Small-Scale Fracture Detection via Anisotropic Bayesian Ant-Tracking Colony Optimization Driven by Azimuthal Seismic Data

Abstract

Fractures play a very important role in hydrocarbon accumulation and migration as well as hydraulic fracturing. Small-scale fractures contain dense sets of fractures that extend from meters to tens of meters in length. They are at risk resulted in drilling loss and fracturing disturbance. It is therefore vital to build a comprehensive fracture system, especially a dedicated description of small-scale fractures is desired. Conventional large-scale fracture identification mostly depends on the post-stack seismic attributes. Ant tracking places ants as seeds on discontinuous anomalies to locate fracture areas. Taking advantage of wide-azimuth pre-stack seismic, we design an azimuthal anisotropy-driven ant-tracking scheme for small-scale fracture detection. First, we fit ellipses to six azimuthal sectors to obtain fracture intensity and strike. Second, we propose an anisotropic Bayes Ant Colony Optimization (ani-Bayes ACO). The novel algorithm is built on an anisotropic transfer mechanism via a Bayesian framework to optimize the detection of small-scale fractures. Specifically, the computed fracture intensity and strike automatically allocate the range of ants’ detection. The anisotropic transfer mechanism strengthens the detection in the fracture-dense zone and constrains ants to track the geometric structure along the fracture. Finally, we combine conventional ant-tracking to construct an across-scale fracture volume. The bandwidth of the resultant fracture system has been enhanced by incorporating the fracture intensity and geometry into tracing. This new theme may accelerate the automated interpretation by simultaneously characterizing the across-scale fractures and fracture zones. The application of field data in the Sichuan Basin, China demonstrated the robustness of our proposed scheme.

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