Semiautomated fault interpretation based on seismic attributes

Abstract

Three-dimensional fault interpretation is a time-consuming and tedious task. Huge efforts have been invested in attempts to accelerate this procedure. We present a novel workflow to perform semiautomated fault illumination that uses a discontinuity attribute as input and provides labeled fault surfaces as output. The procedure is modeled after a biometric algorithm to recognize capillary vein patterns in human fingers. First, a coherence or discontinuity volume is converted to binary form indicating possible fault locations. This binary volume is then skeletonized to produce a suite of fault sticks. Finally, the fault sticks are grouped to construct fault surfaces using a classic triangulation method. The processing in the first two steps is applied time slice by time slice, thereby minimizing the influence of staircase artifacts seen in discontinuity volumes. We illustrate this technique by applying it to a seismic volume acquired over the Netherlands Sector of the North Sea Basin and find that the proposed strategy can produce highly precise fault surfaces.

Semi‐automatic fault/fracture interpretation based on seismic geometry analysis

“…; Hale ; Wang and AlRegib ; Zhang et al . ; Wu and Hale ; Wu ). Ideally, the faults and fractures are expected as continuous lineaments or surfaces with consistent attribute values, for example, semblance and variance.…”

“…Zhang et al . () binarized a discontinuity volume for fault skeletonization using a biometric algorithm. Machado et al .…”

“…() and Zhang et al . () proposed grouping fault points into local planar patches and then merging these small patches into larger fault surfaces under certain geometric constraints. Admasu, Back and Toennies () and Wang and AlRegib () developed an auto‐tracking method of fault line propagation from one section to another within the entire seismic volume for fault patch extraction.…”

Patch-level MLP classification for improved fault detection

“…However, it is limited by the interpretation efficiency especially for a large seismic dataset with complicated deformation history (e.g., folding and faulting). Correspondingly, the computer-aided fault interpretation becomes the research focus with the progress in computer graphics and image processing since 2000, and various methods/algorithms have been developed for refining the edge-detection attributes and interpreting fault surfaces (e.g., Pedersen et al, 2002;Barnes, 2006;Admasu et al, 2006;Hale, 2013;Zhang et al, 2014;Machado et al, Patch-level MLP classification 2016). For example, Pedersen et al (2002) introduced the concept of ant colony optimization from computer science and developed an ant-tracking algorithm for sharpening the lineaments in a variance volume.…”

“…Hale (2013) developed a discrete-scanning algorithm over dips and strikes for lineament thinning from a semblance volume and a dynamic time wrapping algorithm to generate fault surfaces based on the boundary constraints from the thinned semblance volume. Zhang et al (2014) first applied a biometric algorithm to the coherence attribute for fault skeletonization and then grouped discrete fault points into one fault patch under local planar constraints. borrowed the ideas of motion vectors in video coding and processing to assist seismic fault extraction.…”