Well Log Prediction of TOC in Unsampled Source Rock Intervals

The patent introduces machine learning models as intermediary components between wireline log data and net source rock thickness estimation. These models process multiple log parameters (gamma ray, resistivity, porosity, density) through trained algorithms to produce more accurate predictions than direct wireline measurements alone, resolving the contradiction by adding computational mediation to enhance precision.

The patent combines multiple wireline log data types (gamma ray, resistivity, porosity, density) into a composite predictive framework. By integrating diverse measurement modalities through machine learning ensembles and stacking methods, the system achieves superior estimation accuracy compared to any single log type, effectively creating a composite information structure that overcomes individual measurement limitations.

The patent divides the machine learning prediction system into separate specialized models for different targets: one model for TOC prediction and another for sensitive elements prediction. Each model is trained on specific relevant features and validated independently, allowing for targeted optimization and reduced computational burden compared to a single monolithic model attempting to predict all parameters simultaneously.

The patent implements feedback mechanisms through cross-validation and error analysis where model predictions are continuously evaluated against validation datasets. The system uses validation results to adjust model parameters and select optimal architectures, creating a feedback loop that refines model performance while monitoring computational resource usage to prevent unnecessary complexity escalation.