Seismic Texture Analysis Applied to Unconventional Reservoir Characterization and Calibration: A Case Study in Central Pennsylvania, North Central Appalachian Basin

Abstract

Enhancing seismic interpretation capabilities often times relies on the application of object oriented attributes to better understand subsurface geology. This research intends to extract and calibrate seismic texture attributes with well log data for better characterization of an unconventional reservoir in the north central Appalachian basin. Seismic texture refers to the lateral and vertical variations in reflection amplitude and waveform at a specific sample location in the 3-D seismic domain. Among various texture analysis algorithms, here seismic texture is characterized via an algorithm called waveform model regression, utilizing model-derived or data derived waveforms for calibration. Altering the calibrating waveforms facilitates the conversion of amplitude volumes to purpose-driven texture volumes of the reservoirs. Seismic data calibration is crucial due to the resolution and uncertainty in the interpretation of the data. Because texture is a more unique descriptor of seismic data than amplitude, it can provide more statistically and geologically significant correlations to well data. Our new results show that seismic texture is a viable attribute for reservoir feature visualization, discrimination, and calibration. Comparative analysis indicates that the new results help better define fracture trend, mode, and facies that are important in predicting the heterogeneity and anisotropy of the unconventional reservoir in the basin. Provisions of this research include a case study applying seismic texture attributes and an assessment of the viability of the attributes to be calibrated with well data from a hydrocarbon producing reservoir. Examples from this study will provide insight in to its capabilities and practical applications of seismic texture attributes in unconventional reservoirs in the Appalachian basin and other basins around the world.

AAPG Datapages/Search and Discovery Article #90291 ©2017 AAPG Annual Convention and Exhibition, Houston, Texas, April 2-5, 2017