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Abstract

This presentation outlines an integrated workflow that incorporates 4D LoFS data into the Ekofisk field reservoir model history matching process. Successful application and associated benefits of the workflow process are also presented. A permanent ocean-bottom cable array was installed in Ekofisk field in 2010 as a part of a Life of Field Seismic (LoFS) program. This program provides frequent 4D seismic data, and the first three surveys have been acquired in years 2010-2011. LoFS monitoring data is used to optimize the Ekofisk waterflood by providing water movement insights and subsequently improving infill well placement. Reservoir depletion and water injection in Ekofisk lead to reservoir rock compaction and fluid substitution. These changes are revealed in space and time through 4D seismic differences. Inconsistencies between predicted (calculated from reservoir model output) and actual 4D differences are therefore used to identify reservoir model shortcomings. This process is captured using the following workflow: prepare and upscale a geologic model; simulate fluid flow and associated rock-physics using a reservoir model; generate a synthetic 4D seismic response from fluid and rock-physics forecasts; and update the reservoir model to better match actual production/injection data and the 4D seismic response. The above-mentioned Seismic History Matching (SHM) workflow employs rock-physics modeling to quantitatively constrain the reservoir model and develop a simulated 4D seismic response. Then parameterization techniques are used to constrain and update the reservoir model. This workflow updates geological parameters in an optimization loop through minimization of a misfit function. It is an automated closed loop system, and optimization is performed using an in-house computer-assisted history matching tool with an evolutionary algorithm. In summary, the Ekofisk LoFS SHM workflow is a multi-disciplinary process that requires collaboration between geological, geomechanical, seismic and reservoir engineering disciplines to optimize reservoir management.

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/content/papers/10.3997/2214-4609-pdb.293.F015
2012-06-04
2024-03-29
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http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609-pdb.293.F015
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