Using Artificial Intelligence to Corellate Multiple Seismic Attributes to Reservoir Properties

Abstract

Abstract Well data gives precise information on reservoir properties at specific field locations with high vertical resolution. 3D seismic surveys cover large areas of the field but reservoir properties are not directly observable due to poor vertical resolution. For this paper, a new methodology has been developed and tested for relating reservoir properties at the well-bore to sets of seismic attributes, in order to predict reservoir properties in two zones of the Nash Draw field in SE New Mexico. Over 350 seismic attributes can be used in regression analyses of reservoir properties. Since using all attributes is computationally unfeasible and labor intensive, fuzzy logic is used to select the most statistically significant attributes for developing regression equations for individual reservoir properties. Non-linear regressions were used, as individual attributes had low correlation coefficients when cross-plotted with reservoir properties, and neural network architectures were developed to relate the selected attributes to each property. In each case the output data used for training was a reservoir property, porosity (ϕ), water saturation(Sw), or net pay, from 19 wells in the field. Each property was estimated using a neural network trained to CC=0.8 or higher using the highest ranking seismic attributes as inputs. The validity of the solutions were tested by removing three wells from the training data, re-computing the weights, and predicting the three absent points. These tests were applied three times for each reservoir property, with different points removed. Each network accurately predicted these nine test points and the solutions are considered robust. ϕ, Sw, and net pay maps were generated using the regression relationships and the seismic attributes at each seismic bin location. Pore volume (ϕh) and hydrocarbon pore volume (hϕSo) maps were derived from those reservoir property maps. These new techniques maximize both the well control and seismic data and generated useful maps for targeted drilling programs in the field.