ML Powered Velocity Modelling and Time to Depth Conversion

True multi-layer approach resulting in globally optimized model

Bayesian technique: automatic correction of velocity model parameters based on input data, prior model and input uncertainty

Stochastic method accounting simultaneously for all sources of uncertainty: velocity field, seismic interpretation and well top uncertainty

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Ensemble-Based Structural Uncertainty Quantification

Automatically generate and assess hundreds of consistent models at once​

Ability to select and retain models based on advanced criteria (e.g. accumulation connectivity)​

Flexible, graphical workflow design, supporting automated model updates​

Decision support: Yields probability of trap success, volume area and thickness probability distributions, P10, P50 and P90 cases, depth uncertainty and sweet-spot maps

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