Integrated Workflow for Model Update Using Geophysical Reservoir Monitoring Data

Zachariassen, E.; Skjervheim, J. A.; Vabø, Jon Gustav; Lunt, Ian A.; Hove, Joakim

doi:10.3997/2214-4609.201410330

Cited by 5 publications

(2 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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An ensemble-based decision workflow for reservoir management

Chang

Evensen²

2022

Journal of Petroleum Science and Engineering

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Added Value by Fast and Robust Conditioning of Structural Surfaces to Horizontal Wells for Real-World Reservoir Models

Stenerud

Kallekleiv

Abrahamsen

et al. 2012

SPE Annual Technical Conference and Exhibition

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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.

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Integrated Workflow for Model Update Using Geophysical Reservoir Monitoring Data

Cited by 5 publications

References 0 publications

Reservoir Management Under Geological Uncertainty Using Fast Model Update

Reservoir Management Under Geological Uncertainty Using Fast Model Update

An ensemble-based decision workflow for reservoir management

Added Value by Fast and Robust Conditioning of Structural Surfaces to Horizontal Wells for Real-World Reservoir Models

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