![]() Among a total of 64 wells, 25 wells were selected for CCK and NN-MAT and 39 wells were withheld for validation. In this study, we analyzed 3-D seismic and well-log data from the Second Wall Creek Sand of the Teapot Dome field, Wyoming, USA to investigate: (1) how CCK and NN-MAT perform in the prediction of porosity and (2) how the number of wells affects the results. NN-MAT, based on a nonlinear relationship between seismic attributes and log values, treats data as spatially independent observations. CCK is a linear-weighted averaging method based on spatial covariance model. ![]() Collocated cokriging (CCK) and neural-network multi-attribute transform (NN-MAT) are widely used in the prediction of reservoir properties because they can integrate sparsely-distributed, high-resolution well-log data and densely-sampled, low-resolution seismic data.
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