Machine learning methods for estimating permeability of a reservoir

The prediction of permeability from the information of a well log is a crucial and extensive task that is observed in the earth sciences. The permeability of a reservoir is greatly dependent on the pressure of a rock which is that trait of a rock that determines the ease of flow of fluids (gas or liquid) in that medium to percolate through rocks. When a single fluid totally saturates the porous media, the permeability is characterized as absolute. If the porous medium is occupied by more than one fluid, the permeability is described as effective. Over the recent years, many machine learning approaches have been used for the estimation of permeability of a reservoir which would match with the predefined range of permeability in a reservoir for an accurate and computationally faster result. These approaches involved the application of Genetic Algorithms (GR), Machine Learning Algorithms like Artificial Neural Networks (ANN), Multiple Variable Regression (MVR), Support Vector Machine (SVM), and some other Artificial Intelligence Techniques like Artificial Neuro-Fuzzy Inference System (ANFIS). A succinct review of many advanced machine learning algorithms such as MVR, ANN, SVM, or ANFIS and a few ensemble techniques will be conducted for a survey to predict the permeability of a reservoir over 12 years between 2008 and 2020. The second half of this review concludes that machine learning approaches provide better results, create robust models, and have much more room for improvement than traditional empirical, statistical and basic journal integration methods that are limited and computationally more expensive.

Automated summary

Prediction of permeability from well log information is a crucial task in earth sciences. Machine learning approaches have been widely used in recent years for accurate and computationally faster estimation of permeability in reservoirs. These approaches show better results and have more room for improvement compared to traditional methods.