Abstract

More efficient hydrocarbon exploration and production to supply low carbon fuel to meet targets of energy demand within climate goals guidelines, can allow a continued improvement in the quality of life around the globe and especially in areas such as Southeast Asia and Africa.

Seismic data have been and continues to be the main tool for hydrocarbon exploration. Making very large quantities of seismic data easily accessible and with machine learning functionality is the way forward to gain regional and local understanding of petroleum systems, which in turn leads to an increase in the chance of success and less waste and inefficiency. Seismic data are now available as a streamed service through sAIsmic^™, a web-based platform allowing seismic data access on the spot, from large datasets stored in the cloud. 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 concept is already changing the way we access seismic data, making seismic investment dollars go further, and enhancing the efficiency of gaining insight into any hydrocarbon basin. As can be demonstrated by various case examples from Peru and Argentina (Fig. 1), “sAIsmifying” seismic data is proving invaluable in our efforts to improve efficiency, and as an industry we will make hydrocarbon exploration more efficient and successful, bringing energy, growth and wealth to large areas of the developing world.