Connor Gieger

Date of Graduation


Document Type


Degree Type



Eberly College of Arts and Sciences


Geology and Geography

Committee Chair

Dengliang Gao

Committee Co-Chair

Timothy Carr

Committee Member

Thomas Wilson


Enhancing seismic interpretation capabilities often 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 the Marcellus gas shale in 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 waveforms for reservoir property calibration. Altering the calibrating waveforms facilitates the conversion of amplitude volumes to purpose-driven texture volumes to be calibrated with well logs for prediction of reservoir properties in untested regions throughout the reservoir.;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 provides more statistically and geologically significant correlations to well data. Our new results show that seismic texture is a viable attribute not only for reservoir feature visualization and discrimination, but also for reservoir property calibration and prediction. Comparative analysis indicates that the new results help better define seismic signal properties that are important in predicting the heterogeneity 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 the Marcellus Shale in the north central Appalachian basin. Examples from this study will provide insight in its capabilities in practical applications of seismic texture attributes in unconventional reservoirs in the Appalachian basin and other basins around the world.