Automated Surface Identification for Geological-Hydrodynamic Models

The method segments the geological modeling process into distinct iterative cycles: initial grid construction, surface identification from seismic data, model building, accuracy evaluation, and refinement. Each cycle focuses on specific tasks, allowing systematic improvement of modeling accuracy through multiple passes rather than attempting to solve all problems simultaneously.

The methodology implements dynamic refinement where the model grid and surfaces are continuously adjusted based on accuracy evaluations. The iterative process allows the model to evolve from an initial approximation to a high-accuracy representation, with each cycle adapting the model based on seismic data correlation and functional deviation metrics.

The method performs preliminary actions by constructing an initial model grid and identifying initial surfaces from seismic data before iterative refinement begins. This preliminary model provides a starting point that captures major geological features, allowing subsequent iterations to focus on refining details rather than building from scratch, thus reducing total processing time.

The methodology implements feedback mechanisms where each iterative cycle evaluates model accuracy against seismic data and uses this information to guide refinements in the next cycle. The functional deviation metrics and correlation analyses provide quantitative feedback that directs where and how the model should be adjusted, preventing wasted computational effort on already-accurate regions.