Abstract
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 mainly vary according to the frequency content of the seismic data, lateral variations of amplitudes along reflectors, and how the reflectors terminate around faults when fault reflections are not present in the seismic image. 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 at 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 7 % higher and 12 % lower (P10 and P90). 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 the reservoir laterally strongly affects the gross rock volume predicted. The multiple realizations obtained are geologically consistent with the available data, and their differences in geometry and dimensions of the reservoir allow us to improve the understanding of the reservoir structure. The uncertainty modeling workflows applied are easy to design and allow us 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.
Highlights
Geological modeling is a powerful tool that allows us to obtain realistic representations of the subsurface, which in turn gives a better understanding of the most geologically complex scenarios (e.g., Jolie et al, 2015; Hoffman et al, 2008; Latief et al, 2012)
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
In the Lubina culmination, both the upper and lower Basal Tertiary Group (BTG) reservoirs thins out towards the SSW, and the upper BTG pinches out few meters to the SSW of the Lubina-1 well position (Fig. 9c)
Summary
Geological modeling is a powerful tool that allows us to obtain realistic representations of the subsurface, which in turn gives a better understanding of the most geologically complex scenarios (e.g., Jolie et al, 2015; Hoffman et al, 2008; Latief et al, 2012). Due to software limitations and time constraints, only a limited number of possible modeling scenarios are produced by the interpreters This practice, which ignores the other probable solutions that match all the available data and interpretations, has often led to unpleasant surprises, for example, when new wells have been drilled. Another challenge is that many of these models are not easy to update with new data when they become available (Seiler et al, 2009; Skjervheim et al, 2012; Pettan and Strømsvik, 2013), and companies may run the risk of taking decisions based on models that are no longer valid
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