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Departments & Programs


Joint Inversion of Seismic and Flow Data for Reservoir Parameter Assessment

Figure 1: Three main faults, four regions and well clustering in Norne field reservoir model

Time lapse seismic data has begun to play an important role in reservoir characterization, management and monitoring. It can provide information on the dynamics of fluids in the reservoir based on the relation between variations of seismic signals and movement of hydrocarbons and changes in formation pressure. Reservoir monitoring by repeated seismic or time lapse surveys can help in reducing the uncertainties attached to reservoir models. In combination with geological and flow modeling as a part of history matching process it can provide better description of the reservoir and thus better reservoir forecasting. However joint inversion of seismic and flow data for reservoir parameter is highly non-linear and complex. Stochastic optimization based inversion has shown very good results in integration of time-lapse seismic and production data in reservoir history matching. In this project we are using different stochastic optimizers for inversion of data from the Norne field, offshore Norway.

The Norne field is located on a horst block in the southern part of the Nordland II area in the Norwegian Sea. The horst block is approximately 9 km x 3 km. It has 29 producer and 10 injector wells. The rocks within the Norne reservoir are of Late Triassic to Middle Jurassic age. The present geological model consists of five reservoir zones. They are Garn, Not, Ile, Tofte and Tilje. The zonation is made to correspond as much as possible to the actual change of lithology in the layers of the reservoir. Hence, boundaries between zones are chosen at sequence boundaries and maximum flooding surfaces. Lithological boundaries and distinct breaks in porosity or permeability that correlate across the field can also be basis for the zonation. Oil is mainly found in the Ile and Tofte Formations, and gas in the Garn formation. The sandstones are buried at a depth of 2500-2700 m. The porosity is in the range of 25-30 % while permeability varies from 20 to 2500 mD.

The general strategy in seismic history matching is to optimally constrain our reservoir models to flow response as well as seismic response for better predictions. This is done by minimizing the misfit between the observed flow and seismic data and the computed flow and seismic response from the model. In this ongoing research we analyze the performance of the different particle swarm optimizers, both in terms of exploration of the model space and convergence to an optimal model.