Understanding what part of the horizontal well will be stimulated more effectively is important in high grading zones, predicting well performance and determining horizontal well spacing. We analyze the properties of the rock material where a microseismic event occurred. We then describe a method of predicting a stimulated reservoir volume using microseismic data, surface seismic rock properties, and structural attributes. We then correlate this volume to well bore production chemical and radioactive tracers. A dataset from the Eagle Ford Formation in South Texas is used in the analysis.
A microseismic density volume is created from microseismic events. In two Eagle Ford datasets these volumes correlate directly with chemical tracers and can be used for input into a stimulated reservoir volume. Surface seismic 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. Most of the microseismic events occurred in the Eagle Ford shale with Young's modulus values between 30 and 36 GPa and Poisson ratio values between .26 and .29. But simultaneously examining all the different attributes and determining what attribute is the most important in determining where the most events will occur is difficult. To solve this problem we used a stepwise regression analysis technique.
Rock properties and structural attributes are combined with an ellipsoid stimulation model around the well bore. A stepwise regression is then used to determine what attribute has the greatest impact on the shape of the microseismic density volume. We found that curvature had 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.
This same transform is then used to create a microseismic density volume around another nearby well where microseismic data was not recorded. This volume was then compared with chemical tracers gathered from the well. The estimated microseismic density correlated directly with the chemical tracers.