Added Value by Fast and Robust Conditioning of Structural Surfaces to Horizontal Wells for Real-World Reservoir Models

Structural updates for a complex reservoir model require time-consuming manual work, therefore, updates are rarely performed. This leads to an outdated model that gradually loses its predictability. Eventually, this results in model breakdown, and a new model must be built from scratch. Continuously updatable reservoir models avoid this and increase the value of models as a tool in decision making. In addition, easily updateable structural surfaces enable several structural realizations for spanning the uncertainty.

We present the use of a method for fast and robust updates of structural surfaces in reservoir models. We will focus on updates using zone data from horizontal wells (zone-log conditioning), since this traditionally has been a bottleneck that needs tedious manual work prone to error. In zone-log conditioning, we try to generate horizon surfaces that honor the geological zonation along the well paths. This is important for property modeling, and is crucial for fluid-flow simulations. Our method is robust, fully automated, and is built on a consistent mathematical framework that includes specified input-data uncertainties. It has provided satisfactory results for large real-world reservoir models where standard methods and work processes have failed. The field example presented shows a reduction from 22.9 % to 0.9 % in incorrectly honoring of the zone logs by applying this method rather than the standard approach. The remaining 0.9 % is due to conflicting data, gridding errors, and is difficult to get rid of even with manual editing. We consider this a large step forward with respect to providing an up-to-date basis for decisions that also can account for structural uncertainties.