Machine-learning assisted interpretation: Integrated fault prediction and extraction case study from the Groningen gas field, Netherlands

Seismic fault detection using machine-learning techniques, in particular the convolution neural network (CNN), is becoming a widely accepted practice in the field of seismic interpretation. Machine-learning algorithms are trained to mimic the capabilities of an experienced interpreter by recognizing patterns within seismic data and classifying them. Regardless of the method of seismic fault detection, interpretation or extraction of 3D fault representations from edge evidence or fault probability volumes is routine. Extracted fault representations are important to the understanding of the subsurface geology and are a critical input to upstream workflows including structural framework definition, static reservoir and petroleum system modeling, and well planning and derisking activities. Efforts to automate the detection and extraction of geologic features from seismic data have evolved in line with advances in computer algorithms, hardware, and machine-learning techniques. We have developed an assisted fault interpretation workflow for seismic fault detection and extraction, demonstrated through a case study from the Groningen gas field of the Upper Permian, Dutch Rotliegend; a heavily faulted, subsalt gas field located onshore, northeast Netherlands. Supervised using interpreter-led labeling, we apply a 2D multi-CNN to detect faults within a 3D prestack depth migrated seismic data set. After prediction, we apply a geometric evaluation of predicted faults, using a principal component analysis to produce geometric attribute representations (strike azimuth and planarity) of the fault prediction. Strike azimuth and planarity attributes are used to validate and automatically extract consistent 3D fault geometries, providing geologic context to the interpreter and input to dependent workflows more efficiently.