploration more efficient and successful. Machine learning algorithms are helping to automate the more mechanical tasks, leaving time for the more valuable task of analysing the results. The geological insights gained by combining these 2 aspects confirm the value of seismic data lakes.