Structural Uncertainty Modelling The superior structural modelling tools in RMS have been further strengthened in RMS 2013 with the addition of new functionality for exploring uncertainty in fault and horizon representation in the reservoir model. Traditionally, best-case estimates of both faults and horizons have been used throughout the reservoir modelling process, neglecting the inherent uncertainty in these interpretations, and thereby running the risk of severely underestimating the uncertainty in reservoir volumes. With RMS 2013, new fault and horizon uncertainty tools tightly linked to structural modelling and 3D gridding have been added, making it fast and easy to build several geological scenarios to investigate the effect of structural uncertainty. Fault Uncertainty Modelling The figure below shows two realisations of possible fault throws where the upper row realisation has larger throw on the two leftmost faults than the lower realisation. Since the Fault Uncertainty tool is tightly integrated with structural modelling and 3D gridding in RMS, the user can now rapidly build these models in full, to investigate the scenarios corresponding to the uncertainty in the input data. Changes in fault throw created through an automatic workflow. Perturbing fault parameters like throw dip, strike and location in the new Fault Uncertainty Modelling job is also accessible from the RMS’ Uncertainty Management module, allowing fast investigations of multiple fault scenarios, where also the dependency between the faults are properly accounted for. Horizon Uncertainty Modelling The new Horizon Uncertainty tools adds the possibility to incorporate realistic uncertainties in the horizon models by specifying uncertainties in the form of standard deviations for all input data used in the horizon modelling process. These include isochore thicknesses, seismic interpretations in time with corresponding interval velocities, depth maps and well data in the form of both well picks and zone log information. Allowing a user-specified uncertainty on the well picks reduces bull’s eye effects in the resulting set of horizons, but importantly also provides the software a measure that is used to identify possible outliers and errors in the data, giving the users extremely valuable feedback that can be used to evaluate the data quality. The resulting horizons are all dependent on each other, such that an observation on one horizon also affects the neighbouring horizons ensuring a consistent, geologically valid result. Moreover, the set of horizons represents the most likely outcome based on the uncertainty in the input parameters whereby the user gets results that corresponds to the actual data uncertainty, traditionally neglected. In addition to the full set of horizons, the uncertainty in these can also be directly obtained: see the map view on the next page for an example illustrating how the uncertainty is low around well picks and along well trajectories. IN SUMMARY • Fault sensitivity studies made easy. • Horizon sensitivity studies includes uncertainty of all data. • Horizontal well data automatically accounted for. • Realistic structural scenarios.