Multi-scale Bayesian Based Horizon Matchings Across Faults in 3d Seismic Data

Oil and gas exploration decisions are made based on inferences obtained from seismic data interpretation. While 3D seismic data become widespread and the data-sets get larger, the demand for automation to speed up the seismic interpretation process is increasing as well. However, the development of intelligent tools which can do more to assist interpreters has been difficult due to low information content in seismic data. Image processing tools such as auto-trackers assist manual interpretation of horizons, seismic events representing boundaries between rock layers. Auto-trackers works to the extent of observed data continuity; they fail to track horizons 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 multi-resolution simulated annealing with reversible jump Markov Chain Monte Carlo algorithm is employed to sample from aposteriori distribution. The multi-resolution is obtained in scale-space like representation using perceptual resolution of the scene. The model was applied to real 3d seismic data, and has shown to produce horizons matchings which compare well with manually obtained matching references. Further tests show that the inclusions of 3d spatial continuity and multi-resolution aspects of the dataset lead to more robust and correct results.