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Application of Geostatistical Seismic AVA Inversion for Shale Reservoir Characterization and Brittleness Prediction with Machine LearningNormal access

Authors: M. Cyz, L. Azevedo and M. Malinowski
Event name: 81st EAGE Conference and Exhibition 2019
Session: Poster: Seismic Interpretation - Quantitative Interpretation Case Studies A
Publication date: 03 June 2019
DOI: 10.3997/2214-4609.201900691
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 1014.44Kb )
Price: € 20

Summary:
In this study we present an application of geostatistical AVA seismic inversion method for characterization of a unconventional Lower Paleozoic shale reservoir in Northern Poland. The target formations are of a small thickness (up tp 25 meters) and deeply buried (ca. 3 km) what makes their delineation and characterization especially difficult. An application of the iterative geostatistical AVA inversion method allowed for obtaining the high-resolution density, P-wave and S-wave velocity models together with the assessment of the uncertainty on the predictions. The obtained elastic property models were compared with the results of the deterministic simultaneous Amplitude-versus-Offset inversion proving that the application of a such sophisticated (geostatistical) inversion technique is a must while dealing with the thin and highly variable layers. The inverted elastic models where further used to improve the prediction of a spatial distribution of the brittleness index with a machine learning (PSVM) algorithm by integrating well-log data and seismic rock property volumes.


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