Predictive painting across faults

Abstract

Predictive painting can effectively spread information in 3D volumes following the local structures (dips) of seismic events. However, it has trouble spreading information across faults with significant displacement. To address this problem, we incorporate fault-slip information into predictive painting to correctly spread information across faults. The fault slip is obtained using a local similarity scan to measure local shifts of the different sides of a fault. We have developed three methods to use the fault-slip information: (1) the area partition method, which uses the fault slip to correct the painting result after predictive painting in each divided area; (2) the fault-zone replacement method, which replaces fault zones with smooth transitions calculated with the fault slip information to avoid sharp jumps; and (3) the unfaulting method, in which we use the fault slip information to unfault the volume, perform predictive painting in the unfaulted domain, and then map the painting result back to the original space. Our methods are tested in application of predictive painting to horizon picking. Numerical examples demonstrate that predictive painting after incorporating fault slip information can correctly spread information across faults, which makes the proposed three approaches of using fault-slip information effective and applicable.