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

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.

Introduction: Handling uncertainty in the geosciences: identification, mitigation and communication

“…A further example is presented by Bodur and Rey (2019), who discuss the uncertainties in dynamic topography estimates made on the basis of mantle tomography observations. Viscous stresses transmitted vertically to the lithosphere from zones of contrasting buoyancy in the Earth's mantle are known to be responsible for long wavelength uplift or subsidence.…”

“…The difference in estimates from the two approaches is an illustration of uncertainty resulting from our incomplete knowledge of the structure of the lithosphere and the viscosity structure in the Earth's interior. In their manuscript, Bodur and Rey (2019) hypothesise that these discrepancies in dynamic topography estimates may be related to an oversimplification of mantle rheology in numerical models of present-day mantle flow. They do this on the basis of the results of a series of coupled 3-D thermomechanical numerical experiments simulating the dynamic topography signal resulting from a sphere located at a certain depth in the mantle, whose density is greater than that of its surroundings and under a variety of different rheological assumptions.…”

“…This special issue on uncertainty encompasses 12 articles covering the quantification and management of uncertainty in a broad range of geological disciplines, including seismic interpretation (Alcalde et al, 2019;Schaaf and Bond, 2019), mantle dynamic models (Bodur and Rey, 2019;Mather and Fullea, 2019), field geology (Andrews et al, 2019;Bárbara et al, 2019;Pakyuz-Charrier et al, 2019;Stamm et al, 2019;Wilson et al, 2019), plate kinematic modelling (Causer et al, 2020) and subsurface resource evaluation (Miocic et al, 2019;Wilkinson and Polson, 2019). This collection of original research contributions, some of which were presented at the aforementioned EGU 2018 session, highlights the importance of understanding uncertainty, often neglected by interpreters, geomodellers and experimentalists.…”

comment on probabilistic geological modelling and oil and gas aspects/applications.

“…P10 and P90, which you rightly mention as being commonly used for uncertainty evaluation and decision making, are, more often than not, derived at least in part from such modelling. The field of stochastic/probabilistic seismic inver-sion has been gaining traction in oil and gas E&P industry for some time now (many examples are available in the literature), while uncertainty in seismic interpretation is gaining interest (see for instance Bond (2015), Alcalde et al (2017), and Solid Earth Discussion manuscripts by Schaaf and Bond (2019) and Bárbara et al (2019), both of which postdate your submission). The same applies, to a certain extent, to the study of uncertainty and geological risk management using electromagnetic methods in hydrocarbon frontier exploration (see for instance Meju (2019) and Baltar and Roth (2013)).…”