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Discussion papers
https://doi.org/10.5194/se-2019-64
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/se-2019-64
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 02 Apr 2019

Research article | 02 Apr 2019

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal Solid Earth (SE).

Quantifying the impact of the structural uncertainty on the gross rock volume in the Lubina and Montanazo oil fields (Western Mediterranean)

Carla Patricia Bárbara1, Patricia Cabello2, Alexandre Bouche3, Ingrid Aarnes4, Carlos Gordillo5, Oriol Ferrer2, Maria Roma2, and Pau Arbués2 Carla Patricia Bárbara et al.
  • 1Independent researcher, Nottingham, United Kingdom
  • 2Dept. Dinàmica de la Terra i de l’Oceà. Institut de Recerca Geomodels. Facultat de Ciències de la Terra, Universitat de Barcelona, c/Martí i Franquès s/n, 08028, Barcelona, Spain
  • 3Emerson Automation Solutions, Lysaker Torg 45, 1366, Lysaker, Norway
  • 4Norwegian Computing Center, Gaustadalleen 32a, NO-0373 Oslo, Norway
  • 5Repsol, Repsol Campus Madrid, Blue Building, c/Méndez Álvaro 44, 1st floor, 28045, Madrid, Spain

Abstract. Structural uncertainty is a key parameter affecting the accuracy of the information contained in static and dynamic reservoir models. However, quantifying and assessing its real impact on reservoir property distribution, in-place volume estimates and dynamic simulation has always been a challenge. Due to the limitation of the existing workflows and time constraints, the exploration of all potential geological configurations matching the interpreted data has been limited to a small number of scenarios, making the future field-development decisions uncertain.

We present a case study in the Lubina and Montanazo mature oil fields (Western Mediterranean) in which the structural uncertainty in the seismic interpretation of faults and horizons has been captured using modern reservoir modeling workflows. We model the fault and horizon uncertainty by means of two workflows, the manually interpreted and the constant uncertainty cases. In the manually interpreted case, the zones of ambiguity in the position of horizons and faults are defined as locally varying envelopes around the best interpretation, whose dimensions vary according to the diffractions and amplitudes of the seismic data throughout the surface interpretation. In the constant case, the envelope dimensions are kept constant for each horizon and each fault. Both faults and horizons are simulated within their respective uncertainty envelopes as provided to the user. In all simulations, conditioning to available well data is ensured. Stochastic simulation was used to obtain 200 realizations for each uncertainty modeling workflow. The realizations were compared in terms of gross rock volumes above the oil-water contact considering three scenarios in the depths of the contact.

The results show that capturing the structural uncertainty in the picking of horizons and faults in seismic data has a relevant impact on the volume estimation. The models predict percentage differences in the mean gross rock volume with respect to best estimate interpretation up to 16 % higher and 22 % lower. The manually interpreted uncertainty workflow reports narrower gross rock volume predictions and more consistent results from the simulated structural models than the constant case. This work has also revealed that, for the Lubina and Montanazo fields, the fault uncertainty associated with the major faults that bound laterally the reservoir strongly affect the GRV predicted. The multiple realizations obtained are geologically consistent with the available data and their differences in geometry and dimensions of the reservoir allows us to improve the understanding of the reservoir structure.

The uncertainty modeling workflows applied are easy to design and allow to update the models when required. This work demonstrates that knowledge of the data and the sources of uncertainty is important to set up the workflows correctly. Further studies can combine other sources of uncertainty in the modeling process to improve the risk assessment.

Carla Patricia Bárbara et al.
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Carla Patricia Bárbara et al.
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