This presentation outlines an integrated workflow that incorporates 4D seismic data into the Ekofisk field reservoir model history matching process. Successful application and associated benefits of the workflow benefits are also presented. A seismic monitoring programme has been established at Ekofisk with 4D seismic surveys that were acquired over the field in 1989, 1999, 2003, 2006 and 2008. Ekofisk 4D seismic data is becoming a quantitative tool for describing the spatial distribution of reservoir properties and compaction. The seismic 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 4D differences (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: (1) prepare and upscale a geologic model, (2) simulate fluid flow and associated rockphysics using a reservoir model, (3) generate a synthetic 4D seismic response from fluid and rock physics forecasts, and (4) update the reservoir model to better match actual production/injection data and/or 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. Parameterization techniques are then 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 using evolutionary algorithm. In summary, the Ekofisk 4D SHM workflow is a multi-disciplinary process that requires collaboration between geological, geomechanical, geophysical and reservoir engineering disciplines to optimize well placement and reservoir management.

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Reservoir surveillance using 4D seismic has become a valuable resource for managing decisions under uncertainty. This paper highlights an integrated workflow to preserve geological consistency while calibrating a reservoir model using 4D seismic and production data. We demonstrate a successful application of this approach on our North Sea chalk reservoir undergoing waterflood, where a number of repeat seismic surveys have been acquired over time and leveraged as a quantitative source of information for describing the spatial distribution of reservoir properties and compaction. This seismic monitoring data has resulted in the ability to better manage the waterflood by providing fluid movement insights and subsequent improvement of infill well placement. To capture geologic variability and ensure model predictability, geostatistical parameterization techniques using multiple-point statistics are used to represent the uncertainty in the reservoir model. Additionally, the workflow employs a rock physics model to generate a synthetic 4D seismic response from flow simulation. Inconsistencies between the predicted and observed 4D differences are used to classify the reservoir model shortcomings. The uncertain geological parameters are updated in an optimization loop through the minimization of a misfit function comprised of both production and 4D seismic misfit formulations. The closed-loop workflow is managed by an in-house computer-assisted history matching tool using a stochastic optimization algorithm. The integrated approach yields improved reservoir management by encouraging multi-disciplinary collaboration between geological, geomechanical, geophysical and reservoir engineering disciplines.

This paper focuses on maximizing oil recovery from thin oil rim in a high-dipping, multilayered reservoir in the North Sea. Oil output from thin oil zones between a gas cap and an aquifer is limited to uneconomical rates caused by water coning and gas cresting. Horizontal wells are widely used in thin oil rim development due to the limited pressure drawdown applied to the formation. The horizontal wells should be optimally positioned within the oil zone to avoid premature gas and water breakthrough. Critical issues for thin oil zone reservoir modeling are correct grid representation of internal heterogeneities, fluid saturation distribution, fluid contacts, horizontal well trajectories and flow displacement properties. In a stratigraphic grid, the lack of resolution affects the fluid contacts movement within the high-dipping reservoir layers. Similarly for a horizontal grid, thin reservoir intervals are truncated due to a 'stair-casing' effect, which leads to loss of communication within the layers. A hybrid grid was constructed to combine the benefits of the horizontal grid in the oil zone together with the stratigraphic grid in the water and gas zones. Such a grid framework provides the best modeling approach for representing the transition zone and fluid contact movements. In the study, the prediction of oil, gas and water production in the transition zone was difficult because the crude oil altered formation wettability after hydrocarbon migration and reservoir structure tilting. Multiphase flow was modeled by integrating the key dynamic reservoir controls, translated into end-point correlations above the free water level based on multivariate SCAL data analyses. Comparative analyses of alternative modeling techniques exhibited clear differences in the response for coning development and optimal well placements.