Seismic Inversion Using AVA Neural Models for Frontier Reservoir Prediction

The patent applies preliminary action by pre-training machine learning models using synthetic seismic data generated from well logs before actual inversion is needed. This pre-computation of training datasets and model preparation enables rapid inversion processing without time-consuming traditional methods during actual exploration, directly resolving the time-cost contradiction while maintaining accuracy.

The patent substitutes traditional mechanical seismic inversion methods with machine learning-based computational approaches. Instead of using conventional iterative inversion algorithms that require significant manual input and computational resources, the system employs trained neural networks that can rapidly predict subsurface properties from seismic data, dramatically reducing processing time while preserving measurement precision.

The patent applies universality by training machine learning models on diverse synthetic seismic data from multiple wells and geological scenarios, creating a universal model that can adapt to different exploration environments including frontier areas without drilled wells. The model learns general subsurface property relationships that transfer across different geological settings, enabling effective prediction where traditional well-constrained methods fail.

The patent uses copying by creating synthetic seismic data and corresponding subsurface property models from existing well logs to generate training datasets. These synthetic copies simulate various geological scenarios including frontier exploration conditions, allowing the machine learning model to learn from virtual representations of target environments and apply this knowledge to actual frontier exploration where no real well data exists.

The patent applies self-service by implementing automated machine learning pipelines that perform seismic inversion without significant manual intervention. The system automatically preprocesses seismic data, applies trained models, and generates subsurface property predictions, eliminating the need for expert operators to manually guide each inversion process while maintaining high productivity and efficiency.

The patent implements feedback mechanisms where the machine learning model continuously refines predictions based on input seismic data patterns and previously learned relationships. The system incorporates iterative optimization where prediction results feed back into the processing pipeline, automatically adjusting parameters and improving accuracy without manual intervention, thereby enhancing both ease of operation and productivity simultaneously.