Machine-learning-driven semiauto multiscenario horizon interpretation with uncertainty quantification

ABSTRACT Horizon interpretation is one of the most labor-intensive and interpreter-dependent processes in the reservoir characterization workflow. This issue becomes critical, particularly when the quality of a seismic reflector is poor. The uncertainty caused by inaccurate seismic horizon interpretations often misleads the volumetric analyses of subsurface reservoirs. To resolve these issues, we develop a four-step machine-learning-based semiautomated horizon interpretation workflow. In the first step, we generate multiple virtual stratigraphic labels. We then generate multiple synthetic data points to mimic the features of field data by applying a cycle-consistent generative adversarial network. In the third step, we train a U-Net augmented with feature pyramid attention and a convolutional block attention module on the synthetic data set. Finally, the probabilistic distribution results are used to obtain multiple horizon selection scenarios, yielding various possible stratigraphic results. As our method yields a probability distribution rather than a single value for a given field data and seed point, we can obtain multiple possible horizon scenarios rather than a single horizon. Through uncertainty quantification, we can select an appropriate horizon among multiple scenarios. We apply our method to the Smeaheia field data set, which is characterized by numerous and large faults, and compare the results with reference interpretation to confirm its accuracy. The field data example indicates that our method can be applied to complex geologic structures.