Over the last few years, because of the increase in low cost computer power, individuals and companies have stepped up investigations into the use of machine learning in many areas of E&P. For the geosciences, the emphasis has been in reservoir characterization, seismic data processing and to a lesser extent interpretation. The benefits of using machine learning (whether supervised or unsupervised) has been demonstrated throughout the literature and yet the technology is still not a standard workflow for most seismic interpreters. This lack of uptake can be attributed to several factors including: a lack of software tools, clear and well-defined case histories, and training. Fortunately, all of these factors are being mitigated as the technology matures. Rather than looking at machine learning as an adjunct to the traditional interpretation methodology, machine learning techniques should be considered the first step in the interpretation workflow.
The presentation will introduce terminology around machine learning and delve into how these methods are employed for use in seismic interpretation. It will then focus on case studies of unsupervised machine learning for improved application of multi-attribute analysis followed by the application of supervised learning to 3D seismic volumes. The presentation will be from an interpreter’s perspective.
7 сен 2024
