Reservoir tortuosity prediction: Coupling stochastic generation of porous media and machine learning

Accurate reservoir tortuosity prediction is the foundation of the high-quality evaluation of reservoir petrophysical properties. However, conventional empirical equations as a form of experimental data fitting lacks universality because the data are usually from a single horizon or block. We developed a model combining the stochastic generation of porous media with machine learning (ML) to predict reservoir tortuosity based on pore structure parameters. Real core scanning images from public databases were employed in stochastic generation as reference, which is an economic and accurate method of meeting the dataset quality and scale requirements of ML. The particle swarm optimization algorithm, an efficient method of obtaining the best hyperparameter combination, was introduced for the hyperparameter tuning of six commonly used ML algorithms to determine the optional model for tortuosity prediction. Our trained ML models demonstrated superior tortuosity prediction accuracy over deterministic linear and exponential empirical equations with porosity as the only variable, which effectively demonstrates the potential of tortuosity prediction using pore structure parameters. The proposed ML model enables precise tortuosity predictions based on a few measurable pore structural features, which can be obtained from well logging data and CT scanning; thus, it can be widely used in petroleum and logging fields.

Automated summary

A model combining machine learning and stochastic generation of porous media was developed to predict reservoir tortuosity. Real core scanning images were used as reference for stochastic generation, and the particle swarm optimization algorithm was introduced for obtaining the best parameter combination. The model enables precise tortuosity predictions based on a few measurable pore structural features, and can be widely used in the petroleum and logging fields.