Datapages, Inc.Print this page


Microseismic Data Analysis and Prediction using Seismic Derived Rock Properties and Structural Attributes in the Eagle Ford Shale of South Texas

Rob Meek, Bailo Suliman, Rob Hull, Hector Bello, and Doug Portis
Pioneer Natural Resources

Wells performance in the Eagle Ford play (in South Texas) show regional variation with the heterogeneous rock properties and locally along wellbore. Traditionally, microseismic mapping is used to understand stimulation efficiency along horizontal wells, wells interference, optimum wells spacing and depletion efficiency. In this paper, we introduce a new technique to predict the Stimulated Reservoir Volume (SRV) using microseismic data, surface seismic-derived rock mechanical properties and seismic attributes (curvature and coherence). A model transform is derived from existing microseismic data and then applied to predict SRV’s along wellbores where microseismic mapping was not conducted. Chemical and radioactive tracers and production allocation surveillance are used to test the veracity of the predicted stimulated reservoir volume.

During hydraulic stimulation fractures occur where rocks are more brittle, weaker and along preexisting natural fractures and faults. A microseismic density volume is created from microseismic events after careful analysis of the events. In Eagle Ford datasets, these volumes correlate directly with chemical tracers and can be used for input into a reservoir simulators for dynamic flow modeling. Surface seismic derived rock and structural properties are extracted at event locations, along a vector from the perforation to the event, and inside the microseismic density volume. Young’s modulus, density, p-wave velocity, s-wave velocity, minimum and maximum structural curvature, and Lame’s constants lambda and mu are examined. In general, moderate Young’s modulus and low Poisson ratio are areas that have the highest microseismic density. Simultaneous examination of the different attributes and determining what attribute is the most important in determining where the most events will occur is difficult. Stepwise regression analysis is used to mathematically quantify the relation between microseismic density and rock properties and structural attributes.

Rock mechanical properties and structural attributes were combined with an ellipsoid stimulation model around the wellbore and a stepwise regression is used to determine what attribute has the greatest impact on the shape and properties of the microseismic density volume. Results show that curvature has the greatest impact followed by density and Young’s modulus. The resulting transform volume was found to be very consistent with the density and shape of the actual microseismic density.

Furthermore, the same transform created with full dataset is then used to create a microseismic density volume around another nearby well where microseismic mapping was not recorded. The resultant microseismic volume was found to be comparable with the chemical tracer data gathered from the well suggesting areas that were better stimulated.


AAPG Search and Discovery Article #90164©2013 AAPG Southwest Section Meeting, Fredericksburg, Texas, April 6-10, 2013