Integrated Workflow for Consistent Model Building from Depth Conversion to Flow Simulation - North Sea Field Case

Skjervheim, J. A.; Lanen, X. van; Hulme, D.; Stenerud, Vegard R.; Zachariassen, E.; Liu, S.; Hove, Joakim; Evensen, Geir

doi:10.3997/2214-4609.20148221

Cited by 12 publications

(7 citation statements)

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“…The system is based on a big-loop ensemble-based modeling process and system for model conditioning, named Fast Model Update (FMU) (Zachariassen et al, 2011;Skjervheim et al, 2012). FMU is currently being implemented in Statoil-operated assets and the aim is now to further develop methods that can optimally exploit the information available from FMU.…”

Section: Discussionmentioning

confidence: 99%

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Reservoir Management Under Geological Uncertainty Using Fast Model Update

Hanea

Evensen

Hustoft

et al. 2015

SPE Reservoir Simulation Symposium

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Statoil is implementing "Fast Model Update (FMU)," an integrated and automated workflow for reservoir modeling and characterization. FMU connects all steps and disciplines from seismic depth conversion to prediction and reservoir management taking into account relevant reservoir uncertainty. FMU delivers an ensemble of geologically-consistent and history-matched model realizations that together characterizes the reservoir uncertainty. FMU facilitates management of the reservoir (e.g. field development plan, well planning, and drainage strategies) in both early-phase as well as mature projects, properly taking geological uncertainty (including structure, facies, and rock/fluid properties) into account. The focus of the paper is to demonstrate how FMU is used in an algorithm for robust optimization of wells (e.g. well targets, infill wells, drilling priority, rate control). In the current paper we demonstrate some early results where the drilling sequence of wells is optimized under geological uncertainty, using an ensemble of models conditioned on all available data. The final product provides an optimized drilling schedule and drilling time. The paper includes examples from one synthetic and one real field application.

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Section: Discussionmentioning

confidence: 99%

“…FMU is based on an automation of the modeling process from depth conversion to simulation (Zachariassen et al, 2011;Skjervheim et al, 2012). Whenever inputs to the modeling process are changed one can automatically generate new realizations and simulate the dynamical response.…”

Section: Fast Model Update (Fmu)mentioning

confidence: 99%

Reservoir Management Under Geological Uncertainty Using Fast Model Update

Hanea

Evensen

Hustoft

et al. 2015

SPE Reservoir Simulation Symposium

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“…This seamless connection is a requirement and has been used for state-of-the-art reservoir model updating (e.g. Skjervheim et al, 2012) and hence can be adapted for fault facies modelling studies. For full-field applications, future fault facies modelling studies should put efforts on automating and streamlining the workflow detailed in the present study.…”

Section: On Full-field Application Workflowmentioning

confidence: 99%

The impact of fault envelope structure on fluid flow: A screening study using fault facies

et al. 2011

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Many people had a hand in forming this dissertation one way or another, either by direct contribution, giving input into the research, or keeping me motivated to finish the work. First and foremost, I thank my supervisors, Jan Tveranger and William Helland-Hansen, for their encouragement, guidance, and advice throughout my time as a PhD student. I thank Jan for sharing his insight and ideas on reservoir modelling. Our collaborations during the writing stages of the papers have significantly improved my writing skill. Jan's constant challenges to most research works I brought to his desk have helped elevating the quality of this dissertation and, I believe, making me a better scientist.

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“…This practice, which ignores the other equally 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.…”

Section: Introductionmentioning

confidence: 99%

“…The integrated modeling workflows presented in Leahy and Skorstad (2013), Aarnes et al (2014), and Howley and Meyer (2015) are capable of capturing uncertainties associated with any property and step of the modeling workflow. These workflows have evolved from methodologies described in MacDonald et al (2009), Skjervheim et al (2012) and Stenerud et al (2012), and in turn integrate algorithms for the horizon simulation based on a Bayesian statistical approach by Abrahamsen (1993Abrahamsen ( , 2005 and Abrahamsen and Benth (2001).…”

Section: Introductionmentioning

confidence: 99%

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

Bárbara¹,

Cabello

Bouche

et al. 2019

Solid Earth

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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.

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Integrated Workflow for Consistent Model Building from Depth Conversion to Flow Simulation - North Sea Field Case

Cited by 12 publications

References 0 publications

Reservoir Management Under Geological Uncertainty Using Fast Model Update

Reservoir Management Under Geological Uncertainty Using Fast Model Update

The impact of fault envelope structure on fluid flow: A screening study using fault facies

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

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