On the application of machine learning algorithms in predicting the permeability of oil reservoirs

Permeability is one of the main oil reservoir characteristics. It affects potential oil production, well-completion technologies, the choice of enhanced oil recovery methods, and more. The methods used to determine and predict reservoir permeability have serious shortcomings. This article aims to refine and adapt machine learning techniques using historical data from hydrocarbon field development to evaluate and predict parameters such as the skin factor and permeability of the remote reservoir zone. The article analyzes data from 4045 wells tests in oil fields in the Perm Krai (Russia). An evaluation of the performance of different Machine Learning (ML) algorithms in the prediction of the well permeability is performed. Three different real datasets are used to train more than 20 machine learning regressors, whose hyperparameters are optimized using Bayesian Optimization (BO). The resulting models demonstrate significantly better predictive performance compared to traditional methods and the best ML model found is one that never was applied before to this problem. The permeability prediction model is characterized by a high R² adjusted value of 0.799. A promising approach is the integration of machine learning methods and the use of pressure recovery curves to estimate permeability in real-time. The work is unique for its approach to predicting pressure recovery curves during well operation without stopping wells, providing primary data for interpretation. These innovations are exclusive and can improve the accuracy of permeability forecasts. It also reduces well downtime associated with traditional well-testing procedures. The proposed methods pave the way for more efficient and cost-effective reservoir development, ultimately supporting better decision-making and resource optimization in oil production.