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Seismic Brittleness Index Volume Estimation From Well Logs in Unconventional Reservoirs

Abstract

Brittleness is a key rock property for effective reservoir stimulation in unconventional reservoirs. Differentiating brittle from ductile rocks is key to perform an efficient well location and completion. I calculate a brittleness index (BI) volume from surface seismic data calibrated by well logs in the Barnett Shale. Completion effectiveness is function of the interaction between multiple engineering variables (length of the horizontal wells, number of stages, number and size of the hydraulic fracture treatments in a multistage completion, volume of proppant placed, proppant concentration, total perforation length, and number of clusters) and the spatial variation between geological factors (permeability, porosity, maximum stress field, among others) in shale gas reservoirs. I correlate a BI log from a well with core descriptions and mineralogy log information with lithological (gamma ray) and geomechanically (?? and μ?) related well logs building a non-linear relationship between these variables. Using prestack simultaneous inversion, I derived geomechanical seismic attributes such as ?? and μ? seismic volumes and I used them to predict lithology and geomechanical behavior in the reservoir. Additionally, I generated a pseudo gamma ray (GR) seismic cube using probabilistic neural network (PNN). I combined these seismic attributes using the non-linear relationship developed from well logs to generate a pseudo BI seismic cube. I propose a methodology to integrate well logs and seismic derived attributes using non-linear relationships to highlight and identify brittle zones in unconventional reservoirs. Finally, I correlated the resulting BI seismic volume with production and volume of proppant placed into the reservoir validating the effectiveness of this technique.