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3D structural-orientation vector guided autotracking for weak seismic reflections; a new tool for shale reservoir visualization and interpretation

Haibin Di, Gao Dengliang and Ghassan AlRegib
3D structural-orientation vector guided autotracking for weak seismic reflections; a new tool for shale reservoir visualization and interpretation
Interpretation (Tulsa) (November 2018) 6 (4): SN47-SN56

Abstract

Recognizing and tracking weak reflections, which are characterized by low amplitude, low signal-to-noise ratio, and low degree of lateral continuity, is a long-time issue in 3D seismic interpretation and reservoir characterization. The problem is particularly acute with unconventional, fractured shale reservoirs, in which the impedance contrast is low and/or reservoir beds are below the tuning thickness. To improve the performance of interpreting weak reflections associated with shale reservoirs, we have developed a new workflow for weak-reflection tracking guided by a robust structural-orientation vector (SOV) estimation algorithm. The new SOV-guided auto-tracking workflow first uses the reflection orientation at the seed location as a constraint to project the most-likely locations in the neighboring traces, and then locally adjust them to maximally match the target reflection. We verify our workflow through application to a test seismic data set that is typical of routine 3D seismic surveys over shale oil and gas fields. The results demonstrate the improved quality of the resulting horizons compared with the traditional autotracking algorithms. We conclude that this new SOV-guided autotracking workflow can be used to enhance the performance and effectiveness of weak reflection mapping, which should have important implications for improved shale reservoirs visualization and characterization.


ISSN: 2324-8858
EISSN: 2324-8866
Serial Title: Interpretation (Tulsa)
Serial Volume: 6
Serial Issue: 4
Title: 3D structural-orientation vector guided autotracking for weak seismic reflections; a new tool for shale reservoir visualization and interpretation
Affiliation: Georgia Institute of Technology, School of Electrical and Computer Engineering, Atlanta, GA, United States
Pages: SN47-SN56
Published: 201811
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 35
Accession Number: 2019-007239
Categories: Economic geology, geology of energy sourcesApplied geophysics
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus.
Secondary Affiliation: Sinopec, CHN, China
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2019, American Geosciences Institute.
Update Code: 201906
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