TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractA non-subjective technique is presented to combine seismic based fracture detection from shear wave anisotropy with traditional well and structure data to produce a new generation of fracture intensity maps and volumes. Neural networks are easy to use and extremely useful for nonlinear regression where the functional form of the nonlinear equation between the independent and dependent variables are not known. We use a neural network for nonlinearly regressing a number of independent variables to predict fracture intensity within a reservoir. Several of the independent variables are derived from an amplitude versus angle and azimuth (AVAZ) process applied to pre-stack, P-wave seismic data. By examining the changes in reflectivity amplitude with respect to azimuth and incident angle on pre-stack seismic gathers, we can detect the presence of open, near-vertical fractures and their orientation. Seismic data has good lateral coverage, but poor vertical resolution versus well data, which has good vertical resolution, but is sparse laterally. The seismic attributes extracted from the AVAZ process are combined with 3D model attributes, such as porosity and lithology and structural attributes, such as the first and second derivatives of the structural surfaces to predict fracture intensity. Because fracture intensity information is sparse and difficult to obtain, we use expected ultimate recovery (EUR) as a substitute for fracture intensity. The four-month cumulative production provides a good estimate for EUR and is used as the fracture indicator in this study. The attributes are ranked according to their correlation with fracture intensity, and a subset of variables is selected as input into the neural network for the prediction of the fracture indicator. Multiple neural networks are trained on the data providing multiple solutions. These solutions, in the form of 2D maps or 3D volumes, are analyzed statistically to predict the fracture indicator and to high grade prospective drilling locations. In addition, the fracture indicator maps and volumes can be input into discrete fracture modeling tools for further analysis.