Improving Prediction of Fracture Distribution Using Microseismic Data and Acoustic Logging Measurements

Abstract

The complex fracture network from hydraulic fracturing can significantly improve oilwell productivity, so it is widely used in the field of unconventional reservoir development. However, accurate evaluation of the fracture spatial distribution remains a challenge. As a result, how to combine a variety of data to avoid data islands and identify and predict the space of fracture zone is of great importance. In this paper, we present a method and workflow based on the microseismic (MS) data combined with shear wave velocity data to estimate the physical parameters of subsurface media and improve the description and prediction accuracy for hydraulic fractures. The method analyzes MS events to construct the fracture spatial distribution and uses acoustic logging measurements to correct the magnitude of MS events and enhance the resolution. The corrected MS magnitude is mapped to the MS event space for Kriging interpolation analysis to predict the improved spatial distribution of fractures, which is available in the format of a 3D cloud image.