Abstract: Porosity and permeability is plays an important effect in the production of oil and gas in a reservoir. The accumulation of oil and gas plays a very important role in the formation of gas reservoir. In other to have a better flowrate in a reservoir, this paper presents a smart system that will be used in estimating the permeability and porosity in carbonate rocks. The methodology or technique used in this work is Random Forest Classifier. Random Forest Classifier was used in training a machine model for classifying the porosity and permeability in carbonate rocks. The model was trained using a reservoir data consist of a total of 14 columns which was reduced to just three columns. The reduction was done by selecting just the most important feature by means of feature extraction. The model was trained using just the selected features with an estimator function of 100, the model for evaluate based on accuracy and precision. The result generated shows that the model achieved an accuracy of about 100%. The model was saved and deployed to production using python flask. The result of the deployed model shows a correct classification of the porosity and permeability in carbonate rocks.

Keywords: Porosity, Permeability, Carbonate Rocks, Oil Reservoir, Random Forest Classifier.

PDF | DOI: 10.17148/IJARCCE.2022.11117

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