Exploiting 3D Spatial Continuity for Robust Automatic Horizon Matching across Faults

Oil and gas exploration decisions are made based on inferences obtained from seismic data interpretation. The interpretation task is getting very time-consuming as seismic data sets become larger. Image processing tools such as auto-trackers assist manual interpretation of horizons-visible boundaries between certain sediment layers in seismic data. Auto-trackers assume data continuities; therefore, their assistance is very limited in areas of discontinuities such as faults. In this paper, we present a method for automatic horizon matching across faults based on a Bayesian approach. A stochastic matching model which integrates 3D spatial information of seismic data and prior geological knowledge is introduced. The optimal matching solution is found by MAP estimate of this model. A simulated annealing with reversible jump Markov Chain Monte Carlo algorithm is employed to sample from a-posteriori distribution. The model was applied to real 3D seismic data, and has shown to produce geologically acceptable horizons matchings.