Improving porosity and gamma ray prediction for the Middle Jurassic Hugin sandstones in southern Norwegian North Sea with the application of deep neural networks

The complete characterization of a reservoir requires accurate determination of properties such as porosity, gamma ray and density, amongst others. A common workflow is to predict the spatial distribution of properties measured by well logs to those that can be computed from the seismic data. Generally, a high degree of scatter of data points is seen on crossplots between P-impedance and porosity, or P-impedance and gamma ray suggesting large uncertainty in the determined relationship. Although for many rocks there is a well established petrophysical model correlating P-impedance to porosity, there is not a comparable model correlating P-impedance to gamma ray. To address this issue, interpreters can use crossplots to graphically correlate two seismically derived variables to well measurements plotted in color. When there are more than two seismically derived variables, the interpreter can use multilinear regression or artificial neural network (ANN) analysis that uses a percentage of the upscaled well data for training to establish an empirical relation with the input seismic data and then uses the remaining well data to validate the relationship. Once validated at the wells, this relationship can then be used to predict the desired reservoir property volumetrically. We describe the application of deep neural network (DNN) analysis for the determination of porosity and gamma ray over the Volve Field in the southern Norwegian North Sea. After employing several quality-control steps in the deep neural network workflow and observing encouraging results, we validate the final prediction of both porosity and gamma ray properties using blind well correlation. The application of this workflow promises significant improvement to the reservoir property determination for fields that have good well control and exhibit lateral variations in the sought properties.