INTRODUCTION

Detailed interpretation of complex facies intervals within high-resolution 3D seismic data is a tedious and time-consuming process, even with the assistance of traditional deep learning methods. Traditional windowed waveform classification algorithms can have a non-unique solution and are impacted heavily by interpreter bias and laterally varying data quality. This is especially the case in deformed facies intervals such as post-depositional deformation of complex geological sequences, where tectonic reactivation and/or salt tectonism have re-worked sequences of post-salt siliciclastics into complicated packages that are difficult to interpret. These heavily re-worked zones subjected to erosion and re-deposition of growth sequences are prolific throughout the North Sea and can play an important role in fluid migration and containment. The sediments within the sequence of interest are post-depositional to original salt deposition and syn-depositional to halokinesis. Their complexity usually means such sequences are under-interpreted, which introduces pre-drill uncertainties about the well path or target itself (Tang et. al. 2012).

Therefore, we propose a new, data-centric, and interactive deep learning methodology using InteractivAI, which leverages neural networks to accurately yet quickly predict separate deformed facies in the Groningen study area. The results presented below were obtained in a fraction of the time compared to traditional interpretation workflows and allow geoscientists to better characterize complex geologic units while also determining potential impacts on prospective petroleum systems or planned well paths.