Semi-auto horizon tracking guided by strata histograms generated with transdimensional Markov-chain Monte Carlo

Abstract

Although horizon interpretation is a routine task for building reservoir models and accurately estimating hydrocarbon production volumes, it is a labour-intensive and protracted process. Hence, many scientists have worked to improve the horizon interpretation efficiency via auto-picking algorithms. Nevertheless, the implementation of a classic auto-tracking method becomes challenging when addressing reflections with weak and discontinuous signals, which are associated with complicated structures. As an alternative, we propose a workflow consisting of two steps: (1) the computation of strata histograms using transdimensional Markov-chain Monte Carlo and (2) horizon auto-tracking using waveform-based auto-tracking guided by those strata histograms. These strata histograms generate signals that are vertically sharper and more laterally continuous than original seismic signals; therefore, the proposed workflow supports the propagation of waveform-based auto-picking without terminating against complicated geological structures. We demonstrate the performance of the novel horizon auto-tracking workflow through seismic data acquired from the Gulf of Mexico, and the Markov-chain Monte Carlo inversion results are validated using log data. The auto-tracked results show that the proposed method can successfully expand horizon seed points even though the seismic signal continuity is relatively low around salt diapirs and large-scale faults.