Added Value by Fast and Robust Conditioning of Structural Surfaces to Horizontal Wells for Real-World Reservoir Models
- Vegard R. Stenerud (Statoil ASA) | Hans Kallekleiv (Roxar Software Solutions) | Petter Abrahamsen (Norwegian Computing Center) | Paal Dahle (Norwegian Computing Center) | Arne Skorstad (Roxar Software Solutions) | May Hege Aalmen Viken (Statoil ASA)
- Document ID
- Society of Petroleum Engineers
- SPE Annual Technical Conference and Exhibition, 8-10 October, San Antonio, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2012. Society of Petroleum Engineers
- 5.1.2 Faults and Fracture Characterisation, 5.1.8 Seismic Modelling, 5.6.9 Production Forecasting, 5.5 Reservoir Simulation, 5.1.5 Geologic Modeling, 5.5.8 History Matching
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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 This leads to an outdated model that gradually loses its predictability.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.
It is well known that building models for reservoirs with long horizontal wells targeting specific zones can be challenging and tedious. The main challenge in using standard approaches is that the well paths frequently end up in incorrect zones within the reservoir models. Having the well paths located in the intended zone is important when properties are populated from wells and is crucial for flow simulations. A lot of work is put into interpreting the correct zonation in wells, and lots of manual editing and dummy data is typically required to get a satisfactory match in the structural models. Moreover, new or modified well data may require significant additional work to update the structural model. A robust and automated approach is therefore essential for maintaining up-to-date reservoir models and flow simulations.
Modern reservoir-modeling software allows the whole modeling workflow to be run in batch. This makes it possible to update the model in an automated fashion by updating the input data and then re-running the workflow. It is therefore easy to study the effect of changes in the input data and thereby screen sensitivities. To get every step of the model update into an automated workflow requires much effort but it yields a reservoir model better suited for continuous decision making and the need to re-do the modeling job from scratch disappears. The automated workflow also allows us to span the structural uncertainty by generating alternative realizations of the reservoir model. Such realizations may be used to calculate volume uncertainties, to assess production-forecast uncertainties, or for big-loop history matching. Automated reservoir-modeling workflows have been demonstrated by Zachariassen et al. (2011a, 2011b) and Skjervheim et al. 2012 for two different fields offshore Norway. An important component was the automated approach for locating the well paths into the correct zones. We will describe that approach in this paper.
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