Improved permeability prediction from seismic and log data using artificial intelligence techniques

Accurate prediction of permeability remains the key to the determination of oil and gas reservoir quality. A number of studies have been carried out to investigate the predictability of reservoir permeability from log measurements. More recent studies have attempted to predict permeability from seismic signals. Both log measurements and seismic signals have shown to provide rich information about the structure and texture of the subsurface and hence have jointly proven to be good predictors of permeability. However, previous studies on this subject were limited to the application of Artificial Neural Networks (ANN). With the persistent quest for more accurate predictions for more successful exploration and improved production, this paper investigates the effect of combining both seismic and log datasets with the application of more advanced Artificial Intelligence techniques on the accuracy of reservoir permeability predictions. Log measurements and seismic signals obtained from several wells in a giant oil and gas reservoir were used to train and evaluate the performance of Support Vector Machine (SVM) and Type-2 Fuzzy Logic (T2FL) models in the prediction of permeability. The log measurements were matched with the seismic signals of the exact corresponding wells taken from 10-, 20-, 30- and 40ms seismic zones. When compared with the long-existing ANN model, the SVM model gave the most accurate permeability predictions, with the highest correlation coefficient and the least error measures. The results also showed that a combination of seismic and log data has the potential to give more accurate permeability predictions than using either of them separately. A wider field application of the proposed techniques will give more insight, and is expected to save more time, effort and improve hydrocarbon recovery.