Enhanced ant tracking: Using a multispectral seismic attribute workflow to improve 3D fault detection
| Autor: | Darwin Mateus, María Camila Pico-Forero, Paul Goyes-Peñafiel, Mateo Acuña-Uribe |
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| Rok vydání: | 2021 |
| Předmět: |
010504 meteorology & atmospheric sciences
Computer science Multispectral image Seismic attribute Geology 010502 geochemistry & geophysics computer.software_genre Fault (power engineering) 01 natural sciences Fault detection and isolation Task (project management) Visualization Geophysics Workflow Key (cryptography) Data mining computer 0105 earth and related environmental sciences |
| Zdroj: | The Leading Edge. 40:502-512 |
| ISSN: | 1938-3789 1070-485X |
| Popis: | Fault interpretation is a complex task that requires time and effort on behalf of the interpreter. Moreover, it plays a key role during subsurface structural characterization either for hydrocarbon exploration and development or well planning and placement. Seismic attributes are tools that help interpreters identify subsurface characteristics that cannot be observed clearly. Unfortunately, indiscriminate and random seismic attribute use affects the fault interpretation process. We have developed a multispectral seismic attribute workflow composed of dip-azimuth extraction, structural filtering, frequency filtering, detection of amplitude discontinuities, enhancement of amplitude discontinuities, and automatic fault extraction. The result is an enhanced ant-tracking volume in which faults are improved compared to common fault-enhanced workflows that incorporate the ant-tracking algorithm. To prove the effectiveness of the enhanced ant-tracking volume, we have applied this methodology in three seismic volumes with different random noise content and seismic characteristics. The detected and extracted faults are continuous, clean, and accurate. The proposed fault identification workflow reduces the effort and time spent in fault interpretation as a result of the integration and appropriate use of various types of seismic attributes, spectral decomposition, and swarm intelligence. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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