Automating Velocity Model Building Using Monte Carlo Simulations - A West African Case Study

Building velocity models for depth imaging can be time-consuming and regularly requires manual intervention. The workflows tend to be stop-go chained processes. An approach using pseudo-randomness can be used to build a velocity model, mitigating some of the inefficiency challenges associated with traditional velocity model building. Monte Carlo simulations use random sampling to resolve problems where the solution may be insufficiently defined. Using a Monte Carlo approach for velocity model building firstly requires an understanding of how the data quality impacts the model, prior to creating a population of models to invert. A statistical analysis loop of the inverted population of models, followed by numerous repeated cycles, enables a level of automation in velocity model building. The protracted chained approach of classical model building can be replaced by parallelized compute intensive methods to achieve an accurate velocity model in a reduced timeframe. In this abstract we demonstrate the benefits for both quality and time, of weight of statistics and automation when using a Monte Carlo simulation for velocity model building.