Machine learning assisted seismic interpretation: An integrated workflow for structural/stratigraphic interpretation, combined with reservoir characterisation.

• Main Author: Behzad Alaei, Steve Purves, Eirik Larsen, and Dimitrios Oikonomou
• Format: Conference Object
• Language: English
• Published: 2019
• Subjects: machine learning; artificial intelligence; geoscience; subsurface; ASI; seismic interpretation; reservoir characterization; Barents Sea
• Online Access: https://zenodo.org/record/2643218; https://doi.org/10.5281/zenodo.2643218

Introduction Interpretation of reflection seismic data has come a long way, from structural travel time interpretation, via stratigraphic amplitude and waveform driven interpretation, to full geobody extraction and quantitative interpretation of rock properties. Irrespective of the purpose of seismic interpretation (e.g. regional exploration to field development studies), the main steps are mapping the structure of the subsurface layers and characterizing different properties of beds. A great deal of effort has been placed on finding new approaches to carry out seismic interpretation in semi- or fully automatic ways. Machine learning (ML) has contributed to more rapid and accurate data analysis in many disciplines including earth sciences. The latest developments in ML algorithms, high performance computing, and open source libraries, have enabled new and different workflows for petroleum data science including seismic interpretation. New workflows are more data driven and efficient, with improved measures of uncertainty. Method and Data Different ML approaches have been applied to individual steps of seismic interpretation. We have recently (Larsen et al., 2018) showed the role of ML in greater petroleum geoscience workflows. This means that given the developments made in different ML applications in petroleum geoscience, it is the right time to deliver integrated workflows for interpretation which benefit from ML in all stages of seismic interpretation. We present one workflow which includes a variety of ML algorithms (including fully convolutional deep neural networks) and covers most stages of seismic interpretation including seismic to well tie, structural, stratigraphic, and quantitative interpretation. We have applied the workflow on a 3D seismic survey from Norwegian Barents Sea with one well within the survey. The seismic data is prestack time migrated with 12.5x12.5m bin size. Full stack volume, three angle stacks, and migration velocity field and complete suite of wireline logs were used to carry out ...