Reservoir Productivity Estimation Using Machine Learning

The system segments the reservoir characterization process into distinct functional modules: data acquisition module, machine learning model module, and visualization module. Each module handles specific tasks (obtaining subsurface data, applying trained models, generating visual representations), allowing complex analysis to be broken down into manageable components that improve precision without overwhelming complexity.

The patent introduces machine learning algorithms as intermediary components between raw subsurface data and productivity estimates. These algorithms act as mediators that process complex relationships between rock properties, well designs, and production outcomes, enabling high-precision estimation without requiring direct complex physical measurements.

The system performs preliminary actions by pre-training machine learning models using historical well design data and production outcomes before actual reservoir analysis. This pre-computation stores learned relationships in the model parameters, allowing rapid productivity estimation during actual use without re-processing all training data, thus reducing analysis time while maintaining comprehensive parameter consideration.

The patent uses machine learning models as virtual copies of the complex physical and production relationships. Instead of performing exhaustive physical simulations or experiments for each well design scenario, the system uses trained model copies that replicate these relationships, enabling rapid evaluation of multiple well design options with comprehensive parameters without proportional increase in computational time.

The machine learning model serves as a universal processing framework that handles multiple production parameters simultaneously. The model is designed to accept various input parameters (rock properties, well designs, production data) and process them through unified algorithms, ensuring consistent treatment of all parameters without selective information loss. This multi-functional approach maintains reliability by systematically analyzing all relevant parameters.

The system implements feedback mechanisms where model predictions are compared against actual production outcomes, and this information feeds back into model refinement. The feedback loop ensures that all production parameters are continuously evaluated for their informational value, with the model learning to weight and utilize parameters that provide the most reliable characterization while filtering out redundant or noisy data.