Sweet Spot Identification and SRV Estimation by Correlation of Microseismic Data and the Shale Capacity^SM Concept: Application to the Haynesville

Abstract

With low gas prices, Haynesville well economics dramatically improve when drilled and fractured in locations capable of high gas production rates. Finding such sweet spots requires seismically driven 3D estimation of four shale drivers: Total Organic Carbon (TOC), porosity, brittleness, and natural fracture density. These four shale drivers are estimated from post-stack and pre-stack inversions, post-stack volumetric curvature and spectral imaging attributes, and previously derived shale drivers. These are placed in a neural network capable of determining the correlation between the log of each shale driver and the seismic attributes. With the four shale drivers available in the 3D volume of the Haynesville study area, the Shale Capacity^SM model is computed by multiplying the four shale drivers. Previously published studies in the Haynesville showed a strong correlation between well production and Shale Capacity. In this study, the focus is on the microseismic and its relationship to sweet spots identified by the Shale Capacity model. Although the interpreted microseismic shows good correlation with the fracture density, a stronger quantitative correlation is found with the microseismic when only the Shale Capacity model is considered. Ultimately, it is determined that the microseismic may be used to validate the Shale Capacity model. As the validated Shale Capacity model is available throughout the limits of the seismic data, it may be used to estimate the Stimulated Reservoir Volume (SRV) in another location, before a well is drilled and before microseismic is acquired. Additional acquired microseismic may then be used to further validate the Shale Capacity model.

AAPG Datapages/Search and Discovery Article #90193 © 2014 Rocky Mountain Section AAPG Annual Meeting, Denver, Colorado, July 20-22, 2014