Designing an Ensemble model for hydrocarbon reservoir Permeability prediction by petrophysical lithology Labeling

Permeability is an important feature of oil and gas reservoirs that is difficult to predict. At present, experimental and regression models are used to predict permeability. On the other hand, increasing the accuracy of permeability prediction for points that do not have a core sample is of particular importance in analyzing reservoir behavior. In recent times, due to better predictability, machine learning algorithms have been used to predict permeability. In this study, a new group machine learning model for permeability prediction in oil and gas tanks is introduced. In this method, the input data is labeled using log lithology information and separated into a number of clusters and each cluster was modeled by machine learning algorithm. Unlike previous studies that worked independently on models, here we design a group model using augmented decision tree (ETR), decision tree (DTR) regression, and enhanced gradient (GBR) algorithms. And petrophysical data, we were able to dramatically improve the accuracy of the prediction as well as the mean square error and predict the permeability with 99.82% accuracy. The results showed that group models have a great effect on improving the accuracy of permeability prediction compared to individual models and also the separation of samples based on lithology information was a reason to optimize the Trojan estimate compared to previous studies.