Static Model Building Using Artificial Intelligence and Machine Learning Techniques

The study demonstrates the application of supervised machine learning algorithms to classify reservoir rock types using labeled data from well logs, cores, and other sources, improving accuracy and reducing manual interpretation time.

A 3D structural grid was constructed using Python, incorporating well tops, fault sticks, and depth horizons. Facies were predicted and populated throughout the reservoir zone using a Random Forest classifier algorithm.

Additional reservoir properties such as porosity, volume of shale, and saturation were populated across the entire grid using the XGBoost regressor algorithm, with the derived facies serving as an independent variable.

In-place resources were estimated using both deterministic and probabilistic methods, including sensitivity analysis, with estimates varying within 5% of those from a benchmark conventional model.

The study highlights that AI and ML methodologies can automate static modeling processes, facilitating data-centric reservoir description and swift updates to models. This is particularly beneficial for mature fields with extensive well data.