Understanding and Modeling Connectivity in a Deep Water Clastic Reservoir—The Schiehallion Experience
Published:December 01, 2006
Alastair Govan, Paul Freeman, Chris Macdonald, Merv Davies, Davide Casabianca, Ferry Nieuwland, Neil Moodie, Tim Primmer, 2006. "Understanding and Modeling Connectivity in a Deep Water Clastic Reservoir—The Schiehallion Experience", Reservoir Characterization: Integrating Technology and Business Practices, Roger M. Slatt, Norman c. Rosen, Michael Bowman, John Castagna, Timothy Good, Robert Loucks, Rebecca Latimer, Mark Scheihing, Hu Smith
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Schiehallion is a two billion barrel deepwater clastic reservoir, situated on the Atlantic margin of the UKCS, one of the world’s most hostile environments for hydrocarbon production. The field has been developed via subsea wells tied back to an FPSO, and is one of the first developments of its kind anywhere in the world.
The field may be characterized as high productivity but low energy and, as a consequence, water injection is essential to maintaining production. However, the reservoir is channelized, faulted, and has varying degrees of connectivity between the compartments, so that a good understanding of these factors is necessary to optimize the water injection distribution.
Our understanding of the ‘plumbing’, or connectivity between the wells, has evolved and matured over time, using a wide range of different data types, from the initial extended well test, through RFT’s, pressure transient analyses, interference testing, PLT’s, tracer and geochemical sampling, to bi-annual 4D seismic surveys using increasingly sophisticated processing and interpretation.
Much of this understanding has been incorporated in a 3D model, which uses object modeling and seismic conditioning to represent the sand distribution. Potential barriers to flow are identified from seismic coherency analysis, and the strengths of these barriers have been used as the main history matching parameters.
A key learning has been that all data needs to be interpreted with great care, and it is essential to integrate several data types in order to obtain reliable conclusions. The paper gives examples of data which has been invaluable, as well as examples where the data is ambiguous or misleading.