3D seismic image processing for interpretation

Abstract

Extracting fault, unconformity, and horizon surfaces from a seismic image is useful for interpretation of geologic structures and stratigraphic features. Although interpretation of these surfaces has been automated to some extent by others, significant manual effort is still required for extracting each type of these geologic surfaces. I propose methods to automatically extract all the fault, unconformity, and horizon surfaces from a 3D seismic image. To a large degree, these methods just involve image processing or array processing which is achieved by efficiently solving partial differential equations. For fault interpretation, I propose a linked data structure, which is simpler than triangle or quad meshes, to represent a fault surface. In this simple data structure, each sample of a fault corresponds to exactly one image sample. Using this linked data structure, I extract complete and intersecting fault surfaces without holes from 3D seismic images. I use the same structure in subsequent processing to estimate fault slip vectors. I further propose two methods, using precomputed fault surfaces and slips, to undo faulting in seismic images by simultaneously moving fault blocks and faults themselves. For unconformity interpretation, I first propose a new method to compute a unconformity likelihood image that highlights both the termination areas and the corresponding parallel unconformities and correlative conformities. I then extract unconformity surfaces from the likelihood image and use these surfaces as constraints to more accurately estimate seismic normal vectors that are discontinuous near the unconformities. Finally, I use the estimated normal vectors and use the unconformities as constraints to compute a flattened image, in which seismic reflectors are all flat and vertical gaps correspond to the unconformities. Horizon extraction is straightforward after computing a map of image flattening; we can first extract horizontal slices in the flattened space and then map these slices back to the original space to obtain the curved seismic horizon surfaces. The fault and unconformity processing methods above facilitate automatic flattening and horizon extraction by providing an unfaulted image with continuous reflectors across faults and unconformities as constraints for an automatic flattening method. However, human interaction is still desirable for flattening and horizon extraction because of limitations in seismic imaging and computing systems, but the interaction can be enhanced. Instead of picking or tracking horizons one at a time, I propose a method to compute a volume of horizons that honor interpreted constraints, specified as sets of control points in a seismic image. I incorporate the control points with simple constraint preconditioners in the conjugate gradient method used to compute horizons.